diff --git a/models/tts/zipvoice/.gitignore b/models/tts/zipvoice/.gitignore new file mode 100644 index 0000000..84031a2 --- /dev/null +++ b/models/tts/zipvoice/.gitignore @@ -0,0 +1,5 @@ +.venv/ +build/ +upstream/ +uv.lock +__pycache__/ diff --git a/models/tts/zipvoice/README.md b/models/tts/zipvoice/README.md new file mode 100644 index 0000000..efe6f93 --- /dev/null +++ b/models/tts/zipvoice/README.md @@ -0,0 +1,55 @@ +# ZipVoice / LuxTTS → CoreML + +CoreML conversion for [LuxTTS](https://huggingface.co/YatharthS/LuxTTS) (48 kHz +zero-shot voice cloning), a distilled [ZipVoice](https://github.com/k2-fsa/ZipVoice) +(k2-fsa) with a 4-step flow-matching solver and a dual-head 48 kHz Vocos vocoder. +The same pipeline applies to upstream ZipVoice checkpoints (en+zh, dialog) — +identical graphs, different weights/vocoder config. + +Requested in FluidAudio issue #49 ([comment](https://github.com/FluidInference/FluidAudio/issues/49#issuecomment-4762131530)). + +## Status + +| Component | CoreML | Parity (vs torch) | Notes | +|---|---|---|---| +| TextEncoder (Zipformer, 4M) | ✅ fp16, 8.3 MB | cos 0.999997 | fixed 256-token bucket + mask | +| FmDecoder (Zipformer, 119M) | ✅ fp16, 238 MB | cos ≥0.9987/step | fixed 1024-frame bucket + mask | +| Duration expansion + solver | host (Python/Swift) | exact | anchor-Euler, 4 steps, t_shift 0.5 | +| Vocoder (dual-head Vocos 48k) | ⬜ torch for now | — | ISTFT×2 + Linkwitz-Riley crossover; needs DFT-matmul ISTFT | +| Prompt transcription | out of scope | — | upstream uses Whisper; FluidAudio would use Parakeet | +| End-to-end audio | — | log-mel cos 0.99925, RMS match | phase-only waveform divergence | + +## Performance (M5 Pro, macOS 26.5) + +| Model | CPU | GPU | ANE (98–99% resident) | +|---|---|---|---| +| FmDecoder (per step) | 155 ms | **14.1 ms** | 286 ms | +| TextEncoder | 5.5 ms | 1.4 ms | 4.8 ms | + +`all` compute units picks GPU. ~8 s utterance ≈ 5 + 4×14 ≈ **~60 ms** for the +core (≈130× realtime) before vocoder. ANE residency is high but latency is +poor (seq-first layouts / rel-pos attention transposes) — see trials.md. + +## Layout + +- `coreml/convert_coreml.py` — export TextEncoder + FmDecoder (component split + mirrors upstream `zipvoice/bin/onnx_export.py`; duration expansion stays host-side) +- `coreml/parity.py` — per-component + end-to-end parity vs torch oracle +- `scripts/reference_infer.py` — pure-torch reference (parity oracle) +- `upstream/` — clones of ysharma3501/LuxTTS and k2-fsa/ZipVoice (gitignored) + +## Usage + +```bash +uv venv --python 3.11 .venv +# install deps (see pyproject; universal uv sync fights the cu126 pins upstream) +.venv/bin/python scripts/reference_infer.py --prompt-audio +.venv/bin/python coreml/convert_coreml.py +.venv/bin/python -m coreml.parity +``` + +## Licenses + +LuxTTS & ZipVoice: Apache-2.0. Frontend uses piper-phonemize (espeak-ng +data — GPL-3.0); a FluidAudio integration should reuse the in-house G2P +frontend instead. diff --git a/models/tts/zipvoice/coreml/__init__.py b/models/tts/zipvoice/coreml/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/tts/zipvoice/coreml/ane/__init__.py b/models/tts/zipvoice/coreml/ane/__init__.py new file mode 100644 index 0000000..a0ac4ba --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/__init__.py @@ -0,0 +1,5 @@ +"""ANE-canonical (B, C, 1, S) rewrite of Zipformer2EncoderLayer. + +See layer.py for the module, parity.py for fp32 gates, bench.py for CoreML +conversion + ANE/GPU benchmarks. +""" diff --git a/models/tts/zipvoice/coreml/ane/bench.py b/models/tts/zipvoice/coreml/ane/bench.py new file mode 100644 index 0000000..c032ade --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/bench.py @@ -0,0 +1,181 @@ +"""CoreML benchmark: original Zipformer2EncoderLayer vs AneZipformerLayer. + +Converts both to CoreML (iOS17, fp16, mlprogram, fixed S=1024), times each +under CPU_AND_NE and CPU_AND_GPU (3 warmup + 10 timed), checks the ANE +layer's fp16 output against the torch fp32 reference, and runs +coreml-cli --fallback on the compiled ANE package. + +Run: .venv/bin/python -m coreml.ane.bench +""" + +import shutil +import subprocess +import sys +import time +from pathlib import Path + +sys.path.insert(0, ".") + +import coremltools as ct +import numpy as np +import torch +from torch import Tensor, nn + +from coreml.ane.layer import AneZipformerLayer, ane_to_tbc, tbc_to_ane +from coreml.convert_coreml import ( + load_model, + patch_coremltools_int, + patch_simple_downsample, +) + +SEQ_LEN = 1024 +EMBED_DIM = 512 +OUT_DIR = Path("build/coreml/ane_trial") +COREML_CLI = Path("/Users/hanweng/Documents/mobius-zipvoice/tools/coreml-cli") + + +class OrigLayerWrapper(nn.Module): + """(S, 1, C) seq-first original layer with frozen pos_emb constant.""" + + def __init__(self, layer, pos_emb: Tensor): + super().__init__() + self.layer = layer + self.register_buffer("pos_emb", pos_emb) + + def forward(self, src: Tensor, time_emb: Tensor) -> Tensor: + return self.layer(src, self.pos_emb, time_emb=time_emb) + + +def convert(traced, inputs, name): + mlmodel = ct.convert( + traced, + inputs=inputs, + outputs=[ct.TensorType(name="out", dtype=np.float32)], + minimum_deployment_target=ct.target.iOS17, + compute_precision=ct.precision.FLOAT16, + convert_to="mlprogram", + ) + path = OUT_DIR / f"{name}.mlpackage" + if path.exists(): + shutil.rmtree(path) + mlmodel.save(str(path)) + print(f"saved {path}") + return path + + +def bench(path, feeds, units, warmup=3, runs=10): + model = ct.models.MLModel(str(path), compute_units=units) + for _ in range(warmup): + out = model.predict(feeds) + times = [] + for _ in range(runs): + t0 = time.perf_counter() + out = model.predict(feeds) + times.append((time.perf_counter() - t0) * 1e3) + return float(np.mean(times)), out["out"] + + +def cosine(a, b): + a = np.asarray(a, dtype=np.float64).ravel() + b = np.asarray(b, dtype=np.float64).ravel() + return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))) + + +def fallback_check(mlpackage: Path): + compiled = ct.models.utils.compile_model(str(mlpackage)) + dest = mlpackage.with_suffix(".mlmodelc") + if dest.exists(): + shutil.rmtree(dest) + shutil.copytree(compiled, dest) + print(f"\n--- coreml-cli --fallback: {dest.name} ---") + try: + res = subprocess.run( + ["uv", "run", "coreml-cli", str(dest.resolve()), "--fallback"], + cwd=COREML_CLI, + capture_output=True, + text=True, + timeout=600, + ) + print(res.stdout) + if res.returncode != 0: + print(res.stderr[-2000:]) + except Exception as e: # noqa: BLE001 — report and continue + print(f"coreml-cli failed: {e}") + + +def main(): + torch.manual_seed(0) + OUT_DIR.mkdir(parents=True, exist_ok=True) + patch_coremltools_int() + patch_simple_downsample() + model, _ = load_model() + model.eval() + + enc0 = model.fm_decoder.encoders[0] + layer = enc0.layers[0] if hasattr(enc0, "layers") else enc0.encoder.layers[0] + with torch.no_grad(): + pos_emb = enc0.encoder_pos(torch.randn(SEQ_LEN, 1, EMBED_DIM)).detach() + + src = torch.randn(SEQ_LEN, 1, EMBED_DIM) + time_emb = torch.randn(1, EMBED_DIM) + + # torch fp32 reference + with torch.no_grad(): + ref = layer(src, pos_emb, time_emb=time_emb).numpy() + + # --- original layer, seq-first --- + orig = OrigLayerWrapper(layer, pos_emb).eval() + with torch.no_grad(): + traced_orig = torch.jit.trace(orig, (src, time_emb)) + orig_path = convert( + traced_orig, + [ + ct.TensorType(name="src", shape=(SEQ_LEN, 1, EMBED_DIM), dtype=np.float32), + ct.TensorType(name="time_emb", shape=(1, EMBED_DIM), dtype=np.float32), + ], + "OrigLayer", + ) + + # --- ANE-canonical layer --- + ane = AneZipformerLayer(layer, pos_emb, SEQ_LEN).eval() + x_ane = tbc_to_ane(src) + t_ane = time_emb.reshape(1, EMBED_DIM, 1, 1) + with torch.no_grad(): + traced_ane = torch.jit.trace(ane, (x_ane, t_ane)) + ane_path = convert( + traced_ane, + [ + ct.TensorType(name="x", shape=(1, EMBED_DIM, 1, SEQ_LEN), dtype=np.float32), + ct.TensorType(name="time_emb", shape=(1, EMBED_DIM, 1, 1), dtype=np.float32), + ], + "AneLayer", + ) + + orig_feeds = {"src": src.numpy(), "time_emb": time_emb.numpy()} + ane_feeds = {"x": x_ane.numpy(), "time_emb": t_ane.numpy()} + + print(f"\n{'model':<12} {'units':<12} {'mean ms':>10}") + results = {} + for name, path, feeds in ( + ("orig", orig_path, orig_feeds), + ("ane", ane_path, ane_feeds), + ): + for units in (ct.ComputeUnit.CPU_AND_NE, ct.ComputeUnit.CPU_AND_GPU): + ms, out = bench(path, feeds, units) + results[(name, units.name)] = (ms, out) + print(f"{name:<12} {units.name:<12} {ms:>10.2f}") + + # fp16 CoreML (ANE) vs torch fp32 accuracy + ane_ne_out = results[("ane", "CPU_AND_NE")][1] # (1, C, 1, S) + ane_out_tbc = ane_to_tbc(torch.from_numpy(ane_ne_out)).numpy() + orig_ne_out = results[("orig", "CPU_AND_NE")][1] + print(f"\nane CoreML fp16 (NE) vs torch fp32: cos={cosine(ane_out_tbc, ref):.6f}, " + f"max_abs={np.abs(ane_out_tbc - ref).max():.4f}") + print(f"orig CoreML fp16 (NE) vs torch fp32: cos={cosine(orig_ne_out, ref):.6f}") + + fallback_check(ane_path) + fallback_check(orig_path) + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/ane/convert_decoder.py b/models/tts/zipvoice/coreml/ane/convert_decoder.py new file mode 100644 index 0000000..586c0b1 --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/convert_decoder.py @@ -0,0 +1,121 @@ +"""Convert AneFmDecoder to CoreML (iOS17 fp16, fixed S=1024). + +Same I/O contract as the original FmDecoder (t, x, text_condition, +speech_condition, guidance_scale, padding_mask -> v), so coreml/parity.py +feeds and swift/RssBench.swift work unchanged. Saves +build/coreml-ane/AneFmDecoder.mlpackage, copies TextEncoder.mlpackage from +build/coreml, and compiles both to .mlmodelc (the decoder as +FmDecoder.mlmodelc so rss_bench finds it). + +Run: .venv/bin/python -m coreml.ane.convert_decoder +""" + +import argparse +import shutil +import sys +from pathlib import Path + +sys.path.insert(0, ".") + +import coremltools as ct +import numpy as np +import torch + +from coreml.ane.decoder import AneFmDecoder, AneFmDecoderIO +from coreml.convert_coreml import FEAT_DIM, load_model, patch_coremltools_int + +SEQ_LEN = 1024 +SRC_DIR = Path("build/coreml") + + +def compile_to(mlpackage: Path, dest: Path): + compiled = ct.models.utils.compile_model(str(mlpackage)) + if dest.exists(): + shutil.rmtree(dest) + shutil.copytree(compiled, dest) + print(f"compiled {dest}") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--out-dir", default="build/coreml-ane") + parser.add_argument( + "--deployment-target", default="iOS17", choices=["iOS17", "iOS18"], + help="iOS18 enables grouped-channel palettization downstream", + ) + args = parser.parse_args() + out_dir = Path(args.out_dir) + + out_dir.mkdir(parents=True, exist_ok=True) + patch_coremltools_int() + model, _ = load_model() + + ane = AneFmDecoderIO(AneFmDecoder(model.fm_decoder, SEQ_LEN)).eval() + for sl, err in ane.core.basis_errs.items(): + print(f"pos basis S={sl}: rel_max_err={err:.2e}") + + t = torch.tensor([0.5]) + x = torch.randn(1, SEQ_LEN, FEAT_DIM) + text = torch.randn(1, SEQ_LEN, FEAT_DIM) + speech = torch.randn(1, SEQ_LEN, FEAT_DIM) + g = torch.tensor([3.0]) + mask = torch.zeros(1, SEQ_LEN) + mask[0, SEQ_LEN - 124 :] = 1.0 + + with torch.no_grad(): + ref = ane(t, x, text, speech, g, mask) + traced = torch.jit.trace(ane, (t, x, text, speech, g, mask)) + + mlmodel = ct.convert( + traced, + inputs=[ + ct.TensorType(name="t", shape=(1,), dtype=np.float32), + ct.TensorType(name="x", shape=(1, SEQ_LEN, FEAT_DIM), dtype=np.float32), + ct.TensorType( + name="text_condition", shape=(1, SEQ_LEN, FEAT_DIM), dtype=np.float32 + ), + ct.TensorType( + name="speech_condition", shape=(1, SEQ_LEN, FEAT_DIM), dtype=np.float32 + ), + ct.TensorType(name="guidance_scale", shape=(1,), dtype=np.float32), + ct.TensorType(name="padding_mask", shape=(1, SEQ_LEN), dtype=np.float32), + ], + outputs=[ct.TensorType(name="v", dtype=np.float32)], + minimum_deployment_target=getattr(ct.target, args.deployment_target), + compute_precision=ct.precision.FLOAT16, + convert_to="mlprogram", + ) + pkg = out_dir / "AneFmDecoder.mlpackage" + if pkg.exists(): + shutil.rmtree(pkg) + mlmodel.save(str(pkg)) + print(f"saved {pkg}") + + # Sanity: fp16 CoreML (CPU) vs torch fp32 on the trace inputs. + cm = ct.models.MLModel(str(pkg), compute_units=ct.ComputeUnit.CPU_ONLY) + out = cm.predict( + { + "t": t.numpy(), + "x": x.numpy(), + "text_condition": text.numpy(), + "speech_condition": speech.numpy(), + "guidance_scale": g.numpy(), + "padding_mask": mask.numpy(), + } + )["v"] + n = SEQ_LEN - 124 + a, b = ref.numpy()[:, :n].ravel(), out[:, :n].ravel() + c = float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))) + print(f"CoreML fp16 (CPU) vs torch fp32: cos={c:.6f} max_abs={np.abs(a - b).max():.4f}") + + # TextEncoder + compiled models. + te_dst = out_dir / "TextEncoder.mlpackage" + if not te_dst.exists(): + shutil.copytree(SRC_DIR / "TextEncoder.mlpackage", te_dst) + print(f"copied {te_dst}") + compile_to(pkg, out_dir / "FmDecoder.mlmodelc") + compile_to(te_dst, out_dir / "TextEncoder.mlmodelc") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/ane/decoder.py b/models/tts/zipvoice/coreml/ane/decoder.py new file mode 100644 index 0000000..ff6f895 --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/decoder.py @@ -0,0 +1,405 @@ +"""ANE-canonical rewrite of the full TTSZipformer fm_decoder. + +Layout: activations are (1, C, 1, S) fp32, S fixed. Reuses the submodules of +coreml.ane.layer (1x1-conv linears, channel-axis BiasNorm/Bypass, per-head +attention with S on the softmax axis, split depthwise convs) and adds the +whole-decoder plumbing: in/out projections, the t + guidance_scale embedding +path, per-stack time projections, SimpleDownsample/SimpleUpsample in +channel-first form, stack out-combiners, and float padding-mask support +(additive -1000 attention bias + zeroing before the depthwise convs, exactly +mirroring upstream masked_fill semantics). + +pos_abs sharing: linear_pos weights are NOT shared across layers, so the +folded per-layer (H, phd, S, S) constant of the single-layer trial would cost +~260 MB decoder-wide. Instead, every layer's posproj = PE @ W_l^T lies in the +column space of the (2S-1, 48) positional encoding PE, and the SVD of the +concatenated per-layer posproj matrices shows numerical rank ~27. We build +ONE orthonormal basis U_R (R=32, reconstruction ~1e-7 relative) per distinct +sequence length, bake pos_basis[r, q, j] = U_R[S-1-q+j, r] as a shared +constant, and fold the per-layer basis coefficients A_l = U_R^T posproj_l +into that layer's attention in_proj (p block becomes H*R channels). This +keeps the no-gather broadcast-mul+reduce form of the layer trial while +sharing 3 buffers across all 16 layers (~88 MB fp16 total). + +Weights are imported from the POST convert_scaled_to_non_scaled module tree. +""" + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from coreml.ane.layer import ( + AneBiasNorm, + AneBypass, + AneFeedforward, + AneNonlinAttention, + AneSelfAttention, + _adl_to_conv2d, + linear_to_conv2d, + swoosh_r, +) + +POS_RANK = 32 + + +def build_pos_basis(pos_emb: Tensor, layers, rank: int = POS_RANK): + """Shared rel->abs positional basis for all layers at one seq len. + + pos_emb: (1, 2S-1, pos_dim) from the (parameter-free) encoder_pos. + layers: every Zipformer2EncoderLayer that runs at this seq len. + + Returns (U (2S-1, R) float64, coeffs {id(layer): A_l (R, H*phd) float64}). + posproj_l ~= U @ A_l to ~1e-7 relative; U is rescaled so that basis and + coefficient magnitudes match (fp16 balance in the converted model). + """ + pe = pos_emb.squeeze(0).double() # (2S-1, pos_dim) + projs = [ + F.linear(pe, l.self_attn_weights.linear_pos.weight.double()) for l in layers + ] + M = torch.cat(projs, dim=1) # (2S-1, 16*L) + U, _, _ = torch.linalg.svd(M, full_matrices=False) + U = U[:, :rank] # (2S-1, R) + coeffs = {id(l): U.T @ p for l, p in zip(layers, projs)} # (R, H*phd) + # Balance magnitudes: |c*U| == |A/c| at the max. + amax = max(a.abs().max().item() for a in coeffs.values()) + umax = U.abs().max().item() + c = (amax / umax) ** 0.5 + U = U * c + coeffs = {k: a / c for k, a in coeffs.items()} + # Report reconstruction error (should be ~1e-7 relative). + rec = torch.cat([U @ coeffs[id(l)] for l in layers], dim=1) + err = (M - rec).abs().max().item() / M.abs().max().item() + return U, coeffs, err + + +def pos_basis_buffer(U: Tensor, seq_len: int) -> Tensor: + """pos_basis[1, r, q, j] = U[S-1-q+j, r] (rel->abs baked, no gather).""" + idx = (seq_len - 1) - torch.arange(seq_len).unsqueeze(1) + torch.arange(seq_len) + return U[idx].permute(2, 0, 1).unsqueeze(0).contiguous().float() # (1, R, S, S) + + +class AneSharedPosAttentionWeights(nn.Module): + """RelPositionMultiheadAttentionWeights with the shared positional basis. + + The layer's own linear_pos is folded away: the p block of in_proj is + replaced by H*R channels producing ptilde[h, r, q] = sum_c A[r, h*phd+c] + * p[h, c, q], and pos_scores[h, q, j] = sum_r ptilde[h, r, q] + * pos_basis[r, q, j]. pos_basis is passed in at forward time so the + traced graph holds ONE constant shared by every layer at that seq len. + """ + + def __init__(self, saw, coeff: Tensor, seq_len: int): + super().__init__() + h = saw.num_heads + qd = saw.query_head_dim + pd = saw.pos_head_dim + r = coeff.shape[0] + self.num_heads = h + self.query_head_dim = qd + self.rank = r + self.seq_len = seq_len + + w = saw.in_proj.weight.double() # (2*h*qd + h*pd, E) + b = saw.in_proj.bias.double() + qk = 2 * h * qd + wp = w[qk:].reshape(h, pd, -1) # (h, pd, E) + bp = b[qk:].reshape(h, pd) + a = coeff.reshape(r, h, pd).permute(1, 0, 2) # (h, R, pd) + wpt = torch.einsum("hrc,hce->hre", a, wp).reshape(h * r, -1) + bpt = torch.einsum("hrc,hc->hr", a, bp).reshape(h * r) + + e = w.shape[1] + self.in_proj = nn.Conv2d(e, qk + h * r, 1, bias=True) + with torch.no_grad(): + self.in_proj.weight.copy_( + torch.cat([w[:qk], wpt], dim=0).float().reshape(qk + h * r, e, 1, 1) + ) + self.in_proj.bias.copy_(torch.cat([b[:qk], bpt], dim=0).float()) + + def forward(self, x: Tensor, pos_basis: Tensor, attn_bias: Tensor) -> Tensor: + h = self.num_heads + qd = self.query_head_dim + r = self.rank + s = self.seq_len + + proj = self.in_proj(x) # (1, 2*h*qd + h*R, 1, S) + q = proj[:, : h * qd].reshape(h, qd, 1, s) + k = proj[:, h * qd : 2 * h * qd].reshape(h, qd, 1, s) + pt = proj[:, 2 * h * qd :].reshape(h, r, 1, s) + + attn_scores = torch.einsum("hcoq,hcok->hqok", q, k) # (H, S_q, 1, S_k) + # (h, R, S_q, 1) * (1, R, S_q, S_k) -> reduce R -> (h, 1, S_q, S_k) + pos_scores = (pt.permute(0, 1, 3, 2) * pos_basis).sum(dim=1, keepdim=True) + attn_scores = attn_scores + pos_scores.reshape(h, s, 1, s) + # attn_bias: (1, 1, 1, S_k) float, -1000 at padded keys (as upstream). + attn_scores = attn_scores + attn_bias + return attn_scores.softmax(dim=-1) + + +class AneConvModule(nn.Module): + """ConvolutionModule for kernel sizes 7/15/31 with padding-mask zeroing. + + k <= 15 places directly on ANE; k=31 splits into 15+15+1 exactly as in + coreml.ane.layer.AneConvolutionModule. + """ + + def __init__(self, cm): + super().__init__() + self.in_proj = linear_to_conv2d(cm.in_proj) + dw = cm.depthwise_conv # Conv1d(C, C, k, groups=C, padding=k//2) + c, _, k = dw.weight.shape + self.channels = c + self.kernel = k + if k <= 15: + self.dw = nn.Conv2d(c, c, (1, k), groups=c, padding=(0, k // 2), bias=True) + with torch.no_grad(): + self.dw.weight.copy_(dw.weight.reshape(c, 1, 1, k)) + self.dw.bias.copy_(dw.bias) + else: + assert k == 31, k + self.dw_a = nn.Conv2d(c, c, (1, 15), groups=c, padding=(0, 15), bias=True) + self.dw_b = nn.Conv2d(c, c, (1, 15), groups=c, padding=(0, 15), bias=False) + self.dw_c = nn.Conv2d(c, c, (1, 1), groups=c, padding=(0, 15), bias=False) + with torch.no_grad(): + self.dw_a.weight.copy_(dw.weight[:, :, :15].reshape(c, 1, 1, 15)) + self.dw_b.weight.copy_(dw.weight[:, :, 15:30].reshape(c, 1, 1, 15)) + self.dw_c.weight.copy_(dw.weight[:, :, 30:].reshape(c, 1, 1, 1)) + self.dw_a.bias.copy_(dw.bias) + assert cm.out_proj.activation == "SwooshR" + self.out_proj = _adl_to_conv2d(cm.out_proj) + + def forward(self, x: Tensor, conv_keep: Tensor) -> Tensor: + c = self.channels + s = x.shape[-1] + x = self.in_proj(x) # (1, 2C, 1, S) + x = x[:, :c] * torch.sigmoid(x[:, c:]) + # Upstream zeroes padded frames before the depthwise conv. + x = x * conv_keep + if self.kernel <= 15: + y = self.dw(x) + else: + y = ( + self.dw_a(x)[..., :s] + + self.dw_b(x)[..., 15 : 15 + s] + + self.dw_c(x)[..., 30 : 30 + s] + ) + return self.out_proj(swoosh_r(y)) + + +class AneDecoderLayer(nn.Module): + """Zipformer2EncoderLayer with shared positional basis + mask support.""" + + def __init__(self, layer, coeff: Tensor, seq_len: int): + super().__init__() + self.self_attn_weights = AneSharedPosAttentionWeights( + layer.self_attn_weights, coeff, seq_len + ) + h = layer.self_attn_weights.num_heads + self.self_attn1 = AneSelfAttention(layer.self_attn1, h) + self.self_attn2 = AneSelfAttention(layer.self_attn2, h) + self.feed_forward1 = AneFeedforward(layer.feed_forward1) + self.feed_forward2 = AneFeedforward(layer.feed_forward2) + self.feed_forward3 = AneFeedforward(layer.feed_forward3) + self.nonlin_attention = AneNonlinAttention(layer.nonlin_attention) + self.conv_module1 = AneConvModule(layer.conv_module1) + self.conv_module2 = AneConvModule(layer.conv_module2) + self.norm = AneBiasNorm(layer.norm) + self.bypass = AneBypass(layer.bypass) + self.bypass_mid = AneBypass(layer.bypass_mid) + + def forward( + self, + x: Tensor, + time_emb: Tensor, + pos_basis: Tensor, + attn_bias: Tensor, + conv_keep: Tensor, + ) -> Tensor: + src_orig = x + attn_weights = self.self_attn_weights(x, pos_basis, attn_bias) # (H, S, 1, S) + + x = x + time_emb + x = x + self.feed_forward1(x) + x = x + self.nonlin_attention(x, attn_weights[0:1]) + x = x + self.self_attn1(x, attn_weights) + + x = x + time_emb + x = x + self.conv_module1(x, conv_keep) + x = x + self.feed_forward2(x) + x = self.bypass_mid(src_orig, x) + + x = x + self.self_attn2(x, attn_weights) + x = x + time_emb + x = x + self.conv_module2(x, conv_keep) + x = x + self.feed_forward3(x) + + x = self.norm(x) + return self.bypass(src_orig, x) + + +class AneEncoderStack(nn.Module): + """Zipformer2Encoder: per-stack time projection + layer chain.""" + + def __init__(self, enc, coeffs: dict, seq_len: int): + super().__init__() + # enc.time_emb = Sequential(SwooshROnnx, Linear(time_embed_dim, E)) + self.time_proj = linear_to_conv2d(enc.time_emb[1]) + self.layers = nn.ModuleList( + AneDecoderLayer(l, coeffs[id(l)], seq_len) for l in enc.layers + ) + + def forward( + self, + x: Tensor, + time_emb: Tensor, + pos_basis: Tensor, + attn_bias: Tensor, + conv_keep: Tensor, + ) -> Tensor: + te = self.time_proj(swoosh_r(time_emb)) # (1, E, 1, 1) + for layer in self.layers: + x = layer(x, te, pos_basis, attn_bias, conv_keep) + return x + + +class AneDownsampledStack(nn.Module): + """DownsampledZipformer2Encoder: weighted-sum downsample (depthwise + strided conv), inner stack at S/ds, nearest-repeat upsample, bypass + out-combiner. seq_len must be divisible by ds (1024 is, for ds in 2/4).""" + + def __init__(self, enc, coeffs: dict, seq_len: int): + super().__init__() + ds = enc.downsample_factor + self.ds = ds + c = enc.out_combiner.bypass_scale.numel() + w = enc.downsample.bias.softmax(dim=0) # (ds,) + self.down = nn.Conv2d(c, c, (1, ds), stride=(1, ds), groups=c, bias=False) + with torch.no_grad(): + self.down.weight.copy_(w.reshape(1, 1, 1, ds).expand(c, 1, 1, ds)) + self.stack = AneEncoderStack(enc.encoder, coeffs, seq_len // ds) + self.out_combiner = AneBypass(enc.out_combiner) + + def forward( + self, + x: Tensor, + time_emb: Tensor, + pos_basis: Tensor, + attn_bias: Tensor, + conv_keep: Tensor, + ) -> Tensor: + y = self.down(x) # (1, C, 1, S/ds) + y = self.stack(y, time_emb, pos_basis, attn_bias, conv_keep) + y = F.interpolate(y, scale_factor=(1.0, float(self.ds)), mode="nearest") + return self.out_combiner(x, y) + + +class AneFmDecoder(nn.Module): + """Full TTSZipformer fm_decoder, ANE-canonical. + + forward(t, x, guidance_scale, mask): + t, guidance_scale: (1,) runtime scalars + x: (1, in_dim=300, 1, S) pre-concatenated [xt, text_cond, speech_cond] + mask: (1, 1, 1, S) float, 1.0 = padded + returns velocity (1, out_dim=100, 1, S). + """ + + def __init__(self, fm, seq_len: int = 1024, rank: int = POS_RANK): + super().__init__() + self.seq_len = seq_len + self.dsf = list(fm.downsampling_factor) + self.in_proj = linear_to_conv2d(fm.in_proj) + self.out_proj = linear_to_conv2d(fm.out_proj) + + # --- t / guidance_scale embedding (timestep_embedding + MLPs) --- + dim = fm.time_embed_dim + assert dim % 2 == 0 and fm.guidance_scale_embed_dim == dim + half = dim // 2 + freqs = torch.exp( + -torch.log(torch.tensor(10000.0)) * torch.arange(half).float() / half + ) + self.register_buffer("freqs", freqs.reshape(1, half, 1, 1)) + self.guid_proj = linear_to_conv2d(fm.guidance_scale_embed) + self.time_lin1 = linear_to_conv2d(fm.time_embed[0]) + self.time_lin2 = linear_to_conv2d(fm.time_embed[2]) + + # --- shared positional bases per distinct (downsampled) seq len --- + by_len = {} + for i, enc in enumerate(fm.encoders): + sl = seq_len // self.dsf[i] + inner = enc.encoder if hasattr(enc, "encoder") else enc + by_len.setdefault(sl, []).extend(inner.layers) + self.basis_errs = {} + coeffs = {} + for sl, layers in by_len.items(): + inner = next( + (e.encoder if hasattr(e, "encoder") else e) + for i, e in enumerate(fm.encoders) + if seq_len // self.dsf[i] == sl + ) + with torch.no_grad(): + pos_emb = inner.encoder_pos(torch.zeros(sl, 1, 2)).detach() + u, cf, err = build_pos_basis(pos_emb, layers, rank) + self.register_buffer(f"pos_basis_{sl}", pos_basis_buffer(u, sl)) + coeffs.update(cf) + self.basis_errs[sl] = err + + # --- stacks --- + stacks = [] + for i, enc in enumerate(fm.encoders): + if self.dsf[i] == 1: + stacks.append(AneEncoderStack(enc, coeffs, seq_len)) + else: + stacks.append(AneDownsampledStack(enc, coeffs, seq_len)) + self.stacks = nn.ModuleList(stacks) + + def _time_embed(self, t: Tensor, guidance_scale: Tensor) -> Tensor: + targs = t.reshape(1, 1, 1, 1) * self.freqs + te = torch.cat([targs.cos(), targs.sin()], dim=1) # (1, dim, 1, 1) + gargs = guidance_scale.reshape(1, 1, 1, 1) * self.freqs + ge = torch.cat([gargs.cos(), gargs.sin()], dim=1) + emb = te + self.guid_proj(ge) + return self.time_lin2(swoosh_r(self.time_lin1(emb))) + + def forward( + self, t: Tensor, x: Tensor, guidance_scale: Tensor, mask: Tensor + ) -> Tensor: + time_emb = self._time_embed(t, guidance_scale) # (1, dim, 1, 1) + + bias = {1: mask * -1000.0} + keep = {1: 1.0 - mask} + for ds in sorted({d for d in self.dsf if d != 1}): + bias[ds] = bias[1][..., ::ds] + keep[ds] = keep[1][..., ::ds] + + h = self.in_proj(x) + for i, stack in enumerate(self.stacks): + ds = self.dsf[i] + pb = getattr(self, f"pos_basis_{self.seq_len // ds}") + h = stack(h, time_emb, pb, bias[ds], keep[ds]) + return self.out_proj(h) + + +class AneFmDecoderIO(nn.Module): + """Drop-in wrapper with the SAME I/O contract as the original FmDecoder: + t (1,), x/text_condition/speech_condition (1, S, 100), guidance_scale + (1,), padding_mask (1, S) float 1.0 = padded -> v (1, S, 100).""" + + def __init__(self, core: AneFmDecoder): + super().__init__() + self.core = core + + def forward( + self, + t: Tensor, + x: Tensor, + text_condition: Tensor, + speech_condition: Tensor, + guidance_scale: Tensor, + padding_mask: Tensor, + ) -> Tensor: + s = self.core.seq_len + xt = torch.cat([x, text_condition, speech_condition], dim=2) # (1, S, 300) + h = xt.permute(0, 2, 1).unsqueeze(2) # (1, 300, 1, S) + mask = padding_mask.reshape(1, 1, 1, s) + v = self.core(t, h, guidance_scale, mask) # (1, 100, 1, S) + return v.squeeze(2).permute(0, 2, 1) diff --git a/models/tts/zipvoice/coreml/ane/decoder_parity.py b/models/tts/zipvoice/coreml/ane/decoder_parity.py new file mode 100644 index 0000000..d0231a6 --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/decoder_parity.py @@ -0,0 +1,142 @@ +"""fp32 eager parity: AneFmDecoder vs torch fm_decoder on the oracle inputs. + +Two comparisons per solver step: + 1. GATE — Ane @ S=1024 + mask vs torch @ S=1024 + mask (apples-to-apples + rewrite parity): max_abs_diff < 5e-2 and cos > 0.9995. + 2. REPORT — Ane @ S=1024 + mask vs torch @ exact S=751 (the parity.py + oracle path). Upstream padding semantics leak one garbage frame through + SimpleDownsample at the 751 boundary (mask[::ds] keeps it), so + 1024+mask vs 751-exact differs at cos ~0.9991-0.9997 / max_abs ~1-2 in + PURE TORCH already; that floor is inherent to the shipped FmDecoder + bucket too, not to this rewrite. + +Run: .venv/bin/python -m coreml.ane.decoder_parity +""" + +import json +import sys +from pathlib import Path + +sys.path.insert(0, ".") + +import numpy as np +import torch + +from coreml.ane.decoder import AneFmDecoder, AneFmDecoderIO +from coreml.convert_coreml import load_model +from zipvoice.models.modules.solver import get_time_steps + +SEQ_LEN = 1024 +GATE_MAX_ABS = 5e-2 +GATE_COS = 0.9995 + + +def cos(a, b): + a = np.asarray(a, dtype=np.float64).ravel() + b = np.asarray(b, dtype=np.float64).ravel() + return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-12)) + + +def main(): + oracle = Path("build/oracle") + meta = json.loads((oracle / "meta.json").read_text()) + prompt_features = torch.from_numpy(np.load(oracle / "prompt_features.npy")) + prompt_tokens = np.load(oracle / "prompt_tokens.npy").tolist() + text_tokens = np.load(oracle / "text_tokens.npy").tolist() + pfl = meta["prompt_features_lens"] + guidance_scale = meta["guidance_scale"] + num_steps = meta["num_steps"] + speed = 1.0 * 1.3 # generate() multiplies speed by 1.3 + + model, _ = load_model() + + with torch.no_grad(): + text_condition, _ = model.forward_text_inference_ratio_duration( + tokens=[text_tokens], + prompt_tokens=[prompt_tokens], + prompt_features_lens=torch.tensor([pfl]), + speed=speed, + ) + features_len = text_condition.shape[1] + speech_condition = torch.nn.functional.pad( + prompt_features, (0, 0, 0, features_len - prompt_features.size(1)) + ) + + ane = AneFmDecoderIO(AneFmDecoder(model.fm_decoder, SEQ_LEN)).eval() + for sl, err in ane.core.basis_errs.items(): + print(f"pos basis S={sl}: reconstruction rel_max_err={err:.2e}") + + def pad_T(x): + return torch.nn.functional.pad(x, (0, 0, 0, SEQ_LEN - x.size(1))) + + mask_f = torch.zeros(1, SEQ_LEN) + mask_f[0, features_len:] = 1.0 + mask_b = mask_f > 0.5 + ref_mask = torch.zeros(1, features_len, dtype=torch.bool) + text_pad, speech_pad = pad_T(text_condition), pad_T(speech_condition) + + timesteps = get_time_steps(num_step=num_steps, t_shift=0.5) + torch.manual_seed(meta["seed"]) + x0 = torch.randn(1, features_len, 100) + x_a, x_p, x_r = x0.clone(), x0.clone(), x0.clone() + + failures = [] + for step in range(num_steps): + t_cur, t_next = float(timesteps[step]), float(timesteps[step + 1]) + with torch.no_grad(): + v_a = ane( + torch.tensor([t_cur]), + pad_T(x_a), + text_pad, + speech_pad, + torch.tensor([guidance_scale]), + mask_f, + )[:, :features_len] + v_p = model.forward_fm_decoder( + t=torch.tensor(t_cur), + xt=pad_T(x_p), + text_condition=text_pad, + speech_condition=speech_pad, + padding_mask=mask_b, + guidance_scale=torch.tensor(guidance_scale), + )[:, :features_len] + v_r = model.forward_fm_decoder( + t=torch.tensor(t_cur), + xt=x_r, + text_condition=text_condition, + speech_condition=speech_condition, + padding_mask=ref_mask, + guidance_scale=torch.tensor(guidance_scale), + ) + gate_max = float((v_a - v_p).abs().max()) + gate_cos = cos(v_a.numpy(), v_p.numpy()) + rep_max = float((v_a - v_r).abs().max()) + rep_cos = cos(v_a.numpy(), v_r.numpy()) + ok = gate_max < GATE_MAX_ABS and gate_cos > GATE_COS + if not ok: + failures.append(step) + print( + f"[step {step} t={t_cur:.3f}] GATE vs torch@1024+mask: " + f"max_abs={gate_max:.3e} cos={gate_cos:.8f} {'PASS' if ok else 'FAIL'} | " + f"vs torch@751: max_abs={rep_max:.3e} cos={rep_cos:.6f}" + ) + + def euler(x_s, v_s): + x1p = x_s + (1.0 - t_cur) * v_s + x0p = x_s - t_cur * v_s + return (1.0 - t_next) * x0p + t_next * x1p if step < num_steps - 1 else x1p + + x_a, x_p, x_r = euler(x_a, v_a), euler(x_p, v_p), euler(x_r, v_r) + + print( + f"[final mel] ane vs torch@1024+mask cos={cos(x_a.numpy(), x_p.numpy()):.8f} " + f"| ane vs torch@751 cos={cos(x_a.numpy(), x_r.numpy()):.6f}" + ) + if failures: + print(f"FAILED gate at steps {failures}") + sys.exit(1) + print("all decoder parity gates passed") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/ane/layer.py b/models/tts/zipvoice/coreml/ane/layer.py new file mode 100644 index 0000000..7c34daf --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/layer.py @@ -0,0 +1,300 @@ +"""ANE-canonical rewrite of one Zipformer2EncoderLayer. + +Layout: x is (1, C, 1, S) fp32, S fixed. Every nn.Linear becomes a 1x1 +Conv2d, norms/bypass operate on the channel axis, attention runs per-head +with S on the last axis of the softmax input, and the rel->abs positional +conversion is a constant-shape pad/reshape/slice skew (no gather). + +Weights are imported (never re-initialized) from the POST +convert_scaled_to_non_scaled(is_onnx=True) module tree, so Balancer / +Whiten / Dropout are already Identity and the activations are the +SwooshL/ROnnx formulas. +""" + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + + +def _logaddexp0(x: Tensor) -> Tensor: + # logaddexp(0, x) in the numerically-stable form used by + # scaling.logaddexp_onnx: max(0,x) + log1p(exp(-|x|)). + return torch.relu(x) + torch.log1p(torch.exp(-torch.abs(x))) + + +def swoosh_l(x: Tensor) -> Tensor: + """SwooshL: logaddexp(0, x-4) - 0.08x - 0.035 (scaling.SwooshLOnnx).""" + return _logaddexp0(x - 4.0) - 0.08 * x - 0.035 + + +def swoosh_r(x: Tensor) -> Tensor: + """SwooshR: logaddexp(0, x-1) - 0.08x - 0.313261687 (scaling.SwooshROnnx).""" + return _logaddexp0(x - 1.0) - 0.08 * x - 0.313261687 + + +def linear_to_conv2d(linear: nn.Linear) -> nn.Conv2d: + """nn.Linear(in, out) -> nn.Conv2d(in, out, 1) with identical weights.""" + out_f, in_f = linear.weight.shape + conv = nn.Conv2d(in_f, out_f, 1, bias=linear.bias is not None) + with torch.no_grad(): + conv.weight.copy_(linear.weight.reshape(out_f, in_f, 1, 1)) + if linear.bias is not None: + conv.bias.copy_(linear.bias) + return conv + + +def _adl_to_conv2d(adl) -> nn.Conv2d: + """ActivationDropoutAndLinear's linear part -> 1x1 Conv2d.""" + out_f, in_f = adl.weight.shape + conv = nn.Conv2d(in_f, out_f, 1, bias=adl.bias is not None) + with torch.no_grad(): + conv.weight.copy_(adl.weight.reshape(out_f, in_f, 1, 1)) + if adl.bias is not None: + conv.bias.copy_(adl.bias) + return conv + + +class AneBiasNorm(nn.Module): + """scaling.BiasNorm over the channel axis of (1, C, 1, S).""" + + def __init__(self, norm): + super().__init__() + c = norm.bias.numel() + self.register_buffer("bias", norm.bias.detach().clone().reshape(1, c, 1, 1)) + self.register_buffer("scale", norm.log_scale.detach().exp().clone()) + + def forward(self, x: Tensor) -> Tensor: + # rsqrt + explicit square: ANE has no pow op. + d = x - self.bias + scales = torch.rsqrt(torch.mean(d * d, dim=1, keepdim=True)) + return x * scales * self.scale + + +class AneBypass(nn.Module): + """scaling BypassModule (eval path): src_orig + (src - src_orig) * scale.""" + + def __init__(self, bypass): + super().__init__() + c = bypass.bypass_scale.numel() + self.register_buffer( + "bypass_scale", bypass.bypass_scale.detach().clone().reshape(1, c, 1, 1) + ) + + def forward(self, src_orig: Tensor, src: Tensor) -> Tensor: + return src_orig + (src - src_orig) * self.bypass_scale + + +class AneFeedforward(nn.Module): + """FeedforwardModule: 1x1 conv -> SwooshL -> 1x1 conv.""" + + def __init__(self, ff): + super().__init__() + self.in_proj = linear_to_conv2d(ff.in_proj) + assert ff.out_proj.activation == "SwooshL" + self.out_proj = _adl_to_conv2d(ff.out_proj) + + def forward(self, x: Tensor) -> Tensor: + return self.out_proj(swoosh_l(self.in_proj(x))) + + +class AneAttentionWeights(nn.Module): + """RelPositionMultiheadAttentionWeights on (1, C, 1, S). + + Returns per-head attention weights of shape (H, S_q, 1, S_k), softmax + over the last (S_k) axis. + + Rel->abs positional handling: linear_pos(pos_emb) is folded eagerly and + the relative->absolute reindexing (out[q, j] = rel[q, S-1-q+j]) is baked + into a constant buffer pos_abs[h, c, q, j] = pos_proj[h, c, S-1-q+j], + so at runtime pos_scores = sum_c p[h,c,q] * pos_abs[h,c,q,j] — one + broadcast multiply + channel reduce, no gather/as_strided and no >16K + flatten (which the ANE cannot tile; the pad+reshape+slice skew trick + needs a 2*S*S flat axis and falls back to CPU). + """ + + def __init__(self, saw, pos_emb: Tensor, seq_len: int): + super().__init__() + self.num_heads = saw.num_heads + self.query_head_dim = saw.query_head_dim + self.pos_head_dim = saw.pos_head_dim + self.seq_len = seq_len + self.in_proj = linear_to_conv2d(saw.in_proj) + + # pos_emb: (1, 2S-1, pos_dim). Fold linear_pos eagerly: + # (1, 2S-1, H*phd) -> (H, phd, 2S-1), feature index = h*phd + d. + with torch.no_grad(): + pe = F.linear(pos_emb, saw.linear_pos.weight, saw.linear_pos.bias) + n = 2 * seq_len - 1 + assert pe.shape == (1, n, self.num_heads * self.pos_head_dim) + pe = pe.reshape(n, self.num_heads, self.pos_head_dim).permute(1, 2, 0) + # Absolute-indexed constant: (H, phd, S_q, S_k). + idx = (seq_len - 1) - torch.arange(seq_len).unsqueeze(1) + torch.arange(seq_len) + self.register_buffer("pos_abs", pe[:, :, idx].contiguous()) + + def forward(self, x: Tensor) -> Tensor: + h = self.num_heads + qd = self.query_head_dim + pd = self.pos_head_dim + s = self.seq_len + + proj = self.in_proj(x) # (1, (2*qd+pd)*H, 1, S) + q = proj[:, : h * qd].reshape(h, qd, 1, s) + k = proj[:, h * qd : 2 * h * qd].reshape(h, qd, 1, s) + p = proj[:, 2 * h * qd :].reshape(h, pd, 1, s) + + # (H, S_q, 1, S_k): contract channel axis, keep S on the last axis. + attn_scores = torch.einsum("hcoq,hcok->hqok", q, k) + # p: (H, phd, 1, S_q) -> (H, phd, S_q, 1); broadcast over S_k, reduce phd. + pos_scores = (p.permute(0, 1, 3, 2) * self.pos_abs).sum(dim=1, keepdim=True) + # (H, 1, S_q, S_k) -> (H, S_q, 1, S_k): layout-preserving reshape. + attn_scores = attn_scores + pos_scores.reshape(h, s, 1, s) + return attn_scores.softmax(dim=-1) + + +class AneSelfAttention(nn.Module): + """SelfAttention (apply precomputed weights) on (1, C, 1, S).""" + + def __init__(self, sa, num_heads: int): + super().__init__() + self.num_heads = num_heads + self.in_proj = linear_to_conv2d(sa.in_proj) + self.out_proj = linear_to_conv2d(sa.out_proj) + self.value_head_dim = sa.in_proj.out_features // num_heads + + def forward(self, x: Tensor, attn_weights: Tensor) -> Tensor: + h = self.num_heads + vd = self.value_head_dim + s = x.shape[-1] + v = self.in_proj(x).reshape(h, vd, 1, s) # feature index = h*vd + c + out = torch.einsum("hqok,hcok->hcoq", attn_weights, v) + out = out.reshape(1, h * vd, 1, s) + return self.out_proj(out) + + +class AneNonlinAttention(nn.Module): + """NonlinAttention on (1, C, 1, S); consumes head-0 attention weights.""" + + def __init__(self, na): + super().__init__() + self.hidden_channels = na.hidden_channels + self.in_proj = linear_to_conv2d(na.in_proj) + self.out_proj = linear_to_conv2d(na.out_proj) + + def forward(self, x: Tensor, attn_weights: Tensor) -> Tensor: + # attn_weights: (1, S, 1, S) — head 0 only. + hc = self.hidden_channels + x = self.in_proj(x) # (1, 3*hc, 1, S) + s_gate = torch.tanh(x[:, :hc]) + v = x[:, hc : 2 * hc] * s_gate + y = x[:, 2 * hc :] + v = torch.einsum("bqok,bcok->bcoq", attn_weights, v) + return self.out_proj(v * y) + + +class AneConvolutionModule(nn.Module): + """ConvolutionModule: 1x1 conv -> GLU-ish gate -> depthwise (1,31) conv + -> SwooshR -> 1x1 conv, all on (1, C, 1, S). + + The ANE rejects grouped convs with kernel width > 15 ("large kernel + size"; empirically 16 falls back, 15 places). The kernel-31 depthwise + conv is split exactly into taps 0..14 + 15..29 (kernel 15 each) + tap + 30 (kernel 1): + y[t] = sum_{k=0..30} w[k] x[t+k-15] = yA[t] + yB[t] + yC[t] + Asymmetric offsets are realized with the convs' own symmetric padding + plus output slices (standalone pad ops are not ANE-placeable and drag + the convs onto CPU with them). + """ + + def __init__(self, cm): + super().__init__() + self.in_proj = linear_to_conv2d(cm.in_proj) + dw = cm.depthwise_conv # Conv1d(C, C, 31, groups=C, padding=15) + c, _, k = dw.weight.shape + assert k == 31 + self.channels = c + self.dw_a = nn.Conv2d( + c, c, (1, 15), groups=c, padding=(0, 15), bias=dw.bias is not None + ) + self.dw_b = nn.Conv2d(c, c, (1, 15), groups=c, padding=(0, 15), bias=False) + self.dw_c = nn.Conv2d(c, c, (1, 1), groups=c, padding=(0, 15), bias=False) + with torch.no_grad(): + self.dw_a.weight.copy_(dw.weight[:, :, :15].reshape(c, 1, 1, 15)) + self.dw_b.weight.copy_(dw.weight[:, :, 15:30].reshape(c, 1, 1, 15)) + self.dw_c.weight.copy_(dw.weight[:, :, 30:].reshape(c, 1, 1, 1)) + if dw.bias is not None: + self.dw_a.bias.copy_(dw.bias) + assert cm.out_proj.activation == "SwooshR" + self.out_proj = _adl_to_conv2d(cm.out_proj) + + def forward(self, x: Tensor) -> Tensor: + c = self.channels + s = x.shape[-1] + x = self.in_proj(x) # (1, 2C, 1, S) + x = x[:, :c] * torch.sigmoid(x[:, c:]) + # With padding p, output index i covers window starting at x[i-p]. + # A taps x[t-15..t-1]: start t-15 => i=t => [:S] + # B taps x[t..t+14]: start t => i=t+15 => [15:15+S] + # C tap x[t+15]: start t+15 => i=t+30 => [30:30+S] + ya = self.dw_a(x)[..., :s] + yb = self.dw_b(x)[..., 15 : 15 + s] + yc = self.dw_c(x)[..., 30 : 30 + s] + return self.out_proj(swoosh_r(ya + yb + yc)) + + +class AneZipformerLayer(nn.Module): + """Numerically-exact ANE-canonical Zipformer2EncoderLayer. + + forward(x, time_emb): x (1, C, 1, S), time_emb (1, C, 1, 1) — the + already-projected per-timestep embedding the original layer receives + (added to src at three points, broadcast over S). + """ + + def __init__(self, layer, pos_emb: Tensor, seq_len: int): + super().__init__() + self.self_attn_weights = AneAttentionWeights( + layer.self_attn_weights, pos_emb, seq_len + ) + h = layer.self_attn_weights.num_heads + self.self_attn1 = AneSelfAttention(layer.self_attn1, h) + self.self_attn2 = AneSelfAttention(layer.self_attn2, h) + self.feed_forward1 = AneFeedforward(layer.feed_forward1) + self.feed_forward2 = AneFeedforward(layer.feed_forward2) + self.feed_forward3 = AneFeedforward(layer.feed_forward3) + self.nonlin_attention = AneNonlinAttention(layer.nonlin_attention) + self.conv_module1 = AneConvolutionModule(layer.conv_module1) + self.conv_module2 = AneConvolutionModule(layer.conv_module2) + self.norm = AneBiasNorm(layer.norm) + self.bypass = AneBypass(layer.bypass) + self.bypass_mid = AneBypass(layer.bypass_mid) + + def forward(self, x: Tensor, time_emb: Tensor) -> Tensor: + src_orig = x + attn_weights = self.self_attn_weights(x) # (H, S, 1, S) + + x = x + time_emb + x = x + self.feed_forward1(x) + x = x + self.nonlin_attention(x, attn_weights[0:1]) + x = x + self.self_attn1(x, attn_weights) + + x = x + time_emb + x = x + self.conv_module1(x) + x = x + self.feed_forward2(x) + x = self.bypass_mid(src_orig, x) + + x = x + self.self_attn2(x, attn_weights) + x = x + time_emb + x = x + self.conv_module2(x) + x = x + self.feed_forward3(x) + + x = self.norm(x) + return self.bypass(src_orig, x) + + +def tbc_to_ane(x: Tensor) -> Tensor: + """(T, B=1, C) -> (1, C, 1, S=T).""" + return x.permute(1, 2, 0).unsqueeze(2).contiguous() + + +def ane_to_tbc(x: Tensor) -> Tensor: + """(1, C, 1, S) -> (S, 1, C).""" + return x.squeeze(2).permute(2, 0, 1).contiguous() diff --git a/models/tts/zipvoice/coreml/ane/parity.py b/models/tts/zipvoice/coreml/ane/parity.py new file mode 100644 index 0000000..e1c7893 --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/parity.py @@ -0,0 +1,172 @@ +"""fp32 eager parity: AneZipformerLayer vs original Zipformer2EncoderLayer. + +Gates (hard-fail): + - per-submodule max_abs_diff < 1e-3 + - whole-layer max_abs_diff < 1e-2 and cosine > 0.9999 + +Run: .venv/bin/python -m coreml.ane.parity +""" + +import sys + +sys.path.insert(0, ".") + +import torch + +from coreml.ane.layer import AneZipformerLayer, ane_to_tbc, tbc_to_ane +from coreml.convert_coreml import ( + load_model, + patch_coremltools_int, + patch_simple_downsample, +) + +SEQ_LEN = 1024 +EMBED_DIM = 512 + +SUBMODULE_GATE = 1e-3 +LAYER_GATE = 1e-2 +LAYER_COS_GATE = 0.9999 + + +def stats(a: torch.Tensor, b: torch.Tensor): + a = a.detach().flatten().double() + b = b.detach().flatten().double() + max_abs = (a - b).abs().max().item() + cos = torch.nn.functional.cosine_similarity(a, b, dim=0).item() + return max_abs, cos + + +def main(): + torch.manual_seed(0) + patch_coremltools_int() + patch_simple_downsample() + model, _ = load_model() + model.eval() + + enc0 = model.fm_decoder.encoders[0] + layer = enc0.layers[0] if hasattr(enc0, "layers") else enc0.encoder.layers[0] + + # Real positional embedding, captured once from the jit-scripted encoder_pos. + with torch.no_grad(): + pos_emb = enc0.encoder_pos(torch.randn(SEQ_LEN, 1, EMBED_DIM)).detach() + + src = torch.randn(SEQ_LEN, 1, EMBED_DIM) + time_emb = torch.randn(1, EMBED_DIM) # already projected, as the layer sees it + + # Capture original submodule inputs/outputs via hooks. + captured = {} + names = [ + "self_attn_weights", + "feed_forward1", + "feed_forward2", + "feed_forward3", + "nonlin_attention", + "self_attn1", + "self_attn2", + "conv_module1", + "conv_module2", + "norm", + "bypass_mid", + "bypass", + ] + hooks = [] + for name in names: + mod = getattr(layer, name) + + def hook(m, args, output, name=name): + captured[name] = ( + tuple(a.detach().clone() if isinstance(a, torch.Tensor) else a for a in args), + output.detach().clone(), + ) + + hooks.append(mod.register_forward_hook(hook)) + + with torch.no_grad(): + ref = layer(src, pos_emb, time_emb=time_emb) + for h in hooks: + h.remove() + + ane = AneZipformerLayer(layer, pos_emb, SEQ_LEN).eval() + + rows = [] + failures = [] + + def check(name, orig_out, ane_out, gate=SUBMODULE_GATE): + max_abs, cos = stats(orig_out, ane_out) + ok = max_abs < gate + rows.append((name, max_abs, cos, gate, ok)) + if not ok: + failures.append(name) + + def w_to_ane(w): # (H, 1, Sq, Sk) -> (H, Sq, 1, Sk) + return w.permute(0, 2, 1, 3).contiguous() + + with torch.no_grad(): + # Attention weights. + (aw_args, aw_out) = captured["self_attn_weights"] + ane_aw = ane.self_attn_weights(tbc_to_ane(aw_args[0])) + check("self_attn_weights", aw_out, ane_aw.permute(0, 2, 1, 3)) + + # Feedforwards. + for name in ("feed_forward1", "feed_forward2", "feed_forward3"): + args, out = captured[name] + check(name, out, ane_to_tbc(getattr(ane, name)(tbc_to_ane(args[0])))) + + # Nonlin attention (head-0 weights). + args, out = captured["nonlin_attention"] + check( + "nonlin_attention", + out, + ane_to_tbc(ane.nonlin_attention(tbc_to_ane(args[0]), w_to_ane(args[1]))), + ) + + # Self attentions (all heads). + for name in ("self_attn1", "self_attn2"): + args, out = captured[name] + check( + name, + out, + ane_to_tbc(getattr(ane, name)(tbc_to_ane(args[0]), w_to_ane(args[1]))), + ) + + # Convolution modules. + for name in ("conv_module1", "conv_module2"): + args, out = captured[name] + check(name, out, ane_to_tbc(getattr(ane, name)(tbc_to_ane(args[0])))) + + # Norm and bypasses. + args, out = captured["norm"] + check("norm", out, ane_to_tbc(ane.norm(tbc_to_ane(args[0])))) + for name in ("bypass_mid", "bypass"): + args, out = captured[name] + check( + name, + out, + ane_to_tbc(getattr(ane, name)(tbc_to_ane(args[0]), tbc_to_ane(args[1]))), + ) + + # Whole layer. + ane_out = ane_to_tbc( + ane(tbc_to_ane(src), time_emb.reshape(1, EMBED_DIM, 1, 1)) + ) + layer_max_abs, layer_cos = stats(ref, ane_out) + + print(f"{'submodule':<20} {'max_abs_diff':>14} {'cosine':>12} {'gate':>8} ok") + for name, max_abs, cos, gate, ok in rows: + print(f"{name:<20} {max_abs:>14.3e} {cos:>12.8f} {gate:>8.0e} {'PASS' if ok else 'FAIL'}") + layer_ok = layer_max_abs < LAYER_GATE and layer_cos > LAYER_COS_GATE + print( + f"{'WHOLE LAYER':<20} {layer_max_abs:>14.3e} {layer_cos:>12.8f} " + f"{LAYER_GATE:>8.0e} {'PASS' if layer_ok else 'FAIL'} (cos gate {LAYER_COS_GATE})" + ) + if not layer_ok: + failures.append("WHOLE LAYER") + + if failures: + print(f"FAILED gates: {failures}") + sys.exit(1) + print("all parity gates passed") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/ane/pipeline.py b/models/tts/zipvoice/coreml/ane/pipeline.py new file mode 100644 index 0000000..c3d5b9e --- /dev/null +++ b/models/tts/zipvoice/coreml/ane/pipeline.py @@ -0,0 +1,209 @@ +"""Full-pipeline quality + speed harness for the ANE-canonical FmDecoder. + +Adapts coreml/parity.py: CoreML TextEncoder + AneFmDecoder (4-step host +solver, torch vocoder), compared against the PyTorch oracle at each stage. +Adds wav metrics (log-mel cos, RMS delta), whisper-base transcription, and +predict latency / core RTFx for the requested compute units. + +Run: .venv/bin/python -m coreml.ane.pipeline --compute-units CPU_AND_NE +""" + +import argparse +import json +import sys +import time +from pathlib import Path + +sys.path.insert(0, ".") + +import coremltools as ct +import numpy as np +import soundfile as sf +import torch + +from coreml.convert_coreml import MAX_TOKENS, load_model +from coreml.parity import cos, expand_text_condition +from zipvoice.models.modules.solver import get_time_steps + +SEQ_LEN = 1024 +FRAME_RATE = 93.75 # feature frames per second + + +def timeit(fn, runs=10, warmup=3): + for _ in range(warmup): + fn() + ts = [] + for _ in range(runs): + t0 = time.perf_counter() + fn() + ts.append((time.perf_counter() - t0) * 1e3) + ts = np.array(ts) + return ts.mean(), ts.std() + + +def log_mel_cos(ref, test, sr=48000): + import librosa + + def lm(x): + m = librosa.feature.melspectrogram(y=x, sr=sr, n_fft=2048, hop_length=512, n_mels=128) + return np.log(np.maximum(m, 1e-10)) + + return cos(lm(ref), lm(test)) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--oracle-dir", default="build/oracle") + parser.add_argument("--coreml-dir", default="build/coreml-ane") + parser.add_argument( + "--compute-units", + default="CPU_AND_NE", + choices=["ALL", "CPU_ONLY", "CPU_AND_NE", "CPU_AND_GPU"], + ) + parser.add_argument("--runs", type=int, default=10) + parser.add_argument("--skip-quality", action="store_true", help="latency only") + parser.add_argument("--transcribe", action="store_true") + args = parser.parse_args() + + oracle = Path(args.oracle_dir) + meta = json.loads((oracle / "meta.json").read_text()) + prompt_features = torch.from_numpy(np.load(oracle / "prompt_features.npy")) + prompt_tokens = np.load(oracle / "prompt_tokens.npy").tolist() + text_tokens = np.load(oracle / "text_tokens.npy").tolist() + prompt_len = meta["prompt_features_lens"] + guidance_scale = meta["guidance_scale"] + num_steps = meta["num_steps"] + speed = 1.0 * 1.3 + + cu = getattr(ct.ComputeUnit, args.compute_units) + t0 = time.perf_counter() + te_ml = ct.models.MLModel(str(Path(args.coreml_dir) / "TextEncoder.mlpackage"), compute_units=cu) + fm_ml = ct.models.MLModel(str(Path(args.coreml_dir) / "AneFmDecoder.mlpackage"), compute_units=cu) + print(f"[load] {args.compute_units}: {(time.perf_counter() - t0) * 1e3:.0f} ms") + + model, _ = load_model() + + # ---- conditions (CoreML text encoder + host expansion, as parity.py) ---- + cat_tokens = prompt_tokens + text_tokens + S = len(cat_tokens) + tok_in = np.full((1, MAX_TOKENS), model.pad_id, dtype=np.int32) + tok_in[0, :S] = cat_tokens + tmask = np.zeros((1, MAX_TOKENS), dtype=np.float32) + tmask[0, S:] = 1.0 + cm_embed = te_ml.predict({"tokens": tok_in, "padding_mask": tmask})["token_embeds"] + cm_embed = torch.from_numpy(cm_embed[:, : S + 1, :].astype(np.float32)) + + features_len = prompt_len + int(np.ceil(prompt_len / len(prompt_tokens) * len(text_tokens) / speed)) + gen_seconds = (features_len - prompt_len) / FRAME_RATE + text_condition = expand_text_condition(cm_embed, S, features_len) + speech_condition = torch.nn.functional.pad( + prompt_features, (0, 0, 0, features_len - prompt_features.size(1)) + ) + + with torch.no_grad(): + ref_text_condition, _ = model.forward_text_inference_ratio_duration( + tokens=[text_tokens], prompt_tokens=[prompt_tokens], + prompt_features_lens=torch.tensor([prompt_len]), speed=speed, + ) + + def pad_T(z): + return torch.nn.functional.pad(z, (0, 0, 0, SEQ_LEN - z.size(1))) + + fmask = np.zeros((1, SEQ_LEN), dtype=np.float32) + fmask[0, features_len:] = 1.0 + text_np = pad_T(text_condition).numpy().astype(np.float32) + speech_np = pad_T(speech_condition).numpy().astype(np.float32) + + def predict(t_cur, x_np): + return fm_ml.predict( + { + "t": np.array([t_cur], dtype=np.float32), + "x": x_np, + "text_condition": text_np, + "speech_condition": speech_np, + "guidance_scale": np.array([guidance_scale], dtype=np.float32), + "padding_mask": fmask, + } + )["v"] + + # ---- solver loop: CoreML ANE decoder vs torch oracle ---- + timesteps = get_time_steps(num_step=num_steps, t_shift=0.5) + if not args.skip_quality: + torch.manual_seed(meta["seed"]) + x = torch.randn(1, features_len, 100) + x_ref = x.clone() + ref_mask = torch.zeros(1, features_len, dtype=torch.bool) + for step in range(num_steps): + t_cur, t_next = float(timesteps[step]), float(timesteps[step + 1]) + v = predict(t_cur, pad_T(x).numpy().astype(np.float32)) + v = torch.from_numpy(v[:, :features_len, :].astype(np.float32)) + with torch.no_grad(): + v_ref = model.forward_fm_decoder( + t=torch.tensor(t_cur), xt=x_ref, text_condition=ref_text_condition, + speech_condition=speech_condition, padding_mask=ref_mask, + guidance_scale=torch.tensor(guidance_scale), + ) + print( + f"[fm_decoder step {step} t={t_cur:.3f}] cos={cos(v_ref.numpy(), v.numpy()):.6f} " + f"max_abs_diff={float((v_ref - v).abs().max()):.4e}" + ) + + def euler(x_s, v_s): + x1p = x_s + (1.0 - t_cur) * v_s + x0p = x_s - t_cur * v_s + return (1.0 - t_next) * x0p + t_next * x1p if step < num_steps - 1 else x1p + + x, x_ref = euler(x, v), euler(x_ref, v_ref) + + print(f"[final mel] cos={cos(x_ref.numpy(), x.numpy()):.6f}") + + # ---- vocoder + wav metrics ---- + from scripts.reference_infer import load_models_cpu_torch + + _, _, vocos, _, _ = load_models_cpu_torch() + vocos.freq_range = 12000 + vocos.return_48k = True + with torch.no_grad(): + mel = x[:, prompt_len:features_len, :].permute(0, 2, 1) / 0.1 + wav = vocos.decode(mel).squeeze(1).clamp(-1, 1) + wav_np = wav.numpy().squeeze() + if meta["prompt_rms"] < 0.1: + wav_np = wav_np * (meta["prompt_rms"] / 0.1) + + ref_wav, sr = sf.read(oracle / "reference_48k.wav") + n = min(len(ref_wav), len(wav_np)) + rms_ref = float(np.sqrt((ref_wav[:n] ** 2).mean())) + rms_cm = float(np.sqrt((wav_np[:n] ** 2).mean())) + print( + f"[wav] waveform cos={cos(ref_wav[:n], wav_np[:n]):.4f} " + f"log-mel cos={log_mel_cos(ref_wav[:n].astype(np.float32), wav_np[:n].astype(np.float32)):.5f} " + f"RMS {rms_cm:.4f} vs {rms_ref:.4f} (delta {20 * np.log10(rms_cm / rms_ref):+.3f} dB)" + ) + out = Path(args.coreml_dir) / "parity_48k.wav" + sf.write(out, wav_np, 48000) + print(f"wrote {out}") + + if args.transcribe: + import librosa + from transformers import pipeline + + asr = pipeline("automatic-speech-recognition", model="openai/whisper-base", device="cpu") + wav16 = librosa.resample(wav_np.astype(np.float32), orig_sr=48000, target_sr=16000) + ref16 = librosa.resample(np.asarray(ref_wav, dtype=np.float32), orig_sr=48000, target_sr=16000) + print(f"[transcript coreml-ane] {asr(wav16)['text'].strip()}") + print(f"[transcript oracle] {asr(ref16)['text'].strip()}") + + # ---- latency + core RTFx ---- + x_rand = np.random.default_rng(0).standard_normal((1, SEQ_LEN, 100)).astype(np.float32) + te_ms, te_sd = timeit(lambda: te_ml.predict({"tokens": tok_in, "padding_mask": tmask}), args.runs) + fm_ms, fm_sd = timeit(lambda: predict(0.5, x_rand), args.runs) + core_ms = te_ms + num_steps * fm_ms + print( + f"[latency {args.compute_units}] text_encoder {te_ms:.2f}±{te_sd:.2f} ms | " + f"fm_decoder/step {fm_ms:.2f}±{fm_sd:.2f} ms | core (te+{num_steps} steps) {core_ms:.1f} ms | " + f"gen {gen_seconds:.3f}s -> core RTFx {gen_seconds * 1e3 / core_ms:.1f}x" + ) + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/audio_similarity.py b/models/tts/zipvoice/coreml/audio_similarity.py new file mode 100644 index 0000000..e4cd464 --- /dev/null +++ b/models/tts/zipvoice/coreml/audio_similarity.py @@ -0,0 +1,97 @@ +"""Energy/dB similarity between the CoreML-pipeline wav and the PyTorch oracle wav. + +Reports level (dBFS), frame-energy envelope agreement, per-band energy deltas, +and spectral distances. Waveform-phase-insensitive by design, since the flow +decoder's fp16 noise shifts vocoder phase without perceptual effect. + +Usage: + .venv/bin/python -m coreml.audio_similarity \ + --ref build/oracle/reference_48k.wav --test build/coreml/parity_48k.wav +""" + +import argparse + +import numpy as np +import soundfile as sf + +EPS = 1e-12 + + +def db(x): + return 10.0 * np.log10(np.maximum(x, EPS)) + + +def frame_rms(x, frame, hop): + n = 1 + max(0, (len(x) - frame) // hop) + idx = np.arange(frame)[None, :] + hop * np.arange(n)[:, None] + return np.sqrt((x[idx] ** 2).mean(axis=1)) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--ref", default="build/oracle/reference_48k.wav") + parser.add_argument("--test", default="build/coreml/parity_48k.wav") + parser.add_argument("--sr", type=int, default=48000) + args = parser.parse_args() + + ref, sr = sf.read(args.ref) + test, sr2 = sf.read(args.test) + assert sr == sr2 == args.sr + n = min(len(ref), len(test)) + ref, test = ref[:n].astype(np.float64), test[:n].astype(np.float64) + dur = n / sr + + print(f"samples={n} ({dur:.3f}s @ {sr}Hz)") + + # ---- overall level ---- + rms_ref, rms_test = np.sqrt((ref**2).mean()), np.sqrt((test**2).mean()) + print("\n== level ==") + print(f"RMS ref {20*np.log10(rms_ref):+7.2f} dBFS | coreml {20*np.log10(rms_test):+7.2f} dBFS " + f"| delta {20*np.log10(rms_test/rms_ref):+.3f} dB") + print(f"peak ref {20*np.log10(np.abs(ref).max()):+7.2f} dBFS | coreml {20*np.log10(np.abs(test).max()):+7.2f} dBFS " + f"| delta {20*np.log10(np.abs(test).max()/np.abs(ref).max()):+.3f} dB") + print(f"total energy ratio {db((test**2).sum()) - db((ref**2).sum()):+.3f} dB") + + # ---- short-time energy envelope (25 ms / 10 ms) ---- + frame, hop = int(0.025 * sr), int(0.010 * sr) + er, et = frame_rms(ref, frame, hop), frame_rms(test, frame, hop) + env_corr = np.corrcoef(er, et)[0, 1] + dr, dt = 20 * np.log10(np.maximum(er, EPS)), 20 * np.log10(np.maximum(et, EPS)) + active = dr > (dr.max() - 40) # ignore silence tails below -40 dB rel + diff_db = dt[active] - dr[active] + print("\n== short-time energy envelope (25ms/10ms) ==") + print(f"frames={len(er)} active={active.sum()}") + print(f"envelope corr (linear RMS) {env_corr:.5f}") + print(f"per-frame level diff (active): mean {diff_db.mean():+.3f} dB | median {np.median(diff_db):+.3f} dB " + f"| std {diff_db.std():.3f} dB | max|.| {np.abs(diff_db).max():.3f} dB") + print(f"envelope SNR: {db((er**2).sum()) - db(((er-et)**2).sum()):.2f} dB") + + # ---- per-band energy (octave bands) ---- + spec_r = np.abs(np.fft.rfft(ref)) ** 2 + spec_t = np.abs(np.fft.rfft(test)) ** 2 + freqs = np.fft.rfftfreq(n, 1 / sr) + print("\n== per-band energy delta (coreml - ref) ==") + edges = [63, 125, 250, 500, 1000, 2000, 4000, 8000, 12000, 16000, 24000] + lo = 20 + for hi in edges: + m = (freqs >= lo) & (freqs < hi) + delta = db(spec_t[m].sum()) - db(spec_r[m].sum()) + print(f" {lo:>5}-{hi:<5} Hz: {delta:+6.2f} dB") + lo = hi + + # ---- spectral distances (STFT 1024/256, magnitude) ---- + win = np.hanning(1024) + def stft_mag(x): + nf = 1 + (len(x) - 1024) // 256 + idx = np.arange(1024)[None, :] + 256 * np.arange(nf)[:, None] + return np.abs(np.fft.rfft(x[idx] * win, axis=1)) + mr, mt = stft_mag(ref), stft_mag(test) + sc = np.linalg.norm(mr - mt) / np.linalg.norm(mr) + lsd = np.sqrt(((db(mt**2) - db(mr**2)) ** 2).mean(axis=1)).mean() + print("\n== spectral ==") + print(f"spectral convergence {sc:.4f} (lower=better; <0.15 is near-identical)") + print(f"log-spectral distance {lsd:.3f} dB (mean over frames)") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/benchmark.py b/models/tts/zipvoice/coreml/benchmark.py new file mode 100644 index 0000000..9909a2d --- /dev/null +++ b/models/tts/zipvoice/coreml/benchmark.py @@ -0,0 +1,133 @@ +"""Benchmark the CoreML LuxTTS pipeline vs the PyTorch reference. + +Times each component (text encoder, per-step fm decoder, torch vocoder) and +reports end-to-end synthesis latency + RTFx for the oracle utterance, across +compute units. Uses the same oracle inputs as coreml/parity.py. + +Usage: + .venv/bin/python -m coreml.benchmark --compute-units ALL --runs 10 +""" + +import argparse +import json +import time +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch + +from coreml.convert_coreml import MAX_FRAMES, MAX_TOKENS, load_model +from zipvoice.models.modules.solver import get_time_steps + + +def timeit(fn, runs, warmup=2): + for _ in range(warmup): + fn() + ts = [] + for _ in range(runs): + t0 = time.perf_counter() + fn() + ts.append((time.perf_counter() - t0) * 1000) + ts = np.array(ts) + return ts.mean(), ts.std(), ts.min() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--oracle-dir", default="build/oracle") + parser.add_argument("--coreml-dir", default="build/coreml") + parser.add_argument("--compute-units", default="ALL", choices=["ALL", "CPU_ONLY", "CPU_AND_NE", "CPU_AND_GPU"]) + parser.add_argument("--runs", type=int, default=10) + parser.add_argument("--torch-baseline", action="store_true", help="also time the pure-torch pipeline") + args = parser.parse_args() + + oracle = Path(args.oracle_dir) + meta = json.loads((oracle / "meta.json").read_text()) + prompt_features = torch.from_numpy(np.load(oracle / "prompt_features.npy")) + prompt_tokens = np.load(oracle / "prompt_tokens.npy").tolist() + text_tokens = np.load(oracle / "text_tokens.npy").tolist() + prompt_len = meta["prompt_features_lens"] + num_steps = meta["num_steps"] + gen_seconds = meta["wav_seconds_48k"] + + cu = getattr(ct.ComputeUnit, args.compute_units) + t0 = time.perf_counter() + te = ct.models.MLModel(str(Path(args.coreml_dir) / "TextEncoder.mlpackage"), compute_units=cu) + fm = ct.models.MLModel(str(Path(args.coreml_dir) / "FmDecoder.mlpackage"), compute_units=cu) + load_ms = (time.perf_counter() - t0) * 1000 + + cat = prompt_tokens + text_tokens + S = len(cat) + tok_in = np.zeros((1, MAX_TOKENS), dtype=np.int32) + tok_in[0, :S] = cat + tmask = np.zeros((1, MAX_TOKENS), dtype=np.float32) + tmask[0, S:] = 1.0 + + speed = 1.3 + features_len = prompt_len + int(np.ceil(prompt_len / len(prompt_tokens) * len(text_tokens) / speed)) + fmask = np.zeros((1, MAX_FRAMES), dtype=np.float32) + fmask[0, features_len:] = 1.0 + x = np.random.default_rng(0).standard_normal((1, MAX_FRAMES, 100)).astype(np.float32) + cond = np.random.default_rng(1).standard_normal((1, MAX_FRAMES, 100)).astype(np.float32) + timesteps = get_time_steps(num_step=num_steps, t_shift=0.5) + + print(f"compute_units={args.compute_units} runs={args.runs}") + print(f"utterance: prompt {prompt_len} + gen {features_len - prompt_len} frames " + f"(bucket {MAX_FRAMES}), gen audio {gen_seconds:.3f}s @48k") + print(f"model load: {load_ms:.0f} ms\n") + + m, s, lo = timeit(lambda: te.predict({"tokens": tok_in, "padding_mask": tmask}), args.runs) + print(f"text_encoder : {m:7.2f} ± {s:.2f} ms (min {lo:.2f})") + te_ms = m + + def one_step(t): + return fm.predict({ + "t": np.array([t], dtype=np.float32), "x": x, + "text_condition": cond, "speech_condition": cond, + "guidance_scale": np.array([3.0], dtype=np.float32), "padding_mask": fmask, + }) + m, s, lo = timeit(lambda: one_step(0.5), args.runs) + print(f"fm_decoder/step: {m:7.2f} ± {s:.2f} ms (min {lo:.2f})") + step_ms = m + + def full_core(): + te.predict({"tokens": tok_in, "padding_mask": tmask}) + for i in range(num_steps): + one_step(float(timesteps[i])) + m, s, lo = timeit(full_core, args.runs) + print(f"core pipeline : {m:7.2f} ± {s:.2f} ms (min {lo:.2f}) " + f"[te + {num_steps} steps; predicted {te_ms + num_steps * step_ms:.1f}]") + core_ms = m + + # torch vocoder on the generated mel region + from scripts.reference_infer import load_models_cpu_torch + _, _, vocos, _, _ = load_models_cpu_torch() + vocos.freq_range = 12000 + vocos.return_48k = True + mel = torch.randn(1, 100, features_len - prompt_len) / 0.1 * 0.05 + with torch.no_grad(): + m, s, lo = timeit(lambda: vocos.decode(mel), args.runs) + print(f"vocoder (torch): {m:7.2f} ± {s:.2f} ms (min {lo:.2f})") + voc_ms = m + + total = core_ms + voc_ms + print(f"\nend-to-end : {total:7.2f} ms -> RTFx {gen_seconds * 1000 / total:.1f}x " + f"(core only: {gen_seconds * 1000 / core_ms:.1f}x)") + + if args.torch_baseline: + model, _ = load_model() + xt = torch.from_numpy(x[:, :features_len, :]) + ct_t = torch.from_numpy(cond[:, :features_len, :]) + pm = torch.zeros(1, features_len, dtype=torch.bool) + with torch.no_grad(): + m, s, lo = timeit( + lambda: model.forward_fm_decoder( + t=torch.tensor(0.5), xt=xt, text_condition=ct_t, + speech_condition=ct_t, padding_mask=pm, guidance_scale=torch.tensor(3.0)), + args.runs) + print(f"\n[torch cpu baseline] fm_decoder/step: {m:7.2f} ± {s:.2f} ms (min {lo:.2f})") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/convert_coreml.py b/models/tts/zipvoice/coreml/convert_coreml.py new file mode 100644 index 0000000..b0c3ac1 --- /dev/null +++ b/models/tts/zipvoice/coreml/convert_coreml.py @@ -0,0 +1,254 @@ +"""Convert LuxTTS (ZipVoice-Distill) text_encoder + fm_decoder to CoreML. + +Component boundaries mirror upstream zipvoice/bin/onnx_export.py, except the +data-dependent token-duration expansion stays on the host (CoreML cannot +express dynamic expand), so the text encoder emits per-token embeddings. +The 4-step anchor-Euler solver loop and the vocoder also stay on the host +for this trial. + +Usage: + .venv/bin/python coreml/convert_coreml.py --output-dir build/coreml +""" + +import argparse +import json +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch +from huggingface_hub import snapshot_download +from torch import Tensor, nn + +from zipvoice.models.zipvoice_distill import ZipVoiceDistill +from zipvoice.tokenizer.tokenizer import EmiliaTokenizer +from zipvoice.utils.checkpoint import load_checkpoint +from zipvoice.utils.scaling_converter import convert_scaled_to_non_scaled + +MAX_TOKENS = 256 # default fixed token bucket (prompt + text) +MAX_FRAMES = 1024 # default fixed feature bucket (~10.9 s at 93.75 Hz) +FEAT_DIM = 100 + + +class FrozenPosEmb(nn.Module): + """Constant replacement for CompactRelPositionalEncoding under fixed shapes.""" + + def __init__(self, pos_emb: Tensor): + super().__init__() + self.register_buffer("pos_emb", pos_emb) + + def forward(self, x: Tensor, left_context_len: int = 0) -> Tensor: + return self.pos_emb + + +def patch_pos_encodings(module: nn.Module, example_inputs: tuple): + """Freeze positional-encoding outputs as constants for tracing. + + convert_scaled_to_non_scaled jit-scripts encoder_pos, whose in-graph + shape math becomes a slice_by_index with fp32 begin that CoreML rejects. + Shapes are fixed here, so probe each Zipformer2Encoder's input length + eagerly and swap encoder_pos for a constant-buffer module. + """ + parents = [m for m in module.modules() if hasattr(m, "encoder_pos")] + captured = {} + + hooks = [ + m.register_forward_pre_hook(lambda mod, args: captured.__setitem__(id(mod), args[0].detach().clone())) + for m in parents + ] + with torch.no_grad(): + module(*example_inputs) + for h in hooks: + h.remove() + + for m in parents: + assert id(m) in captured, "probe pass did not reach an encoder stack" + with torch.no_grad(): + pos_emb = m.encoder_pos(captured[id(m)]).detach().clone() + m.encoder_pos = FrozenPosEmb(pos_emb) + + +def patch_coremltools_int(): + """Let aten::Int handle 1-element constant arrays (shape math folds to size-1 + arrays under fixed shapes; stock handler only accepts 0-d).""" + from coremltools.converters.mil import Builder as mb + from coremltools.converters.mil.frontend.torch import ops as torch_ops + + reg = torch_ops._TORCH_OPS_REGISTRY + orig = reg.get_func("int") + + def _int(context, node): + x = context[node.inputs[0]] + val = getattr(x, "val", None) + if val is not None and np.asarray(val).size == 1: + res = mb.const(val=int(np.asarray(val).reshape(-1)[0]), name=node.name) + context.add(res) + return + return orig(context, node) + + reg.set_func_by_name(_int, "int") + + +def patch_simple_downsample(): + """Skip the repeat-last-row padding when seq_len is already a multiple of + the downsample factor. The zero-size expand+cat that upstream emits for + pad=0 becomes a spurious extra row under coremltools, breaking the + downstream reshape. MAX_FRAMES is chosen divisible by all ds factors.""" + from zipvoice.models.modules.zipformer import SimpleDownsample + + def forward(self, src: Tensor) -> Tensor: + seq_len, batch_size, in_channels = src.shape + ds = self.downsample + d_seq_len = (seq_len + ds - 1) // ds + pad = d_seq_len * ds - seq_len + if pad > 0: # python int under fixed-shape trace + src_extra = src[src.shape[0] - 1 :].expand(pad, src.shape[1], src.shape[2]) + src = torch.cat((src, src_extra), dim=0) + src = src.reshape(d_seq_len, ds, batch_size, in_channels) + weights = self.bias.softmax(dim=0) + weights = weights.unsqueeze(-1).unsqueeze(-1) + return (src * weights).sum(dim=1) + + SimpleDownsample.forward = forward + + +class CoreMLTextEncoder(nn.Module): + """embed + Zipformer text encoder -> per-token embeddings (B, S, feat_dim).""" + + def __init__(self, model: nn.Module): + super().__init__() + self.embed = model.embed + self.text_encoder = model.text_encoder + + def forward(self, tokens: Tensor, padding_mask: Tensor) -> Tensor: + # tokens: int32 (1, S); padding_mask: float (1, S), 1.0 = padded + embed = self.embed(tokens) + mask = padding_mask > 0.5 + return self.text_encoder(x=embed, t=None, padding_mask=mask) + + +class CoreMLFmDecoder(nn.Module): + """Single flow-matching step: velocity prediction (distill, guidance embedded).""" + + def __init__(self, model: nn.Module): + super().__init__() + self.fm_decoder = model.fm_decoder + + def forward( + self, + t: Tensor, + x: Tensor, + text_condition: Tensor, + speech_condition: Tensor, + guidance_scale: Tensor, + padding_mask: Tensor, + ) -> Tensor: + # t, guidance_scale: float (1,); x/conditions: (1, T, feat); mask: float (1, T) + xt = torch.cat([x, text_condition, speech_condition], dim=2) + mask = padding_mask > 0.5 + return self.fm_decoder(x=xt, t=t, padding_mask=mask, guidance_scale=guidance_scale) + + +def load_model(): + model_path = snapshot_download("YatharthS/LuxTTS") + tokenizer = EmiliaTokenizer(token_file=f"{model_path}/tokens.txt") + with open(f"{model_path}/config.json") as f: + config = json.load(f) + model = ZipVoiceDistill( + **config["model"], vocab_size=tokenizer.vocab_size, pad_id=tokenizer.pad_id + ) + load_checkpoint(filename=f"{model_path}/model.pt", model=model, strict=True) + model.eval() + convert_scaled_to_non_scaled(model, inplace=True, is_onnx=True) + return model, tokenizer + + +def convert_text_encoder(model, out_dir: Path, max_tokens: int = MAX_TOKENS, target=ct.target.iOS17): + wrapper = CoreMLTextEncoder(model).eval() + tokens = torch.zeros(1, max_tokens, dtype=torch.int32) + tokens[0, :10] = torch.arange(2, 12, dtype=torch.int32) + mask = torch.zeros(1, max_tokens) + mask[0, 10:] = 1.0 + + patch_pos_encodings(wrapper, (tokens, mask)) + with torch.no_grad(): + traced = torch.jit.trace(wrapper, (tokens, mask)) + + mlmodel = ct.convert( + traced, + inputs=[ + ct.TensorType(name="tokens", shape=(1, max_tokens), dtype=np.int32), + ct.TensorType(name="padding_mask", shape=(1, max_tokens), dtype=np.float32), + ], + outputs=[ct.TensorType(name="token_embeds", dtype=np.float32)], + minimum_deployment_target=target, + compute_precision=ct.precision.FLOAT16, + convert_to="mlprogram", + ) + path = out_dir / "TextEncoder.mlpackage" + mlmodel.save(str(path)) + print(f"saved {path}") + + +def convert_fm_decoder(model, out_dir: Path, max_frames: int = MAX_FRAMES, target=ct.target.iOS17): + wrapper = CoreMLFmDecoder(model).eval() + t = torch.tensor([0.5]) + x = torch.randn(1, max_frames, FEAT_DIM) + text_cond = torch.randn(1, max_frames, FEAT_DIM) + speech_cond = torch.randn(1, max_frames, FEAT_DIM) + guidance = torch.tensor([3.0]) + mask = torch.zeros(1, max_frames) + mask[0, max_frames - 124:] = 1.0 + + patch_pos_encodings(wrapper, (t, x, text_cond, speech_cond, guidance, mask)) + with torch.no_grad(): + traced = torch.jit.trace(wrapper, (t, x, text_cond, speech_cond, guidance, mask)) + + mlmodel = ct.convert( + traced, + inputs=[ + ct.TensorType(name="t", shape=(1,), dtype=np.float32), + ct.TensorType(name="x", shape=(1, max_frames, FEAT_DIM), dtype=np.float32), + ct.TensorType(name="text_condition", shape=(1, max_frames, FEAT_DIM), dtype=np.float32), + ct.TensorType(name="speech_condition", shape=(1, max_frames, FEAT_DIM), dtype=np.float32), + ct.TensorType(name="guidance_scale", shape=(1,), dtype=np.float32), + ct.TensorType(name="padding_mask", shape=(1, max_frames), dtype=np.float32), + ], + outputs=[ct.TensorType(name="v", dtype=np.float32)], + minimum_deployment_target=target, + compute_precision=ct.precision.FLOAT16, + convert_to="mlprogram", + ) + path = out_dir / "FmDecoder.mlpackage" + mlmodel.save(str(path)) + print(f"saved {path}") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output-dir", default="build/coreml") + parser.add_argument("--skip-text-encoder", action="store_true") + parser.add_argument("--skip-fm-decoder", action="store_true") + parser.add_argument("--max-tokens", type=int, default=MAX_TOKENS) + parser.add_argument("--max-frames", type=int, default=MAX_FRAMES) + parser.add_argument( + "--deployment-target", default="iOS17", choices=["iOS17", "iOS18"], + help="iOS18 enables grouped-channel palettization downstream", + ) + args = parser.parse_args() + target = getattr(ct.target, args.deployment_target) + + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + patch_coremltools_int() + patch_simple_downsample() + model, _ = load_model() + if not args.skip_text_encoder: + convert_text_encoder(model, out_dir, max_tokens=args.max_tokens, target=target) + if not args.skip_fm_decoder: + convert_fm_decoder(model, out_dir, max_frames=args.max_frames, target=target) + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/dump_swift_fixtures.py b/models/tts/zipvoice/coreml/dump_swift_fixtures.py new file mode 100644 index 0000000..93a81cb --- /dev/null +++ b/models/tts/zipvoice/coreml/dump_swift_fixtures.py @@ -0,0 +1,279 @@ +"""Dump fixtures for the FluidAudio Swift LuxTts backend. + +Produces small JSON + raw-binary fixtures that pin every host-side algorithm +the Swift port implements: + + - EmiliaTokenizer output (prompt + 2 test texts): token strings + ids + - VocosFbank prompt mel (24 kHz, n_fft 1024, hop 256, 100 mels, x0.1 scale) + - duration expansion (prepare_avg_tokens_durations / get_tokens_index) + - anchor-Euler solver timesteps (t_shift 0.5, 4 steps) + a mini numeric + solver trajectory + - an end-to-end CoreML (gpu graphs + Vocoder555) synthesis stat block for + TEXTS[0] (duration + RMS gates; exact waveform match is not expected) + +Usage: + .venv/bin/python -m coreml.dump_swift_fixtures \ + --prompt-audio build/wavs/07_prompt_clip.wav \ + --out-dir /Tests/FluidAudioTests/TTS/LuxTts/Resources +""" + +import argparse +import json +import math +import shutil +from pathlib import Path + +import coremltools as ct +import librosa +import numpy as np +import soundfile as sf +import torch + +from zipvoice.models.modules.solver import get_time_steps +from zipvoice.tokenizer.tokenizer import EmiliaTokenizer +from zipvoice.utils.common import get_tokens_index, prepare_avg_tokens_durations +from zipvoice.utils.feature import VocosFbank +from zipvoice.utils.infer import rms_norm + +TEXTS = [ + "The quick brown fox jumps over the lazy dog, and honestly, it felt great.", + "FluidAudio runs speech models locally on Apple silicon, no cloud required.", +] + +MAX_TOKENS = 256 +MAX_FRAMES = 1024 +FEAT_DIM = 100 +TARGET_RMS = 0.1 +FEAT_SCALE = 0.1 +GUIDANCE_SCALE = 3.0 +NUM_STEPS = 4 +T_SHIFT = 0.5 +SPEED = 1.0 # NOT upstream's hidden 1.3 (clips sentence onsets) +HOP_48K = 512 # 256 at 24 kHz -> 512 at 48 kHz + + +def expansion_fixture(tokens_len: int, features_len: int): + durations = prepare_avg_tokens_durations( + torch.tensor([features_len]), torch.tensor([tokens_len]) + ) + index = get_tokens_index(durations, features_len)[0].tolist() + return { + "tokens_len": tokens_len, + "features_len": features_len, + "avg_token_duration": int(durations[0][0]), + "tokens_index_len": len(index), + "tokens_index_first20": index[:20], + "tokens_index_last20": index[-20:], + "tokens_index": index, + } + + +def features_len_for(prompt_len: int, prompt_tokens: int, text_tokens: int, speed: float) -> int: + return prompt_len + int(np.ceil(prompt_len / prompt_tokens * text_tokens / speed)) + + +def synth_coreml(te, fm, voc555, prompt_ids, text_ids, prompt_features, prompt_len, seed): + """CoreML gpu-path synthesis, mirroring what the Swift host does.""" + cat = prompt_ids + text_ids + S = len(cat) + assert S + 1 <= MAX_TOKENS + + tok = np.zeros((1, MAX_TOKENS), dtype=np.int32) + tok[0, :S] = cat + tmask = np.zeros((1, MAX_TOKENS), dtype=np.float32) + tmask[0, S:] = 1.0 + embeds = te.predict({"tokens": tok, "padding_mask": tmask})["token_embeds"] + embeds = torch.from_numpy(embeds[:, : S + 1, :].astype(np.float32)) + + features_len = features_len_for(prompt_len, len(prompt_ids), len(text_ids), SPEED) + assert features_len <= MAX_FRAMES + + durations = prepare_avg_tokens_durations( + torch.tensor([features_len]), torch.tensor([S]) + ) + index = get_tokens_index(durations, features_len) + text_cond = torch.gather( + embeds, dim=1, index=index.unsqueeze(-1).expand(1, features_len, FEAT_DIM) + ) + speech_cond = torch.nn.functional.pad( + prompt_features, (0, 0, 0, features_len - prompt_features.size(1)) + ) + + pad = lambda z: torch.nn.functional.pad( + z, (0, 0, 0, MAX_FRAMES - z.size(1)) + ).numpy().astype(np.float32) + fmask = np.zeros((1, MAX_FRAMES), dtype=np.float32) + fmask[0, features_len:] = 1.0 + + timesteps = get_time_steps(num_step=NUM_STEPS, t_shift=T_SHIFT) + torch.manual_seed(seed) + x = torch.randn(1, features_len, FEAT_DIM) + text_np, speech_np = pad(text_cond), pad(speech_cond) + for step in range(NUM_STEPS): + t_cur, t_next = float(timesteps[step]), float(timesteps[step + 1]) + v = fm.predict({ + "t": np.array([t_cur], dtype=np.float32), + "x": pad(x), + "text_condition": text_np, + "speech_condition": speech_np, + "guidance_scale": np.array([GUIDANCE_SCALE], dtype=np.float32), + "padding_mask": fmask, + })["v"] + v = torch.from_numpy(v[:, :features_len, :].astype(np.float32)) + x1p, x0p = x + (1.0 - t_cur) * v, x - t_cur * v + x = (1.0 - t_next) * x0p + t_next * x1p if step < NUM_STEPS - 1 else x1p + + gen = features_len - prompt_len + mel = (x[0, prompt_len:features_len, :].numpy().T / FEAT_SCALE).astype(np.float32) + mel_padded = np.full((1, FEAT_DIM, 555), math.log(1e-7), dtype=np.float32) + mel_padded[0, :, :gen] = mel + wav = voc555.predict({"mel": mel_padded})["audio"].squeeze().astype(np.float32) + wav = np.clip(wav[: (gen - 1) * HOP_48K], -1.0, 1.0) + return wav, features_len, gen + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--prompt-audio", default="build/wavs/07_prompt_clip.wav") + parser.add_argument("--staging-dir", default="build/hf-staging") + parser.add_argument("--out-dir", required=True) + parser.add_argument("--seed", type=int, default=42) + args = parser.parse_args() + + out = Path(args.out_dir) + out.mkdir(parents=True, exist_ok=True) + staging = Path(args.staging_dir) + + tokenizer = EmiliaTokenizer(token_file=str(staging / "tokens.txt")) + # The Swift tokenizer tests parse the same token table. + shutil.copy(staging / "tokens.txt", out / "tokens.txt") + + # ---- prompt: load 24k, transcribe, rms-norm, mel ---- + wav24, _ = librosa.load(args.prompt_audio, sr=24000, duration=5) + wav16, _ = librosa.load(args.prompt_audio, sr=16000, duration=5) + + from transformers import pipeline + + transcriber = pipeline( + "automatic-speech-recognition", model="openai/whisper-tiny", device="cpu" + ) + prompt_text = transcriber(wav16)["text"].strip() + print(f"[prompt] transcript: {prompt_text}") + + prompt_wav = torch.from_numpy(wav24).unsqueeze(0) + prompt_wav, prompt_rms = rms_norm(prompt_wav, TARGET_RMS) + prompt_rms = float(prompt_rms) + + fbank = VocosFbank() + prompt_features = fbank.extract(prompt_wav, sampling_rate=24000).unsqueeze(0) * FEAT_SCALE + prompt_len = prompt_features.size(1) + print(f"[prompt] rms={prompt_rms:.5f} frames={prompt_len}") + + # Binary fixtures: post-rms-norm 24k waveform + mel (row-major T x 100, f32le) + prompt_wav_np = prompt_wav[0].numpy().astype(np.float32) + prompt_wav_np.tofile(out / "prompt_24k_f32le.bin") + mel_np = prompt_features[0].numpy().astype(np.float32) + mel_np.tofile(out / "prompt_mel_f32le.bin") + + # ---- tokens ---- + prompt_tokens = tokenizer.texts_to_tokens([prompt_text])[0] + prompt_ids = tokenizer.tokens_to_token_ids([prompt_tokens])[0] + texts_entries = [] + for text in TEXTS: + toks = tokenizer.texts_to_tokens([text])[0] + ids = tokenizer.tokens_to_token_ids([toks])[0] + S = len(prompt_ids) + len(ids) + features_len = features_len_for(prompt_len, len(prompt_ids), len(ids), SPEED) + texts_entries.append({ + "text": text, + "tokens": toks, + "phoneme_string": "".join(toks), + "token_ids": ids, + "cat_tokens_len": S, + "features_len_speed1": features_len, + "expansion": expansion_fixture(S, features_len), + }) + + # ---- solver fixtures ---- + timesteps = get_time_steps(num_step=NUM_STEPS, t_shift=T_SHIFT).tolist() + rng = np.random.RandomState(0) + x = rng.randn(8) + vs = rng.randn(NUM_STEPS, 8) + x_solver = {"x0": x.tolist(), "v_steps": vs.tolist()} + for step in range(NUM_STEPS): + t_cur, t_next = timesteps[step], timesteps[step + 1] + x1p = x + (1.0 - t_cur) * vs[step] + x0p = x - t_cur * vs[step] + x = x1p if step == NUM_STEPS - 1 else (1.0 - t_next) * x0p + t_next * x1p + x_solver["x_final"] = x.tolist() + + # ---- e2e CoreML (gpu graphs + Vocoder555) for TEXTS[0] ---- + cu = ct.ComputeUnit.CPU_AND_GPU + te = ct.models.CompiledMLModel(str(staging / "gpu/TextEncoder.mlmodelc"), compute_units=cu) + fm = ct.models.CompiledMLModel(str(staging / "gpu/FmDecoder.mlmodelc"), compute_units=cu) + voc = ct.models.CompiledMLModel( + str(staging / "vocoder/Vocoder555.mlmodelc"), compute_units=cu + ) + wav, features_len, gen = synth_coreml( + te, fm, voc, prompt_ids, texts_entries[0]["token_ids"], + prompt_features, prompt_len, args.seed, + ) + if prompt_rms < TARGET_RMS: + wav = wav * (prompt_rms / TARGET_RMS) + rms = float(np.sqrt(np.mean(np.square(wav)))) + sf.write(out / "e2e_python_coreml_48k.wav", wav, 48000) + print(f"[e2e] features_len={features_len} gen={gen} " + f"samples={len(wav)} ({len(wav)/48000:.3f}s) rms={rms:.5f}") + + fixtures = { + "prompt": { + "audio_source": str(Path(args.prompt_audio).name), + "transcript": prompt_text, + "rms_pre_norm": prompt_rms, + "target_rms": TARGET_RMS, + "wav_24k_samples": int(prompt_wav_np.shape[0]), + "mel_frames": prompt_len, + "mel_dim": FEAT_DIM, + "mel_first3_frames": mel_np[:3].tolist(), + "mel_mean": float(mel_np.mean()), + "mel_std": float(mel_np.std()), + "tokens": prompt_tokens, + "phoneme_string": "".join(prompt_tokens), + "token_ids": prompt_ids, + }, + "texts": texts_entries, + "solver": { + "num_steps": NUM_STEPS, + "t_shift": T_SHIFT, + "timesteps": timesteps, + "mini_trajectory": x_solver, + }, + "e2e": { + "text_index": 0, + "seed": args.seed, + "speed": SPEED, + "guidance_scale": GUIDANCE_SCALE, + "features_len": features_len, + "gen_frames": gen, + "wav_samples": int(len(wav)), + "wav_seconds": float(len(wav) / 48000.0), + "rms": rms, + "wav_file": "e2e_python_coreml_48k.wav", + }, + "constants": { + "max_tokens": MAX_TOKENS, + "max_frames": MAX_FRAMES, + "feat_dim": FEAT_DIM, + "feat_scale": FEAT_SCALE, + "sample_rate_mel": 24000, + "sample_rate_out": 48000, + "n_fft": 1024, + "hop_length": 256, + }, + } + (out / "luxtts_fixtures.json").write_text(json.dumps(fixtures, ensure_ascii=False, indent=1)) + print(f"wrote {out / 'luxtts_fixtures.json'}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/g2p/__init__.py b/models/tts/zipvoice/coreml/g2p/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/tts/zipvoice/coreml/g2p/build_corpus.py b/models/tts/zipvoice/coreml/g2p/build_corpus.py new file mode 100644 index 0000000..24b97c9 --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/build_corpus.py @@ -0,0 +1,163 @@ +"""Build the 1,000-sentence English G2P validation corpus for LuxTTS phase 2. + +Mix (fixed seed, deterministic): + - 100 conversational phrases (MiniMax TTS-Multilingual-Test-Set, english) + - 550 LibriSpeech test-clean transcripts (lowercased, sentence-cased, + length-filtered, final period added) + - 250 template sentences exercising numbers, currency, times, dates, + ordinals, decimals, fractions and percentages + - 100 template sentences exercising proper names (/usr/share/dict/propernames) + +The resulting corpus file is committed (corpus_en_1000.txt) so scoring is +reproducible without the source datasets. Re-run only to regenerate it. + +Usage: + .venv/bin/python -m coreml.g2p.build_corpus \ + --minimax /Benchmarks/tts/corpus/minimax/english.txt \ + --librispeech "~/Library/Application Support/FluidAudio/Datasets/LibriSpeech/test-clean" \ + --out coreml/g2p/corpus_en_1000.txt +""" + +import argparse +import random +from pathlib import Path + +SEED = 20260707 + +NUMERIC_TEMPLATES = [ + "The meeting starts at {time} on {month} {ordinal}.", + "It costs {dollars}, which is {percent} more than last year.", + "She ran {decimal} miles in under {cardinal} minutes.", + "The invoice total came to {dollars} after the discount.", + "Chapter {cardinal} begins on page {cardinal2}.", + "The train leaves at {time}, so be there by {time2}.", + "About {percent} of the {cardinal} respondents agreed.", + "The recipe needs {fraction} of a cup of sugar and {cardinal} eggs.", + "He was born in {year} and moved here in {year2}.", + "The temperature dropped to {cardinal} degrees overnight.", + "Their new apartment is {decimal} miles from the office.", + "The {ordinal} floor has {cardinal} rooms and {cardinal2} windows.", + "We waited {cardinal} minutes for the {time} bus.", + "The stock fell {percent} to {dollars} a share.", + "Version {cardinal} ships on {month} {ordinal}, {year}.", + "Only {cardinal} of the {cardinal2} tickets are left.", + "The marathon record is {cardinal} hours and {cardinal2} minutes.", + "My grandfather turned {cardinal} on the {ordinal} of {month}.", + "The bill was {dollars} plus a {percent} tip.", + "Flight {cardinal2} departs at {time} from gate {cardinal}.", +] + +NAME_TEMPLATES = [ + "{name} and {name2} drove to the coast on Saturday.", + "Have you met {name}, the new engineer from the {city} office?", + "{name} said the project would be done by Friday.", + "According to {name2}, the results were never published.", + "{name} introduced {name2} to everyone at the party.", + "The award went to {name} for her work on the harbor bridge.", + "{name} couldn't believe what {name2} had written.", + "Later that evening, {name} called {name2} about the missing report.", + "{name2} grew up near {city} before moving abroad.", + "Everyone agreed that {name} deserved the promotion.", +] + +CITIES = [ + "Boston", "Denver", "Chicago", "Seattle", "Atlanta", "Portland", + "Austin", "Phoenix", "Dallas", "Miami", +] +MONTHS = [ + "January", "February", "March", "April", "May", "June", "July", + "August", "September", "October", "November", "December", +] +FRACTIONS = ["1/2", "1/4", "3/4", "2/3"] + + +def load_minimax(path: Path, limit: int) -> list[str]: + lines = [ + line.strip() + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() and not line.startswith("#") + ] + return lines[:limit] + + +def load_librispeech(root: Path, limit: int, rng: random.Random) -> list[str]: + sentences = [] + for trans in sorted(root.rglob("*.trans.txt")): + for line in trans.read_text(encoding="utf-8").splitlines(): + parts = line.split(" ", 1) + if len(parts) != 2: + continue + text = parts[1].strip().lower() + if not (40 <= len(text) <= 120): + continue + sentences.append(text[0].upper() + text[1:] + ".") + rng.shuffle(sentences) + return sentences[:limit] + + +def numeric_sentences(count: int, rng: random.Random) -> list[str]: + out = [] + while len(out) < count: + template = NUMERIC_TEMPLATES[len(out) % len(NUMERIC_TEMPLATES)] + hour, minute = rng.randint(1, 12), rng.randint(0, 59) + hour2, minute2 = rng.randint(1, 12), rng.randint(0, 59) + n = rng.randint(2, 99) + out.append( + template.format( + time=f"{hour}:{minute:02d} {rng.choice(['AM', 'PM'])}", + time2=f"{hour2}:{minute2:02d} {rng.choice(['AM', 'PM'])}", + month=rng.choice(MONTHS), + ordinal=f"{n}{'th' if 11 <= n % 100 <= 13 else {1: 'st', 2: 'nd', 3: 'rd'}.get(n % 10, 'th')}", + dollars=f"${rng.randint(1, 900)}.{rng.randint(0, 99):02d}", + percent=f"{rng.randint(1, 99)}%", + decimal=f"{rng.randint(1, 40)}.{rng.randint(1, 9)}", + fraction=rng.choice(FRACTIONS), + cardinal=str(rng.randint(2, 120)), + cardinal2=str(rng.randint(2, 900)), + year=str(rng.randint(1900, 2029)), + year2=str(rng.randint(1900, 2029)), + ) + ) + return out + + +def name_sentences(count: int, rng: random.Random) -> list[str]: + names = [ + n.strip() + for n in Path("/usr/share/dict/propernames").read_text().splitlines() + if n.strip() + ] + out = [] + for i in range(count): + template = NAME_TEMPLATES[i % len(NAME_TEMPLATES)] + out.append( + template.format( + name=rng.choice(names), + name2=rng.choice(names), + city=rng.choice(CITIES), + ) + ) + return out + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--minimax", required=True) + parser.add_argument("--librispeech", required=True) + parser.add_argument("--out", default="coreml/g2p/corpus_en_1000.txt") + args = parser.parse_args() + + rng = random.Random(SEED) + corpus = ( + load_minimax(Path(args.minimax).expanduser(), 100) + + load_librispeech(Path(args.librispeech).expanduser(), 550, rng) + + numeric_sentences(250, rng) + + name_sentences(100, rng) + ) + assert len(corpus) == 1000, len(corpus) + Path(args.out).write_text("\n".join(corpus) + "\n", encoding="utf-8") + print(f"wrote {len(corpus)} sentences to {args.out}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/g2p/corpus_en_1000.txt b/models/tts/zipvoice/coreml/g2p/corpus_en_1000.txt new file mode 100644 index 0000000..25c432a --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/corpus_en_1000.txt @@ -0,0 +1,1000 @@ +I guess it comes down a simple choice. Get busy living or get busy dying. +You got a dream, you gotta protect it. People can't do something themselves, they wanna tell you you can't do it. If you want something, go get it. +Life was like a box of chocolates, you never know what you're gonna get. +Is life always this hard. Or is it just when you're a kid?Always like this. +Whoever saves one life, saves the world entire. +There is no such thing as a great talent without great will - power. +I don't wait for moods. You accomplish nothing if you do that. Your mind must know it has got down to work +We take care of the future best by taking care of the present now +There is no development physically or intellectually without effort, and effort means work +You can overcome anything, if and only if you love something enough +Harris's foray on to a network that hosts some of her most vocal critics comes amid a flurry of media appearances with less than three weeks to go to polling day. +However, no transgender surgeries took place in the federal prison system while Trump was president. +The Harris campaign has said this “is not what she is proposing or running on” in the 2024 election. +She went further than she has gone before in trying to place some distance between herself and her boss. +Baier also pressed her on why one of her campaign promises is to “turn the page” when she has been vice-president for more than three years. +Everyone's heart is a piece of sea, shining bottomless blue. +Dance as if no one is watching, work as if never need money, Love as if never been hurt. +Today, at the edge of our hope, at the end of our time, we have chosen not only to believe in ourselves, but in each other. +Everything that needs to be said has already been said. But since no one was listening, everything must be said again. +I always like walking in the rain, So no one can see me crying. +Okay, everybody relax. This is not even a date.It's just two people going out to dinner +Daddy, I just... I can't marry him!I'm sorry. I just don't love him. +Independence. Taking control of your life.And hey, you need anything, you can always come to Joey. +Just try to think of nice calm things...Raindrops on roses and whiskers on kittens,doorbells and sleigh bells and something with mittens... +He finally asked you out?Oh, this is a Dear Diary moment. +Good morning, did you sleep well last night or were you up late working on that project? +I really enjoyed the dinner we had yesterday, especially the homemade pasta with that amazing sauce. +Have you heard about the new coffee shop that just opened around the corner from our office? +The weather forecast says it's going to rain all weekend, so we might need to reschedule our hiking trip. +I've been trying to learn Spanish for the past few months, but I'm finding the pronunciation quite challenging. +My daughter just started kindergarten this week, and she's already made several new friends. +Could you recommend a good book to read on my upcoming vacation to the beach? +I noticed you've been working out regularly, and I must say the results are really impressive. +Do you think we should take the train or drive to the conference next month in Chicago? +I've been having trouble sleeping lately, do you have any suggestions that might help me get better rest? +The traffic was terrible this morning because of construction on the main highway into the city. +Have you tried that new recipe I sent you last week for vegetable curry with coconut milk? +My computer has been running slowly lately, do you know anyone who could help me fix it? +I'm thinking about redecorating my living room with a more modern style, what colors do you think would work well? +We should plan a reunion with our college friends, it's been almost five years since we all got together. +Thank you for helping me move last weekend, I couldn't have done it without your assistance. +I've started practicing meditation every morning, and it's really helping me stay focused throughout the day. +What do you think about the new policy at work regarding flexible hours and working from home? +My son has been asking for a pet dog for his birthday, but I'm not sure our apartment is suitable for one. +Have you seen any good movies lately that you would recommend watching? +Breaking news: Scientists have discovered a new species of deep-sea creatures living near hydrothermal vents in the Pacific Ocean. +The president announced a comprehensive infrastructure plan today that aims to rebuild roads and bridges across the nation over the next decade. +According to the latest economic report, inflation rates have decreased for the third consecutive month, signaling potential stability in the market. +Local authorities have issued evacuation orders for coastal areas as the tropical storm approaches the southeastern shoreline. +The national women's soccer team has advanced to the finals after a thrilling victory in yesterday's semifinal match. +Health officials are urging residents to get vaccinated as the seasonal flu is expected to be particularly severe this year. +The tech giant unveiled its newest smartphone model today, featuring breakthrough battery technology and enhanced camera capabilities. +A major international agreement on climate change was signed by representatives from over one hundred countries at yesterday's summit. +Police are asking for public assistance in locating a missing teenager who was last seen leaving her school on Tuesday afternoon. +The city council has approved plans for a new public park that will transform the abandoned industrial area into a green space with recreational facilities. +Stock markets experienced significant volatility following the central bank's announcement about interest rate adjustments. +A renowned artist's previously unknown painting was discovered in an attic and is expected to fetch millions at auction next month. +The annual film festival will be held virtually this year, allowing movie enthusiasts from around the world to participate online. +Scientists warn that the ongoing drought could lead to water restrictions in several states if rainfall doesn't increase in the coming weeks. +A recent study suggests that regular consumption of dark chocolate may have positive effects on cardiovascular health when consumed in moderation. +The historic building downtown will undergo extensive renovation starting next month to preserve its architectural significance. +Education officials have announced a new curriculum that emphasizes practical skills and digital literacy for elementary school students. +The space agency successfully launched a new satellite designed to monitor climate patterns and collect data on atmospheric conditions. +Researchers have identified a potential breakthrough in Alzheimer's treatment that shows promising results in early clinical trials. +The international music competition has attracted record numbers of participants this year, with contestants from over sixty different countries. +The old lighthouse keeper stared out at the stormy sea, knowing that the approaching ship was in grave danger as it navigated toward the jagged rocks. +She opened the dusty journal she had found in her grandmother's attic and was transported to a world of secret romances and wartime adventures. +The detective examined the crime scene carefully, noticing a small detail that everyone else had overlooked, which would ultimately solve the case. +As the spacecraft broke through the atmosphere of the alien planet, the crew held their breath, unsure of what new life forms they might encounter. +The ancient spell book glowed with an eerie blue light as the young apprentice cautiously turned its pages for the first time. +Walking through the abandoned mansion, she couldn't shake the feeling that someone or something was watching her from the shadows. +The two rival kingdoms had maintained an uneasy peace for generations, but the assassination of the young prince threatened to ignite a devastating war. +Time seemed to slow down as he fell from the bridge, his entire life flashing before his eyes in those few seconds before hitting the water below. +The small village had kept its magical secret for centuries, hidden from the outside world by an enchanted forest that confused any unwelcome visitors. +After fifty years of separation, the twins reunited at their childhood home, instantly recognizing each other despite the decades that had passed. +The coastal restaurant served the freshest seafood imaginable, with dishes prepared using traditional recipes passed down through generations. +Walking through the ancient olive groves of Tuscany, I was struck by how the silvery leaves shimmered in the afternoon sunlight. +The annual food festival brings together culinary traditions from around the world, creating a vibrant celebration of global cuisine. +The mountain trail offered breathtaking views of the valley below, with wildflowers carpeting the meadows in a riot of colors. +This documentary explores the rich cultural heritage of traditional tea ceremonies across different Asian countries. +The historic castle stands majestically on the hilltop, its stone walls telling stories of medieval knights and royal banquets. +The family recipe for apple pie has remained unchanged for over a century, bringing comfort and joy to each new generation. +The crystal-clear waters of the island lagoon revealed an underwater paradise of colorful coral reefs and tropical fish. +The film beautifully captures the relationship between a young artist and his mentor as they navigate challenges together. +Autumn in New England transforms the landscape into a spectacular canvas of red, orange, and golden hues. +The international cooking class taught participants how to blend spices and flavors to create authentic dishes from around the world. +The ancient temple, nestled among mist-covered mountains, offered visitors a sense of peace and spiritual connection. +The award-winning documentary follows the journey of coffee from farm to cup, highlighting the dedication of growers worldwide. +The botanical gardens showcase over five thousand plant species from different climate zones, creating a living museum of biodiversity. +The local market bustles with activity as vendors display fresh produce, handcrafted goods, and regional specialties. +The lighthouse has guided ships safely to harbor for over two centuries, standing as a symbol of hope and direction. +The animated film takes viewers on a magical journey through imaginary worlds filled with endearing characters and valuable life lessons. +The vineyard tours allow visitors to witness the winemaking process firsthand, from grape harvesting to the final tasting. +The historical drama portrays the friendship between two musicians from different backgrounds who find common ground through their art. +The national park preserves ancient redwood forests, some trees dating back more than two thousand years. +The traditional dessert combines local honey, fresh berries, and hand-whipped cream, creating a perfect balance of flavors. +The hot air balloon festival transforms the sky into a colorful canvas as hundreds of balloons take flight at dawn. +The coming-of-age film resonates with audiences of all ages as it explores universal themes of friendship and self-discovery. +The coastal hiking trail winds along dramatic cliffs, offering panoramic views of the ocean and opportunities to spot migrating whales. +The culinary tour introduces visitors to hidden neighborhood restaurants where local chefs prepare authentic regional specialties. +Another circumstance was most remarkable. +When i approached your room i examined the window. +Old dances are simplified of their yearning bleached by time. +Larkspur bit me again this morning for the third time. +Then she gave rosalie back her magic ring thanking the kind witch for all she had done for them. +You will take me on board count will you not. +This outward mutability indicated and did not more than fairly express the various properties of her inner life. +But here he was at a terrible disadvantage as compared with the owls hawks and eagles he had no rending claws. +Why it's in missouri somewhere on the frontier i think we'll get a map. +We have heard something of your story said kenneth and are interested in it. +In this world evidently the vestibule of another there are no fortunate. +He wouldn't search so don't worry replied cyril quietly and the two looked at each other and knew that it was so. +We've tortured each other enough for tonight. +We moderns however see the absurdity of it. +Yes and with all your fingers it took you a year to catch me the king frowned more angrily. +Most of all robin thought of his father what would he counsel. +Frank read english slowly and the more he read about this divorce case the angrier he grew. +She saw that the bed was gilded and so rich that it seemed that of a prince rather than of a private gentleman. +Take your place and let us see what the crystal can show to you. +To diminish the number of the shady to augment the number of the luminous that is the object. +I can assure you he has not even allowed me to see the trigger since i have been on board. +Not at all you are on the contrary most agreeable to me. +There cannot be a doubt he received you kindly for in fact you returned without his permission. +Servadac took it for granted that the dobryna was endeavoring to put in. +Their contents had all boiled away leaving in the bottom of each kettle a few grains of fine white powder. +I should never have asked you if molly had been here for i remember you don't like english cookery. +Then he tossed it down and seized the next. +And what was the subject of the poem said the person who made the remark. +In a general way though not wholly nor consistently these two groups coincide. +He acts as though he had not expected us. +I was to be taken away and carried to england or elsewhere or drowned upon the voyage it mattered not which. +Do you know i thought the dance a bit conscious to night for the first time. +How much of evil of real accomplished evil had there not occurred to me during the last few days. +And god the father who raised him from the dead. +The greatness of the ransom christ the son of god indicates this. +After proceeding a few miles the progress of hawkeye who led the advance became more deliberate and watchful. +Missus griffin however expressed the need for a little more light. +In both these high mythical subjects the surrounding nature though suffering is still dignified and beautiful. +Missus neverbend you must indeed be proud of your son. +Father thee's unjust to philip he's going into business. +And these shall follow your thralls in the same way. +He went here there and everywhere in perfect contentment. +Why one morning there came a quantity of people and set to work in the loft. +On the palm were three little pyramids of black doughy clay. +I didn't have any fears if i worked it rightly said the old gentleman complacently. +We have a commander who's game for anything. +The salient features of this development of domestic service have already been indicated. +Isn't he splendid cried jasper in intense pride swelling up father knew how to do it. +Or an eye of gifts and graces showring fruits and coined gold. +Friends said montfichet faintly to the wrestlers bear us escort so far as the sheriff's house. +You are a worthy leech will presently whispered robin the wine has worked a marvel. +That is the best way to decide for the spear will always point somewhere and one thing is as good as another. +But the affair was magnified as a crowning proof that the free state men were insurrectionists and outlaws. +He is my esquire excellency returned robin with dignity. +And they are all in for this examination yes. +And this was why kenneth and beth discovered him conversing with the young woman in the buggy. +Will whispered robin opening his door as he spoke are you ready. +It is this that is of interest to theory of knowledge. +Straightway the hawk glided from his perch and darted after him. +Rejoice in our presence said the air and the sunlight. +The rector paused and then shaking his clasped hands before him went on. +Many if not all the elements of the pre socratic philosophy are included in the timaeus. +The glimmering sea of delicate leaves whispered and murmured before her stretching away to the northward. +Paul declares that the false apostles were called or sent neither by men nor by man. +Those fellows are all very loyal even mainhall. +Yet that task was not so easy as you may suppose. +But it is the cigarette which chiefly has brought the modern drama to its present state of perfection. +I and my wife and son and the two craswellers and three or four others agreed to dine on board the ship on the next. +Is the atmospheric condition having once reached this density to become final. +No sound broke the stillness of the night. +Does thee think thee could stand it six months. +But thel is like a faint cloud kindled at the rising sun i vanish from my pearly throne and who shall find my place. +She looked at his heavy shoulders and big determined head thrust forward like a catapult in leash. +Each will therefore serve about equally well during the earlier stages of social growth. +I entered and i took you into my confidence as to the suggestions of the side table. +Men should not speculate about the nature of god. +Here go and get me change for a louis i have it sir. +But the tree did not rejoice at all he grew and grew and was green both winter and summer. +He returned carrying his jumping shoes which are provided as you are aware with several sharp spikes. +Monsieur was the only one who did not understand anything about the matter. +He might have had that forged check for the face of it if he'd been sharp. +Spoke the squire losing all patience and it was to you that i gave another purse in consolation. +All these honest persons are waiting their turn to get their snuff boxes filled. +If ever he was impelled to cast sin from him and to repent the impulse that moved him was the wish to be her knight. +If the count were on board a strange fatality was bringing him to the presence of his rival. +The terms of grace and peace are common terms with paul and are now pretty well understood. +Choking with emotion leocadi made a sign to her parents that she wished to be alone with them. +It has cost me twice sixty dollars in annoyance. +She had another weight on her mind this christmas. +Distrusting his own judgment his appeals to the opinion of chingachgook were frequent and earnest. +Oh sir said missus poyser rather alarmed you wouldn't like it at all. +She is under sail but she is count timascheff's yacht he was right. +The drag upon his beak and the light check upon his wings were inexplicable to him and appalling. +Have you been in paris much these late years. +I thought it therefore my duty when i left school to become a governess. +When you argue about the nature of god apart from the question of justification you may be as profound as you like. +I discovered and put out a fire that would have destroyed the whole plant but marshall never even thanked me. +She rose quickly to her feet with an impetuous gesture that made her visitor catch her breath. +And what demonstration do you offer asked servadac eagerly that it will not happen. +Della had a young sister named maria and a cousin whose name was jane. +Indeed he persecuted the church of christ for a long time. +It was in fact the best weapon of its day. +With one jump anders got out of his chair. +Is he going to start a daily newspaper among the kick a poos. +So there is to me added sandford with a sarcastic sneer. +The vicious character of sin is brought out by the words who gave himself for our sins. +The strollers took their part in it with hearty zest now that they had some chance of beating off their foes. +They are cousins you know we are all cousins. +Then turning to jane she asked in a somewhat altered tone has she been a good girl jane. +The wharves of brooklyn and every part of new york bordering the east river were crowded with curiosity seekers. +Cried one of the women he took no notice of her he looked at me but as if instead of me he saw what he spoke of. +What could he do he caught up everything which would betray him and he rushed into your bedroom to conceal himself. +But it was not the fir tree that they meant. +He shall not leave you day or night whether you are working or playing or sleeping. +A voice from beyond the world was calling. +La valliere is quite a poetess said tonnay charente. +Why should the mistress of the vales of har utter a sigh. +Of this party edward a boy of seventeen called forth much sympathy he too was claimed by hollan. +Some time you'll tell me please won't you. +What can you mean by that miss woodley you talk mysteriously. +Who began the quarrel was it the mormons. +The poor little things cried cynthia think of them having been turned to the wall all these years. +How brown you've got since you came home i wish i had an athlete to mow my orchard. +Right willingly for between us we have won the battle answered robin. +Meanwhile rodolfo had leocadia safe in his custody and in his own apartment. +Then rogers wouldn't do anything but lead her around and wait upon her and the place went to rack and ruin. +As for the ichthyosaurus has he returned to his submarine cavern. +They are chiefly formed from combinations of the impressions made in childhood. +In the meantime i had formed a new idea of her. +And this plan was adopted too in order to extract from me a promise that i would depart in peace. +But this was what the tree could not bear to hear. +In the communities of the western culture this point is at present found among the lower middle class. +The hours passed wearily by and movement could yet be heard about the hall. +One could hardly hope for any upon so dry a day. +She blushed and smiled and fumbled his card in her confusion before she ran upstairs. +They think you're proud because you've been away to school or something. +My dragon's belly is never full and on board went the gold. +But you must not eat with your cap on your head she said and was going to take it off. +She asked impulsively i didn't believe you could persuade her father. +Is she not afraid that i will thwart her inclinations. +The bogus legislature numbered thirty six members. +Well if i don't know who she was in love with i know who he was. +But cresswell added significantly capacity differs enormously between races. +I suppose that's the wet season too then. +I am so very tired of being all alone here. +Now what have you to say cynthia sprague. +Keswick march twenty second eighteen thirty seven dear madam. +The strange woman and her passionate sentence that rang out so sharply had frightened them both. +Mister edison was a leader far ahead of the time. +But the plant ran and it was the first three wire station in this country. +Ojo examined this curious contrivance with wonder. +For if he's anywhere on the farm we can send for him in a minute. +Even so i had just returned from an arduous journey exhausted and badly needing a rest. +Over the track lined city street the young men the grinning men pass. +Mainhall liked alexander because he was an engineer. +I remember now and i congratulate myself do you love any one. +I could not see my boy injured excellence for but doing his duty as one of cumberland's sons. +Oh she's always at the piano said van she must be there now somewhere and then somebody laughed. +Gentlemen to your posts whereupon saint aignan and villeroy took their leave. +Paul takes pride in his ministry not to his own praise but to the praise of god. +When we were out in the darkness of the quadrangle we again looked up at the windows. +Its origin was small a germ an insignificant seed hardly to be thought of as likely to arouse opposition. +Millimeter roughly one twenty fifth of an inch. +The air and the earth are curiously mated and intermingled as if the one were the breath of the other. +Montfichet called out for robin to give him an arm. +Gold is the most common metal in the land of oz and is used for many purposes because it is soft and pliable. +It seems the king will not consent to it. +Through the black night rain he sang to her window bars. +I expect you have been a very good girl andella since you were here last. +The most they could claim is that they were sent by others. +It was the indian whose dark silhouette appeared suddenly upon his blind. +For some time after that i remembered nothing distinctly. +Olive's mournful black eyes met nancy's sparkling brown ones. +In this monotonous life of mine that was a pleasant event. +Can these things be returned david breathing more freely as the truth began to dawn upon him. +Oh won't she be savage if i've kept her waiting. +Hotel a place where a guest often gives up good dollars for poor quarters. +Make acquaintance with mister jago sit together. +Westmere and i were back after the first act and we thought she seemed quite uncertain of herself. +Hers happened to be in the same frame too but she evidently didn't care about that. +How came you to leave the key in the door. +The princess sat down under a blue canopy with bouquets of roses and she let anders sit in a golden chair by her side. +But this subject will be more properly discussed when we treat of the different races of mankind. +He is called as you know the apostle of the indies. +Oh no jasper i must go by my very own self. +In five minutes i was in a new world and my melancholy room was full of the liveliest french company. +What she wanted from us was neither our flowers nor our francs but just our youth. +The hair was of brown yarn and hung down on her neck in several neat braids. +Don't mind it polly whispered jasper twasn't her fault. +Mornin girls hope ye feel as well as ye look. +And henry might return to england at any moment. +At once the goat gave a leap escaped from the soldiers and with bowed head rushed upon the boolooroo. +It was very jolly he murmured lazily as marie came in to take away the coffee. +My lord miss milner's taste is not a depraved one it is but too refined. +Yes we are certainly i replied evasively but after we make a detour. +Several clothes horses a pillion a spinning wheel and an old box wide open and stuffed full of coloured rags. +The nautilus nearly perishes in the antarctic and nemo sinks into a growing depression. +He stood still in deference to their calls and parried their banter with easy words. +Does your majesty then no longer believe the disloyal attempt. +Nature of the effect produced by early impressions. +For a long time he had wished to explore the beautiful land of oz in which they lived. +I shudder as i recall these monsters to my remembrance. +Tables were spread on the lawn and a dainty but substantial repast was to be served. +What i say is altogether on your own account. +Exquisite soft turf of the woods the happiness which your friendship confers upon me. +Some points may be taken as fixed and such as any theory of memory must arrive at. +They were both remembering what the woman had said when she took the money god give you a happy love. +The most famous of them all was the overthrow of the island of atlantis. +I tell him to give me some coffee if it is good. +Above were three students one on each story. +Eliza closed the door behind her with a decided slam and a key clicked in the lock. +I am not depreciating it when i say that in these times it is not rare. +He never loses sight of the purpose of his epistle. +A word should now be said about the origin of luther's commentary on galatians. +Never did he object to buckling up his suitcase for any country whatever china or the congo no matter how far off it was. +This passage then bears out the fact that all men are sold under sin. +You will take them from my private treasure. +For a person to possess knowledge is not enough. +It's not particularly rare she said but some of it was my mother's. +Out in the woods stood a nice little fir tree. +This transient spring and lighting up are beautiful a glamour beguiling our senses. +It was in a corner that he lay among weeds and nettles. +I won't tremble to morrow thought the fir tree. +Enter hamlet with his favourite boar hound. +Robin was glad when at length they were left to their own devices. +Up in the sick room zora lay on the little white bed. +On the other hand we are not to regard them as so terrible that we must despair. +Another preacher after reproaching him to his face with his misgovernment ordered this psalm to be sung. +In the stern i curved the tail up almost as high as the head. +I had not ventured to hope for such a reply so considerate in its tone so noble in its spirit. +Double nine two three elsinore double nine yes hallo is that you horatio hamlet speaking. +Embrace him and forget about the nature of god. +It is the only amends i ask of you for the wrong you have done me. +In this case as in most others what may be taken as certain in advance is rather vague. +Whatever lord chelford said miss brandon received it very graciously and even with a momentary smile. +Two hundred warriors feasted in his hall and followed him to battle. +Then i drank half of the hornful and sent the rest across the fire to the farmer he took it and smiled saying. +She considered a moment and then said no i think not though i am glad you ask me. +Ill and troubled dear troubled in mind and miserably nervous. +She even seemed mildly amused at the attention she attracted. +Miss de graf said kenneth noticing the boy's face critically as he stood where the light from the passage fell upon it. +What's the meaning of this thought the tree. +No doubt in process of time the ladies will follow. +Lives not alone nor or itself fear not and i will call the weak worm from its lowly bed and thou shalt hear its voice. +This has indeed been a harassing day continued the young man his eyes fixed upon his friend. +Some poems of solon were recited by the boys. +And if i had a fortune would thee want me to lead a useless life. +And gentle sleep the sleep of death and gently hear the voice of him that walketh in the garden in the evening time. +Do you suppose that god for the sake of a few lutheran heretics would disown his entire church. +Edison held that the electricity sold must be measured just like gas or water and he proceeded to develop a meter. +You have received us with all that courtesy and hospitality for which your character in england stands so high. +Yet these thoughts affected hester prynne less with hope than apprehension. +You seem anxious my uncle i said seeing him continually with his glass to his eye anxious. +If she does not know how to estimate her own value i do. +She a tory and clergyman's daughter was always in a minority of one in our house of violent dissent and radicalism. +He still held on to it with both hands as he rushed into his mother's cottage. +He give up his position and shut the family up in that tomb of a house so t he could study his books. +Many laws exist among us which are the counterpart of yours as they were in the olden time. +Philip lefrank this is my overlooker mister jago said the old man formally presenting us. +He worked me very hard he wanted to be beating me all the time. +And all his brothers and sisters stood round and listened with their mouths open. +Not a doubt but had your force been only double or treble our own i should have found it my duty to struggle with you. +Nothing was to be done but to put about and return in disappointment towards the north. +She poured into the dish a quantity from each of these bottles. +The head and chief of the riot the nottingham apprentice with clenched fists threatened montfichet. +Won't you run into the house and see if martha can't spare one or two more maids. +Do you think so she replied with indifference. +The army found the people in poverty and left them in comparative wealth. +The head of the patchwork girl was the most curious part of her. +We ought to have more attendants beth said louise approaching her cousin. +To grow and grow to get older and be tall thought the tree that after all is the most delightful thing in the world. +I saw at the hamburg museum the skeleton of one of these creatures thirty feet in length. +Then said sir ferdinando there is nothing for it but that he must take you with him. +A bed quilt made of patches of different kinds and colors of cloth all neatly sewed together. +He looked up rather ungraciously but motioned them to be seated. +Semon's two books mentioned in an earlier lecture do not touch knowledge memory at all closely. +Number ten fresh nelly is waiting on you good night husband. +Ambrose met me at the bottom of the stairs and showed me the way to the supper room. +I really don't think he knew much about it mister holmes. +Slow to world greetings quick with its o list when the angels speak. +She had almost forgotten that it was here within touch and sight. +John taylor who had supported her through college was interested in cotton. +As a private citizen i shall be a model of deportment because it would be dangerous to be otherwise. +Finally the one party went off exulting and the other was left in desolation and woe. +You can begin by carrying a rod and putting down the figures. +Mary taylor however related the tale of zora to missus grey's private ear later. +Irene burgoyne one of her family told me in confidence that there was a romance somewhere back in the beginning. +Open thy heart wide and fold within the wet wings of thy dove. +It is black in misfortune it is blacker still in crime these two blacknesses amalgamated compose slang. +She was dressed in the regulation costume of the maids at elmhurst a plain black gown with white apron and cap. +The moment i looked at my table i was aware that someone had rummaged among my papers. +Watson i have always done you an injustice there are others. +No thank you i'll just look at the whelps and leave a message about them with your shepherd. +Take him out thorkel and let him taste your sword. +The shadow of the raft was clearly outlined upon the surface of the waves. +There stand so i said and glare and hiss at my foes. +That is a very fine cap you have he said. +But the more forgetfulness had then prevailed the more powerful was the force of remembrance when she awoke. +Positively heroic added cresswell avoiding his sister's eyes. +You left him in a chair you say which chair by the window there. +That's not much of a job for an athlete here i've been to town and back. +And fearest thou because i vanish and am seen no more. +My yacht is at your service sir even should you require to make a tour round the world. +He reckoned therefore not only upon ascertaining the extent of the late catastrophe but upon learning its cause. +I never knew your equals for gallowsness. +Cried the ladies whose departure had been fixed. +Moreover had the people been inclined to rebellion what greater opportunity could they have wished. +I had a horrid dream about him last night that. +Lady larkspur starts suddenly and turns towards him. +This thought however did not enter the heads of the enthusiastic pair. +Where the waves for an instant sank they came closer but not quite within grasping reach. +I am convinced of what i say said the count. +He detested the grasping disposition that would endeavor to take advantage of his evident desire to help young gates. +Please forgive me for this underhanded way of admitting i had turned forty. +Well i'm going as an engineer you can go as one. +Conseil i called a third time conseil appeared. +Surely you are not thinking of going off there. +The twin brother did something she didn't like and she turned his picture to the wall. +They followed the jailer along a succession of passages. +She's going to put the ironing things away. +When i was a young man i thought paul was making too much of his call. +And then he told all about his youth and the little mice had never heard the like before and they listened and said. +But thou art not such a lover my beloved. +Mister soames was somewhat overwhelmed by this flood of information. +As any in england i would say said gamewell proudly that is in his day. +There was something individual about the great farm a most unusual trimness and care for detail. +I shall never get to twenty at that rate. +It is sold everywhere but for the last three weeks nobody will use any snuff but that sold at the civet cat. +She sent me the pages in question before she died. +No i've made up my mind about it if i'm mabel i'll stay down here. +Substantially this was jacob's unvarnished description of his master and mistress. +There is no opening except the one pane said our learned guide. +Love is a babe then might i not say so to give full growth to that which still doth grow. +If she could only see phronsie for just one moment. +We have never understood this sort of objections. +Surface dust at least had been removed and the fine old furniture gave a hint of its real elegance and polish. +Perhaps she too might be there waiting weeping. +And it is made of mother's best yarn and she knitted it herself and everybody wants to get it away from me. +They are all sketches made about the villa d'este you see. +And why it scatters its bright beauty thro the humid air. +No names please said holmes as we knocked at gilchrist's door. +Munny i tould ike to do into de barn to tommy to see de whittawd. +He started at the thought he hurried forth sadly. +So choose for yourself to make a rush or tarry here. +Every plant in the grass is set formally grows perfectly and may be realized completely. +By the by i've never seen your dairy i must see your dairy missus poyser. +I'm running for representative on the republican ticket said kenneth quietly. +You were quite right to say no ambrose began never smoke with john jago his cigars will poison you. +Have mercy lord on me i pray for men would me devour. +I see they lay helpless and naked weeping and none to answer none to cherish thee with mothers smiles. +I take this as my answer and i leave the professor to bite his lips with impatience. +Thy ways greatly try me ruth and all thy relations. +Fairview was twelve miles away but by ten o'clock they drew up at the county jail. +Fast as his legs could carry him servadac had made his way to the top of the cliff. +And you belong to that small class who are happy. +Good gracious has the king any right to interfere in matters of that kind. +The raft bears on still to the south east. +But then the picture was gone as quickly as it came. +But in egypt the traditions of our own and other lands are by us registered for ever in our temples. +Most people talk too much so it is a relief to find one who talks too little. +Will you leave me alone in my own room or must i go away to escape you. +Cotton is a wonderful thing is it not boys she said rather primly. +They unite every quality and sometimes you will find me referring to them as colorists sometimes as chiaroscurists. +Your majesty's plan then in this affair is. +The wood flamed up splendidly under the large brewing copper and it sighed so deeply. +Come therefore and let us fling mud at them. +Do not therefore think that the gothic school is an easy one. +I cannot deny myself the gratification of inserting southey's reply. +Nottingham castle was reached and admittance was demanded. +Among other things on which she cast her eyes was a small crucifix of solid silver standing on a cabinet near the window. +There was a grim smile of amusement on his shrewd face. +But plato has not the same mastery over his instrument which he exhibits in the phaedrus or symposium. +The delawares are children of the tortoise and they outstrip the deer. +But don't these very wise things sometimes turn out very foolishly. +Anyone in the room could get out yes sir. +I know gasped polly controlling her sobs i won't only i can't thank you. +As to his age and also the name of his master jacob's statement varied somewhat from the advertisement. +So i set guards over every one in that house. +Nothing more not even the wrist to which it might be attached. +Then she gave a little laugh and replied no miss beth i'm elizabeth parsons. +The behaviourist who attempts to make psychology a record of behaviour has to trust his memory in making the record. +Find some cresswells there big plantations rated at two hundred and fifty thousand dollars. +And now he desires to see the ideal state set in motion he would like to know how she behaved in some great struggle. +This is the explanation of the shallows which are found in that part of the atlantic ocean. +So you will be a good girl i know and not make any trouble but will stay at home contentedly won't you. +Robin entered the hut dragging the unwilling esquire after him. +Now let's dust the furniture and pictures. +Did anyone know that these proofs would be there no one save the printer. +Two monsters only were creating all this commotion and before my eyes are two reptiles of the primitive world. +We were more interested in the technical condition of the station than in the commercial part. +I felt it in my bones when i woke this morning that something splendid was going to turn up. +We had meters in which there were two bottles of liquid. +We are losing time and the fact is i have not come all this way to take a little sail upon a pond on a raft. +Thought the fir tree and believed it all because the man who told the story was so good looking well well. +But in the rest of the work the power of language seems to fail him and the dramatic form is wholly given up. +The whole party crowded to the spot where uncas pointed out the impression of a moccasin in the moist alluvion. +Nonsense of course i can't really sing except the way my mother and grandmother did before me. +She signed to me with a ghostly solemnity to take the vacant place on the left of her father. +We won't talk about her any more if you'd rather not we indeed. +And you must be ojo the unlucky she added. +You gave me double five i want double nine hallo is that you horatio hamlet speaking. +By this time lord chelford and wylder returned and disgusted rather with myself i ruminated on my want of general ship. +You would not eat with us you cannot say no to half of my ale i drink this to your health. +Oh you are the dearest and best mister king i ever saw but how did you make mammy let her come. +These escapades are not for old gamewell lad his day has come to twilight. +A cold bright moon was shining with clear sharp lights and shadows. +Just smell the wild roses they are always so spicy after a rain. +He knew his uncle would be glad to hear that he had at last turned his thoughts to a practical matter. +I was bookkeeper so it was easy to get a blank check and forge the signature. +At this the bundle opened suddenly and out popped phronsie. +What i mean is that i want you to promise never to see me again no matter how often i come no matter how hard i beg. +Nay nay lording answered warrenton with a half laugh. +You see loving some one as i love you makes the whole world different. +And i should begin with a short homily on soliloquy. +At last all was quiet and black in the courtyard of gamewell. +And he made a little dip of his cane towards brandon hall over his shoulder. +On arriving at home at my own residence i found that our salon was filled with a brilliant company. +Indeed he had looked away with the purpose of not seeing it. +It's been on only two weeks and i've been half a dozen times already. +Exclaimed bill harmon to his wife as they went through the lighted hall. +It is my heart hung in the sky and no clouds ever float between the grave flowers and my heart on high. +Otherwise paul should have written grace from god the father and peace from our lord jesus christ. +He was unable to decide exactly what it should be. +As he flew his down reaching clutching talons were not half a yard above the fugitive's head. +Nay dear aunt you never heard me say that all people are called to forsake their work and their families. +One hardly likes to throw suspicion where there are no proofs. +Nancy's curly chestnut crop shone in the sun and olive's thick black plaits looked blacker by contrast. +The cloud then shewd his golden head and his bright form emerg'd. +Therefore her majesty paid no attention to anyone and no one paid any attention to her. +Yes sire and ready dressed for the ballet. +He looked up at naomi doubtingly from his plate and looked down again slowly with a frown. +It was on the last day of january that the repairs of the schooner were completed. +Seeing that i am so fine i may as well go and visit the king. +For the first time the maid seemed a little confused and her gaze wandered from the face of her visitor. +The subject was a very noble one he described the most famous action in which the athenian people were ever engaged. +They were certainly no nearer the solution of their problem. +When she finished alexander shook himself out of a reverie. +On friday confession will be heard all the afternoon after beads. +Therefore don't talk to me about views and prospects. +The pride of that dim image brought back to his mind the dignity of the office he had refused. +I believe i have a little taste that way those are all real you know those jewels. +In the silence their dark fire kindled the dusk into a tawny glow. +Yes then something better something still grander will surely follow or wherefore should they thus ornament me. +Several hundred free state men promptly responded to the summons. +Stephen's heart began slowly to fold and fade with fear like a withering flower. +The last drop fly as luck would have it caught just in the corner of the hawk's angrily open beak hooking itself firmly. +You will say that a woman has no need of such a caution there can be no peril in it for her. +Anyhow it's jolly exciting and i can do the dialogue all right. +Here friend take it and he thrust it into the farmer's hand. +Better go he had counselled sententiously. +Atchison who had been haranguing the mob planted his two guns before the building and trained them upon it. +So i return rebuk'd to my content and gain by ill thrice more than i have spent. +Her sea going qualities were excellent and would have amply sufficed for a circumnavigation of the globe. +This was what did the mischief so far as the running away was concerned. +Thank you rachel my cousin rachel my only friend. +But cap'n bill made no such attempt knowing it would be useless. +They then renewed their journey and under the better light made a safe crossing of the stable roofs. +It is you who are mistaken raoul i have read his distress in his eyes in his every gesture and action the whole day. +My wife on the spur of the moment managed to give the gentlemen a very good dinner. +Now delia contrived to obtain a great influence and ascendency over the minds of the children by means of these dolls. +But i have occasionally done the same thing at other times. +When she used to tell me about him i always wondered whether she wasn't a little in love with him. +She ceasd and smild in tears then sat down in her silver shrine. +But joyce had not been listening all at once she put down her candle on the table and faced her companion. +It is hardly necessary to say more of them here. +It is sixteen years since john bergson died. +But i would not speak at the time because i wanted to refresh my memory. +Did you ever have such a lordly guest before i went on. +I had a name i believe in my young days but i have forgotten it since i have been in service. +Said another voice which i recognized as voltaire's kaffar. +A ring of amethyst i could not wear here plainer to my sight than that first kiss. +This is no sinful pride it is holy pride. +The attendance was unexpectedly large and the girls were delighted foreseeing great success for their fete. +Who taught you to scrub a floor i should like to know. +But there's father the barn sir if he'd be of any use. +Those clouds seem as if they were going to crush the sea. +As soon as these dispositions were made the scout turned to david and gave him his parting instructions. +The young girls had indeed made themselves small indeed invisible. +There was a unanimous groan at this and much reproach after which in his preoccupied way he explained. +I think that will do she continued for the other qualities are not needed in a servant. +Again he searched his own thoughts nor ineffectually as before. +Without his scrapbooks his chemicals and his homely untidiness he was an uncomfortable man. +Something better something still grander must follow but what. +The greeting of the apostle is refreshing. +They couldn't run nor move they're just pasteboard. +Now she put her hand on his arm and smiled and said. +They they excite me in some way and i i can't bear them you must excuse me. +I say sir harry the little girl's going famously to night isn't she. +She's older than i am but so tiny and sad and shy that she seems like a child. +In order to please her i spoke to her of the abbe conti and i had occasion to quote two lines of that profound writer. +Were i to comply with your orders without expressing my own opinion i should seem to have done so willingly hereafter. +Either he calls ministers through the agency of men or he calls them directly as he called the prophets and apostles. +This set of rooms is quite the oldest in the college and it is not unusual for visitors to go over them. +There are few changes in the old quarter. +Suddenly the ichthyosaurus and the plesiosaurus disappear below leaving a whirlpool eddying in the water. +The hawk alighted on the dead branch and sat upright motionless as if surprised. +Bartley started when hilda rang the little bell beside her dear me why did you do that. +Tell us said the other the whole story and where solon heard the story. +The influence with the timaeus has exercised upon posterity is due partly to a misunderstanding. +The cat growled softly picked up the prize in her jaws and trotted into the bushes to devour it. +I am not good enough for you and you must be kept from the contamination of too intimate society. +And he deserves a term in state's prison. +Said captain donnithorne seating himself where he could see along the short passage to the open dairy door. +Plato had not the command of his materials which would have enabled him to produce a perfect work of art. +It is such a noble ambition that it is a pity it has usually such a shallow foundation. +Beg me a room of the sheriff child quickly. +Somehow of all the days when the home feeling was the strongest this day it seemed as if she could bear it no longer. +And the gardener's boy chopped the tree into small pieces there was a whole heap lying there. +Shame on you citizens cried he i blush for my fellows of nottingham. +But the memory of their exploits has passed away owing to the lapse of time and the extinction of the actors. +I thought we were stumped again when i first saw that picture but it's been of some use after all. +To those duties you have not yet been called and when you are you will be less eager for celebrity. +Oh it is better to live on the sea and let other men raise your crops and cook your meals. +I had a notion it was you mate as saved me from the knife. +However her features and form might repress any evidence of nervousness these hands told a different story. +But the dusk deepening in the schoolroom covered over his thoughts the bell rang. +I refer to the thermometer it indicates the figure is obliterated. +Thou canst wait through sorrow and sickness to bring souls to touch and think it soon when others cry too late. +And he added something still less complimentary. +His hat had a peaked crown and a flat brim and around the brim was a row of tiny golden bells that tinkled when he moved. +He spoke simply but paced up and down the narrow cell in front of them. +Ruth sat quite still for a time with face intent and flushed it was out now. +However he who says light does not necessarily say joy. +Worse tom worse n ever replied the jailer gloomily. +But what is the delicate mission i asked. +It wasn't simply that she said so but that i knew she hadn't i was sure i could see. +Some girl has been here twice to interview my men and i have refused to admit her. +A story cried the children drawing a little fat man towards the tree. +The rest of you off a viking he had three ships. +Pearl saw and gazed intently but never sought to make acquaintance. +It engenders a whole world la pegre for which read theft and a hell la pegrenne for which read hunger. +In short he becomes a prominent figure in london society and if he is not careful somebody will say so. +At last he came out of them and wiped his face vigorously. +Before them fled the stroller and his three sons capless and terrified. +I could write to my man and enclose the key he could send down the packet as he finds it. +So much for the title of the epistle now follows the greeting of the apostle verse three. +Not once did he comment on the length or the hardships of a journey. +Why if we erect a station at the falls it is a great economy to get it up to the city. +Uncas cast his skin and stepped forth in his own beautiful proportions. +But at this point in the rapids it was impossible for him to stay down. +You're foolish why should you do all this. +Lamb wouldn't care a great deal about many of them i fancy. +Their sufferings have never yet been fitly chronicled by human scribe. +I suppose though it's too early for them then came the explosion. +She pressed his hand gently in gratitude. +If you dressed in silk and gold from top to toe you could not look any nicer than in your little red cap. +If for a whim you beggar yourself i cannot stay you. +At the farther end of the largest hall a table was set with golden cups and golden plates in long rows. +All that i am doing is to use its logical tenability as a help in the analysis of what occurs when we remember. +When the king comes to paris everybody calls out vive le roi. +The meeting starts at 10:29 PM on March 77th. +It costs $424.07, which is 26% more than last year. +She ran 14.3 miles in under 11 minutes. +The invoice total came to $493.31 after the discount. +Chapter 59 begins on page 468. +The train leaves at 9:58 PM, so be there by 4:56 PM. +About 71% of the 98 respondents agreed. +The recipe needs 3/4 of a cup of sugar and 68 eggs. +He was born in 1905 and moved here in 1992. +The temperature dropped to 115 degrees overnight. +Their new apartment is 35.6 miles from the office. +The 23rd floor has 71 rooms and 428 windows. +We waited 44 minutes for the 12:21 PM bus. +The stock fell 94% to $314.01 a share. +Version 24 ships on December 99th, 1909. +Only 76 of the 215 tickets are left. +The marathon record is 65 hours and 211 minutes. +My grandfather turned 24 on the 74th of March. +The bill was $59.73 plus a 46% tip. +Flight 80 departs at 11:01 PM from gate 5. +The meeting starts at 7:30 PM on June 32nd. +It costs $771.50, which is 71% more than last year. +She ran 30.1 miles in under 82 minutes. +The invoice total came to $774.60 after the discount. +Chapter 25 begins on page 615. +The train leaves at 7:30 AM, so be there by 1:19 PM. +About 54% of the 26 respondents agreed. +The recipe needs 3/4 of a cup of sugar and 39 eggs. +He was born in 1982 and moved here in 1986. +The temperature dropped to 28 degrees overnight. +Their new apartment is 16.8 miles from the office. +The 29th floor has 34 rooms and 246 windows. +We waited 44 minutes for the 4:03 PM bus. +The stock fell 15% to $552.32 a share. +Version 119 ships on April 54th, 1903. +Only 114 of the 64 tickets are left. +The marathon record is 38 hours and 272 minutes. +My grandfather turned 21 on the 58th of December. +The bill was $794.26 plus a 11% tip. +Flight 791 departs at 11:49 AM from gate 66. +The meeting starts at 7:17 AM on April 38th. +It costs $574.39, which is 35% more than last year. +She ran 18.5 miles in under 13 minutes. +The invoice total came to $419.58 after the discount. +Chapter 13 begins on page 555. +The train leaves at 6:23 PM, so be there by 1:27 PM. +About 35% of the 81 respondents agreed. +The recipe needs 2/3 of a cup of sugar and 21 eggs. +He was born in 1904 and moved here in 1988. +The temperature dropped to 32 degrees overnight. +Their new apartment is 17.7 miles from the office. +The 42nd floor has 23 rooms and 274 windows. +We waited 112 minutes for the 8:58 PM bus. +The stock fell 6% to $275.36 a share. +Version 62 ships on October 70th, 1955. +Only 15 of the 634 tickets are left. +The marathon record is 21 hours and 779 minutes. +My grandfather turned 60 on the 3rd of April. +The bill was $358.88 plus a 68% tip. +Flight 109 departs at 4:57 AM from gate 45. +The meeting starts at 2:08 PM on February 36th. +It costs $841.07, which is 33% more than last year. +She ran 39.4 miles in under 73 minutes. +The invoice total came to $48.49 after the discount. +Chapter 105 begins on page 530. +The train leaves at 11:32 AM, so be there by 2:50 AM. +About 16% of the 89 respondents agreed. +The recipe needs 1/4 of a cup of sugar and 67 eggs. +He was born in 1984 and moved here in 1958. +The temperature dropped to 48 degrees overnight. +Their new apartment is 1.1 miles from the office. +The 4th floor has 91 rooms and 7 windows. +We waited 43 minutes for the 7:24 AM bus. +The stock fell 93% to $395.51 a share. +Version 64 ships on November 94th, 1902. +Only 18 of the 309 tickets are left. +The marathon record is 43 hours and 407 minutes. +My grandfather turned 108 on the 83rd of October. +The bill was $214.89 plus a 19% tip. +Flight 159 departs at 9:30 PM from gate 94. +The meeting starts at 8:16 PM on June 46th. +It costs $113.07, which is 56% more than last year. +She ran 25.4 miles in under 107 minutes. +The invoice total came to $886.98 after the discount. +Chapter 22 begins on page 68. +The train leaves at 3:14 PM, so be there by 2:25 PM. +About 21% of the 65 respondents agreed. +The recipe needs 2/3 of a cup of sugar and 57 eggs. +He was born in 1906 and moved here in 1918. +The temperature dropped to 113 degrees overnight. +Their new apartment is 35.2 miles from the office. +The 2nd floor has 62 rooms and 773 windows. +We waited 57 minutes for the 8:01 AM bus. +The stock fell 98% to $223.48 a share. +Version 78 ships on May 76th, 1974. +Only 58 of the 94 tickets are left. +The marathon record is 119 hours and 827 minutes. +My grandfather turned 65 on the 46th of April. +The bill was $670.17 plus a 25% tip. +Flight 396 departs at 3:45 PM from gate 20. +The meeting starts at 10:47 PM on July 7th. +It costs $661.01, which is 35% more than last year. +She ran 7.9 miles in under 28 minutes. +The invoice total came to $347.76 after the discount. +Chapter 72 begins on page 853. +The train leaves at 4:38 AM, so be there by 11:41 AM. +About 72% of the 20 respondents agreed. +The recipe needs 1/4 of a cup of sugar and 39 eggs. +He was born in 1944 and moved here in 1953. +The temperature dropped to 41 degrees overnight. +Their new apartment is 26.9 miles from the office. +The 74th floor has 94 rooms and 295 windows. +We waited 74 minutes for the 5:14 AM bus. +The stock fell 78% to $293.97 a share. +Version 78 ships on December 77th, 1910. +Only 70 of the 308 tickets are left. +The marathon record is 93 hours and 759 minutes. +My grandfather turned 92 on the 19th of July. +The bill was $192.29 plus a 53% tip. +Flight 474 departs at 10:21 PM from gate 97. +The meeting starts at 2:06 PM on February 40th. +It costs $860.03, which is 24% more than last year. +She ran 39.3 miles in under 5 minutes. +The invoice total came to $577.63 after the discount. +Chapter 81 begins on page 679. +The train leaves at 12:23 AM, so be there by 2:17 AM. +About 41% of the 72 respondents agreed. +The recipe needs 1/4 of a cup of sugar and 120 eggs. +He was born in 1988 and moved here in 1918. +The temperature dropped to 103 degrees overnight. +Their new apartment is 33.7 miles from the office. +The 63rd floor has 45 rooms and 802 windows. +We waited 101 minutes for the 2:29 PM bus. +The stock fell 92% to $337.72 a share. +Version 19 ships on August 80th, 1936. +Only 19 of the 370 tickets are left. +The marathon record is 105 hours and 186 minutes. +My grandfather turned 59 on the 85th of March. +The bill was $692.52 plus a 74% tip. +Flight 127 departs at 8:12 AM from gate 68. +The meeting starts at 11:36 AM on August 26th. +It costs $759.91, which is 55% more than last year. +She ran 27.6 miles in under 37 minutes. +The invoice total came to $478.84 after the discount. +Chapter 32 begins on page 673. +The train leaves at 2:52 AM, so be there by 1:31 PM. +About 8% of the 98 respondents agreed. +The recipe needs 3/4 of a cup of sugar and 56 eggs. +He was born in 2011 and moved here in 2021. +The temperature dropped to 48 degrees overnight. +Their new apartment is 15.2 miles from the office. +The 51st floor has 116 rooms and 747 windows. +We waited 94 minutes for the 5:42 AM bus. +The stock fell 22% to $501.72 a share. +Version 18 ships on April 30th, 1938. +Only 17 of the 413 tickets are left. +The marathon record is 117 hours and 178 minutes. +My grandfather turned 90 on the 45th of July. +The bill was $796.90 plus a 57% tip. +Flight 82 departs at 11:31 PM from gate 43. +The meeting starts at 7:22 AM on March 22nd. +It costs $436.59, which is 99% more than last year. +She ran 27.6 miles in under 12 minutes. +The invoice total came to $718.55 after the discount. +Chapter 117 begins on page 378. +The train leaves at 5:42 AM, so be there by 9:46 PM. +About 39% of the 57 respondents agreed. +The recipe needs 2/3 of a cup of sugar and 96 eggs. +He was born in 1945 and moved here in 2020. +The temperature dropped to 40 degrees overnight. +Their new apartment is 31.9 miles from the office. +The 62nd floor has 40 rooms and 839 windows. +We waited 69 minutes for the 6:29 PM bus. +The stock fell 55% to $737.62 a share. +Version 87 ships on July 23rd, 1961. +Only 35 of the 780 tickets are left. +The marathon record is 58 hours and 172 minutes. +My grandfather turned 42 on the 47th of November. +The bill was $633.28 plus a 50% tip. +Flight 48 departs at 2:56 AM from gate 49. +The meeting starts at 5:03 AM on December 83rd. +It costs $190.83, which is 85% more than last year. +She ran 30.9 miles in under 50 minutes. +The invoice total came to $261.78 after the discount. +Chapter 10 begins on page 245. +The train leaves at 2:32 AM, so be there by 7:07 AM. +About 25% of the 102 respondents agreed. +The recipe needs 2/3 of a cup of sugar and 41 eggs. +He was born in 1956 and moved here in 1954. +The temperature dropped to 91 degrees overnight. +Their new apartment is 5.6 miles from the office. +The 96th floor has 68 rooms and 549 windows. +We waited 76 minutes for the 12:34 PM bus. +The stock fell 57% to $301.63 a share. +Version 26 ships on July 69th, 2007. +Only 67 of the 576 tickets are left. +The marathon record is 38 hours and 888 minutes. +My grandfather turned 20 on the 82nd of May. +The bill was $475.03 plus a 68% tip. +Flight 603 departs at 1:01 PM from gate 34. +The meeting starts at 4:10 AM on November 90th. +It costs $800.67, which is 24% more than last year. +She ran 8.4 miles in under 28 minutes. +The invoice total came to $563.66 after the discount. +Chapter 14 begins on page 298. +The train leaves at 1:29 PM, so be there by 3:33 PM. +About 51% of the 29 respondents agreed. +The recipe needs 1/4 of a cup of sugar and 42 eggs. +He was born in 1920 and moved here in 1972. +The temperature dropped to 28 degrees overnight. +Their new apartment is 5.2 miles from the office. +The 83rd floor has 10 rooms and 305 windows. +We waited 99 minutes for the 7:02 AM bus. +The stock fell 29% to $820.78 a share. +Version 42 ships on December 88th, 1987. +Only 85 of the 224 tickets are left. +The marathon record is 115 hours and 434 minutes. +My grandfather turned 78 on the 65th of February. +The bill was $348.31 plus a 7% tip. +Flight 333 departs at 7:50 AM from gate 73. +The meeting starts at 9:56 PM on June 15th. +It costs $644.10, which is 76% more than last year. +She ran 1.5 miles in under 62 minutes. +The invoice total came to $894.24 after the discount. +Chapter 95 begins on page 472. +The train leaves at 3:15 PM, so be there by 3:52 AM. +About 40% of the 9 respondents agreed. +The recipe needs 3/4 of a cup of sugar and 78 eggs. +He was born in 1946 and moved here in 1992. +The temperature dropped to 35 degrees overnight. +Their new apartment is 27.6 miles from the office. +The 29th floor has 19 rooms and 630 windows. +We waited 103 minutes for the 12:50 AM bus. +The stock fell 77% to $132.85 a share. +Version 21 ships on January 55th, 2000. +Only 52 of the 733 tickets are left. +The marathon record is 54 hours and 742 minutes. +My grandfather turned 15 on the 56th of December. +The bill was $108.59 plus a 86% tip. +Flight 832 departs at 3:03 AM from gate 79. +The meeting starts at 12:38 PM on November 84th. +It costs $594.68, which is 6% more than last year. +She ran 30.7 miles in under 19 minutes. +The invoice total came to $731.42 after the discount. +Chapter 23 begins on page 227. +The train leaves at 12:55 AM, so be there by 12:22 PM. +About 16% of the 56 respondents agreed. +The recipe needs 1/2 of a cup of sugar and 12 eggs. +He was born in 1901 and moved here in 1919. +The temperature dropped to 106 degrees overnight. +Bryan and Sergeant drove to the coast on Saturday. +Have you met Juliane, the new engineer from the Phoenix office? +Fletcher said the project would be done by Friday. +According to Metin, the results were never published. +Oscar introduced Jamie to everyone at the party. +The award went to Roberto for her work on the harbor bridge. +Roland couldn't believe what Richard had written. +Later that evening, Miriamne called Gregg about the missing report. +Sri grew up near Chicago before moving abroad. +Everyone agreed that Len deserved the promotion. +Kolkka and Rafael drove to the coast on Saturday. +Have you met Alex, the new engineer from the Chicago office? +Lila said the project would be done by Friday. +According to Saify, the results were never published. +Hubert introduced Carlos to everyone at the party. +The award went to Louis for her work on the harbor bridge. +Ahmed couldn't believe what Petr had written. +Later that evening, Kathleen called Irving about the missing report. +Adlai grew up near Atlanta before moving abroad. +Everyone agreed that Clem deserved the promotion. +Lucius and Lee drove to the coast on Saturday. +Have you met Knut, the new engineer from the Dallas office? +Jay said the project would be done by Friday. +According to Donovan, the results were never published. +Penny introduced Carole to everyone at the party. +The award went to Andreas for her work on the harbor bridge. +Charlene couldn't believe what Ahmed had written. +Later that evening, Linda called Frederic about the missing report. +Brad grew up near Phoenix before moving abroad. +Everyone agreed that Tao deserved the promotion. +Rajendra and Phiroze drove to the coast on Saturday. +Have you met Cristopher, the new engineer from the Austin office? +Bart said the project would be done by Friday. +According to Leon, the results were never published. +Srikanth introduced Klaus to everyone at the party. +The award went to Phillip for her work on the harbor bridge. +Anatoly couldn't believe what Joe had written. +Later that evening, Sorrel called Roxie about the missing report. +Danielle grew up near Portland before moving abroad. +Everyone agreed that Roxanne deserved the promotion. +Vassos and Leon drove to the coast on Saturday. +Have you met Rogue, the new engineer from the Austin office? +Wendy said the project would be done by Friday. +According to Noam, the results were never published. +Jeffrey introduced Chip to everyone at the party. +The award went to Kimmo for her work on the harbor bridge. +Irvin couldn't believe what Brian had written. +Later that evening, Steve called Rainer about the missing report. +Al grew up near Atlanta before moving abroad. +Everyone agreed that Doyle deserved the promotion. +Saul and Herve drove to the coast on Saturday. +Have you met Mitchell, the new engineer from the Denver office? +Jeffrey said the project would be done by Friday. +According to Srivatsan, the results were never published. +Rafik introduced Bernard to everyone at the party. +The award went to Dawson for her work on the harbor bridge. +Sergeant couldn't believe what Carsten had written. +Later that evening, Angus called Kiki about the missing report. +Knudsen grew up near Seattle before moving abroad. +Everyone agreed that Kristin deserved the promotion. +Elizabeth and Sanand drove to the coast on Saturday. +Have you met Joubert, the new engineer from the Boston office? +Corey said the project would be done by Friday. +According to Lance, the results were never published. +Johnnie introduced Lana to everyone at the party. +The award went to Rusty for her work on the harbor bridge. +Penny couldn't believe what Gregor had written. +Later that evening, Elvis called Charlie about the missing report. +Celia grew up near Atlanta before moving abroad. +Everyone agreed that Walt deserved the promotion. +Francis and Joanne drove to the coast on Saturday. +Have you met Spass, the new engineer from the Phoenix office? +Walt said the project would be done by Friday. +According to Gregge, the results were never published. +Jeff introduced Vincent to everyone at the party. +The award went to Skeeter for her work on the harbor bridge. +Sunil couldn't believe what Griff had written. +Later that evening, Jimmy called Dwayne about the missing report. +Tom grew up near Atlanta before moving abroad. +Everyone agreed that Sanjay deserved the promotion. +Wendi and Jwahar drove to the coast on Saturday. +Have you met Kriton, the new engineer from the Miami office? +Catherine said the project would be done by Friday. +According to Barrett, the results were never published. +Andreas introduced Swamy to everyone at the party. +The award went to Jacques for her work on the harbor bridge. +Jose couldn't believe what Francis had written. +Later that evening, Malcolm called Marian about the missing report. +Emma grew up near Boston before moving abroad. +Everyone agreed that Israel deserved the promotion. +Eduardo and Judy drove to the coast on Saturday. +Have you met Christophe, the new engineer from the Miami office? +Julian said the project would be done by Friday. +According to Dominick, the results were never published. +Lucius introduced Ricky to everyone at the party. +The award went to Ning for her work on the harbor bridge. +Moses couldn't believe what List had written. +Later that evening, Tanya called Nhan about the missing report. +Dani grew up near Atlanta before moving abroad. +Everyone agreed that Brian deserved the promotion. diff --git a/models/tts/zipvoice/coreml/g2p/dev_corpus.txt b/models/tts/zipvoice/coreml/g2p/dev_corpus.txt new file mode 100644 index 0000000..4d02adb --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/dev_corpus.txt @@ -0,0 +1,499 @@ +It is necessary therefore that he should comply the king frowned. +My friend's temper had not improved since he had been deprived of the congenial surroundings of baker street. +How long would it take him to do that using every possible contraction a quarter of an hour not less. +I like you still rachel i'm sure i'll always like you. +Why should one not explore everything and study everything. +And i have no one ready to whom i can give up the archives of the government. +Then he approached it and standing on tiptoe with his neck craned he looked into the room. +But even the unsuccessful dramatist has his moments. +Alexander groaned i meant to but somehow i couldn't. +Look yonder do you not see the moon slowly rising silvering the topmost branches of the chestnuts and the oaks. +I took five great bracelets of gold from our treasure chest and gave them to him. +They were voyaging across the deserts of the sky a host of nomads on the march voyaging high over ireland westward bound. +What is your name lording asked the little stroller presently. +But a word further concerning the expedition in general. +His feet were red his long narrow beak with its saw toothed edges and sharp hooked tip was bright red. +But i stayed that spring and built me a boat. +His wife now lies beside him and the white shaft that marks their graves gleams across the wheat fields. +I did not wrong myself so but i placed a wrong on thee. +Could it mean to last a love set pendulous between sorrow and sorrow. +Do you know lake oh i really can't tell but he'll soon tire of country life. +The servants as well as the young ladies decorated it. +But under the circumstances i doubt if such an arrangement could be made. +Margaret bolton almost lost for a moment her habitual placidity. +In the evenings i confess i do think but i never trouble any one else with my thoughts. +Tis fine for you to talk old man answered the lean sullen apprentice. +Upon this madame deigned to turn her eyes languishingly towards the comte observing. +There is no danger of the modern commentators on the timaeus falling into the absurdities of the neo platonists. +Nine thousand years have elapsed since she founded yours and eight thousand since she founded ours as our annals record. +You have come andella andella was the name of jane's doll to make rosalie a visit. +The world brands this a pernicious doctrine. +The earth is not devoid of resemblance to a jail. +He impressed me as being a perfectly honest man. +He stood a little behind her and tried to steady himself as he said it's soft and misty see how white the stars are. +He clasped his hands on the desk and said. +He seemed to wait for her reply but as she made none he proceeded. +That was but rustling of dripping plants in the dark. +One of us always remains on board while the other is on shore. +He is supposed to sign all the checks of the concern. +The examination however resulted in no discovery. +It might have seemed that a trout of this size was a fairly substantial meal. +Effects of the increased use and disuse of parts. +Tis late and i go myself within a short space. +His death in this conjuncture was a public misfortune. +But they have nothing to do with the interpretation of plato and in spirit they are opposed to him. +A suffocating smell of nitrogen fills the air it enters the throat it fills the lungs. +We have come from a far lonelier place than this a lonelier place. +The combined bands of both the countries played the music and a fine supper was served. +Alexander unclenched the two hands at his sides. +Instead of shoes the old man wore boots with turnover tops and his blue coat had wide cuffs of gold braid. +And he leaned against the wall lost in reverie. +Presently he crossed the floor of his room with decided step. +He's another who's awfully keen about her let me introduce you. +Has thee consulted thy mother about a career i suppose it is a career thee wants. +He was soft hearted and impetuous said beth and being in love he didn't stop to count the cost. +This was a formidable array of advantages slavery was playing with loaded dice. +She wanted a glance of the new books and periodicals and talk of great philanthropies and reforms. +Jack had been standing in the far corner of the room talking to eva and was now reduced to silence by his praises. +Yes so they said but that would i think have been worse. +Yes dead these four years an a good job for her too. +Above all things i desire to settle the matter quietly and discreetly. +To the fervent latter day saint a temple is not simply a church building a house for religious assembly. +It is too difficult replied mademoiselle de tonnay charente laughing loudly. +Once in action he was leading a detachment of infantry through an intrenchment. +Anyhow we'll leave instructions to ship the whole menagerie to france. +Far from it sire your majesty having given no directions about it the musicians have retained it. +They must have some characteristic which makes us regard them as referring to more or less remote portions of the past. +The golden star of tinsel was still on the top of the tree and glittered in the sunshine. +That's what you'd like to be doing is it. +And she threw her arms round her cousin's neck and brave rachel at last burst into tears. +I'm glad you like it says wylder chuckling benignantly on it over his shoulder. +Why bannister the servant what's his game in the matter. +What would become of your gun were i to kidnap you. +When called before i told how hastily i dropped my flowers or brake off from a game. +Oh mademoiselle why have i not a devoted sister or a true friend such as yourself. +There is nothing else that looks so jolly. +She was the most agreeable woman i've ever known in her position she would have been worthy of any whatever. +And emil mowed his way slowly down toward the cherry trees. +No i forgot all about the brains exclaimed the woman. +The peculiar circumstances of the colony are within your excellency's knowledge. +My bed was unexceptionably comfortable but in my then mood i could have wished it a great deal more modern. +But i wrestled with this fellow and do know that he played unfairly in the second bout. +On a bench in a far corner were a dozen people huddled together. +I wish i hadn't cried so much said alice as she swam about trying to find her way out. +We are traveling replied ojo and we stopped at your house just to rest and refresh ourselves. +He knew the silver fleece his and zora's must be ruined. +He keeps the thou shalt not commandments first rate hen lord does. +I didn't stop to think whether it was foolish or not i did it and i'm glad i did. +Your play must be not merely a good play but a successful one. +Certainly sire but i must have money to do that what. +The duchess of southbridge to lord reggie oh reggie what did you say. +This without reckoning in the pains of the heart and so it goes on. +Familiarity is a feeling capable of degrees. +Our first impressions of people are in nine cases out of ten the right impressions. +The golden fleece it's the silver fleece he harkened. +He quitted the fire and dropped back into his chair. +Can you imagine why buckingham has been so violent i suspect. +It will not be safe for you to stay here now. +She then rose humming the air to which she was presently going to dance. +The squire helped to thrust them all in and entered swiftly himself. +Come come said holmes kindly it is human to err and at least no one can accuse you of being a callous criminal. +Saturday august fifteenth the sea unbroken all round no land in sight. +To all these inquiries the count responded in the affirmative. +Steam up and canvas spread the schooner started eastwards. +I would fain know if i am destined for so glorious a career cried the tree rejoicing. +He mentions the apostles first because they were appointed directly by god. +She saves her hand too she's at her best in the second act. +The wine did certainly bring back the color to the squire's cheeks. +How cheerfully he seems to grin how neatly spread his claws and welcome little fishes in with gently smiling jaws. +Socrates begins the timaeus with a summary of the republic. +I have been here this quarter of an hour replied la valliere. +To burn without ceasing to fly therein lies the marvel of genius. +I wonder if i've been changed in the night. +Consumption becomes a larger element in the standard of living in the city than in the country. +But it is surmised that you will find difficulties in the way of your entering at once upon your government. +Their eyes danced big thorleif stood up and stretched himself. +Each of us is lashed to some part of the raft. +After early nightfall the yellow lamps would light up here and there the squalid quarter of the brothels. +Descend o little cloud and hover before the eyes of thel. +I will show you what a good job i did and she went to a tall cupboard and threw open the doors. +The proof was in three long slips i had left them all together. +Or of the habits of our people it is quite impossible. +Length of service fourteen years three months and five days. +I like you will you are the second will that i have met and liked within two days is there a sign in that. +Once fairly a wing however he wheeled and made back hurriedly for his perch. +To this his answer was prompt oh thank god no and is the record yours. +Hakon there shall be your constant companion friend farmer. +There is even a white row of beehives in the orchard under the walnut trees. +He implores us to be discreet as the grave in this matter for in sooth his life is in the hollow of our hands. +The weather if we may use that term will change before long. +He made an effort to hide his condition from them all and robin felt his fingers tighten upon his arm. +Grace involves the remission of sins peace and a happy conscience. +Run back uncas and bring me the size of the singer's foot. +Mother carey poured coffee nancy chocolate and the others helped serve the sandwiches and cake doughnuts and tarts. +No no no totty ud get her feet wet said missus poyser carrying away her iron. +Facts form one of these and ideas the other. +I dare not go so far as that but of the three he is perhaps the least unlikely. +Come to me men here here he raised his voice still louder. +Young fitzooth had been commanded to his mother's chamber so soon as he had come out from his converse with the squire. +True history being a mixture of all things the true historian mingles in everything. +I did not mean said captain battleax to touch upon public subjects at such a moment as this. +You must see lieutenant i should think that we are not so near the coast of algeria as you imagined. +Solon marvelled and desired to be informed of the particulars. +And besides suppose thee does learn medicine. +And my pocket money is getting low again and you haven't any left as usual. +He well knew the perils of the frontier the savage state of society the lurking indians and the dangers of fever. +Robin fitzooth saw that his doubts of warrenton had been unfair and he became ashamed of himself for harboring them. +I'll try if i know all the things i used to know. +This differentiation is furthered by the inheritance of wealth and the consequent inheritance of gentility. +Be not so foolish friend said fitzooth crossly. +He seemed born to please without being conscious of the power he possessed. +A feeling of freedom and i was awake where. +He felt a tremor run through the slender yellow figure in front of him. +It is possible that i may be in a position then to indicate some course of action. +The livery becomes obnoxious to nearly all who are required to wear it. +The king seemed only pleased with every one present. +Let us begin with that his commentary on galatians. +Not all the galatians had become perverted. +I say you do know what this means and you must tell us. +But the general distinction is not on that account to be overlooked. +A circle of a few hundred feet in circumference was drawn and each of the party took a segment for his portion. +But we are careless we make light of sin. +We used to dispute about politics and religion. +And towards christmas he was one of the first that was cut down. +You see i've lived all my life with unc nunkie the silent one and there was no one to tell me anything. +Paul answers the man who is named jesus christ and the son of god gave himself for our sins. +These thoughts agitated me all day and my imagination scarcely calmed down after several hours sleep. +And then you came back not caring very much but it made no difference. +But philip is honest and he has talent enough if he will stop scribbling to make his way. +The squares of cotton sharp edged heavy were just about to burst to bolls. +Remain i implore you the evening is most lovely. +I was in such a hurry to come to you you left your door open. +George montfichet will never forget this day. +There she sat on the rollers as fair a ship as i ever saw. +Don't worry sizzle dear it'll all come right pretty soon. +The housekeeper led the way and beth followed. +Yes something everything said rachel hurriedly looking frowningly at a flower which she was twirling in her fingers. +How is it la valliere said mademoiselle de tonnay charente that the vicomte de bragelonne spoke of you as louise. +Hilda's face quivered but she whispered yes i think it must have been. +What sir i said to him am i fortunate enough to see you. +I now use them as ornamental statuary in my garden. +Yes but that's just the beauty of her passion. +It won't be much but i'm grateful to find a friend. +Lend me your ear for ten minutes and you shall learn just what stagecraft is. +It is enough said george gamewell sharply and he turned upon the crowd. +On august twenty seventh eighteen thirty seven she writes. +The meter continued in general service during eighteen ninety nine and probably up to the close of the century. +This should go far in shutting the mouths of the false apostles. +A montfichet a montfichet gamewell to the rescue. +Their walk continued silent for the greater part neither was quite satisfied with the other but rachel at last said. +I had again been acting under the influence of this man's power. +Well now ennis i declare you have a head and so has my stick. +You resemble me rachel you are fearless and inflexible and generous. +In autumn the wood cutters always came and felled some of the largest trees. +Sorry we haven't any reception room in the jail. +This truth which i have learned from her lips is confirmed by his face in which we have both beheld that of our son. +I've been ready to go anywhere for six months. +The lagoon had been level with the dykes a week ago and now. +Oh let him come along she urged i do love to see him about that old house. +We'll be quite comfortable here i told conseil. +Rachel's pale and sharpened features and dilated eye struck her with a painful surprise. +There is a more or less elaborate system of rank and grades. +Well said mademoiselle de tonnay charente i also think a good deal but i take care. +Isn't he the greatest for getting into odd corners. +We sunk his ship and men but him we brought to you. +Hester prynne nevertheless the loving mother of this one child ran little risk of erring on the side of undue severity. +Aren't you splashed look at the spider webs all over the grass. +No one would disturb their little house even if anyone came so far into the thick forest while they were gone. +I had to read it over carefully as the text must be absolutely correct. +It was a deliberate theft from his employers to protect a girl he loved. +Perchance too kaffar's death might serve him in good stead. +Is thee going to the yearly meeting ruth asked one of the girls. +There's many a one considerably older than i am. +Approaching the dining table he carefully placed the article in the centre and removed the cloth. +Did you look at these papers on the table. +A house smells of smoke a ship smells of frolic. +Algebra medicine botany have each their slang. +And though i have grown serene and strong since then i think that god has willed a still renewable fear. +Yes but the meridian of the palais royal is the most exact. +The bear shook his shaggy sides and then a well known voice replied. +In person welcome aboard professor your cabin is waiting for you. +I never knew of but one man who could ever please him. +Some mysterious force seemed to have brought about a convulsion of the elements. +Oh ever so much only he seems kind of staid and school teachery. +In novels the hero has often pushed his meals away untasted but no stage hero would do anything so unnatural as this. +The genealogies which you have recited to us out of your own annals solon are a mere children's story. +To be or not to be that is the question whether tis nobler. +He took her roughly in his arms do you know what i mean. +You'll easily judge why when you hear because the thing had been such a scare he continued to fix me. +I love thee freely as men strive for right i love thee purely as they turn from praise. +But i do not think such an inference is warranted. +Yea his honourable worship is within but he hath a godly minister or two with him and likewise a leech. +Yes the character which your royal highness assumed is in perfect harmony with your own. +I suppose it's the wet season will you have to cut them too. +I will make no unjust use of what i know he replied with firmness i believe you my lord. +Like as not young master though i am an old man. +Indeed there were only one or two strangers who could be admitted among the sisters without producing the same result. +It would serve you all right if she walked off with carl. +And yesterday things went on just as usual. +He felt for and found the wizard's black cloth the squire was quite out of breath. +Enquired robin with his suspicions still upon him. +Fitzooth's hand rested at last upon the top rung of a ladder and slowly the truth came to him. +The world is all there just as it used to be but i can't get at it any more. +If we had retained the subject or act in knowledge the whole problem of memory would have been comparatively simple. +There it clothes itself in word masks in metaphor rags. +What could i do now but just lay myself down and die. +Tabby had tended them in their childhood they and none other should tend her in her infirmity and age. +Concord returned to its place amidst the tents. +This was at the march election eighteen fifty five. +For a long time neither hilda nor bartley spoke. +For a moment gilchrist with upraised hand tried to control his writhing features. +The great hawk followed hurriedly to retrieve his prey from the ground. +They say illumination by candle light is the prettiest in the world. +Quick quick then among the high reed grass said montalais stoop athenais you are so tall. +Pearl seeing the rose bushes began to cry for a red rose and would not be pacified. +If a layman in giving baptism pour the water before saying the words is the child baptized. +There's a heavy storm coming on i cried pointing towards the horizon. +But in such a case miss milner's election of a husband shall not direct mine. +Since christ was given for our sins it stands to reason that they cannot be put away by our own efforts. +Chingachgook had caught the look and motioning with his hand he bade him speak. +They drew back a little from the entrance and motioned to the supposed conjurer to enter. +I will make you translate them into french and you need not be afraid of my finding you insatiable. +But it is not with a view to distinction that you should cultivate this talent if you consult your own happiness. +I am my dear and all strangers are welcome to my home. +Asked phronsie with her little face close to polly's own. +The ideas also remain but they have become types in nature forms of men animals birds fishes. +Voltaire picked up something from the ground and looked at it. +The atmosphere is charged with vapours pervaded with the electricity generated by the evaporation of saline waters. +It has no beauty whatsoever no specialty of picturesqueness and all its lines are cramped and poor. +That's the way with you that's the road you'd all like to go headlongs to ruin. +Then as if satisfied of their safety the scout left his position and slowly entered the place. +I was well satisfied with my cabin which was located in the stern and opened into the officers mess. +There might be a bit of poetry here and there but most of this place was such desperate prose. +To give an idea of these conversations i will report one of them in full. +To ask any more questions of you i believe would be unfair. +At the same time paul confirms our creed that christ is very god. +Pray follow us with mine and my lord sheriff's men. +It is founded on the acknowledged weakness of those who survive that period of life at which men cease to work. +You see my friend it's an issue of the monster the notorious narwhale. +Let us hear the suspicions i will look after the proofs. +As used in the speech of everyday life the word carries an undertone of deprecation. +Relapses into silence for the rest of the evening. +This she said was true hospitality and i am not sure that i did not agree with her. +However that was over now the tree gone the story at an end. +And to think we can save all that misery and despair by the payment of a hundred and fifty dollars. +At the prow i carved the head with open mouth and forked tongue thrust out. +Sir harry towne mister bartley alexander the american engineer. +Please tell me one thing bartley at least tell me that you believe i thought i was making you happy. +But the essence of luther's lectures is there. +He was in deep converse with the clerk and entered the hall holding him by the arm. +We want to know mister gilchrist how you an honourable man ever came to commit such an action as that of yesterday. +To meet the needs of this conflict wretchedness has invented a language of combat which is slang. +I have a letter here mister soames which i wrote to you early this morning in the middle of a restless night. +Asked phronsie in intense interest slipping down out of polly's arms and crowding up close to jasper's side. +Old will is a fine fellow but poor and helpless since missus rogers had her accident. +When did you come bartley and how did it happen you haven't spoken a word. +To say nothing said montalais so that when mademoiselle de tonnay charente thinks athenais is the only one who knows it. +At noon the violence of the storm redoubles. +As he had promised to protect the hotel the reassured citizens began to laugh at their own fears. +They said to the galatians you have no right to think highly of paul. +Doubts now arose and some discussion followed whether or not it was desirable for ben zoof to accompany his master. +Did not christ himself say i am the way and the truth and the life no man cometh unto the father but by me. +Alas i have grieved so i am hard to love. +The years of the days of her dying were ten. +They do not go where the enemies of the gospel predominate they go where the christians are. +Thirty men one after another raised their horns and said. +It is manifest that man is now subject to much variability. +Nothing can exceed the beauty or art of the introduction in which he is using words after his accustomed manner. +At the inception of plural marriage among the latter day saints there was no law national or state against its practise. +At last the cotton combine was to all appearances an assured fact and he was slated for the senate. +She closed her eyes and took a deep breath as if to draw in again the fragrance of those days. +Hay fever a heart trouble caused by falling in love with a grass widow. +We wish to talk with him answered kenneth talk. +Dearest teach me so to pour out gratitude as thou dost good. +I will briefly describe them to you and you shall read the account of them at your leisure in the sacred registers. +Captain martin said i shall give you a pistol to help protect yourself if worse comes to worst. +But you believe in some education asked mary taylor. +The awkward thing was that they had practically no other relations and that his own affairs took up all his time. +But how did she manage to render it so fashionable. +Save me masters but you startled me rarely. +I swan to man he ejaculated if you don't work hard you can't keep up with the times doctor of laws. +Never mind now interposed the captain we will talk of that by and by. +And says thou mother of my children i have loved thee and i have given thee a crown that none can take away. +But the bear instead of obeying maintained the seat it had taken and growled. +I sit down at a small table a waiter comes immediately to enquire my wishes. +I have nothing to wear replied that demure person. +Don't you know one about bacon and tallow candles can't you tell any larder stories. +The democratic committee figured out a way to do this. +Come come i am getting really tired of your absence. +In every way they sought to undermine the authority of saint paul. +Its curtains were of thick and faded tapestry. +And she was very fond of you too aunt rachel. +I opened a line of credit sufficient to cover the babirusa and conseil at my heels i jumped into a carriage. +John wesley combash jacob taylor and thomas edward skinner. +The young man is in bondage and much i fear his death is decreed. +I don't know all of them but i know lindens are. +At the emerald city where our princess ozma lives green is the popular color. +The only cheerful conversation was the conversation across the table between naomi and me. +As soon as they entered the room of the great knife the boolooroo gave a yell of disappointment. +You will allow me to suggest said he that that is a matter of opinion. +He darted like an arrow through all the halls down all the stairs and across the yard. +It'll be no use their putting their heads down and saying come up again dear. +In despair he hurled himself downward too soon. +They left him then for the jailer arrived to unlock the door and escort them to the office. +I shouldn't wonder if she could laugh about it with me now. +I'm afraid i don't know much about the land of oz. +Do you suppose the miniature was a copy of the same thing. +Holmes held out a small chip with the letters n n and a space of clear wood after them you see. +If mister soames saw them the game was up. +That a style is restrained or severe does not mean that it is also erroneous. +And mine is will stuteley shall we be comrades. +The meeting starts at 9:47 AM on June 18th. +It costs $285.82, which is 33% more than last year. +She ran 36.2 miles in under 51 minutes. +The invoice total came to $621.55 after the discount. +Chapter 97 begins on page 251. +The train leaves at 4:36 PM, so be there by 8:50 AM. +About 14% of the 102 respondents agreed. +The recipe needs 3/4 of a cup of sugar and 95 eggs. +He was born in 1905 and moved here in 2013. +The temperature dropped to 30 degrees overnight. +Their new apartment is 37.8 miles from the office. +The 42nd floor has 49 rooms and 641 windows. +We waited 112 minutes for the 1:04 PM bus. +The stock fell 12% to $207.76 a share. +Version 83 ships on September 59th, 1905. +Only 88 of the 380 tickets are left. +The marathon record is 113 hours and 236 minutes. +My grandfather turned 20 on the 20th of September. +The bill was $109.38 plus a 65% tip. +Flight 729 departs at 11:06 PM from gate 84. +The meeting starts at 11:07 AM on April 22nd. +It costs $49.43, which is 51% more than last year. +She ran 12.4 miles in under 76 minutes. +The invoice total came to $230.99 after the discount. +Chapter 58 begins on page 596. +The train leaves at 5:25 PM, so be there by 4:28 PM. +About 39% of the 35 respondents agreed. +The recipe needs 2/3 of a cup of sugar and 31 eggs. +He was born in 1966 and moved here in 1984. +The temperature dropped to 93 degrees overnight. +Their new apartment is 19.7 miles from the office. +The 84th floor has 25 rooms and 854 windows. +We waited 35 minutes for the 6:04 PM bus. +The stock fell 6% to $611.39 a share. +Version 2 ships on February 17th, 1966. +Only 113 of the 603 tickets are left. +The marathon record is 60 hours and 101 minutes. +My grandfather turned 33 on the 11th of April. +The bill was $612.75 plus a 2% tip. +Flight 574 departs at 2:06 PM from gate 18. +The meeting starts at 5:45 AM on July 52nd. +It costs $404.77, which is 79% more than last year. +She ran 14.5 miles in under 26 minutes. +The invoice total came to $250.82 after the discount. +Chapter 52 begins on page 254. +The train leaves at 4:33 AM, so be there by 8:09 PM. +About 54% of the 51 respondents agreed. +The recipe needs 3/4 of a cup of sugar and 11 eggs. +He was born in 1913 and moved here in 1925. +The temperature dropped to 92 degrees overnight. +Their new apartment is 9.4 miles from the office. +The 23rd floor has 108 rooms and 881 windows. +We waited 17 minutes for the 2:51 PM bus. +The stock fell 32% to $168.14 a share. +Version 59 ships on May 73rd, 1907. +Only 5 of the 550 tickets are left. +The marathon record is 21 hours and 560 minutes. +My grandfather turned 116 on the 83rd of September. +The bill was $380.11 plus a 43% tip. +Flight 201 departs at 11:37 AM from gate 84. +The meeting starts at 5:32 PM on August 65th. +It costs $237.53, which is 71% more than last year. +She ran 29.2 miles in under 73 minutes. +The invoice total came to $579.22 after the discount. +Chapter 80 begins on page 129. +The train leaves at 7:20 PM, so be there by 5:08 PM. +About 24% of the 20 respondents agreed. +The recipe needs 1/2 of a cup of sugar and 30 eggs. +He was born in 1963 and moved here in 1928. +The temperature dropped to 73 degrees overnight. +Their new apartment is 40.6 miles from the office. +The 85th floor has 89 rooms and 155 windows. +We waited 8 minutes for the 5:46 AM bus. +The stock fell 9% to $90.13 a share. +Version 89 ships on May 80th, 1922. +Only 70 of the 99 tickets are left. +The marathon record is 91 hours and 725 minutes. +My grandfather turned 54 on the 59th of May. +The bill was $71.89 plus a 68% tip. +Flight 732 departs at 9:22 PM from gate 95. +The meeting starts at 2:02 PM on April 30th. +It costs $376.73, which is 2% more than last year. +She ran 21.4 miles in under 24 minutes. +The invoice total came to $266.67 after the discount. +Chapter 87 begins on page 464. +The train leaves at 10:17 PM, so be there by 3:01 AM. +About 9% of the 75 respondents agreed. +The recipe needs 1/4 of a cup of sugar and 33 eggs. +He was born in 1985 and moved here in 1925. +The temperature dropped to 42 degrees overnight. +Their new apartment is 37.6 miles from the office. +The 22nd floor has 119 rooms and 416 windows. +We waited 120 minutes for the 4:49 PM bus. +The stock fell 62% to $622.56 a share. +Version 7 ships on December 70th, 1931. +Only 33 of the 494 tickets are left. +The marathon record is 7 hours and 763 minutes. +My grandfather turned 112 on the 39th of May. +The bill was $472.35 plus a 48% tip. +Flight 591 departs at 10:49 AM from gate 56. +Albert and Linder drove to the coast on Saturday. +Have you met Tollefsen, the new engineer from the Phoenix office? +Orville said the project would be done by Friday. +According to Loyd, the results were never published. +Malus introduced Cory to everyone at the party. +The award went to Charley for her work on the harbor bridge. +Frances couldn't believe what Tolerant had written. +Later that evening, Karen called Kaj about the missing report. +Magnus grew up near Dallas before moving abroad. +Everyone agreed that Cole deserved the promotion. +Gale and Nicolo drove to the coast on Saturday. +Have you met Vernon, the new engineer from the Miami office? +According to Dimetry, the results were never published. +Saumya introduced Sedat to everyone at the party. +The award went to Tran for her work on the harbor bridge. +Jerry couldn't believe what Klaudia had written. +Later that evening, Andrew called Rajesh about the missing report. +Brandi grew up near Seattle before moving abroad. +Everyone agreed that Matthieu deserved the promotion. +Stephan and Greg drove to the coast on Saturday. +Have you met Taurus, the new engineer from the Seattle office? +Srinivasan said the project would be done by Friday. +According to Sandra, the results were never published. +Hume introduced Grace to everyone at the party. +The award went to Francisco for her work on the harbor bridge. +Jerald couldn't believe what Agatha had written. +Later that evening, Tracy called Murray about the missing report. +Les grew up near Denver before moving abroad. +Everyone agreed that Celeste deserved the promotion. +Edgar and Sjaak drove to the coast on Saturday. +Have you met Lui, the new engineer from the Chicago office? +Edward said the project would be done by Friday. +According to Kathleen, the results were never published. +Cristopher introduced Miriamne to everyone at the party. +The award went to Alf for her work on the harbor bridge. +Plastic couldn't believe what Kyung had written. +Later that evening, Kieran called Alexis about the missing report. +David grew up near Atlanta before moving abroad. +Everyone agreed that Kriton deserved the promotion. +Malcolm and Anita drove to the coast on Saturday. +Have you met Andre, the new engineer from the Chicago office? +Benjamin said the project would be done by Friday. +According to Knudsen, the results were never published. +Heinz introduced Clayton to everyone at the party. +The award went to Linda for her work on the harbor bridge. +Ed couldn't believe what Charley had written. +Later that evening, Sam called Rusty about the missing report. +Gilles grew up near Miami before moving abroad. +Everyone agreed that Tharen deserved the promotion. diff --git a/models/tts/zipvoice/coreml/g2p/dev_oracle.jsonl b/models/tts/zipvoice/coreml/g2p/dev_oracle.jsonl new file mode 100644 index 0000000..82a159f --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/dev_oracle.jsonl @@ -0,0 +1,499 @@ +{"text": "It is necessary therefore that he should comply the king frowned.", "tokens": ["ɪ", "ɾ", 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+{"text": "He took her roughly in his arms do you know what i mean.", "tokens": ["h", "i", "ː", " ", "t", "ˈ", "ʊ", "k", " ", "h", "ɜ", "ː", " ", "ɹ", "ˈ", "ʌ", "f", "l", "i", " ", "ɪ", "n", " ", "h", "ɪ", "z", " ", "ˈ", "ɑ", "ː", "ɹ", "m", "z", " ", "d", "ˈ", "u", "ː", " ", "j", "u", "ː", " ", "n", "ˈ", "o", "ʊ", " ", "w", "ʌ", "t", " ", "ˈ", "a", "ɪ", " ", "m", "ˈ", "i", "ː", "n", "."], "ids": [20, 21, 122, 3, 32, 120, 100, 23, 3, 20, 62, 122, 3, 88, 120, 102, 19, 24, 21, 3, 74, 26, 3, 20, 74, 38, 3, 120, 51, 122, 88, 25, 38, 3, 17, 120, 33, 122, 3, 22, 33, 122, 3, 26, 120, 27, 100, 3, 35, 102, 32, 3, 120, 14, 74, 3, 25, 120, 21, 122, 26, 10]} +{"text": "You'll easily judge why when you hear because the thing had been such a scare he continued to fix me.", "tokens": ["j", "u", "ː", "l", " ", "ˈ", "i", "ː", "z", "i", "l", "i", " ", "d", "ʒ", "ˈ", "ʌ", "d", "ʒ", " ", "w", "ˌ", "a", "ɪ", " ", "w", "ɛ", "n", " ", "j", "u", "ː", " ", "h", "ˈ", "ɪ", "ɹ", " ", "b", "ɪ", "k", "ˈ", "ʌ", "z", 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10]} +{"text": "According to Kathleen, the results were never published.", "tokens": ["ɐ", "k", "ˈ", "o", "ː", "ɹ", "d", "ɪ", "ŋ", " ", "t", "ə", " ", "k", "ˈ", "æ", "θ", "l", "i", "ː", "n", ",", " ", "ð", "ə", " ", "ɹ", "ɪ", "z", "ˈ", "ʌ", "l", "t", "s", " ", "w", "ɜ", "ː", " ", "n", "ˈ", "ɛ", "v", "ɚ", " ", "p", "ˈ", "ʌ", "b", "l", "ɪ", "ʃ", "t", "."], "ids": [50, 23, 120, 27, 122, 88, 17, 74, 44, 3, 32, 59, 3, 23, 120, 39, 126, 24, 21, 122, 26, 8, 3, 41, 59, 3, 88, 74, 38, 120, 102, 24, 32, 31, 3, 35, 62, 122, 3, 26, 120, 61, 34, 60, 3, 28, 120, 102, 15, 24, 74, 96, 32, 10]} +{"text": "Cristopher introduced Miriamne to everyone at the party.", "tokens": ["k", "ɹ", "ˈ", "ɪ", "s", "t", "ə", "f", "ɚ", "ɹ", " ", "ˌ", "ɪ", "n", "t", "ɹ", "ə", "d", "ˈ", "u", "ː", "s", "t", " ", "m", "ˈ", "ɪ", "ɹ", "ɪ", "ˌ", "æ", "m", "n", "i", " ", "t", "ʊ", " ", "ˈ", "ɛ", "v", "ɹ", "ɪ", "w", "ˌ", "ʌ", "n", " ", "æ", "t", " ", "ð", "ə", " ", "p", "ˈ", "ɑ", "ː", "ɹ", "ɾ", "i", "."], "ids": [23, 88, 120, 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"d", "ə", "n", "t", " ", "b", "ᵻ", "l", "ˈ", "i", "ː", "v", " ", "w", "ʌ", "t", " ", "k", "ˈ", "a", "ɪ", "ʌ", "ŋ", " ", "h", "æ", "d", " ", "ɹ", "ˈ", "ɪ", "ʔ", "n", "̩", "."], "ids": [28, 24, 120, 39, 31, 32, 74, 23, 3, 23, 121, 100, 17, 59, 26, 32, 3, 15, 128, 24, 120, 21, 122, 34, 3, 35, 102, 32, 3, 23, 120, 14, 74, 102, 44, 3, 20, 39, 17, 3, 88, 120, 74, 109, 26, 144, 10]} +{"text": "Later that evening, Kieran called Alexis about the missing report.", "tokens": ["l", "ˈ", "e", "ɪ", "ɾ", "ɚ", " ", "ð", "æ", "t", " ", "ˈ", "i", "ː", "v", "n", "ɪ", "ŋ", ",", " ", "k", "ˈ", "i", "ə", "ɹ", "ə", "n", " ", "k", "ˈ", "ɔ", "ː", "l", "d", " ", "ɐ", "l", "ˈ", "ɛ", "k", "s", "ɪ", "s", " ", "ɐ", "b", "ˌ", "a", "ʊ", "t", " ", "ð", "ə", " ", "m", "ˈ", "ɪ", "s", "ɪ", "ŋ", " ", "ɹ", "ᵻ", "p", "ˈ", "o", "ː", "ɹ", "t", "."], "ids": [24, 120, 18, 74, 92, 60, 3, 41, 39, 32, 3, 120, 21, 122, 34, 26, 74, 44, 8, 3, 23, 120, 21, 59, 88, 59, 26, 3, 23, 120, 54, 122, 24, 17, 3, 50, 24, 120, 61, 23, 31, 74, 31, 3, 50, 15, 121, 14, 100, 32, 3, 41, 59, 3, 25, 120, 74, 31, 74, 44, 3, 88, 128, 28, 120, 27, 122, 88, 32, 10]} +{"text": "David grew up near Atlanta before moving abroad.", "tokens": ["d", "ˈ", "e", "ɪ", "v", "ɪ", "d", " ", "ɡ", "ɹ", "ˈ", "u", "ː", " ", "ˌ", "ʌ", "p", " ", "n", "ˌ", "ɪ", "ɹ", " ", "ɐ", "t", "l", "ˈ", "æ", "n", "t", "ə", " ", "b", "ᵻ", "f", "ˌ", "o", "ː", "ɹ", " ", "m", "ˈ", "u", "ː", "v", "ɪ", "ŋ", " ", "ɐ", "b", "ɹ", "ˈ", "ɔ", "ː", "d", "."], "ids": [17, 120, 18, 74, 34, 74, 17, 3, 66, 88, 120, 33, 122, 3, 121, 102, 28, 3, 26, 121, 74, 88, 3, 50, 32, 24, 120, 39, 26, 32, 59, 3, 15, 128, 19, 121, 27, 122, 88, 3, 25, 120, 33, 122, 34, 74, 44, 3, 50, 15, 88, 120, 54, 122, 17, 10]} +{"text": "Everyone agreed that Kriton deserved the promotion.", "tokens": ["ˈ", "ɛ", "v", "ɹ", "ɪ", "w", "ˌ", "ʌ", "n", " ", "ɐ", "ɡ", "ɹ", "ˈ", "i", "ː", "d", " ", "ð", "æ", "t", " ", "k", "ɹ", "ˈ", "ɪ", "t", "ə", "n", " ", "d", "ɪ", "z", "ˈ", "ɜ", "ː", "v", "d", " ", "ð", "ə", " ", "p", 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"ˈ", "ɑ", "ː", "n", "d", "ɹ", "e", "ɪ", ",", " ", "ð", "ə", " ", "n", "ˈ", "u", "ː", " ", "ˌ", "ɛ", "n", "d", "ʒ", "ɪ", "n", "ˈ", "ɪ", "ɹ", " ", "f", "ɹ", "ʌ", "m", "ð", "ə", " ", "ʃ", "ᵻ", "k", "ˈ", "ɑ", "ː", "ɡ", "o", "ʊ", " ", "ˈ", "ɑ", "ː", "f", "ɪ", "s", "?"], "ids": [20, 39, 34, 3, 22, 33, 122, 3, 25, 120, 61, 32, 3, 120, 51, 122, 26, 17, 88, 18, 74, 8, 3, 41, 59, 3, 26, 120, 33, 122, 3, 121, 61, 26, 17, 108, 74, 26, 120, 74, 88, 3, 19, 88, 102, 25, 41, 59, 3, 96, 128, 23, 120, 51, 122, 66, 27, 100, 3, 120, 51, 122, 19, 74, 31, 13]} +{"text": "Benjamin said the project would be done by Friday.", "tokens": ["b", "ˈ", "ɛ", "n", "d", "ʒ", "ə", "m", "ɪ", "n", " ", "s", "ˈ", "ɛ", "d", " ", "ð", "ə", " ", "p", "ɹ", "ˈ", "ɑ", "ː", "d", "ʒ", "ɛ", "k", "t", " ", "w", "ʊ", "d", " ", "b", "i", "ː", " ", "d", "ˈ", "ʌ", "n", " ", "b", "a", "ɪ", " ", "f", "ɹ", "ˈ", "a", "ɪ", "d", "e", "ɪ", "."], "ids": [15, 120, 61, 26, 17, 108, 59, 25, 74, 26, 3, 31, 120, 61, 17, 3, 41, 59, 3, 28, 88, 120, 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written.", "tokens": ["ˈ", "ɛ", "d", " ", "k", "ˌ", "ʊ", "d", "ə", "n", "t", " ", "b", "ᵻ", "l", "ˈ", "i", "ː", "v", " ", "w", "ʌ", "t", " ", "t", "ʃ", "ˈ", "ɑ", "ː", "ɹ", "l", "i", " ", "h", "æ", "d", " ", "ɹ", "ˈ", "ɪ", "ʔ", "n", "̩", "."], "ids": [120, 61, 17, 3, 23, 121, 100, 17, 59, 26, 32, 3, 15, 128, 24, 120, 21, 122, 34, 3, 35, 102, 32, 3, 32, 96, 120, 51, 122, 88, 24, 21, 3, 20, 39, 17, 3, 88, 120, 74, 109, 26, 144, 10]} +{"text": "Later that evening, Sam called Rusty about the missing report.", "tokens": ["l", "ˈ", "e", "ɪ", "ɾ", "ɚ", " ", "ð", "æ", "t", " ", "ˈ", "i", "ː", "v", "n", "ɪ", "ŋ", ",", " ", "s", "ˈ", "æ", "m", " ", "k", "ˈ", "ɔ", "ː", "l", "d", " ", "ɹ", "ˈ", "ʌ", "s", "t", "i", " ", "ɐ", "b", "ˌ", "a", "ʊ", "t", " ", "ð", "ə", " ", "m", "ˈ", "ɪ", "s", "ɪ", "ŋ", " ", "ɹ", "ᵻ", "p", "ˈ", "o", "ː", "ɹ", "t", "."], "ids": [24, 120, 18, 74, 92, 60, 3, 41, 39, 32, 3, 120, 21, 122, 34, 26, 74, 44, 8, 3, 31, 120, 39, 25, 3, 23, 120, 54, 122, 24, 17, 3, 88, 120, 102, 31, 32, 21, 3, 50, 15, 121, 14, 100, 32, 3, 41, 59, 3, 25, 120, 74, 31, 74, 44, 3, 88, 128, 28, 120, 27, 122, 88, 32, 10]} +{"text": "Gilles grew up near Miami before moving abroad.", "tokens": ["d", "ʒ", "ˈ", "ɪ", "l", "z", " ", "ɡ", "ɹ", "ˈ", "u", "ː", " ", "ˌ", "ʌ", "p", " ", "n", "ˌ", "ɪ", "ɹ", " ", "m", "a", "ɪ", "ˈ", "æ", "m", "i", " ", "b", "ᵻ", "f", "ˌ", "o", "ː", "ɹ", " ", "m", "ˈ", "u", "ː", "v", "ɪ", "ŋ", " ", "ɐ", "b", "ɹ", "ˈ", "ɔ", "ː", "d", "."], "ids": [17, 108, 120, 74, 24, 38, 3, 66, 88, 120, 33, 122, 3, 121, 102, 28, 3, 26, 121, 74, 88, 3, 25, 14, 74, 120, 39, 25, 21, 3, 15, 128, 19, 121, 27, 122, 88, 3, 25, 120, 33, 122, 34, 74, 44, 3, 50, 15, 88, 120, 54, 122, 17, 10]} +{"text": "Everyone agreed that Tharen deserved the promotion.", "tokens": ["ˈ", "ɛ", "v", "ɹ", "ɪ", "w", "ˌ", "ʌ", "n", " ", "ɐ", "ɡ", "ɹ", "ˈ", "i", "ː", "d", " ", "ð", "æ", "t", " ", "θ", "ˈ", "æ", "ɹ", "ə", "n", " ", "d", "ɪ", "z", "ˈ", "ɜ", "ː", "v", "d", " ", "ð", "ə", " ", "p", "ɹ", "ə", "m", "ˈ", "o", "ʊ", "ʃ", "ə", "n", "."], "ids": [120, 61, 34, 88, 74, 35, 121, 102, 26, 3, 50, 66, 88, 120, 21, 122, 17, 3, 41, 39, 32, 3, 126, 120, 39, 88, 59, 26, 3, 17, 74, 38, 120, 62, 122, 34, 17, 3, 41, 59, 3, 28, 88, 59, 25, 120, 27, 100, 96, 59, 26, 10]} diff --git a/models/tts/zipvoice/coreml/g2p/misaki_mapping.py b/models/tts/zipvoice/coreml/g2p/misaki_mapping.py new file mode 100644 index 0000000..8e2285c --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/misaki_mapping.py @@ -0,0 +1,81 @@ +"""Approach B baseline: Misaki (Kokoro G2P) output + symbol mapping to the +espeak token set, scored against the espeak oracle. + +Runs in a venv with `misaki` installed (spacy en_core_web_sm, num2words): + /tmp/misaki-venv/bin/python coreml/g2p/misaki_mapping.py \ + --corpus coreml/g2p/corpus_en_1000.txt --out /tmp/misaki_dump.jsonl + +The dump is then scored with `coreml.g2p.validate score`. + +Mapping rules (best-effort espeak-parity): + ligatures/diphthong shorthands: ʤ->dʒ ʧ->tʃ A->eɪ I->aɪ W->aʊ O->oʊ Y->ɔɪ + rhotics: əɹ->ɚ ɜɹ->ɜː(+ɹ before vowel is espeak-side; approximated ɜːɹ) + length: ɑ->ɑː ɔ->ɔː(not before n) i->iː(stressed nucleus) u->uː ɜ->ɜː +""" + +import argparse +import json +import re +from pathlib import Path + + +def map_to_espeak(phonemes: str) -> str: + s = phonemes + # multi-char first + s = s.replace("ʤ", "dʒ").replace("ʧ", "tʃ") + s = s.replace("əɹ", "ɚ").replace("ɜɹ", "ɜːɹ") + for src, dst in [ + ("A", "eɪ"), ("I", "aɪ"), ("W", "aʊ"), ("O", "oʊ"), ("Y", "ɔɪ"), + ("ᵊ", "ə"), + ]: + s = s.replace(src, dst) + # length marks: espeak en-us long vowels + s = re.sub(r"ɑ(?!ː)", "ɑː", s) + s = re.sub(r"ɔ(?![ːɪn])", "ɔː", s) + s = re.sub(r"ɜ(?!ː)", "ɜː", s) + s = re.sub(r"u(?!ː)", "uː", s) + # 'i' as a stressed nucleus is long in espeak (sˈiː), word-final happy-i + # stays short; approximate: i after a stress mark cluster gets ː + s = re.sub(r"([ˈˌ][^aeiouɑɔɜʊɪʌæɛə]*)i(?![ː])", r"\1iː", s) + return s + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--corpus", required=True) + parser.add_argument("--tokens", required=True, help="tokens.txt") + parser.add_argument("--out", required=True) + args = parser.parse_args() + + from misaki import en + + g2p = en.G2P(trf=False, british=False) + + token2id = {} + for line in Path(args.tokens).read_text(encoding="utf-8").splitlines(): + tab = line.index("\t") + token2id[line[:tab]] = int(line[tab + 1:]) + + sentences = [ + s.strip() + for s in Path(args.corpus).read_text(encoding="utf-8").splitlines() + if s.strip() + ] + with open(args.out, "w", encoding="utf-8") as f: + for text in sentences: + result = g2p(text) + phonemes = result[0] if isinstance(result, tuple) else result + mapped = map_to_espeak(phonemes) + ids = [token2id[c] for c in mapped if c in token2id] + f.write( + json.dumps( + {"text": text, "phonemes": mapped, "ids": ids}, + ensure_ascii=False, + ) + + "\n" + ) + print(f"wrote {len(sentences)} entries to {args.out}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/g2p/oracle_tokens.jsonl b/models/tts/zipvoice/coreml/g2p/oracle_tokens.jsonl new file mode 100644 index 0000000..0ee89d7 --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/oracle_tokens.jsonl @@ -0,0 +1,1000 @@ +{"text": "I guess it comes down a simple choice. Get busy living or get busy dying.", "tokens": ["a", "ɪ", " ", "ɡ", "ˈ", "ɛ", "s", " ", "ɪ", "t", " ", "k", "ˈ", "ʌ", "m", "z", " ", "d", "ˌ", "a", "ʊ", "n", " ", "ɐ", " ", "s", "ˈ", "ɪ", "m", "p", "ə", "l", " ", "t", "ʃ", "ˈ", "ɔ", "ɪ", "s", ".", "ɡ", "ɛ", "t", " ", "b", "ˈ", "ɪ", "z", "i", " ", "l", "ˈ", "ɪ", "v", "ɪ", "ŋ", " ", "ɔ", "ː", "ɹ", " ", "ɡ", "ɛ", "t", " ", "b", "ˈ", "ɪ", "z", "i", " ", "d", "ˈ", "a", "ɪ", "ɪ", "ŋ", "."], "ids": [14, 74, 3, 66, 120, 61, 31, 3, 74, 32, 3, 23, 120, 102, 25, 38, 3, 17, 121, 14, 100, 26, 3, 50, 3, 31, 120, 74, 25, 28, 59, 24, 3, 32, 96, 120, 54, 74, 31, 10, 66, 61, 32, 3, 15, 120, 74, 38, 21, 3, 24, 120, 74, 34, 74, 44, 3, 54, 122, 88, 3, 66, 61, 32, 3, 15, 120, 74, 38, 21, 3, 17, 120, 14, 74, 74, 44, 10]} +{"text": "You got a dream, you gotta protect it. People can't do something themselves, they wanna tell you you can't do it. If you want something, go get it.", "tokens": ["j", "u", "ː", " ", "ɡ", "ɑ", "ː", "t", " ", "ɐ", " ", "d", "ɹ", "ˈ", "i", "ː", "m", ",", " ", "j", "u", "ː", " ", "ɡ", "ˈ", "ɑ", "ː", "ɾ", "ə", " ", "p", "ɹ", "ə", "t", "ˈ", "ɛ", "k", "t", " ", "ɪ", "t", ".", "p", "ˈ", "i", "ː", "p", "ə", "l", " ", "k", "ˈ", "æ", "n", "t", " ", "d", "ˈ", "u", "ː", " ", "s", "ˈ", "ʌ", "m", "θ", "ɪ", "ŋ", " ", "ð", "ɛ", "m", "s", "ˈ", "ɛ", "l", "v", "z", ",", " ", "ð", "e", "ɪ", " ", "w", "ˈ", "ɔ", "n", "ə", " ", "t", "ˈ", "ɛ", "l", " ", "j", "u", "ː", " ", "j", "u", "ː", " ", "k", "ˈ", "æ", "n", "t", " ", "d", "ˈ", "u", "ː", " ", "ɪ", "t", ".", "ɪ", "f", " ", "j", "u", "ː", " ", "w", "ˈ", "ɔ", "n", "t", " ", "s", "ˈ", "ʌ", "m", "θ", "ɪ", "ŋ", ",", " ", "ɡ", "ˌ", "o", "ʊ", " ", "ɡ", "ˈ", "ɛ", "t", " ", "ɪ", "t", "."], "ids": [22, 33, 122, 3, 66, 51, 122, 32, 3, 50, 3, 17, 88, 120, 21, 122, 25, 8, 3, 22, 33, 122, 3, 66, 120, 51, 122, 92, 59, 3, 28, 88, 59, 32, 120, 61, 23, 32, 3, 74, 32, 10, 28, 120, 21, 122, 28, 59, 24, 3, 23, 120, 39, 26, 32, 3, 17, 120, 33, 122, 3, 31, 120, 102, 25, 126, 74, 44, 3, 41, 61, 25, 31, 120, 61, 24, 34, 38, 8, 3, 41, 18, 74, 3, 35, 120, 54, 26, 59, 3, 32, 120, 61, 24, 3, 22, 33, 122, 3, 22, 33, 122, 3, 23, 120, 39, 26, 32, 3, 17, 120, 33, 122, 3, 74, 32, 10, 74, 19, 3, 22, 33, 122, 3, 35, 120, 54, 26, 32, 3, 31, 120, 102, 25, 126, 74, 44, 8, 3, 66, 121, 27, 100, 3, 66, 120, 61, 32, 3, 74, 32, 10]} +{"text": "Life was like a box of chocolates, you never know what you're gonna get.", "tokens": ["l", "ˈ", "a", "ɪ", "f", " ", "w", "ʌ", "z", " ", "l", "ˈ", "a", "ɪ", "k", " ", "ɐ", " ", "b", "ˈ", "ɑ", "ː", "k", "s", " ", "ʌ", "v", " ", "t", "ʃ", "ˈ", "ɑ", "ː", "k", "l", "ə", "t", "s", ",", " ", "j", "u", "ː", " ", "n", "ˈ", "ɛ", "v", "ɚ", " ", "n", "ˈ", "o", "ʊ", " ", "w", "ʌ", "t", " ", "j", "ʊ", "ɹ", " ", "ɡ", "ˌ", "ə", "n", "ə", " ", "ɡ", "ˈ", "ɛ", "t", "."], "ids": [24, 120, 14, 74, 19, 3, 35, 102, 38, 3, 24, 120, 14, 74, 23, 3, 50, 3, 15, 120, 51, 122, 23, 31, 3, 102, 34, 3, 32, 96, 120, 51, 122, 23, 24, 59, 32, 31, 8, 3, 22, 33, 122, 3, 26, 120, 61, 34, 60, 3, 26, 120, 27, 100, 3, 35, 102, 32, 3, 22, 100, 88, 3, 66, 121, 59, 26, 59, 3, 66, 120, 61, 32, 10]} +{"text": "Is life always this hard. Or is it just when you're a kid?Always like this.", "tokens": ["ɪ", "z", " ", "l", "ˈ", "a", "ɪ", "f", " ", "ˈ", "ɔ", "ː", "l", "w", "e", "ɪ", "z", " ", "ð", "ɪ", "s", " ", "h", "ˈ", "ɑ", "ː", "ɹ", "d", ".", "ɔ", "ː", "ɹ", " ", "ɪ", "z", " ", "ɪ", "t", " ", "d", "ʒ", "ˈ", "ʌ", "s", "t", " ", "w", "ɛ", "n", " ", "j", "ʊ", "ɹ", " ", "ɐ", " ", "k", "ˈ", "ɪ", "d", "ɐ", "l", "w", "ˌ", "e", "ɪ", "z", " ", "l", "ˈ", "a", "ɪ", "k", " ", "ð", "ˈ", "ɪ", "s", "."], "ids": [74, 38, 3, 24, 120, 14, 74, 19, 3, 120, 54, 122, 24, 35, 18, 74, 38, 3, 41, 74, 31, 3, 20, 120, 51, 122, 88, 17, 10, 54, 122, 88, 3, 74, 38, 3, 74, 32, 3, 17, 108, 120, 102, 31, 32, 3, 35, 61, 26, 3, 22, 100, 88, 3, 50, 3, 23, 120, 74, 17, 50, 24, 35, 121, 18, 74, 38, 3, 24, 120, 14, 74, 23, 3, 41, 120, 74, 31, 10]} +{"text": "Whoever saves one life, saves the world entire.", "tokens": ["h", "u", "ː", "ˈ", "ɛ", "v", "ɚ", " ", "s", "ˈ", "e", "ɪ", "v", "z", " ", "w", "ˈ", "ʌ", "n", " ", "l", "ˈ", "a", "ɪ", "f", ",", " ", "s", "ˈ", "e", "ɪ", "v", "z", " ", "ð", "ə", " ", "w", "ˈ", "ɜ", "ː", "l", "d", " ", "ɛ", "n", "t", "ˈ", "a", "ɪ", "ɚ", "."], "ids": [20, 33, 122, 120, 61, 34, 60, 3, 31, 120, 18, 74, 34, 38, 3, 35, 120, 102, 26, 3, 24, 120, 14, 74, 19, 8, 3, 31, 120, 18, 74, 34, 38, 3, 41, 59, 3, 35, 120, 62, 122, 24, 17, 3, 61, 26, 32, 120, 14, 74, 60, 10]} +{"text": "There is no such thing as a great talent without great will - power.", "tokens": ["ð", "ɛ", "ɹ", " ", "ɪ", "z", " ", "n", "ˈ", "o", "ʊ", " ", "s", "ˈ", "ʌ", "t", "ʃ", " ", "θ", "ˈ", "ɪ", "ŋ", " ", "æ", "z", " ", "ɐ", " ", "ɡ", "ɹ", "ˈ", "e", "ɪ", "t", " ", "t", "ˈ", "æ", "l", "ə", "n", "t", " ", "w", "ɪ", "ð", "ˌ", "a", "ʊ", "t", " ", "ɡ", "ɹ", "ˈ", "e", "ɪ", "t", " ", "w", "ɪ", "l", " ", "p", "ˈ", "a", "ʊ", "ɚ", "."], "ids": [41, 61, 88, 3, 74, 38, 3, 26, 120, 27, 100, 3, 31, 120, 102, 32, 96, 3, 126, 120, 74, 44, 3, 39, 38, 3, 50, 3, 66, 88, 120, 18, 74, 32, 3, 32, 120, 39, 24, 59, 26, 32, 3, 35, 74, 41, 121, 14, 100, 32, 3, 66, 88, 120, 18, 74, 32, 3, 35, 74, 24, 3, 28, 120, 14, 100, 60, 10]} +{"text": "I don't wait for moods. You accomplish nothing if you do that. Your mind must know it has got down to work", "tokens": ["a", "ɪ", " ", "d", "ˈ", "o", "ʊ", "n", "t", " ", "w", "ˈ", "e", "ɪ", "t", " ", "f", "ɔ", "ː", "ɹ", " ", "m", "ˈ", "u", "ː", "d", "z", ".", "j", "u", "ː", " ", "ɐ", "k", "ˈ", "ɑ", "ː", "m", "p", "l", "ɪ", "ʃ", " ", "n", "ˈ", "ʌ", "θ", "ɪ", "ŋ", " ", "ɪ", "f", " ", "j", "u", "ː", " ", "d", "ˈ", "u", "ː", " ", "ð", "ˈ", "æ", "t", ".", "j", "ʊ", "ɹ", " ", "m", "ˈ", "a", "ɪ", "n", "d", " ", "m", "ˈ", "ʌ", "s", "t", " ", "n", "ˈ", "o", "ʊ", " ", "ɪ", "t", " ", "h", "ɐ", "z", " ", "ɡ", "ɑ", "ː", "t", " ", "d", "ˌ", "a", "ʊ", "n", " ", "t", "ə", " ", "w", "ˈ", "ɜ", "ː", "k"], "ids": [14, 74, 3, 17, 120, 27, 100, 26, 32, 3, 35, 120, 18, 74, 32, 3, 19, 54, 122, 88, 3, 25, 120, 33, 122, 17, 38, 10, 22, 33, 122, 3, 50, 23, 120, 51, 122, 25, 28, 24, 74, 96, 3, 26, 120, 102, 126, 74, 44, 3, 74, 19, 3, 22, 33, 122, 3, 17, 120, 33, 122, 3, 41, 120, 39, 32, 10, 22, 100, 88, 3, 25, 120, 14, 74, 26, 17, 3, 25, 120, 102, 31, 32, 3, 26, 120, 27, 100, 3, 74, 32, 3, 20, 50, 38, 3, 66, 51, 122, 32, 3, 17, 121, 14, 100, 26, 3, 32, 59, 3, 35, 120, 62, 122, 23]} +{"text": "We take care of the future best by taking care of the present now", "tokens": ["w", "i", "ː", " ", "t", "ˈ", "e", "ɪ", "k", " ", "k", "ˈ", "ɛ", "ɹ", " ", "ʌ", "v", "ð", "ə", " ", "f", "j", "ˈ", "u", "ː", "t", "ʃ", "ɚ", " ", "b", "ˈ", "ɛ", "s", "t", " ", "b", "a", "ɪ", " ", "t", "ˈ", "e", "ɪ", "k", "ɪ", "ŋ", " ", "k", "ˈ", "ɛ", "ɹ", " ", "ʌ", "v", "ð", "ə", " ", "p", "ɹ", "ˈ", "ɛ", "z", "ə", "n", "t", " ", "n", "ˈ", "a", "ʊ"], "ids": [35, 21, 122, 3, 32, 120, 18, 74, 23, 3, 23, 120, 61, 88, 3, 102, 34, 41, 59, 3, 19, 22, 120, 33, 122, 32, 96, 60, 3, 15, 120, 61, 31, 32, 3, 15, 14, 74, 3, 32, 120, 18, 74, 23, 74, 44, 3, 23, 120, 61, 88, 3, 102, 34, 41, 59, 3, 28, 88, 120, 61, 38, 59, 26, 32, 3, 26, 120, 14, 100]} +{"text": "There is no development physically or intellectually without effort, and effort means work", "tokens": ["ð", "ɛ", "ɹ", " ", "ɪ", "z", " ", "n", "ˈ", "o", "ʊ", " ", "d", "ɪ", "v", "ˈ", "ɛ", "l", "ə", "p", "m", "ə", "n", "t", " ", "f", "ˈ", "ɪ", "z", "ɪ", "k", "l", "i", " ", "ɔ", "ː", "ɹ", " ", "ˌ", "ɪ", "n", "t", "ə", "l", "ˈ", "ɛ", "k", "t", "ʃ", "u", "ː", "ə", "l", "i", " ", "w", "ɪ", "ð", "ˌ", "a", "ʊ", "t", " ", "ˈ", "ɛ", "f", "ɚ", "t", ",", " ", "æ", "n", "d", " ", "ˈ", "ɛ", "f", "ɚ", "t", " ", "m", "ˈ", "i", "ː", "n", "z", " ", "w", "ˈ", "ɜ", "ː", "k"], "ids": [41, 61, 88, 3, 74, 38, 3, 26, 120, 27, 100, 3, 17, 74, 34, 120, 61, 24, 59, 28, 25, 59, 26, 32, 3, 19, 120, 74, 38, 74, 23, 24, 21, 3, 54, 122, 88, 3, 121, 74, 26, 32, 59, 24, 120, 61, 23, 32, 96, 33, 122, 59, 24, 21, 3, 35, 74, 41, 121, 14, 100, 32, 3, 120, 61, 19, 60, 32, 8, 3, 39, 26, 17, 3, 120, 61, 19, 60, 32, 3, 25, 120, 21, 122, 26, 38, 3, 35, 120, 62, 122, 23]} +{"text": "You can overcome anything, if and only if you love something enough", "tokens": ["j", "u", "ː", " ", "k", "æ", "n", " ", "ˌ", "o", "ʊ", "v", "ɚ", "k", "ˈ", "ʌ", "m", " ", "ˈ", "ɛ", "n", 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"I won't tremble to morrow thought the fir tree.", "tokens": ["a", "ɪ", " ", "w", "o", "ʊ", "n", "t", " ", "t", "ɹ", "ˈ", "ɛ", "m", "b", "ə", "l", " ", "t", "ə", " ", "m", "ˈ", "ɑ", "ː", "ɹ", "o", "ʊ", " ", "θ", "ˈ", "ɔ", "ː", "t", " ", "ð", "ə", " ", "f", "ˈ", "ɜ", "ː", " ", "t", "ɹ", "ˈ", "i", "ː", "."], "ids": [14, 74, 3, 35, 27, 100, 26, 32, 3, 32, 88, 120, 61, 25, 15, 59, 24, 3, 32, 59, 3, 25, 120, 51, 122, 88, 27, 100, 3, 126, 120, 54, 122, 32, 3, 41, 59, 3, 19, 120, 62, 122, 3, 32, 88, 120, 21, 122, 10]} +{"text": "Enter hamlet with his favourite boar hound.", "tokens": ["ˈ", "ɛ", "n", "t", "ɚ", " ", "h", "ˈ", "æ", "m", "l", "ɪ", "t", " ", "w", "ɪ", "ð", " ", "h", "ɪ", "z", " ", "f", "ˈ", "e", "ɪ", "v", "ɹ", "ɪ", "t", " ", "b", "ˈ", "o", "ː", "ɹ", " ", "h", "ˈ", "a", "ʊ", "n", "d", "."], "ids": [120, 61, 26, 32, 60, 3, 20, 120, 39, 25, 24, 74, 32, 3, 35, 74, 41, 3, 20, 74, 38, 3, 19, 120, 18, 74, 34, 88, 74, 32, 3, 15, 120, 27, 122, 88, 3, 20, 120, 14, 100, 26, 17, 10]} +{"text": "Robin was glad when at length they were left to their own devices.", "tokens": ["ɹ", "ˈ", "ɑ", "ː", "b", "ɪ", "n", " ", "w", "ʌ", "z", " ", "ɡ", "l", "ˈ", "æ", "d", " ", "w", "ɛ", "n", " ", "æ", "t", " ", "l", "ˈ", "ɛ", "ŋ", "θ", " ", "ð", "e", "ɪ", " ", "w", "ɜ", "ː", " ", "l", "ˈ", "ɛ", "f", "t", " ", "t", "ə", " ", "ð", "ɛ", "ɹ", " ", "ˈ", "o", "ʊ", "n", " ", "d", "ɪ", "v", "ˈ", "a", "ɪ", "s", "ᵻ", "z", "."], "ids": [88, 120, 51, 122, 15, 74, 26, 3, 35, 102, 38, 3, 66, 24, 120, 39, 17, 3, 35, 61, 26, 3, 39, 32, 3, 24, 120, 61, 44, 126, 3, 41, 18, 74, 3, 35, 62, 122, 3, 24, 120, 61, 19, 32, 3, 32, 59, 3, 41, 61, 88, 3, 120, 27, 100, 26, 3, 17, 74, 34, 120, 14, 74, 31, 128, 38, 10]} +{"text": "Up in the sick room zora lay on the little white bed.", "tokens": ["ˌ", "ʌ", "p", " ", "ɪ", "n", "ð", "ə", " ", "s", "ˈ", "ɪ", "k", " ", "ɹ", "ˈ", "u", "ː", "m", " ", "z", "ˈ", "o", "ː", "ɹ", "ə", " ", "l", "ˈ", "e", "ɪ", " ", "ɔ", "n", "ð", "ə", " ", "l", "ˈ", "ɪ", "ɾ", "ə", "l", " 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+{"text": "Exclaimed bill harmon to his wife as they went through the lighted hall.", "tokens": ["ɛ", "k", "s", "k", "l", "ˈ", "e", "ɪ", "m", "d", " ", "b", "ˈ", "ɪ", "l", " ", "h", "ˈ", "ɑ", "ː", "ɹ", "m", "ə", "n", " ", "t", "ə", " ", "h", "ɪ", "z", " ", "w", "ˈ", "a", "ɪ", "f", " ", "æ", "z", " ", "ð", "e", "ɪ", " ", "w", "ɛ", "n", "t", " ", "θ", "ɹ", "u", "ː", " ", "ð", "ə", " ", "l", "ˈ", "a", "ɪ", "ɾ", "ᵻ", "d", " ", "h", "ˈ", "ɔ", "ː", "l", "."], "ids": [61, 23, 31, 23, 24, 120, 18, 74, 25, 17, 3, 15, 120, 74, 24, 3, 20, 120, 51, 122, 88, 25, 59, 26, 3, 32, 59, 3, 20, 74, 38, 3, 35, 120, 14, 74, 19, 3, 39, 38, 3, 41, 18, 74, 3, 35, 61, 26, 32, 3, 126, 88, 33, 122, 3, 41, 59, 3, 24, 120, 14, 74, 92, 128, 17, 3, 20, 120, 54, 122, 24, 10]} +{"text": "It is my heart hung in the sky and no clouds ever float between the grave flowers and my heart on high.", "tokens": ["ɪ", "ɾ", " ", "ɪ", "z", " ", "m", "a", "ɪ", " ", "h", "ˈ", "ɑ", "ː", "ɹ", "t", " ", "h", "ˈ", "ʌ", "ŋ", " ", "ɪ", 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"Danielle grew up near Portland before moving abroad.", "tokens": ["d", "ˌ", "æ", "n", "ɪ", "ˈ", "ɛ", "l", " ", "ɡ", "ɹ", "ˈ", "u", "ː", " ", "ˌ", "ʌ", "p", " ", "n", "ˌ", "ɪ", "ɹ", " ", "p", "ˈ", "o", "ː", "ɹ", "t", "l", "ə", "n", "d", " ", "b", "ᵻ", "f", "ˌ", "o", "ː", "ɹ", " ", "m", "ˈ", "u", "ː", "v", "ɪ", "ŋ", " ", "ɐ", "b", "ɹ", "ˈ", "ɔ", "ː", "d", "."], "ids": [17, 121, 39, 26, 74, 120, 61, 24, 3, 66, 88, 120, 33, 122, 3, 121, 102, 28, 3, 26, 121, 74, 88, 3, 28, 120, 27, 122, 88, 32, 24, 59, 26, 17, 3, 15, 128, 19, 121, 27, 122, 88, 3, 25, 120, 33, 122, 34, 74, 44, 3, 50, 15, 88, 120, 54, 122, 17, 10]} +{"text": "Everyone agreed that Roxanne deserved the promotion.", "tokens": ["ˈ", "ɛ", "v", "ɹ", "ɪ", "w", "ˌ", "ʌ", "n", " ", "ɐ", "ɡ", "ɹ", "ˈ", "i", "ː", "d", " ", "ð", "æ", "t", " ", "ɹ", "ˈ", "ɑ", "ː", "k", "s", "æ", "n", " ", "d", "ɪ", "z", "ˈ", "ɜ", "ː", "v", "d", " ", "ð", "ə", " ", "p", "ɹ", "ə", "m", "ˈ", "o", "ʊ", "ʃ", "ə", "n", "."], "ids": [120, 61, 34, 88, 74, 35, 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59, 3, 28, 88, 59, 25, 120, 27, 100, 96, 59, 26, 10]} +{"text": "Eduardo and Judy drove to the coast on Saturday.", "tokens": ["ɛ", "d", "w", "ˈ", "ɑ", "ː", "ɹ", "d", "o", "ʊ", " ", "æ", "n", "d", " ", "d", "ʒ", "ˈ", "u", "ː", "d", "i", " ", "d", "ɹ", "ˈ", "o", "ʊ", "v", " ", "t", "ə", " ", "ð", "ə", " ", "k", "ˈ", "o", "ʊ", "s", "t", " ", "ˌ", "ɔ", "n", " ", "s", "ˈ", "æ", "ɾ", "ɚ", "d", "ˌ", "e", "ɪ", "."], "ids": [61, 17, 35, 120, 51, 122, 88, 17, 27, 100, 3, 39, 26, 17, 3, 17, 108, 120, 33, 122, 17, 21, 3, 17, 88, 120, 27, 100, 34, 3, 32, 59, 3, 41, 59, 3, 23, 120, 27, 100, 31, 32, 3, 121, 54, 26, 3, 31, 120, 39, 92, 60, 17, 121, 18, 74, 10]} +{"text": "Have you met Christophe, the new engineer from the Miami office?", "tokens": ["h", "æ", "v", " ", "j", "u", "ː", " ", "m", "ˈ", "ɛ", "t", " ", "k", "ɹ", "ˈ", "ɪ", "s", "t", "ə", "f", "i", ",", " ", "ð", "ə", " ", "n", "ˈ", "u", "ː", " ", "ˌ", "ɛ", "n", "d", "ʒ", "ɪ", "n", "ˈ", "ɪ", "ɹ", " ", "f", "ɹ", "ʌ", "m", "ð", "ə", " ", "m", "a", "ɪ", "ˈ", "æ", "m", "i", " ", "ˈ", "ɑ", "ː", "f", "ɪ", "s", "?"], "ids": [20, 39, 34, 3, 22, 33, 122, 3, 25, 120, 61, 32, 3, 23, 88, 120, 74, 31, 32, 59, 19, 21, 8, 3, 41, 59, 3, 26, 120, 33, 122, 3, 121, 61, 26, 17, 108, 74, 26, 120, 74, 88, 3, 19, 88, 102, 25, 41, 59, 3, 25, 14, 74, 120, 39, 25, 21, 3, 120, 51, 122, 19, 74, 31, 13]} +{"text": "Julian said the project would be done by Friday.", "tokens": ["d", "ʒ", "ˈ", "u", "ː", "l", "i", "ə", "n", " ", "s", "ˈ", "ɛ", "d", " ", "ð", "ə", " ", "p", "ɹ", "ˈ", "ɑ", "ː", "d", "ʒ", "ɛ", "k", "t", " ", "w", "ʊ", "d", " ", "b", "i", "ː", " ", "d", "ˈ", "ʌ", "n", " ", "b", "a", "ɪ", " ", "f", "ɹ", "ˈ", "a", "ɪ", "d", "e", "ɪ", "."], "ids": [17, 108, 120, 33, 122, 24, 21, 59, 26, 3, 31, 120, 61, 17, 3, 41, 59, 3, 28, 88, 120, 51, 122, 17, 108, 61, 23, 32, 3, 35, 100, 17, 3, 15, 21, 122, 3, 17, 120, 102, 26, 3, 15, 14, 74, 3, 19, 88, 120, 14, 74, 17, 18, 74, 10]} +{"text": "According to Dominick, the results were never published.", "tokens": ["ɐ", "k", "ˈ", "o", "ː", "ɹ", "d", "ɪ", "ŋ", " ", "t", "ə", " ", "d", "ə", "m", "ˈ", "ɪ", "n", "ɪ", "k", ",", " ", "ð", "ə", " ", "ɹ", "ɪ", "z", "ˈ", "ʌ", "l", "t", "s", " ", "w", "ɜ", "ː", " ", "n", "ˈ", "ɛ", "v", "ɚ", " ", "p", "ˈ", "ʌ", "b", "l", "ɪ", "ʃ", "t", "."], "ids": [50, 23, 120, 27, 122, 88, 17, 74, 44, 3, 32, 59, 3, 17, 59, 25, 120, 74, 26, 74, 23, 8, 3, 41, 59, 3, 88, 74, 38, 120, 102, 24, 32, 31, 3, 35, 62, 122, 3, 26, 120, 61, 34, 60, 3, 28, 120, 102, 15, 24, 74, 96, 32, 10]} +{"text": "Lucius introduced Ricky to everyone at the party.", "tokens": ["l", "ˈ", "u", "ː", "ʃ", "ə", "s", " ", "ˌ", "ɪ", "n", "t", "ɹ", "ə", "d", "ˈ", "u", "ː", "s", "t", " ", "ɹ", "ˈ", "ɪ", "k", "i", " ", "t", "ʊ", " ", "ˈ", "ɛ", "v", "ɹ", "ɪ", "w", "ˌ", "ʌ", "n", " ", "æ", "t", " ", "ð", "ə", " ", "p", "ˈ", "ɑ", "ː", "ɹ", "ɾ", "i", "."], "ids": [24, 120, 33, 122, 96, 59, 31, 3, 121, 74, 26, 32, 88, 59, 17, 120, 33, 122, 31, 32, 3, 88, 120, 74, 23, 21, 3, 32, 100, 3, 120, 61, 34, 88, 74, 35, 121, 102, 26, 3, 39, 32, 3, 41, 59, 3, 28, 120, 51, 122, 88, 92, 21, 10]} +{"text": "The award went to Ning for her work on the harbor bridge.", "tokens": ["ð", "ɪ", " ", "ɐ", "w", "ˈ", "ɔ", "ː", "ɹ", "d", " ", "w", "ɛ", "n", "t", " ", "t", "ə", " ", "n", "ˈ", "ɪ", "ŋ", " ", "f", "ɔ", "ː", "ɹ", " ", "h", "ɜ", "ː", " ", "w", "ˈ", "ɜ", "ː", "k", " ", "ɔ", "n", "ð", "ə", " ", "h", "ˈ", "ɑ", "ː", "ɹ", "b", "ɚ", " ", "b", "ɹ", "ˈ", "ɪ", "d", "ʒ", "."], "ids": [41, 74, 3, 50, 35, 120, 54, 122, 88, 17, 3, 35, 61, 26, 32, 3, 32, 59, 3, 26, 120, 74, 44, 3, 19, 54, 122, 88, 3, 20, 62, 122, 3, 35, 120, 62, 122, 23, 3, 54, 26, 41, 59, 3, 20, 120, 51, 122, 88, 15, 60, 3, 15, 88, 120, 74, 17, 108, 10]} +{"text": "Moses couldn't believe what List had written.", "tokens": ["m", "ˈ", "o", "ʊ", "s", "ᵻ", "z", " ", "k", "ˌ", "ʊ", "d", "ə", "n", "t", " ", "b", "ᵻ", "l", "ˈ", "i", "ː", "v", " ", "w", "ʌ", "t", " ", "l", "ˈ", "ɪ", "s", "t", " ", "h", "æ", "d", " ", "ɹ", "ˈ", "ɪ", "ʔ", "n", "̩", "."], "ids": [25, 120, 27, 100, 31, 128, 38, 3, 23, 121, 100, 17, 59, 26, 32, 3, 15, 128, 24, 120, 21, 122, 34, 3, 35, 102, 32, 3, 24, 120, 74, 31, 32, 3, 20, 39, 17, 3, 88, 120, 74, 109, 26, 144, 10]} +{"text": "Later that evening, Tanya called Nhan about the missing report.", "tokens": ["l", "ˈ", "e", "ɪ", "ɾ", "ɚ", " ", "ð", "æ", "t", " ", "ˈ", "i", "ː", "v", "n", "ɪ", "ŋ", ",", " ", "t", "ˈ", "æ", "n", "j", "ə", " ", "k", "ˈ", "ɔ", "ː", "l", "d", " ", "ˈ", "ɛ", "n", "h", "ˈ", "æ", "n", " ", "ɐ", "b", "ˌ", "a", "ʊ", "t", " ", "ð", "ə", " ", "m", "ˈ", "ɪ", "s", "ɪ", "ŋ", " ", "ɹ", "ᵻ", "p", "ˈ", "o", "ː", "ɹ", "t", "."], "ids": [24, 120, 18, 74, 92, 60, 3, 41, 39, 32, 3, 120, 21, 122, 34, 26, 74, 44, 8, 3, 32, 120, 39, 26, 22, 59, 3, 23, 120, 54, 122, 24, 17, 3, 120, 61, 26, 20, 120, 39, 26, 3, 50, 15, 121, 14, 100, 32, 3, 41, 59, 3, 25, 120, 74, 31, 74, 44, 3, 88, 128, 28, 120, 27, 122, 88, 32, 10]} +{"text": "Dani grew up near Atlanta before moving abroad.", "tokens": ["d", "ˈ", "æ", "n", "i", " ", "ɡ", "ɹ", "ˈ", "u", "ː", " ", "ˌ", "ʌ", "p", " ", "n", "ˌ", "ɪ", "ɹ", " ", "ɐ", "t", "l", "ˈ", "æ", "n", "t", "ə", " ", "b", "ᵻ", "f", "ˌ", "o", "ː", "ɹ", " ", "m", "ˈ", "u", "ː", "v", "ɪ", "ŋ", " ", "ɐ", "b", "ɹ", "ˈ", "ɔ", "ː", "d", "."], "ids": [17, 120, 39, 26, 21, 3, 66, 88, 120, 33, 122, 3, 121, 102, 28, 3, 26, 121, 74, 88, 3, 50, 32, 24, 120, 39, 26, 32, 59, 3, 15, 128, 19, 121, 27, 122, 88, 3, 25, 120, 33, 122, 34, 74, 44, 3, 50, 15, 88, 120, 54, 122, 17, 10]} +{"text": "Everyone agreed that Brian deserved the promotion.", "tokens": ["ˈ", "ɛ", "v", "ɹ", "ɪ", "w", "ˌ", "ʌ", "n", " ", "ɐ", "ɡ", "ɹ", "ˈ", "i", "ː", "d", " ", "ð", "æ", "t", " ", "b", "ɹ", "ˈ", "a", "ɪ", "ə", "n", " ", "d", "ɪ", "z", "ˈ", "ɜ", "ː", "v", "d", " ", "ð", "ə", " ", "p", "ɹ", "ə", "m", "ˈ", "o", "ʊ", "ʃ", "ə", "n", "."], "ids": [120, 61, 34, 88, 74, 35, 121, 102, 26, 3, 50, 66, 88, 120, 21, 122, 17, 3, 41, 39, 32, 3, 15, 88, 120, 14, 74, 59, 26, 3, 17, 74, 38, 120, 62, 122, 34, 17, 3, 41, 59, 3, 28, 88, 59, 25, 120, 27, 100, 96, 59, 26, 10]} diff --git a/models/tts/zipvoice/coreml/g2p/probe_lexicon.py b/models/tts/zipvoice/coreml/g2p/probe_lexicon.py new file mode 100644 index 0000000..0b73b91 --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/probe_lexicon.py @@ -0,0 +1,441 @@ +"""Harvest an espeak-parity English lexicon for the LuxTTS Swift G2P. + +espeak-ng (via piper_phonemize, voice en-us) is probed per word in four +controlled carrier contexts; the resulting variants reproduce espeak's +clause-level behavior at runtime without espeak: + + mid "say W trees" mid-clause, before a stressed word (base form) + final "say W" clause-final ($strend/$atend/$u+ forms) + unstr "say W him" before only-unstressed words ($strend2 forms) + vowel "say W apple" before a vowel-initial word (the/to/an forms, + linking-r, final-t flapping) + pause "say W but trees" before a $pause/$brk word (and/or/but/if/which): + no liaison/flap; "to" -> tU + start "W trees" clause-initial ($atstart forms: what -> wˌʌt) + r "say W red" before an r-initial word (word-final schwa is + rendered ɚ: vanilla -> vɐnˈɪlɚ; "the" keeps ðə) + +A second pass probes each word Capitalized; case-sensitive rows are stored +when they differ (espeak: "I" = unstressed pronoun aɪ, "i" = letter ˈaɪ; +$capital words like Polish/polish). + +Sparse storage: variants equal to `mid` are omitted. + +Also harvested (aux JSON): + - multi-word phrase entries (espeak en_list `(a b)` entries, e.g. "in the" + -> merged pron with no space) with mid/final/vowel variants + - homograph verb/noun/past variants ($verbf/$nounf/$pastf machinery) + - flag word sets (verbf/nounf/pastf/verbsf/verbextend) from en_list + - letter names (for all-caps spell-out) + - $capital words (Polish vs polish) + +Inputs (fetch once): + en_list https://raw.githubusercontent.com/rhasspy/espeak-ng/master/dictsource/en_list + (the rhasspy fork matches piper_phonemize's bundled dictionary) + freq https://raw.githubusercontent.com/hermitdave/FrequencyWords/master/content/2018/en/en_full.txt + words-alpha https://raw.githubusercontent.com/dwyl/english-words/master/words_alpha.txt + +Usage: + .venv/bin/python -m coreml.g2p.probe_lexicon \ + --en-list en_list --freq en_full.txt \ + --words-alpha words_alpha.txt --top 200000 \ + --extra-vocab coreml/g2p/corpus_en_1000.txt coreml/g2p/dev_corpus.txt \ + --out-lexicon coreml/g2p/lexicon_en_us.tsv \ + --out-aux coreml/g2p/lexicon_aux.json + +The FluidAudio bundle is the TSV as raw DEFLATE plus the aux JSON: + python -c "import zlib; c=zlib.compressobj(9, zlib.DEFLATED, -15); \ + open('luxtts_en_us_lexicon.tsv.zz','wb').write( \ + c.compress(open('coreml/g2p/lexicon_en_us.tsv','rb').read())+c.flush())" +""" + +import argparse +import json +import re +import sys +from pathlib import Path + +from piper_phonemize import phonemize_espeak + +SAY = "sˈeɪ " +TREES = " tɹˈiːz" +HIM = " hˌɪm" +APPLE = " ˈæpəl" +BUT_TREES = " bˌʌt tɹˈiːz" +RED = " ɹˈɛd" + +CONTRACTIONS = [ + "don't", "won't", "can't", "isn't", "aren't", "wasn't", "weren't", + "hasn't", "haven't", "hadn't", "doesn't", "didn't", "couldn't", + "wouldn't", "shouldn't", "mustn't", "needn't", "ain't", "shan't", + "i'm", "you're", "we're", "they're", "i've", "you've", "we've", + "they've", "could've", "would've", "should've", "i'll", "you'll", + "he'll", "she'll", "we'll", "they'll", "it'll", "that'll", "i'd", + "you'd", "he'd", "she'd", "we'd", "they'd", "it's", "that's", + "there's", "here's", "what's", "who's", "she's", "he's", "let's", + "o'clock", "y'all", "ma'am", "'em", "everyone's", "everybody's", + "somebody's", "someone's", "nobody's", "nothing's", "one's", + "world's", "life's", +] + + +def p(text: str) -> str: + return "".join("".join(c) for c in phonemize_espeak(text, "en-us")) + + +def probe_word(word: str): + """Return (mid, final, unstr, vowel, pause, start) or None.""" + mid_out = p(f"say {word} trees") + if not (mid_out.startswith(SAY) and mid_out.endswith(TREES)): + return None + mid = mid_out[len(SAY):-len(TREES)] + if not mid: + return None + + final_out = p(f"say {word}") + final = final_out[len(SAY):] if final_out.startswith(SAY) else None + + unstr_out = p(f"say {word} him") + unstr = ( + unstr_out[len(SAY):-len(HIM)] + if unstr_out.startswith(SAY) and unstr_out.endswith(HIM) + else None + ) + + vowel_out = p(f"say {word} apple") + vowel = ( + vowel_out[len(SAY):-len(APPLE)] + if vowel_out.startswith(SAY) and vowel_out.endswith(APPLE) + else None + ) + + pause_out = p(f"say {word} but trees") + pause = ( + pause_out[len(SAY):-len(BUT_TREES)] + if pause_out.startswith(SAY) and pause_out.endswith(BUT_TREES) + else None + ) + + start_out = p(f"{word} trees") + start = start_out[:-len(TREES)] if start_out.endswith(TREES) else None + + r_out = p(f"say {word} red") + r = ( + r_out[len(SAY):-len(RED)] + if r_out.startswith(SAY) and r_out.endswith(RED) + else None + ) + + return mid, final, unstr, vowel, pause, start, r + + +def parse_en_list(path: Path): + """Extract phrases, homographs, flag sets, capital words from en_list.""" + phrases = {} + flags_by_word = {} + homograph_words = set() + capital_words = set() + + for raw in path.read_text(encoding="utf-8", errors="replace").splitlines(): + line = raw.split("//")[0].strip() + if not line: + continue + flags = set(re.findall(r"\$[a-z0-9+]+", line)) + if line.startswith("("): + m = re.match(r"\(([^)]*)\)", line) + if not m: + continue + phrase = m.group(1).strip().lower() + if not re.fullmatch(r"[a-z']+( [a-z']+)+", phrase): + continue + phrases.setdefault(phrase, set()).update(flags) + continue + parts = line.split() + word = parts[0].lstrip("?0123456789").lower() + if not re.fullmatch(r"[a-z']+", word): + continue + if flags & {"$verb", "$noun", "$past"}: + homograph_words.add(word) + if "$capital" in flags: + capital_words.add(word) + flags_by_word.setdefault(word, set()).update(flags) + + flag_sets = { + name: sorted( + w for w, f in flags_by_word.items() if f"${name}" in f + ) + for name in [ + "verbf", "verbsf", "nounf", "pastf", "verbextend", "pause", + "brk", "allcaps", "abbrev", + ] + } + return phrases, sorted(homograph_words), flag_sets, sorted(capital_words) + + +def compose(words_prons): + return " ".join(words_prons) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--en-list", required=True) + parser.add_argument("--freq", required=True, help="'word count' per line") + parser.add_argument("--words-alpha", required=True) + parser.add_argument("--top", type=int, default=200000) + parser.add_argument("--extra-vocab", nargs="*", default=[]) + parser.add_argument("--out-lexicon", required=True) + parser.add_argument("--out-aux", required=True) + args = parser.parse_args() + + alpha = set( + w.strip().lower() + for w in Path(args.words_alpha).read_text().splitlines() + if w.strip() + ) + dictwords = set( + w.strip().lower() + for w in Path("/usr/share/dict/words").read_text().splitlines() + if w.strip() + ) + propernames = [ + w.strip() + for w in Path("/usr/share/dict/propernames").read_text().splitlines() + if w.strip() + ] + + words = [] + seen = set() + for line in Path(args.freq).read_text(encoding="utf-8").splitlines(): + parts = line.split() + if len(parts) != 2: + continue + w = parts[0].lower() + if w in seen or not re.fullmatch(r"[a-z]+", w): + continue + if w not in alpha and w not in dictwords: + continue + seen.add(w) + words.append(w) + if len(words) >= args.top: + break + + for extra in args.extra_vocab: + for line in Path(extra).read_text(encoding="utf-8").splitlines(): + for w in re.findall(r"[A-Za-z][A-Za-z']*", line): + lw = w.lower() + if lw not in seen: + seen.add(lw) + words.append(lw) + + for w in CONTRACTIONS: + if w not in seen: + seen.add(w) + words.append(w) + + # hyphenated number compounds from the normalizer (inflect emits + # "twenty-six"; espeak's flapping inside them is irregular, e.g. + # thirty-five -> θˈɜːɾifˈaɪv but thirty-nine -> θˈɜːtinˈaɪn, so they + # are probed whole) plus hyphenated tokens seen in the eval corpora + tens = ["twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"] + ones = ["one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] + ordinals = [ + "first", "second", "third", "fourth", "fifth", "sixth", "seventh", + "eighth", "ninth", + ] + hyphen_words = [f"{t}-{o}" for t in tens for o in ones] + hyphen_words += [f"{t}-{o}" for t in tens for o in ordinals] + for extra in args.extra_vocab: + for line in Path(extra).read_text(encoding="utf-8").splitlines(): + hyphen_words += re.findall( + r"[A-Za-z][A-Za-z']*(?:-[A-Za-z][A-Za-z']*)+", line + ) + for w in hyphen_words: + lw = w.lower() + if lw not in seen: + seen.add(lw) + words.append(lw) + + proper_only = [n for n in propernames if n.lower() not in seen] + for n in proper_only: + seen.add(n.lower()) + + print(f"probing {len(words)} words + {len(proper_only)} proper names") + + lexicon = {} + failed = 0 + for i, w in enumerate(words): + result = probe_word(w) + if result is None: + failed += 1 + continue + lexicon[w] = result + if (i + 1) % 20000 == 0: + print(f" {i + 1}/{len(words)}") + for n in proper_only: + result = probe_word(n) + if result is None: + failed += 1 + continue + lexicon[n.lower()] = result + print(f"lexicon: {len(lexicon)} entries ({failed} extraction failures)") + + phrases_raw, homograph_words, flag_sets, capital_words = parse_en_list( + Path(args.en_list) + ) + + # ---- capitalization pass (before phrases: their baselines need the + # capital rows): any word whose Capitalized mid form differs gets a + # full case-sensitive row (espeak: I vs i, Polish vs polish, ...) + capitals = {} + for w in list(lexicon): + cap = w.capitalize() + if cap == w: + continue + out = p(f"say {cap} trees") + if out.startswith(SAY) and out.endswith(TREES): + form = out[len(SAY):-len(TREES)] + if form != lexicon[w][0]: + row = probe_word(cap) + if row: + capitals[cap] = row + print(f"capital-sensitive words: {len(capitals)}") + + # ---- phrases: keep only variants whose espeak output differs from + # word-by-word composition (those are the merge/special entries). + # Baselines are case-aware so "I had" doesn't produce spurious + # variants that would shadow "had to" at runtime. + phrase_table = {} + for phrase, flags in phrases_raw.items(): + pw = phrase.split() + if not all(w in lexicon for w in pw): + # probe missing constituents on demand + ok = True + for w in pw: + if w not in lexicon: + r = probe_word(w) + if r is None: + ok = False + break + lexicon[w] = r + if not ok: + continue + mid_out = p(f"say {phrase} trees") + final_out = p(f"say {phrase}") + vowel_out = p(f"say {phrase} apple") + cap = phrase[0].upper() + phrase[1:] + start_out = p(f"{phrase} trees") + startcap_out = p(f"{cap} trees") + midcap_out = p(f"say {cap} trees") + entry = {} + mids = [lexicon[w][0] for w in pw] + cap_row = capitals.get(pw[0].capitalize(), lexicon[pw[0]]) + composed = compose(mids) + composed_midcap = compose([cap_row[0]] + mids[1:]) + composed_start = compose( + [lexicon[pw[0]][5] or lexicon[pw[0]][0]] + mids[1:] + ) + composed_startcap = compose([cap_row[5] or cap_row[0]] + mids[1:]) + if mid_out.startswith(SAY) and mid_out.endswith(TREES): + mid = mid_out[len(SAY):-len(TREES)] + if mid != composed: + entry["mid"] = mid + if midcap_out.startswith(SAY) and midcap_out.endswith(TREES): + midcap = midcap_out[len(SAY):-len(TREES)] + if midcap != composed_midcap and midcap != entry.get("mid"): + entry["midcap"] = midcap + if final_out.startswith(SAY): + final = final_out[len(SAY):] + composed_final = compose( + mids[:-1] + [lexicon[pw[-1]][1] or lexicon[pw[-1]][0]] + ) + if final != composed_final: + entry["final"] = final + if vowel_out.startswith(SAY) and vowel_out.endswith(APPLE): + vowel = vowel_out[len(SAY):-len(APPLE)] + composed_vowel = compose( + mids[:-1] + [lexicon[pw[-1]][3] or lexicon[pw[-1]][0]] + ) + if vowel != composed_vowel: + entry["vowel"] = vowel + if start_out.endswith(TREES): + start = start_out[:-len(TREES)] + if start != entry.get("mid", composed_start): + entry["start"] = start + if startcap_out.endswith(TREES): + startcap = startcap_out[:-len(TREES)] + if startcap != entry.get( + "start", entry.get("mid", composed_startcap) + ) and startcap != composed_startcap: + entry["startcap"] = startcap + r_out = p(f"say {phrase} red") + if r_out.startswith(SAY) and r_out.endswith(RED): + r = r_out[len(SAY):-len(RED)] + if r != entry.get("mid", composed): + entry["r"] = r + if entry: + entry["flags"] = sorted(flags) + phrase_table[phrase] = entry + print(f"phrase table: {len(phrase_table)} entries") + + # ---- homographs: verb/noun/past context variants, probed for EVERY + # word (espeak also selects them via en_rules suffix logic — e.g. + # "estimate" — so the en_list $verb/$noun word list is incomplete). + # The leads vary ("to" -> tə/tʊ before vowels), so both are accepted. + homographs = {} + contexts = [ + ("verb", "say to {} trees", [SAY + "tə ", SAY + "tʊ "]), + ("noun", "say the {} trees", [SAY + "ðə ", SAY + "ðɪ "]), + ("past", "say had {} trees", [SAY + "hæd ", SAY + "hˌæd "]), + ] + for i, w in enumerate(sorted(lexicon)): + entry = {} + for key, template, leads in contexts: + out = p(template.format(w)) + if not out.endswith(TREES): + continue + for lead in leads: + if out.startswith(lead): + form = out[len(lead):-len(TREES)] + if form and form != lexicon[w][0]: + entry[key] = form + break + if entry: + homographs[w] = entry + if (i + 1) % 40000 == 0: + print(f" homographs {i + 1}/{len(lexicon)}") + print(f"homographs with context variants: {len(homographs)}") + + # ---- letter names (for all-caps spell-out) + letters = {} + for c in "abcdefghijklmnopqrstuvwxyz": + out = p(f"say {c.upper()}") + if out.startswith(SAY): + letters[c] = out[len(SAY):] + + # ---- write lexicon TSV (sparse variants); capital rows keyed + # case-sensitively and resolved before the lowercase row at runtime + def row_line(word, row): + mid = row[0] + return "\t".join( + [word, mid] + + [(v if v and v != mid else "") for v in row[1:]] + ).rstrip("\t") + + lines = [row_line(w, lexicon[w]) for w in sorted(lexicon)] + lines += [row_line(w, capitals[w]) for w in sorted(capitals)] + Path(args.out_lexicon).write_text("\n".join(lines) + "\n", encoding="utf-8") + + aux = { + "phrases": phrase_table, + "homographs": homographs, + "flag_sets": flag_sets, + "letters": letters, + } + Path(args.out_aux).write_text( + json.dumps(aux, ensure_ascii=False, indent=1), encoding="utf-8" + ) + print(f"wrote {args.out_lexicon} and {args.out_aux}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/g2p/reference_g2p.py b/models/tts/zipvoice/coreml/g2p/reference_g2p.py new file mode 100644 index 0000000..d6bad05 --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/reference_g2p.py @@ -0,0 +1,597 @@ +"""Python reference of the LuxTTS Swift G2P (LuxTtsG2p.swift mirrors this). + +Pipeline (matches EmiliaTokenizer's English path): + 1. map_punctuations + EnglishTextNormalizer (upstream, called directly here; + the Swift side ports it) + 2. tokenize into words / punctuation, split clauses at ,.!?;: (em-dash -> ; + and "…" is a silent clause break) + 3. per clause: phrase-table longest match (espeak multi-word entries), + then right-to-left per-word variant selection: + clause-final -> final + followed only by + unstressed words -> unstr ($strend2, resolved right-to-left) + next word $pause/$brk -> pause (blocks liaison/flap; to -> tU) + next word vowel-onset -> vowel (the/to/an, linking-r, flapping) + clause-initial -> start ($atstart: what -> wˌʌt) + otherwise -> mid + Lookup is case-sensitive first (espeak: "I" pronoun aɪ vs "i" letter + ˈaɪ, Polish vs polish), then lower-case. + 4. homograph verb/noun/past variants driven by the preceding word's + $verbf/$verbsf/$nounf/$pastf flags ($verbextend keeps the state) + 5. assembly: ' ' between words; ",;:" attach + space; ".!?" attach, no + space (espeak clause concat); "…" nothing, no space + +Usage: + .venv/bin/python -m coreml.g2p.reference_g2p \ + --lexicon coreml/g2p/lexicon_en_us.tsv --aux coreml/g2p/lexicon_aux.json \ + --corpus coreml/g2p/dev_corpus.txt --out /tmp/ref_dump.jsonl +""" + +import argparse +import json +import re +from pathlib import Path + +STAGING_TOKENS = Path(__file__).resolve().parents[2] / "build/hf-staging/tokens.txt" + +CLAUSE_PUNCT = set(",.!?;:") +VOWEL_SCALARS = set("aeiouæɑɐɔəɛɜɪʊʌʉɒɚᵻ") +STRESS_MARKS = set("ˈˌ") +VOICELESS = set("ptkfθsʃ") +SIBILANT = set("szʃʒ") + +WORD_RE = re.compile(r"[A-Za-z][A-Za-z']*|[0-9]+|[^\sA-Za-z0-9]") + +MID, FINAL, UNSTR, VOWEL, PAUSE, START, RVAR = range(7) + + +class Lexicon: + def __init__(self, lexicon_path, aux_path): + self.entries = {} + for line in Path(lexicon_path).read_text(encoding="utf-8").splitlines(): + parts = line.split("\t") + row = [parts[1]] + [ + parts[i] if len(parts) > i and parts[i] else None + for i in range(2, 8) + ] + self.entries[parts[0]] = tuple(row) + aux = json.loads(Path(aux_path).read_text(encoding="utf-8")) + self.phrases = aux["phrases"] + self.homographs = aux["homographs"] + self.flag_sets = {k: set(v) for k, v in aux["flag_sets"].items()} + self.letters = aux["letters"] + self.pause_words = self.flag_sets["pause"] | self.flag_sets["brk"] + # all-caps tokens spoken as words (NO, ALL); everything else spells + self.allcaps_words = ( + self.flag_sets["allcaps"] - self.flag_sets["abbrev"] + ) + self.max_phrase_len = max( + (len(k.split()) for k in self.phrases), default=1 + ) + + def lookup(self, word): + """Case-sensitive row first, then lower-case row.""" + entry = self.entries.get(word) + if entry is None: + entry = self.entries.get(word.lower()) + return entry + + +def first_phone(pron): + for ch in pron: + if ch not in STRESS_MARKS: + return ch + return "" + + +def letter_name_with_stress(name, mark): + stripped = "".join(c for c in name if c not in STRESS_MARKS) + for i, ch in enumerate(stripped): + if ch in VOWEL_SCALARS: + return stripped[:i] + mark + stripped[i:] + return mark + stripped + + +def add_suffix_s(mid): + last = mid[-1] + if last in SIBILANT: + return mid + "ɪz" + if last in VOICELESS: + return mid + "s" + return mid + "z" + + +class ReferenceG2p: + """Units: ('word', token) or ('phrase', [tokens], key). Selection is + right-to-left so $strend2 chains resolve on final stress states.""" + + def __init__(self, lexicon: Lexicon): + self.lex = lexicon + self.oov = set() + + # ---- fallbacks ------------------------------------------------------- + + def spell_letters(self, word): + out = [] + letters = [c for c in word.lower() if c.isalpha()] + for i, c in enumerate(letters): + name = self.lex.letters.get(c, "") + mark = "ˈ" if i == len(letters) - 1 else "ˌ" + out.append(letter_name_with_stress(name, mark)) + return "".join(out) + + def oov_pron(self, word): + lower = word.lower() + for suffix in ("'s", "s'"): + if lower.endswith(suffix): + base = self.lex.lookup(lower[: -len(suffix)]) + if base: + return add_suffix_s(base[MID]) + if lower.endswith("s") and self.lex.lookup(lower[:-1]): + return add_suffix_s(self.lex.lookup(lower[:-1])[MID]) + if len(word) >= 2 and word.isupper() and "'" not in word: + return self.spell_letters(word) + # camelCase: split, parts space-joined; single letters -> names + if any(c.isupper() for c in word[1:]): + parts = re.findall(r"[A-Z]?[a-z']+|[A-Z]+(?![a-z])", word) + if len(parts) > 1: + out = [] + for part in parts: + if len(part) == 1: + out.append( + letter_name_with_stress( + self.lex.letters.get(part.lower(), ""), "ˈ" + ) + ) + else: + sub = self.lex.lookup(part) + out.append(sub[MID] if sub else self.oov_pron(part)) + return " ".join(out) + self.oov.add(word) + return self.spell_letters(word) + + # ---- flags ----------------------------------------------------------- + + CONTRACTION_BASE = {"won't": "will", "can't": "can", "shan't": "shall"} + + def flag_word(self, token): + """The word (lower-cased base form) whose espeak flags drive the + expect_verb/noun/past counters. n't/'ll/'ve/'s contractions inherit + the auxiliary's flags (doesn't -> does).""" + lower = token.lower().replace("’", "'") + if lower == "i" and token != "I": + return None # lowercase i is the letter, not the pronoun + if lower in self.CONTRACTION_BASE: + return self.CONTRACTION_BASE[lower] + if lower.endswith("n't"): + return lower[:-3] + if lower.endswith("'ll"): + return "will" + if lower.endswith("'ve"): + return "have" + if lower.endswith("'s"): + return "is" + return lower + + def update_counters(self, counters, token): + """espeak translateword.c: $pastf -> expect_past=3 ; $verbf -> + expect_verb=2 ; $verbsf -> expect_verb_s=2 ; $nounf -> + expect_noun=2 ; then decrement all (unless $verbextend).""" + word = self.flag_word(token) + fs = self.lex.flag_sets + if word is not None: + if word in fs["pastf"]: + counters.update(past=3, verb=0, noun=0) + elif word in fs["verbf"]: + counters.update(verb=2, verb_s=0, noun=0) + elif word in fs["verbsf"]: + counters.update(verb=0, verb_s=2, past=0, noun=0) + elif word in fs["nounf"]: + counters.update(noun=2, verb=0, verb_s=0, past=0) + if word is None or word not in fs["verbextend"]: + for key in counters: + if counters[key] > 0: + counters[key] -= 1 + + def homograph_key(self, counters, token): + if counters["verb"] > 0 or ( + counters["verb_s"] > 0 and token.lower().endswith("s") + ): + return "verb" + if counters["past"] > 0: + return "past" + if counters["noun"] > 0: + return "noun" + return None + + # ---- clause ------------------------------------------------------------ + + def phonemize_clause(self, items, clause_initial, break_before=None): + """items: ('word', token) | ('hyph', [parts]) | ('lit', pron) for + one clause; break_before[i] marks a quote/paren pause boundary + before item i. Returns one pronunciation chunk per unit.""" + n = len(items) + if n == 0: + return [] + if break_before is None: + break_before = [False] * n + + # unit segmentation: phrase spans over consecutive 'word' items + # (a phrase span is only consumed if usable at its position; + # a span may not straddle a quote/paren boundary) + units = [] # (kind, start, length, entry) + i = 0 + while i < n: + kind, value = items[i] + if kind != "word": + units.append((kind, i, 1, None)) + i += 1 + continue + matched = None + for length in range(min(self.lex.max_phrase_len, n - i), 1, -1): + if any(items[k][0] != "word" for k in range(i, i + length)): + continue + if any(break_before[k] for k in range(i + 1, i + length)): + continue + key = " ".join(items[k][1].lower() for k in range(i, i + length)) + entry = self.lex.phrases.get(key) + if not entry: + continue + at_end = i + length == n + usable = ( + "mid" in entry + or "midcap" in entry + or "vowel" in entry + or "r" in entry + or (at_end and "final" in entry) + or (clause_initial and i == 0 and ("start" in entry or "startcap" in entry)) + ) + if usable: + matched = (i, length, entry) + break + if matched: + units.append(("phrase",) + matched) + i += matched[1] + else: + units.append(("word", i, 1, None)) + i += 1 + + # pass 1 (left to right): homograph selection key per unit + # (espeak expect_verb/noun/past counters), and $pause application: + # a pause word only pauses from the 3rd word after the last break, + # and an applied pause starts a new "break" (probed: "gas or water + # and he" -> "or" pauses, resetting, so "and" is 2nd -> liaison). + flags = [None] * len(units) + pause_applied = [False] * len(units) # unit is an APPLIED pause word + counters = {"verb": 0, "verb_s": 0, "noun": 0, "past": 0} + word_pos = 0 # words since last break + for u, (kind, start, length, entry) in enumerate(units): + if break_before[start]: + word_pos = 0 + if kind == "word": + token = items[start][1] + flags[u] = self.homograph_key(counters, token) + if ( + token.lower() in self.lex.pause_words + and word_pos >= 2 + and start != n - 1 + ): + pause_applied[u] = True + word_pos = 0 # pause word restarts the count as word 0 + for k in range(start, start + length): + if items[k][0] == "word": + self.update_counters(counters, items[k][1]) + if not pause_applied[u]: + word_pos += 1 + + # pass 2 (right to left): variant selection + chunks = [None] * len(units) + stressed_after = False + next_first_phone = None + next_is_break = False # next unit is an applied pause word + + for u in range(len(units) - 1, -1, -1): + kind, start, length, entry = units[u] + at_end = start + length == n + at_start = clause_initial and start == 0 + + if kind == "phrase": + tokens = [items[k][1] for k in range(start, start + length)] + chunk = self.select_phrase( + entry, tokens, at_end, at_start, + next_first_phone, next_is_break, stressed_after, + ) + elif kind == "hyph": + chunk = self.select_hyph( + items[start][1], flags[u], at_end, at_start, + next_first_phone, next_is_break, stressed_after, + ) + elif kind == "lit": + chunk = items[start][1] + else: + chunk = self.select_word( + items[start][1], flags[u], at_end, at_start, + next_first_phone, next_is_break, stressed_after, + ) + chunks[u] = chunk + if "ˈ" in chunk: + stressed_after = True + next_first_phone = first_phone(chunk) + next_is_break = pause_applied[u] or break_before[start] + + return chunks + + def select_hyph( + self, parts, flag, at_end, at_start, nxt_phone, nxt_is_break, + stressed_after, + ): + """Hyphen chain: whole-token lexicon row if probed (twenty-six), + else parts pronounced separately, concatenated without space.""" + key = "-".join(parts) + if self.lex.lookup(key) is not None: + return self.select_word( + key, flag, at_end, at_start, nxt_phone, nxt_is_break, + stressed_after, + ) + out = [] + for j, part in enumerate(parts): + if j == len(parts) - 1: + out.append( + self.select_word( + part, None, at_end, False, nxt_phone, nxt_is_break, + stressed_after, + ) + ) + else: + entry = self.lex.lookup(part) + out.append(entry[MID] if entry else self.oov_pron(part)) + return "".join(out) + + def compose_phrase(self, tokens, variant_last): + chunks = [] + for i, w in enumerate(tokens): + entry = self.lex.lookup(w) + if entry is None: + chunks.append(self.oov_pron(w)) + elif i == len(tokens) - 1: + chunks.append(variant_last(entry)) + else: + chunks.append(entry[MID]) + return " ".join(chunks) + + def select_phrase( + self, entry, tokens, at_end, at_start, nxt_phone, nxt_is_break, + stressed_after, + ): + capitalized = tokens[0][0].isupper() + if at_start and capitalized and "startcap" in entry: + return entry["startcap"] + if at_start and "start" in entry: + return entry["start"] + if at_end and "final" in entry: + return entry["final"] + if not at_end and not nxt_is_break: + if nxt_phone == "ɹ" and "r" in entry: + return entry["r"] + if nxt_phone in VOWEL_SCALARS and "vowel" in entry: + return entry["vowel"] + if capitalized and "midcap" in entry and not at_end: + return entry["midcap"] + if "mid" in entry and not at_end: + return entry["mid"] + # fall back to word-by-word composition + if at_end: + return self.compose_phrase( + tokens, lambda e: e[FINAL] or e[MID] + ) + return self.compose_phrase(tokens, lambda e: e[MID]) + + def select_word( + self, token, flag, at_end, at_start, nxt_phone, nxt_is_break, + stressed_after, + ): + # all-caps tokens spell out unless espeak marks them $allcaps words + if ( + len(token) >= 2 + and token.isupper() + and token.isalpha() + and token.lower() not in self.lex.allcaps_words + ): + return self.spell_letters(token) + + if flag: + hom = self.lex.homographs.get(token.lower()) + if hom and flag in hom: + return hom[flag] + + entry = self.lex.lookup(token) + if entry is None: + return self.oov_pron(token) + mid, final, unstr, vowel, pause, start, rvar = entry + + if at_end: + return final or mid + if unstr and not stressed_after and not nxt_is_break: + selected = unstr + if vowel and nxt_phone in VOWEL_SCALARS: + if vowel == mid + "ɹ" and selected.endswith(mid[-1]): + selected += "ɹ" + elif vowel == mid[:-1] + "ɾ" and selected.endswith(mid[-1]): + selected = selected[:-1] + "ɾ" + return selected + if nxt_is_break: + return pause or mid + if nxt_phone == "ɹ" and rvar: + return rvar + if vowel and nxt_phone in VOWEL_SCALARS: + return vowel + if at_start and start: + return start + return mid + + # ---- sentence ---------------------------------------------------------- + + def phonemize(self, normalized_text): + positions = [ + (m.group(0), m.start(), m.end()) + for m in WORD_RE.finditer(normalized_text) + ] + + # hyphen chains: word(-word)+ with no spaces -> single unit, parts + # pronounced separately and concatenated without space + tokens = [] # ("hyph", parts, start, end) | ("tok", tok, start, end) + i = 0 + while i < len(positions): + tok, start, end = positions[i] + if ( + tok[0].isalpha() + and i + 2 < len(positions) + and positions[i + 1][0] == "-" + and positions[i + 1][1] == end + and positions[i + 2][1] == positions[i + 1][2] + and positions[i + 2][0][0].isalpha() + ): + parts = [tok] + j = i + 1 + last_end = end + while ( + j + 1 < len(positions) + and positions[j][0] == "-" + and positions[j][1] == last_end + and positions[j + 1][0][0].isalpha() + and positions[j + 1][1] == positions[j][2] + ): + parts.append(positions[j + 1][0]) + last_end = positions[j + 1][2] + j += 2 + tokens.append(("hyph", parts, start, last_end)) + i = j + continue + tokens.append(("tok", tok, start, end)) + i += 1 + + pieces = [] + clause = [] # ('word', tok) | ('hyph', parts) | ('lit', pron) + breaks = [] # per clause item: break (quote/paren) before it? + clause_initial = True + pending_break = False + glue_next = False # "…"/mid-token "?": join to previous, no space + + def flush(clause_initial_flag): + nonlocal pending_break + pending_break = False + if not clause: + return None + chunks = self.phonemize_clause( + list(clause), clause_initial_flag, list(breaks) + ) + clause.clear() + breaks.clear() + return " ".join(c for c in chunks if c) + + def append_flush(clause_initial_flag, glue): + phon = flush(clause_initial_flag) + if phon: + if glue and pieces and pieces[-1] not in CLAUSE_PUNCT: + pieces.append(phon) # no separator (espeak "…" join) + else: + pieces.append(phon) + return phon + + for idx, entry in enumerate(tokens): + kind, value, start, end = entry + if kind == "hyph": + clause.append((kind, value)) + breaks.append(pending_break) + pending_break = False + continue + tok = value + if tok[0].isalpha() or tok.isdigit(): + clause.append(("word", tok)) + breaks.append(pending_break) + pending_break = False + continue + + # mid-token punctuation (no space on either side): espeak + # reads "." as "dot", "!" as "exclamation", drops "," and "?" + prev_glued = idx > 0 and tokens[idx - 1][3] == start + next_glued = idx + 1 < len(tokens) and tokens[idx + 1][2] == end + mid_token = prev_glued and next_glued + + ch = ";" if tok in "—–" else tok + if ch in CLAUSE_PUNCT or ch == "…": + if mid_token and ch == ".": + clause.append(("lit", "dˈɑːt")) + breaks.append(False) + continue + if mid_token and ch == "!": + clause.append(("lit", "ˈɛkskləmˌeɪʃən")) + breaks.append(False) + continue + if mid_token and ch == ",": + continue # dropped, words stay separated by a space + if mid_token and ch == "?": + append_flush(clause_initial, glue_next) + glue_next = True + clause_initial = True + continue + append_flush(clause_initial, glue_next) + glue_next = False + if ch != "…": + pieces.append(ch) + pieces.append(" " if ch in ",;:" else "") + else: + glue_next = True + clause_initial = True + elif ch in "\"'()[]«»": + # quotes/parens: transparent but insert a pause boundary + if clause: + pending_break = True + # dashes/other symbols: transparent + append_flush(clause_initial, glue_next) + + text = "".join(pieces) + text = re.sub(r" +", " ", text).strip() + return text + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--lexicon", required=True) + parser.add_argument("--aux", required=True) + parser.add_argument("--corpus", required=True) + parser.add_argument("--out", required=True) + args = parser.parse_args() + + from zipvoice.tokenizer.tokenizer import EmiliaTokenizer + + tokenizer = EmiliaTokenizer(token_file=str(STAGING_TOKENS)) + g2p = ReferenceG2p(Lexicon(args.lexicon, args.aux)) + + token2id = tokenizer.token2id + sentences = [ + s.strip() + for s in Path(args.corpus).read_text(encoding="utf-8").splitlines() + if s.strip() + ] + with open(args.out, "w", encoding="utf-8") as f: + for text in sentences: + normalized = tokenizer.english_normalizer.normalize( + tokenizer.preprocess_text(text) + ) + phon = g2p.phonemize(normalized) + ids = [token2id[c] for c in phon if c in token2id] + f.write( + json.dumps( + {"text": text, "phonemes": phon, "ids": ids}, + ensure_ascii=False, + ) + + "\n" + ) + print(f"OOV words: {len(g2p.oov)}") + if g2p.oov: + print(sorted(g2p.oov)[:40]) + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/g2p/validate.py b/models/tts/zipvoice/coreml/g2p/validate.py new file mode 100644 index 0000000..c29f0ec --- /dev/null +++ b/models/tts/zipvoice/coreml/g2p/validate.py @@ -0,0 +1,193 @@ +"""LuxTTS phase-2 G2P validation harness. + +Subcommands: + dump-oracle corpus.txt -> oracle_tokens.jsonl (EmiliaTokenizer ground truth: + map_punctuations + EnglishTextNormalizer + espeak en-us) + word-gap measure whole-sentence vs word-by-word-joined espeak output + (quantifies sentence-level effects a lexicon G2P must model) + score score a Swift token dump (jsonl: {"text":..., "ids":[...]}) + against the oracle: sentence exact-match % + token edit % + +Gates (LuxTTS phase 2): sentence exact >= 90%, overall token edit <= 2%. + +Usage: + .venv/bin/python -m coreml.g2p.validate dump-oracle \ + --corpus coreml/g2p/corpus_en_1000.txt --out coreml/g2p/oracle_tokens.jsonl + .venv/bin/python -m coreml.g2p.validate word-gap --corpus ... --limit 500 + .venv/bin/python -m coreml.g2p.validate score \ + --oracle coreml/g2p/oracle_tokens.jsonl --swift swift_dump.jsonl +""" + +import argparse +import json +import re +from collections import Counter +from functools import lru_cache, reduce +from pathlib import Path + +from piper_phonemize import phonemize_espeak + +from zipvoice.tokenizer.tokenizer import EmiliaTokenizer + +STAGING_TOKENS = Path(__file__).resolve().parents[2] / "build/hf-staging/tokens.txt" + + +def edit_distance(a, b): + if len(a) < len(b): + a, b = b, a + prev = list(range(len(b) + 1)) + for i, ca in enumerate(a, 1): + cur = [i] + for j, cb in enumerate(b, 1): + cur.append(min(prev[j] + 1, cur[-1] + 1, prev[j - 1] + (ca != cb))) + prev = cur + return prev[-1] + + +def load_corpus(path): + return [ + line.strip() + for line in Path(path).read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + +def dump_oracle(args): + tokenizer = EmiliaTokenizer(token_file=str(STAGING_TOKENS)) + sentences = load_corpus(args.corpus) + with open(args.out, "w", encoding="utf-8") as f: + for text in sentences: + tokens = tokenizer.texts_to_tokens([text])[0] + ids = tokenizer.tokens_to_token_ids([tokens])[0] + f.write( + json.dumps({"text": text, "tokens": tokens, "ids": ids}, ensure_ascii=False) + + "\n" + ) + print(f"wrote {len(sentences)} oracle entries to {args.out}") + + +@lru_cache(maxsize=None) +def word_phonemes(word): + result = phonemize_espeak(word, "en-us") + return "".join("".join(clause) for clause in result) + + +WORD_RE = re.compile(r"[A-Za-z][A-Za-z'’]*|[,.!?;:]") + + +def word_gap(args): + """Phonemize each sentence whole vs word-by-word-joined; report the gap.""" + tokenizer = EmiliaTokenizer(token_file=str(STAGING_TOKENS)) + sentences = load_corpus(args.corpus)[: args.limit] + + exact = 0 + total_tokens = 0 + total_edit = 0 + diff_words = Counter() + + for text in sentences: + # Same normalization path the tokenizer applies before espeak. + normalized = tokenizer.english_normalizer.normalize( + tokenizer.preprocess_text(text) + ) + whole = tokenizer.texts_to_tokens([text])[0] + + pieces = WORD_RE.findall(normalized) + joined = "" + for piece in pieces: + if piece in ",.!?;:": + joined = joined.rstrip() + piece + " " + else: + joined += word_phonemes(piece) + " " + approx = list(joined.rstrip()) + + dist = edit_distance(whole, approx) + total_tokens += len(whole) + total_edit += dist + if dist == 0: + exact += 1 + elif args.verbose: + print(f"[{dist}] {text}") + print(" whole :", "".join(whole)) + print(" joined:", "".join(approx)) + + # attribute diffs to words: re-phonemize sentence with each word + # replaced by its isolated pronunciation is expensive; instead do a + # cheap per-word alignment on space-separated chunks. + whole_chunks = "".join(whole).split(" ") + approx_chunks = "".join(approx).split(" ") + if len(whole_chunks) == len(approx_chunks) == len( + [p for p in pieces if p not in ",.!?;:"] + ): + words = [p for p in pieces if p not in ",.!?;:"] + for w, wc, ac in zip(words, whole_chunks, approx_chunks): + if wc != ac: + diff_words[w.lower()] += 1 + + n = len(sentences) + print(f"sentences: {n}") + print(f"exact-match (word-joined == whole): {exact}/{n} = {100 * exact / n:.1f}%") + print(f"token edit rate: {total_edit}/{total_tokens} = {100 * total_edit / total_tokens:.2f}%") + print("top context-sensitive words:") + for word, count in diff_words.most_common(40): + print(f" {count:5d} {word}") + + +def score(args): + oracle = [json.loads(line) for line in open(args.oracle, encoding="utf-8")] + swift = [json.loads(line) for line in open(args.swift, encoding="utf-8")] + assert len(oracle) == len(swift), (len(oracle), len(swift)) + + exact = 0 + total_tokens = 0 + total_edit = 0 + worst = [] + for ref, hyp in zip(oracle, swift): + assert ref["text"] == hyp.get("text", ref["text"]), ref["text"] + dist = edit_distance(ref["ids"], hyp["ids"]) + total_tokens += len(ref["ids"]) + total_edit += dist + if dist == 0: + exact += 1 + else: + worst.append((dist, ref["text"])) + + n = len(oracle) + sent_pct = 100 * exact / n + edit_pct = 100 * total_edit / total_tokens + print(f"sentence exact match: {exact}/{n} = {sent_pct:.1f}% (gate >= 90%)") + print(f"token edit rate: {total_edit}/{total_tokens} = {edit_pct:.2f}% (gate <= 2%)") + worst.sort(reverse=True) + if args.verbose: + for dist, text in worst[:30]: + print(f" [{dist}] {text}") + print("PASS" if sent_pct >= 90 and edit_pct <= 2 else "FAIL") + + +def main(): + parser = argparse.ArgumentParser() + sub = parser.add_subparsers(dest="cmd", required=True) + + p = sub.add_parser("dump-oracle") + p.add_argument("--corpus", required=True) + p.add_argument("--out", required=True) + p.set_defaults(func=dump_oracle) + + p = sub.add_parser("word-gap") + p.add_argument("--corpus", required=True) + p.add_argument("--limit", type=int, default=500) + p.add_argument("--verbose", action="store_true") + p.set_defaults(func=word_gap) + + p = sub.add_parser("score") + p.add_argument("--oracle", required=True) + p.add_argument("--swift", required=True) + p.add_argument("--verbose", action="store_true") + p.set_defaults(func=score) + + args = parser.parse_args() + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/parity.py b/models/tts/zipvoice/coreml/parity.py new file mode 100644 index 0000000..1b30042 --- /dev/null +++ b/models/tts/zipvoice/coreml/parity.py @@ -0,0 +1,172 @@ +"""Parity check: CoreML text_encoder + fm_decoder vs PyTorch, end-to-end. + +Reproduces the LuxTTS pipeline with CoreML components (host-side duration +expansion + 4-step anchor-Euler solver + torch vocoder) and compares against +the pure-torch path at each stage, using the oracle inputs saved by +scripts/reference_infer.py. + +Usage: + .venv/bin/python coreml/parity.py --oracle-dir build/oracle --coreml-dir build/coreml +""" + +import argparse +import json +from pathlib import Path + +import coremltools as ct +import numpy as np +import soundfile as sf +import torch + +from coreml.convert_coreml import MAX_FRAMES, MAX_TOKENS, load_model +from zipvoice.models.modules.solver import get_time_steps +from zipvoice.models.zipvoice import get_tokens_index, prepare_avg_tokens_durations + + +def cos(a, b): + a = np.asarray(a, dtype=np.float64).ravel() + b = np.asarray(b, dtype=np.float64).ravel() + return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-12)) + + +def expand_text_condition(token_embeds: torch.Tensor, tokens_len: int, features_len: int): + """Host-side duration expansion (mirrors forward_text_condition, B=1).""" + features_lens = torch.tensor([features_len]) + tokens_lens = torch.tensor([tokens_len]) + durations = prepare_avg_tokens_durations(features_lens, tokens_lens) + index = get_tokens_index(durations, features_len) # (1, T) + return torch.gather( + token_embeds, dim=1, index=index.unsqueeze(-1).expand(1, features_len, token_embeds.size(-1)) + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--oracle-dir", default="build/oracle") + parser.add_argument("--coreml-dir", default="build/coreml") + parser.add_argument("--compute-units", default="CPU_ONLY", choices=["ALL", "CPU_ONLY", "CPU_AND_NE"]) + args = parser.parse_args() + + oracle = Path(args.oracle_dir) + meta = json.loads((oracle / "meta.json").read_text()) + prompt_features = torch.from_numpy(np.load(oracle / "prompt_features.npy")) + prompt_tokens = np.load(oracle / "prompt_tokens.npy").tolist() + text_tokens = np.load(oracle / "text_tokens.npy").tolist() + prompt_features_len = meta["prompt_features_lens"] + guidance_scale = meta["guidance_scale"] + num_steps = meta["num_steps"] + speed = 1.0 * 1.3 # generate() multiplies speed by 1.3 + + model, _ = load_model() # includes convert_scaled_to_non_scaled, same as conversion + + cu = getattr(ct.ComputeUnit, args.compute_units) + te_ml = ct.models.MLModel(str(Path(args.coreml_dir) / "TextEncoder.mlpackage"), compute_units=cu) + fm_ml = ct.models.MLModel(str(Path(args.coreml_dir) / "FmDecoder.mlpackage"), compute_units=cu) + + # ---- 1. text encoder ---- + cat_tokens = prompt_tokens + text_tokens + S = len(cat_tokens) + S_pad = S + 1 # upstream pad_labels appends one pad slot; its row fills remainder frames + assert S_pad <= MAX_TOKENS, f"{S_pad} tokens > bucket {MAX_TOKENS}" + + with torch.no_grad(): + ref_embed, _ = model.forward_text_embed([cat_tokens]) # (1, S+1, D) + + tok_in = np.full((1, MAX_TOKENS), model.pad_id, dtype=np.int32) + tok_in[0, :S] = cat_tokens + mask_in = np.zeros((1, MAX_TOKENS), dtype=np.float32) + mask_in[0, S:] = 1.0 # upstream masks the pad slot too (make_pad_mask over S) + cm_embed = te_ml.predict({"tokens": tok_in, "padding_mask": mask_in})["token_embeds"] + cm_embed_valid = torch.from_numpy(cm_embed[:, :S_pad, :].astype(np.float32)) + + print(f"[text_encoder] cos={cos(ref_embed.numpy(), cm_embed_valid.numpy()):.6f} " + f"max_abs_diff={float((ref_embed - cm_embed_valid).abs().max()):.4e}") + + # ---- 2. duration + conditions (host) ---- + features_len = prompt_features_len + int( + np.ceil(prompt_features_len / len(prompt_tokens) * len(text_tokens) / speed) + ) + assert features_len <= MAX_FRAMES, f"{features_len} frames > bucket {MAX_FRAMES}" + + text_condition = expand_text_condition(cm_embed_valid, S, features_len) + with torch.no_grad(): + ref_text_condition, _ = model.forward_text_inference_ratio_duration( + tokens=[text_tokens], prompt_tokens=[prompt_tokens], + prompt_features_lens=torch.tensor([prompt_features_len]), speed=speed, + ) + assert ref_text_condition.shape[1] == features_len, (ref_text_condition.shape, features_len) + print(f"[text_condition] cos={cos(ref_text_condition.numpy(), text_condition.numpy()):.6f}") + + speech_condition = torch.nn.functional.pad( + prompt_features, (0, 0, 0, features_len - prompt_features.size(1)) + ) + + # ---- 3. solver loop: CoreML decoder vs torch decoder ---- + def pad_T(x): + return torch.nn.functional.pad(x, (0, 0, 0, MAX_FRAMES - x.size(1))) + + frame_mask = np.zeros((1, MAX_FRAMES), dtype=np.float32) + frame_mask[0, features_len:] = 1.0 + ref_pad_mask = torch.zeros(1, features_len, dtype=torch.bool) + + timesteps = get_time_steps(num_step=num_steps, t_shift=0.5) + torch.manual_seed(meta["seed"]) + x = torch.randn(1, features_len, 100) + x_ref = x.clone() + + text_np, speech_np = pad_T(text_condition).numpy(), pad_T(speech_condition).numpy() + for step in range(num_steps): + t_cur, t_next = float(timesteps[step]), float(timesteps[step + 1]) + + v = fm_ml.predict({ + "t": np.array([t_cur], dtype=np.float32), + "x": pad_T(x).numpy().astype(np.float32), + "text_condition": text_np.astype(np.float32), + "speech_condition": speech_np.astype(np.float32), + "guidance_scale": np.array([guidance_scale], dtype=np.float32), + "padding_mask": frame_mask, + })["v"] + v = torch.from_numpy(v[:, :features_len, :].astype(np.float32)) + + with torch.no_grad(): + v_ref = model.forward_fm_decoder( + t=torch.tensor(t_cur), xt=x_ref, text_condition=ref_text_condition, + speech_condition=speech_condition, padding_mask=ref_pad_mask, + guidance_scale=torch.tensor(guidance_scale), + ) + print(f"[fm_decoder step {step} t={t_cur:.3f}] cos={cos(v_ref.numpy(), v.numpy()):.6f} " + f"max_abs_diff={float((v_ref - v).abs().max()):.4e}") + + def euler_update(x_s, v_s): + x1p = x_s + (1.0 - t_cur) * v_s + x0p = x_s - t_cur * v_s + return (1.0 - t_next) * x0p + t_next * x1p if step < num_steps - 1 else x1p + + x, x_ref = euler_update(x, v), euler_update(x_ref, v_ref) + + print(f"[final mel] cos={cos(x_ref.numpy(), x.numpy()):.6f}") + + # ---- 4. vocoder (torch, shared) + wav comparison ---- + from scripts.reference_infer import load_models_cpu_torch # reuse loader + + _, _, vocos, _, _ = load_models_cpu_torch() + vocos.freq_range = 12000 + vocos.return_48k = True + with torch.no_grad(): + mel = x[:, prompt_features_len:features_len, :].permute(0, 2, 1) / 0.1 + wav = vocos.decode(mel).squeeze(1).clamp(-1, 1) + wav_np = wav.numpy().squeeze() + if meta["prompt_rms"] < 0.1: + wav_np = wav_np * (meta["prompt_rms"] / 0.1) + + ref_wav, sr = sf.read(oracle / "reference_48k.wav") + n = min(len(ref_wav), len(wav_np)) + print(f"[wav] len_ref={len(ref_wav)} len_cm={len(wav_np)} cos(first {n})={cos(ref_wav[:n], wav_np[:n]):.6f}") + + out = Path(args.coreml_dir) / "parity_48k.wav" + sf.write(out, wav_np, 48000) + print(f"wrote {out}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/quantize_variants.py b/models/tts/zipvoice/coreml/quantize_variants.py new file mode 100644 index 0000000..a62c8b9 --- /dev/null +++ b/models/tts/zipvoice/coreml/quantize_variants.py @@ -0,0 +1,58 @@ +"""Weight-compress the FmDecoder: int8 linear + 4-bit palettization variants. + +TextEncoder stays fp16 (8.3 MB, irrelevant). Output dirs are drop-in +replacements for build/coreml so parity.py / benchmarks run unchanged. + +Usage: + .venv/bin/python -m coreml.quantize_variants --source-dir build/coreml +""" + +import argparse +import shutil +from pathlib import Path + +import coremltools as ct +import coremltools.optimize.coreml as cto + + +def size_mb(p: Path) -> float: + return sum(f.stat().st_size for f in p.rglob("*") if f.is_file()) / 1e6 + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--source-dir", default="build/coreml") + args = parser.parse_args() + + src = Path(args.source_dir) + fm = ct.models.MLModel(str(src / "FmDecoder.mlpackage")) + + variants = {} + + # int8: linear symmetric, per-channel (iOS16+) + cfg8 = cto.OptimizationConfig( + global_config=cto.OpLinearQuantizerConfig(mode="linear_symmetric", dtype="int8") + ) + variants["int8"] = cto.linear_quantize_weights(fm, cfg8) + + # 6-bit: k-means palettization, per-tensor (iOS16+). int4 was evaluated and + # scrapped - see trials.md quantization matrix (per-tensor: -2.1dB; grouped + # needs iOS18 and still ~2x the 6-bit error; nothing gained on the ANE path). + cfg6 = cto.OptimizationConfig(global_config=cto.OpPalettizerConfig(nbits=6, mode="kmeans")) + variants["6bit"] = cto.palettize_weights(fm, cfg6) + + for name, model in variants.items(): + out = src.parent / f"{src.name}-{name}" + out.mkdir(parents=True, exist_ok=True) + dst_te = out / "TextEncoder.mlpackage" + if not dst_te.exists(): + shutil.copytree(src / "TextEncoder.mlpackage", dst_te) + dst_fm = out / "FmDecoder.mlpackage" + if dst_fm.exists(): + shutil.rmtree(dst_fm) + model.save(str(dst_fm)) + print(f"{name}: {dst_fm} {size_mb(dst_fm):.1f} MB (fp16 was {size_mb(src / 'FmDecoder.mlpackage'):.1f} MB)") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/synth_samples.py b/models/tts/zipvoice/coreml/synth_samples.py new file mode 100644 index 0000000..67a94c3 --- /dev/null +++ b/models/tts/zipvoice/coreml/synth_samples.py @@ -0,0 +1,107 @@ +"""Synthesize sample wavs with the CoreML pipeline (torch vocoder) and +transcribe them with Whisper to sanity-check intelligibility. + +Usage: + .venv/bin/python -m coreml.synth_samples --out-dir build/samples +""" + +import argparse +import json +from pathlib import Path + +import coremltools as ct +import numpy as np +import soundfile as sf +import torch + +from coreml.convert_coreml import MAX_FRAMES, MAX_TOKENS, load_model +from coreml.parity import expand_text_condition +from scripts.reference_infer import load_models_cpu_torch +from zipvoice.models.modules.solver import get_time_steps + +TEXTS = [ + "The quick brown fox jumps over the lazy dog, and honestly, it felt great.", + "FluidAudio runs speech models locally on Apple silicon, no cloud required.", + "In the last seven days, we've signed the same number of contracts as we signed in the whole of Q4.", + "Voice cloning at forty eight kilohertz, from a five second reference clip.", +] + + +def synth(text, tokenizer, te, fm, prompt_tokens, prompt_features, prompt_len, prompt_rms, vocos, seed=42, speed=1.3, + max_tokens=MAX_TOKENS, max_frames=MAX_FRAMES): + text_tokens = tokenizer.texts_to_token_ids([text])[0] + cat = prompt_tokens + text_tokens + S = len(cat) + assert S + 1 <= max_tokens, f"{S + 1} tokens > {max_tokens}" + + tok_in = np.zeros((1, max_tokens), dtype=np.int32) + tok_in[0, :S] = cat + tmask = np.zeros((1, max_tokens), dtype=np.float32) + tmask[0, S:] = 1.0 + embeds = te.predict({"tokens": tok_in, "padding_mask": tmask})["token_embeds"] + embeds = torch.from_numpy(embeds[:, : S + 1, :].astype(np.float32)) + + # speed: generate() default is 1.0 * 1.3 + features_len = prompt_len + int(np.ceil(prompt_len / len(prompt_tokens) * len(text_tokens) / speed)) + assert features_len <= max_frames, f"{features_len} frames > {max_frames}" + + text_cond = expand_text_condition(embeds, S, features_len) + speech_cond = torch.nn.functional.pad(prompt_features, (0, 0, 0, features_len - prompt_features.size(1))) + + pad = lambda z: torch.nn.functional.pad(z, (0, 0, 0, max_frames - z.size(1))).numpy().astype(np.float32) + fmask = np.zeros((1, max_frames), dtype=np.float32) + fmask[0, features_len:] = 1.0 + + timesteps = get_time_steps(num_step=4, t_shift=0.5) + torch.manual_seed(seed) + x = torch.randn(1, features_len, 100) + text_np, speech_np = pad(text_cond), pad(speech_cond) + for step in range(4): + t_cur, t_next = float(timesteps[step]), float(timesteps[step + 1]) + v = fm.predict({ + "t": np.array([t_cur], dtype=np.float32), "x": pad(x), + "text_condition": text_np, "speech_condition": speech_np, + "guidance_scale": np.array([3.0], dtype=np.float32), "padding_mask": fmask, + })["v"] + v = torch.from_numpy(v[:, :features_len, :].astype(np.float32)) + x1p, x0p = x + (1.0 - t_cur) * v, x - t_cur * v + x = (1.0 - t_next) * x0p + t_next * x1p if step < 3 else x1p + + with torch.no_grad(): + mel = x[:, prompt_len:features_len, :].permute(0, 2, 1) / 0.1 + wav = vocos.decode(mel).squeeze(1).clamp(-1, 1).numpy().squeeze() + if prompt_rms < 0.1: + wav = wav * (prompt_rms / 0.1) + return wav + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--oracle-dir", default="build/oracle") + parser.add_argument("--coreml-dir", default="build/coreml") + parser.add_argument("--out-dir", default="build/samples") + args = parser.parse_args() + + oracle = Path(args.oracle_dir) + meta = json.loads((oracle / "meta.json").read_text()) + prompt_features = torch.from_numpy(np.load(oracle / "prompt_features.npy")) + prompt_tokens = np.load(oracle / "prompt_tokens.npy").tolist() + + _, _, vocos, tokenizer, _ = load_models_cpu_torch() + vocos.freq_range = 12000 + vocos.return_48k = True + + te = ct.models.MLModel(str(Path(args.coreml_dir) / "TextEncoder.mlpackage")) + fm = ct.models.MLModel(str(Path(args.coreml_dir) / "FmDecoder.mlpackage")) + + out = Path(args.out_dir) + out.mkdir(parents=True, exist_ok=True) + for i, text in enumerate(TEXTS): + wav = synth(text, tokenizer, te, fm, prompt_tokens, prompt_features, + meta["prompt_features_lens"], meta["prompt_rms"], vocos) + sf.write(out / f"sample_{i}.wav", wav, 48000) + print(f"sample_{i}.wav ({len(wav)/48000:.2f}s): {text}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/trials.md b/models/tts/zipvoice/coreml/trials.md new file mode 100644 index 0000000..b190647 --- /dev/null +++ b/models/tts/zipvoice/coreml/trials.md @@ -0,0 +1,481 @@ +# Conversion trials + +## Trial 1 — TextEncoder + FmDecoder, fixed shapes, fp16 (2026-07-07) + +Result: both convert and pass parity. GPU is the fast path; ANE resident but slow. + +Gotchas hit (in order): + +1. **`CompactRelPositionalEncoding` slices with in-graph shape math** → + `slice_by_index` with fp32 begin, rejected by coremltools. The module is + jit-scripted by `convert_scaled_to_non_scaled`, so instance `forward` + monkey-patching never applies. Fix: probe each `Zipformer2Encoder`'s input + eagerly, precompute `pos_emb`, swap `encoder_pos` for a constant-buffer + module (`FrozenPosEmb`). +2. **`aten::Int` on 1-element arrays** (`seq_len2 = 2*seq_len - 1` shape math) + → stock handler only takes 0-d. Fix: `patch_coremltools_int()` folds + 1-element constant arrays. +3. **`SimpleDownsample` zero-size pad** — `expand(0, ...)` + `cat` for pad=0 + materializes a spurious row under coremltools, breaking the downstream + reshape ([1025,1,512] → [512,2,1,512]). Fix: skip the pad branch when + `seq_len % ds == 0` (MAX_FRAMES=1024 divisible by all ds factors [1,2,4]). +4. **Token bucket**: phoneme-level tokenization is denser than expected — + 5 s prompt + one sentence = 194 tokens. Bucket at 256. + Upstream `pad_labels` appends one pad slot whose embedding fills + remainder frames in duration expansion; keep S+1 rows, mask from S. + +Parity (CPU_ONLY CoreML vs torch, oracle: 5 s YC prompt, seed 42): + +``` +[text_encoder] cos=0.999997 max_abs=1.0e-03 +[fm_decoder step 0-3] cos=0.9990-0.9991 +[final mel] cos=0.998802 +[wav] waveform cos=0.715 (phase-only) log-mel cos=0.99925 RMS 0.1479 vs 0.1463 +``` + +Profile (M5 Pro): FmDecoder GPU 14.1 ms/step vs ANE 286 ms (98.2% resident), +CPU 155 ms. TextEncoder 1.4–4.8 ms everywhere. `all` → 100% GPU. + +### Pipeline benchmark (coreml/benchmark.py, M5 Pro, oracle utterance) + +3.0 s generated audio, 469 prompt + 282 gen frames padded into the fixed +1024-frame bucket — latency is bucket-constant, so RTFx scales up with +utterance length (~2.4× higher near bucket capacity). + +| compute units | text_enc | fm_dec/step | core (te+4 steps) | +torch vocoder | RTFx e2e | +|---|---|---|---|---|---| +| ALL (→GPU) | 4.7 ms | 14.2 ms | 62 ms | 139 ms | **21.5×** (core 48×) | +| CPU_ONLY | 5.5 ms | 153 ms | 621 ms | 705 ms | 4.2× | +| CPU_AND_NE | 4.9 ms | 286 ms | 1152 ms| 1233 ms | 2.4× | + +torch-cpu fm_decoder baseline: 405 ms/step → CoreML GPU is 28× faster, +CoreML CPU 2.6× faster. Model load: 8.7 s (ALL) / 17.7 s (ANE) cold. +Vocoder (torch cpu, 77 ms) is now the e2e bottleneck — CoreML port next. + +### Audio similarity vs PyTorch wav (coreml/audio_similarity.py) + +Level/energy: RMS −16.69 vs −16.60 dBFS (**Δ −0.094 dB**), peak Δ −0.34 dB, +total energy Δ −0.094 dB. Short-time envelope (25 ms/10 ms): corr **0.9954**, +per-frame diff median +0.01 dB / std 1.14 dB, envelope SNR 24.8 dB. Per-octave +band energy deltas all within **±0.27 dB** (20 Hz–24 kHz). Spectral +convergence 0.154; LSD 2.77 dB (mean). Waveform-domain cos 0.715 is phase +divergence only — energy and spectrum are match-grade. + +## Open items + +- **Vocoder**: dual ISTFT heads (24k + 48k upsampled) + Linkwitz-Riley + crossover (IIR biquads). Plan: DFT-matmul ISTFT + conv_transpose overlap-add + in CoreML; crossover host-side (Accelerate biquads) or FIR-approximated. +- **ANE latency**: 286 ms/step despite 98% residency. Zipformer runs seq-first + (T,B,C) with heavy transposes + rel-pos gather; would need (B,C,1,S) + restructuring per the ANE playbook. GPU path is fast enough to ship macOS; + revisit for iPhone (no coreml-cli --fallback run yet). +- **Shape buckets**: single 1024-frame bucket (~10.9 s). Need enumerated + buckets (256/512/1024/2048) and re-profile; attention is O(T²). +- **fp16 vs guidance**: `guidance_scale` embedding path untested at other + scales; parity run used 3.0 (LuxTTS default). +- **ZipVoice upstream variants**: same script should convert zipvoice / + zipvoice_distill / dialog checkpoints (24k vocoder, different tokens.txt). + +### Transcription sanity check (whisper-base on generated wavs) + +All CoreML samples intelligible; CoreML and PyTorch oracle transcribe +identically. Onset clipping ("The quick" -> "Brown Fox...") reproduces in +the PYTORCH oracle too - not a conversion artifact. + +### Onset-clipping root cause (debugged) + +Hypotheses tested, in order: +1. Whisper missing the onset - NO: +0.5s silence pad changes nothing; + onset RMS shows speech at full level ~50ms in. Words acoustically absent. +2. Uniform avg-duration alignment mapping leading text tokens into the + prompt region (at speed 1.3 the first 47 text tokens land inside the + 469 prompt frames) - NO: a two-segment expansion (prompt tokens pinned + to prompt frames) produces byte-identical transcripts. The flow model + self-aligns; text_condition alignment is a soft hint. +3. PROMPT BOUNDARY CONTINUATION - YES: the model continues speech + seamlessly from the prompt. A prompt hard-cut mid-phrase (fixed 5.000s + slice) makes the model elide sentence-initial function words. Same + text/seed/speed with a prompt ending at a natural sentence boundary + synthesizes the complete sentence including "The". + +Amplifier: generate() silently multiplies speed by 1.3, squeezing the +ratio-based duration estimate; at 1.3 the elision grows from "The" to +"The quick". speed<=1.0 + clean prompt boundary = full sentence. + +Swift integration guidance: trim reference clips at a VAD pause boundary +(Silero VAD already in FluidAudio) instead of a fixed duration; expose +speed, default 1.0. + +### Long-sentence test (2048-frame / 512-token variant) + +Converter now takes --max-tokens/--max-frames; long variant at +build/coreml-long (FmDecoder same 238MB weights). Three texts, 10.2-14.4s +generated audio, sentence-end prompt, speed 1.0: CoreML and PyTorch +transcribe word-for-word identically, sentence onsets intact, full +multi-sentence content preserved. Decoder step scaling (GPU): 14.0ms @1024 +-> 35.4ms @2048 frames (2.5x for 2x frames - subquadratic thanks to the +[1,2,4,2,1] downsampled stacks). 14.4s utterance e2e wall ~0.3-0.4s with +torch vocoder (~40x realtime). Enumerated buckets remain the plan for a +Swift integration; per-bucket compiled variants work today. + +### RSS measurements (python host, minus 348MB harness baseline) + +Steady-state: 1024/GPU ~480MB (mlpackage load) / ~1.1GB (CompiledMLModel); +2048/GPU ~800MB / ~3.6GB; 1024/ANE ~220MB (weights in ANE-managed memory). +Load/compile peak (transient): 1.5GB @1024, 4.3GB @2048, 1.2GB @1024-ANE. +CompiledMLModel python loads hold 2-3x more resident than mlpackage loads +- treat all as macOS upper bounds; measure in Swift on device. +iPhone guidance: 1024 bucket max (chunk long text at sentence boundaries), +consider 512 bucket + 6-bit palettization; 2048 compile peak (4.3GB) will +not fit older-device jetsam limits. + +### Swift harness (swift/RssBench.swift, CoreML.framework, phys_footprint) + +| config | load | steady footprint | fm step | core RTFx | +|---|---|---|---|---| +| 1024 all(GPU) | 8.5s | 996 MB | 14.7 ms | 92x (5.9s gen) | +| 1024 ane | 16.5s | 658 MB | 286 ms | 5.2x | +| 2048 all(GPU) | 22.4s | 3059 MB | 34.3 ms | 114x (16.8s gen) | + +Latency matches the python host exactly. Memory is the real story: +phys_footprint (jetsam metric) is ~1.0GB at the 1024 bucket steady - +activation arenas are preallocated at load for the fixed max shape +(652MB after load, +340MB after first predict). 2048 bucket = 3.0GB +steady: not iPhone-viable. Python mlpackage-path RSS (~480MB) undercounted; +the CompiledMLModel path (~1.1GB) was the honest one. +iPhone plan: 1024 bucket max + sentence chunking, add a 512 bucket +(~2.9s gen) for short utterances, 6-bit palettization to cut the weight +share, and revisit ANE layouts (658MB + lowest power, but 20x slower today). + +### Quantization matrix (FmDecoder, 1024 bucket; TextEncoder stays fp16) + +| variant | weights | GPU step | steady footprint | log-mel cos | RMS delta | transcript | +|---|---|---|---|---|---|---| +| fp16 | 249 MB | 14.7 ms | 996 MB | 0.99925 | -0.09 dB | identical | +| int8 linear | 131 MB | CRASHES on GPU | 674 MB (ane) / 48 MB (cpu) | 0.99870 | -0.05 dB | identical | +| 6-bit palettize | 101 MB | 15.0 ms | 419 MB | 0.99829 | -0.45 dB | identical | +| int4 palettize (per-tensor) | 72 MB | 14.1 ms | 420 MB | 0.93412 | -2.07 dB | intelligible, degraded | + +- int8 linear_symmetric hits an OS bug on GPU: MPSGraph "MLIR pass manager + failed" assertion at first predict (macOS 26.5, M5 Pro). Works on + ane (286ms) and cpu (152ms). Do not ship int8 for the GPU path. +- 6-bit per-tensor palettization is the ship candidate: transparent + transcript, -0.45dB, full GPU speed, footprint 996 -> 419 MB (2.4x), + weights 249 -> 101 MB. Same recipe class as Supertonic-3 int4. +- int4 per-tensor is a bridge too far without grouped channels; retry + with per_grouped_channel group_size=16 under an iOS18+ target. +- Palettized variants land LuxTTS below every existing FluidAudio TTS + backend's peak RSS except Supertonic-3 (197 MB). + +### ANE deep-dive + +Fallback is NOT the problem: 2285/2297 ops on ANE (99.5%); the 12 CPU ops +(depthwise convs, kernel 31/15 "grouped conv with large kernel size") +cost 0.6ms. Quantization does NOT help: fp16/int8/6bit/int4 all 286ms +@1024 - ANE is not weight-bandwidth-bound here. + +Bucket scaling shows the real issue - ANE time grows superlinearly +(doubling frames: x2.3, x3.2, x3.5) while GPU stays near-linear: +256f: ANE 40ms vs GPU 5.9ms (6.8x) | 512f: 91 vs 7.9 (11.4x) +1024f: 286 vs 14.1 (20.3x) | 2048f: 1009 vs 34.3 (29.4x) +=> the O(T^2) rel-pos attention (softmax + gather at [heads,B,T,T], +seq-first permutes) tiles catastrophically on ANE at T>=512. Classic +ane-transformers territory: needs (B,C,1,S) QKV restructuring, split +softmax, and replacing the rel-pos gather - a dedicated trial per the +ANE playbook, not a conversion flag. + +Interim ANE options: 256-frame buckets are only 6.8x off GPU - ANE-only +sentence-chunked synthesis at ~17x realtime core (4x40ms for ~2.7s) is +already usable where power matters, and the ANE/GPU gap will narrow on +iPhone (weaker GPU, comparable ANE). System view: FluidAudio's other +models occupy ANE; TTS-on-GPU diversifies the load. + +### ANE rewrite feasibility (module benchmarks @T=1024, stack0 dim512) + +| module | ANE (T,B,C) | GPU | ANE (B,C,1,S) recast | +|---|---|---|---| +| attention (weights+apply) | 5.08 ms | 2.39 ms | - | +| feedforward | 6.85 ms | 1.37 ms | **0.50 ms (13.7x)** | +| conv_module | 2.52 ms | 1.41 ms | - | + +Summed over 16 layers x per-stack T, the seq-first module costs fully +account for the 286ms whole-model step - it is not partition overhead; +EVERY module pays the (T,B,C) layout tax uniformly (ff worst at 5x vs +GPU). The 1x1-conv2d recast of feedforward (same weights, channels-first) +runs 13.7x faster on ANE, confirming the ane-transformers thesis. + +Projected full rewrite: ff-class modules ~0.5ms, attention needs the +(B,C,1,S) QKV + last-axis softmax + constant-matrix rel-pos treatment +=> plausible 286 -> ~40-60ms/step ANE @1024 (4 steps ~200ms core, +~28x RT, 658MB, low power) - iPhone-viable ANE-first TTS. + +Trial plan (next session): +1. ANE-canonical TTSZipformer forward (weights reused, no retrain): + linear->conv2d(1x1), norms on channel dim, bypass/downsample + channel-first, SwooshL/R kept elementwise. +2. Attention: per-head qkv via conv, softmax over last (S) axis, + rel->abs positional via constant banded matrix (fixed shapes). +3. Per-module parity gates vs torch (the quick ff recast above skipped + submodule details - do it exactly), then whole-decoder parity + wav. +4. coreml-cli --fallback loop per playbook; target zero CPU ops and + subquadratic-ish scaling to 1024f. + +### ANE layer rewrite (trial 2) — one full Zipformer2EncoderLayer, (1,C,1,S) + +coreml/ane/: AneZipformerLayer imports weights from the post +convert_scaled_to_non_scaled layer (enc0.layers[0], dim 512, H=4, qhd=32, +phd=4, vhd=12) into ANE-canonical form: every Linear -> 1x1 conv2d, +BiasNorm/Bypass on the channel axis, per-head attention with S on the +last softmax axis, SwooshL/R elementwise (logaddexp_onnx form). + +Parity (fp32 eager, S=1024, real pos_emb, coreml/ane/parity.py) — +numerically exact, every submodule and the whole layer: + +| submodule | max_abs_diff | cos | +|---|---|---| +| self_attn_weights | 2.4e-07 | 1.0 | +| feed_forward1/2/3 | <2e-06 | 1.0 | +| nonlin_attention, self_attn1/2 | 0.0 | 1.0 | +| conv_module1/2 | 1.9e-06 | 1.0 | +| norm, bypass, bypass_mid | <2e-06 | 1.0 | +| WHOLE LAYER | 2.4e-06 | 1.00000000 | + +CoreML (iOS17 fp16 mlprogram, S=1024, M5 Pro, coreml/ane/bench.py): + +| layer | ANE | GPU | ANE placement | +|---|---|---|---| +| original (T,B,C) | 35.9 ms | 2.2 ms | 136/138 ops (2 dw convs CPU) | +| ANE-canonical | **4.14 ms (8.7x)** | 2.1 ms | **166/166 ops, zero fallback** | + +fp16 CoreML (ANE) vs torch fp32: cos = 1.000000, max_abs 0.015 +(orig layer converted the same way: cos 0.980 — the gather rel->abs +path costs accuracy too). + +What it took (each step verified by --fallback + latency): +1. Baseline recast (skew-trick rel->abs via pad+reshape+slice): 13.6 ms, + 11 CPU ops. The 2*S*S flatten exceeds ANE dim limits -> reshape/slice + on CPU; BiasNorm ** -0.5 pow also CPU. +2. pow -> rsqrt, and rel->abs baked into a constant buffer + pos_abs[h,c,q,j] = linear_pos(pos_emb)[h,c,S-1-q+j] consumed as + broadcast-mul + channel reduce (no gather/as_strided/flatten): 5.35 ms. +3. Depthwise k=31 conv: ANE limit is kernel width <= 15 (16 falls back, + 15 places). Split exactly into k=15 + k=15 + k=1 with symmetric conv + padding + output slices (standalone pad ops are not ANE-placeable and + drag neighbors to CPU): 4.14 ms, 100% ANE. + +Caveats for the whole-decoder rewrite: +- pos_abs is (H, phd, S, S) per layer: 33.5 MB fp16 at S=1024. Fine per + layer; naive replication over all layers/stacks adds ~100-300 MB + (smaller S per downsampled stack shrinks it 4x/16x...). If footprint + matters, revisit a chunked skew or share pos_abs where linear_pos + weights allow folding. +- Projection: dominant stack layer 8.7x faster => 286 ms/step scales to + ~35-60 ms/step ANE @1024 (smaller stacks saw less ANE penalty, so gain + there is smaller); 4 steps ~150-250 ms core, iPhone-viable ANE-first. + +Template for the full rewrite: coreml/ane/layer.py. + +### ANE full-decoder rewrite (trial 3) — AneFmDecoder, all 16 layers, (1,C,1,S) + +coreml/ane/decoder.py: full TTSZipformer fm_decoder in ANE-canonical form — +in/out proj as 1x1 convs, t + guidance_scale sinusoidal embedding ported to +(1,C,1,1) (cos/sin cat on the channel axis, MLPs as 1x1 convs), per-stack +time projections, SimpleDownsample as a strided depthwise conv with +softmax(bias) taps, SimpleUpsample as nearest upsample, stack out-combiners +as channel-axis Bypass, per-stack cnn kernels 31/15/7/15/31 (31 split +15+15+1; 15 and 7 place directly). Same I/O contract as the original +FmDecoder (t, x, text_condition, speech_condition, guidance_scale, +padding_mask -> v), so coreml/parity.py-style feeds and swift/RssBench work +unchanged. Mask ported as float bias: -1000 added pre-softmax on keys + +zeroing before the depthwise convs + [::ds] subsampling — exactly upstream's +masked_fill semantics. + +**pos_abs sharing**: linear_pos weights are NOT shared across layers, so the +folded per-layer (H,phd,S,S) buffer of trial 2 would cost ~260 MB +decoder-wide. Every layer's posproj = PE @ W_l^T lies in col(PE) (pos_dim +48), and the SVD of the per-seq-len concatenated posprojs has numerical rank +~27: an R=32 orthonormal basis reconstructs all of them to <=3.7e-8 +relative. One pos_basis[r,q,j] = U_R[S-1-q+j, r] constant per distinct S +(1024: 67 MB, 512: 16.8 MB, 256: 4.2 MB fp16 = **88 MB total, 3 buffers**), +with the per-layer basis coefficients folded into that layer's attention +in_proj (p block 16 -> H*R=128 channels). mlpackage: 313 MB vs 238 MB orig. + +**Eager fp32 parity** (oracle inputs, 4-step loop): vs torch @1024+mask +max_abs <= 3.8e-4, cos = 1.00000000 every step, final mel cos 1.00000000. +vs the 751-exact oracle path: cos 0.9990-0.9997 — that is the pure-torch +padding-leak floor, NOT the rewrite: upstream SimpleDownsample folds frame +751 (computed garbage in the padded region) into downsampled frame 375 and +mask[::2] keeps it, so torch@1024+mask itself differs from torch@751 by +max_abs 1-2 / cos 0.9991-0.9997. The shipped FmDecoder bucket pays the same +floor (its trial-1 step cos 0.999 was mostly this, not fp16). + +**Consolidated results** (M5 Pro, S=1024 bucket, oracle utterance 469 +prompt + 282 gen frames = 3.008 s, whisper-base, 3 warmup + 10 timed): + +| metric | AneFmDecoder ANE | AneFmDecoder GPU | AneFmDecoder CPU | orig ANE | orig GPU | +|---|---|---|---|---|---| +| fm step (python) | **54.6 ms** | 23.5 ms | 145 ms | 286 ms | 14.2 ms | +| fm step (Swift) | 54.5 ms | - | - | 286 ms | 14.7 ms | +| core te+4 steps | 223 ms | 96 ms | 586 ms | 1149 ms | 62 ms | +| core RTFx (3.0 s oracle) | **13.5x** | 31.3x | 5.1x | 2.6x | 48x | +| core RTFx (5.9 s full bucket) | **26.6x** | 61.7x | - | 5.2x | 92x | +| per-step cos vs torch | 0.978-0.985 | - | 0.9986-0.9990 | 0.904-0.930 | 0.999 | +| final mel cos | 0.9750 | - | 0.99868 | **0.6814** | 0.9988 | +| log-mel cos (wav) | 0.96426 | - | 0.99889 | (wav cos -0.04) | 0.99925 | +| RMS delta | -0.54 dB | - | -0.10 dB | - | -0.09 dB | +| transcript (whisper-base) | "brown fox jumps over the lazy dog and honestly it felt great." | - | identical | GARBLED: "they pack or burn fast, jump through the lazy guards. Usley." | identical | +| ANE placement | **2591/2591 (100%), 0 CPU** | - | - | 2285/2297 (12 CPU) | - | +| Swift phys_footprint steady | **25.5 MB** | - | - | 658 MB | 996 MB | +| model load | 13.7 s py / 11.9 s Swift | 1.4 s | - | 16.5 s | 8.5 s | + +Transcript matches the oracle modulo casing ("Brown Fox jumps...") — the +known prompt-boundary elision, not a regression. + +Takeaways: + +- ANE step 286 -> **54.6 ms (5.2x)**, 100% ANE placement, zero fallback — + inside the 40-60 ms projection from trial 2. +- The rewrite is not just faster on ANE, it is the difference between + broken and shippable there: the ORIGINAL graph on ANE produces garbled + audio (mel cos 0.68, unintelligible whisper transcript). The seq-first + gather/as_strided rel-pos path loses precision on ANE (trial 2 saw the + same per-layer: orig 0.980 vs ane 1.000000). +- Remaining ANE-vs-CPU quality gap (log-mel 0.964 vs 0.999, -0.54 dB, + transcript intact) is fp16 accumulation compounded over 16 layers x 4 + solver steps on the ANE path — the same package at CPU_ONLY matches the + shipped conversion exactly, and eager fp32 parity is exact. +- phys_footprint 25.5 MB steady (vs 658 MB orig-ANE / 996 MB GPU): with + 100% ANE placement, weights + activations live in ANE-managed memory + outside the jetsam-counted footprint. Combined with 54.6 ms steps this is + the iPhone path: ANE-first, low power, no jetsam pressure. +- GPU step of the ANE-canonical graph is 23.5 ms vs 14.2 ms original (the + R=32 broadcast reduce costs more on GPU) — keep the original conversion + for the macOS GPU path, ship AneFmDecoder where ANE/power/memory matter. +- Harnesses: coreml/ane/decoder_parity.py (eager gate), + coreml/ane/convert_decoder.py (build/coreml-ane, compiles + FmDecoder.mlmodelc for rss_bench), coreml/ane/pipeline.py (quality + + whisper + latency). + +### int4 grouped-channel (iOS18) + +Retry of int4 with per_grouped_channel kmeans palettization (group_size=16, +Supertonic-3's recipe class) — needs an iOS18-target source package, so the +original-graph FmDecoder was reconverted with iOS18 (convert_coreml.py grew a +--deployment-target flag; default stays iOS17, existing packages untouched) +to build/coreml-ios18, then coreml/quantize_int4_grouped.py -> +build/coreml-int4g. Quality gauntlet identical to the quantization matrix +(coreml/parity.py CPU_ONLY + 128-mel log-mel cos + whisper-base). + +| variant | weights | GPU step | steady footprint | final mel cos | log-mel cos | RMS delta | transcript | +|---|---|---|---|---|---|---|---| +| 6-bit palettize (iOS17) | 101 MB | 15.0 ms | 419 MB | 0.98501 | 0.99829 | -0.45 dB | identical | +| int4 per-tensor (iOS17) | 72 MB | 14.1 ms | 420 MB | - | 0.93412 | -2.07 dB | intelligible, degraded | +| **int4 grouped g=16 (iOS18)** | 72.7 MB | 16.1 ms | **243.8 MB** | 0.92336 | 0.98589 (80-mel) / 0.98437 (128-mel) | -0.90 dB | **identical** | + +(6-bit final-mel cos measured this session for comparison: its per-step cos +is 0.977-0.988 vs int4g's 0.939-0.964.) + +- Grouped channels recover most of per-tensor's loss (-2.07 -> -0.90 dB, + log-mel 0.934 -> 0.984, transcript garble -> verbatim "Brown Fox jumps + over the lazy dog and honestly it felt great.") at the same 72 MB — but + it is NOT transparent: still 2x the 6-bit level error and audibly softer. +- Footprint surprise: 243.8 MB steady on GPU vs 419 MB for 6-bit — the + iOS18 runtime keeps the grouped-LUT weights compressed in the arena + (6-bit/iOS17 decompresses to fp16 at load). ANE run: 287 ms step + (unchanged, not weight-bound), 121 MB. +- Verdict: 6-bit stays the quality ship candidate on iOS17; int4 grouped is + the memory-floor option (72 MB weights / 244 MB steady, -0.45 extra dB) + if we accept iOS18 minimum. + +**Stacked on the ANE-canonical graph** (AneFmDecoder reconverted iOS18 -> +build/coreml-ane-ios18, palettized -> build/coreml-ane-int4g; pipeline.py +CPU_AND_NE + rss_bench ane): weights 328 -> 149.9 MB (pos_basis constants +resist palettization), step 55.5 ms / footprint 34.8 MB (both ~unchanged vs +fp16's 54.6 ms / 25.5 MB). But quantization error stacks on the fp16-ANE +accumulation floor: final mel cos 0.9038, log-mel 0.93490, RMS -1.77 dB +(fp16 ANE was 0.96426 / -0.54 dB), and whisper drops a word: "...over the +lays dog...". Roughly additive in dB — do NOT ship int4g on the ANE path; +if ANE memory matters it's already 25 MB at fp16. + +### Vocoder CoreML port (2026-07-07) + +coreml/vocoder/: the 48 kHz dual-head linacodec Vocos vocoder (the e2e +bottleneck: torch cpu 77-81 ms) fully in-graph as Vocoder.mlpackage — +backbone (8 ConvNeXt), UpSamplerBlock [2,1], BOTH ISTFT heads including the +ISTFT itself, the 24k->48k resample, and the crossover. mel (1,100,S) fp32 -> +audio (1,(S-1)*512) @48k; iOS17 fp16 mlprogram, 33 MB. Variants: S=282 +(oracle gen region, build/coreml-vocoder) and S=555 (1024-bucket gen region, +build/coreml-vocoder-f555) via --frames. + +Reimplementations (fixed shapes, all constants precomputed): + +- **ISTFT (center)**: linear head -> clamp(logmag, max=ln 100) BEFORE exp + (fp16-safe, identical to upstream clip(mag, 1e2)) -> mag*cos/sin(phase) -> + irfft as constant DFT matmul (Hermitian-weighted cos/sin bases, hann window + folded in) -> overlap-add as 4 shifted-chunk pads+adds (n_fft/hop = 4) -> + precomputed 1/window-envelope constant, center-trimmed. Standalone vs + torch.istft: **SNR 127.4 dB** (both 282 and 565 frame counts). +- **Resample 24k->48k**: torchaudio's sinc_interp_hann kernel (2 phases x 15 + taps) as conv1d + interleave — SNR inf (bit-exact) vs AF.resample. +- **Crossover IN-GRAPH**: linkwitz.py is not IIR — it's an rfft over the + whole waveform with a ~2.6 Hz cubic-fade brickwall at 12 kHz. Rewritten as + merged = low + FIR_hp(high - low) (single filter, branches exactly + complementary); 511-tap linear-phase highpass by spectral inversion of + scipy firwin. Tap sweep vs torch on the real oracle signal: 255 -> 59.6 dB, + 511 -> 62.2, 1023 -> 64.3, 2047 -> 65.6; 511 chosen (already ~20 dB above + the fp16 noise floor). Eager fp32 wrapper vs vocos.decode: 62.2 dB. +- Snake1d's jit-scripted helper monkey-patched to drop its no-op reshapes. + +Parity (coreml/vocoder/parity_vocoder.py — oracle mel regenerated through the +pure-torch reference path, torch vocos.decode vs CoreML Vocoder, S=282): + +| compute units | wav SNR | wav cos | log-mel cos | RMS delta | whisper-base | +|---|---|---|---|---|---| +| CPU_AND_GPU | **43.1 dB** | 0.999975 | 0.99982 | +0.000 dB | identical | +| CPU_AND_NE | 34.0 dB | 0.999801 | 0.99866 | -0.007 dB | - | +| CPU_ONLY | 29.1 dB | 0.999387 | 0.99926 | +0.019 dB | - | + +ISTFT is deterministic, so unlike the decoder this is waveform-level +agreement, all fp16 rounding. + +Latency (coreml/vocoder/bench.py, CompiledMLModel, 3 warmup + 10 runs, +M5 Pro; torch cpu baseline 77-81 ms): + +| variant | CPU_AND_GPU | CPU_AND_NE | rss delta | +|---|---|---|---| +| S=282 (3.0 s audio) | **2.34 ms (RTFx 1281x, 33x vs torch)** | 10.56 ms (284x) | +77 MB gpu / +44 MB ane | +| S=555 (5.9 s audio) | **2.93 ms (RTFx 2014x)** | 19.36 ms (305x) | +80 MB gpu / +69 MB ane | + +coreml-cli --fallback (mlmodelc): 160/228 ops ANE (70.2%), 68 CPU +(slice_by_index x10, reshape x9, pad x9, sin x7, add x7, mul x6, pow x5, ...) +— the OLA pad/slice tail and snake sin/pow land on CPU, and one ANE segment +hits "ANECCompile() FAILED" at load (runtime falls back, results above +include it). GPU is the design target (pairs with the GPU decoder graph); +ANE at 10.6 ms is already 7x faster than torch, left as-is per the playbook. + +Pipeline impact: core 62 ms (GPU) + vocoder 2.3 ms = **~64 ms e2e** vs 139 ms +with the torch vocoder — oracle RTFx 21.5x -> **~47x**, full-bucket ~91x. +The vocoder is no longer the bottleneck (3.6% of e2e). Open: enumerated +frame buckets for the vocoder to match the decoder buckets, and Swift-side +wiring. + +### Decision: int4 scrapped (2026-07-07) + +All int4 variants dropped from the lineup and build artifacts deleted. +Rationale: per-tensor loses 2.1dB; grouped-channel (iOS18) is verbatim- +transcript but still ~2x the 6-bit error and forces an iOS18 floor; on +the ANE graph it degrades quality (word error) with zero latency/memory +win (fp16 ANE graph is already 25.5MB). Ship set: fp16 ANE graph +(iPhone), fp16 or 6-bit original graph (macOS GPU). int8 parked on the +MPSGraph GPU bug. Numbers retained above for the record; +quantize_int4_grouped.py removed (recoverable from git history). + +### HF release + +https://huggingface.co/FluidInference/luxtts-coreml — compiled .mlmodelc +layout: gpu/ (fp16 original graph, 1024f), gpu-6bit/, gpu-long/ (2048f), +ane/ (ANE-canonical graph), vocoder/ (Vocoder282 + Vocoder555), plus +tokens.txt + config.json from upstream. Model card carries the benchmark +matrix, the do-not-run-gpu-graph-on-ANE warning, and integration notes +(VAD prompt trim, speed 1.0, host-side solver). diff --git a/models/tts/zipvoice/coreml/vocoder/__init__.py b/models/tts/zipvoice/coreml/vocoder/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/tts/zipvoice/coreml/vocoder/bench.py b/models/tts/zipvoice/coreml/vocoder/bench.py new file mode 100644 index 0000000..4585bdc --- /dev/null +++ b/models/tts/zipvoice/coreml/vocoder/bench.py @@ -0,0 +1,65 @@ +"""Latency + footprint bench for the CoreML Vocoder (compiled mlmodelc). + +Usage: + .venv/bin/python -m coreml.vocoder.bench --coreml-dir build/coreml-vocoder \ + --frames 282 --compute-units CPU_AND_GPU CPU_AND_NE +""" + +import argparse +import resource +import time +from pathlib import Path + +import coremltools as ct +import numpy as np + + +def rss_mb(): + return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1 << 20) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--coreml-dir", default="build/coreml-vocoder") + parser.add_argument("--frames", type=int, default=282) + parser.add_argument("--compute-units", nargs="+", default=["CPU_AND_GPU", "CPU_AND_NE"], + choices=["ALL", "CPU_ONLY", "CPU_AND_NE", "CPU_AND_GPU"]) + parser.add_argument("--runs", type=int, default=10) + parser.add_argument("--warmup", type=int, default=3) + args = parser.parse_args() + + pkg = Path(args.coreml_dir) / "Vocoder.mlpackage" + mlc = Path(args.coreml_dir) / "Vocoder.mlmodelc" + if not mlc.exists(): + t0 = time.perf_counter() + ct.models.utils.compile_model(str(pkg), str(mlc)) + print(f"compiled {mlc} in {time.perf_counter() - t0:.1f} s") + + mel = np.random.default_rng(0).standard_normal((1, 100, args.frames)).astype(np.float32) * 0.5 + gen_seconds = (args.frames - 1) * 512 / 48000.0 + print(f"frames={args.frames} -> {gen_seconds:.3f} s audio @48k; runs={args.runs} warmup={args.warmup}") + + for cu_name in args.compute_units: + cu = getattr(ct.ComputeUnit, cu_name) + rss0 = rss_mb() + t0 = time.perf_counter() + model = ct.models.CompiledMLModel(str(mlc), compute_units=cu) + model.predict({"mel": mel}) + load_ms = (time.perf_counter() - t0) * 1e3 + + for _ in range(args.warmup): + model.predict({"mel": mel}) + ts = [] + for _ in range(args.runs): + t0 = time.perf_counter() + model.predict({"mel": mel}) + ts.append((time.perf_counter() - t0) * 1e3) + ts = np.array(ts) + print(f"{cu_name:12s}: {ts.mean():7.2f} ± {ts.std():.2f} ms (min {ts.min():.2f}) " + f"RTFx {gen_seconds * 1e3 / ts.mean():.0f}x " + f"load+first {load_ms:.0f} ms rss +{rss_mb() - rss0:.0f} MB") + del model + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/vocoder/convert_vocoder.py b/models/tts/zipvoice/coreml/vocoder/convert_vocoder.py new file mode 100644 index 0000000..ef5ca89 --- /dev/null +++ b/models/tts/zipvoice/coreml/vocoder/convert_vocoder.py @@ -0,0 +1,297 @@ +"""Convert the LuxTTS 48 kHz dual-head Vocos vocoder to CoreML, fully in-graph. + +The linacodec vocoder is: VocosBackbone (8 ConvNeXt blocks, dim 512) -> +{ISTFTHead @24k on L frames, UpSamplerBlock [2,1] -> ISTFTHead @48k on 2L+1 +frames}, 24k path resampled to 48k, then an FFT-domain brickwall crossover at +12 kHz. Everything is reimplemented with fixed shapes so the whole decode is +one CoreML graph: + +- ISTFT (center padding): irfft as a constant DFT matmul with the hann window + folded in, overlap-add as 4 shifted chunk adds (hop 256, n_fft 1024), then a + precomputed 1/window-envelope constant. Validated vs torch.istft at >120 dB. +- 24k->48k resample: torchaudio's sinc kernel (2 phases x 15 taps) applied as + conv1d + interleave, bit-exact vs AF.resample. +- Crossover: torch does rfft over the whole waveform and blends with a ~2.6 Hz + brickwall fade at 12 kHz (linkwitz.py). Reimplemented as + merged = path_24k + FIR_hp(path_48k - path_24k) with a 511-tap linear-phase + highpass (spectral inversion of scipy firwin lowpass, so the two branches + stay exactly complementary). ~62 dB vs the torch crossover on real signals. + +Usage: + .venv/bin/python -m coreml.vocoder.convert_vocoder --frames 282 \ + --output-dir build/coreml-vocoder --validate +""" + +import argparse +import math +import time +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch +import torch.nn.functional as F +from huggingface_hub import snapshot_download +from torch import Tensor, nn +from torch.nn.utils import parametrize + +from coreml.convert_coreml import patch_coremltools_int + +N_FFT = 1024 +HOP = 256 +N_BINS = N_FFT // 2 + 1 +FEAT_DIM = 100 +FIR_TAPS = 511 +CUTOFF_HZ = 12000 +SR = 48000 + + +def load_vocos(): + """LuxTTS linacodec Vocos, weight-norm parametrizations removed (as the + reference loader does) so the upsampler convs are plain tensors.""" + from linacodec.vocoder.vocos import Vocos + + model_path = snapshot_download("YatharthS/LuxTTS") + vocos = Vocos.from_hparams(f"{model_path}/vocoder/config.yaml") + parametrize.remove_parametrizations(vocos.upsampler.upsample_layers[0], "weight") + parametrize.remove_parametrizations(vocos.upsampler.upsample_layers[1], "weight") + vocos.load_state_dict(torch.load(f"{model_path}/vocoder/vocos.bin", map_location="cpu")) + vocos.eval() + vocos.freq_range = CUTOFF_HZ + vocos.return_48k = True + return vocos + + +def patch_snake(): + """Snake1d calls a jit-scripted helper whose reshape shape-math trips the + converter; the reshapes are no-ops for 3D input, so compute directly.""" + from linacodec.vocoder import upsampler_block + + def forward(self, x: Tensor) -> Tensor: + return x + (self.alpha + 1e-9).reciprocal() * torch.sin(self.alpha * x).pow(2) + + upsampler_block.Snake1d.forward = forward + + +def istft_constants(num_frames: int): + """Constant tensors for a fixed-shape center-padded ISTFT. + + Returns (basis_cos (N_BINS, N_FFT), basis_sin, env_inv (1, (T-1)*HOP)): + frame = real @ basis_cos + imag @ basis_sin gives window * irfft(spec). + """ + n = np.arange(N_FFT)[:, None] + k = np.arange(N_BINS)[None, :] + herm = np.where((k == 0) | (k == N_FFT // 2), 1.0, 2.0) / N_FFT + cos_b = herm * np.cos(2 * np.pi * k * n / N_FFT) # (N_FFT, N_BINS) + sin_b = -herm * np.sin(2 * np.pi * k * n / N_FFT) + window = torch.hann_window(N_FFT).numpy().astype(np.float64) + basis_cos = (window[:, None] * cos_b).T # (N_BINS, N_FFT) + basis_sin = (window[:, None] * sin_b).T + + full = num_frames * HOP + (N_FFT - HOP) + env = np.zeros(full) + for t in range(num_frames): + env[t * HOP : t * HOP + N_FFT] += window**2 + env = env[N_FFT // 2 : -(N_FFT // 2)] # center trim; hann/75% NOLA holds + assert env.min() > 1e-3 + return ( + torch.from_numpy(basis_cos).float(), + torch.from_numpy(basis_sin).float(), + torch.from_numpy(1.0 / env[None, :]).float(), + ) + + +class DftIstftHead(nn.Module): + """ISTFTHead (linear -> exp/cos/sin -> center ISTFT) with the ISTFT done as + DFT matmul + shifted-chunk overlap-add, fixed frame count.""" + + def __init__(self, head: nn.Module, num_frames: int): + super().__init__() + self.out = head.out + self.num_frames = num_frames + self.out_len = (num_frames - 1) * HOP + self.full_len = num_frames * HOP + (N_FFT - HOP) + basis_cos, basis_sin, env_inv = istft_constants(num_frames) + self.register_buffer("basis_cos", basis_cos) + self.register_buffer("basis_sin", basis_sin) + self.register_buffer("env_inv", env_inv) + self.log_mag_max = float(np.log(100.0)) # == upstream clip(mag, max=1e2) + + def forward(self, x: Tensor) -> Tensor: + # x: (B, T, 512) -> audio (B, (T-1)*HOP) + z = self.out(x) # (B, T, 2*N_BINS) + log_mag = z[:, :, :N_BINS].clamp(max=self.log_mag_max) # pre-exp: fp16 safe + phase = z[:, :, N_BINS:] + mag = torch.exp(log_mag) + real = mag * torch.cos(phase) + imag = mag * torch.sin(phase) + frames = real @ self.basis_cos + imag @ self.basis_sin # (B, T, N_FFT) + + # Overlap-add: chunk c of every frame lands at flat offset c*HOP. + flat = self.num_frames * HOP + y = None + for c in range(N_FFT // HOP): + chunk = frames[:, :, c * HOP : (c + 1) * HOP].reshape(1, flat) + part = F.pad(chunk, (c * HOP, self.full_len - flat - c * HOP)) + y = part if y is None else y + part + y = y[:, N_FFT // 2 : self.full_len - N_FFT // 2] + return y * self.env_inv + + +class Resample2x(nn.Module): + """torchaudio sinc_interp_hann 24k->48k as conv1d + phase interleave.""" + + def __init__(self, in_len: int): + super().__init__() + from torchaudio.functional.functional import _get_sinc_resample_kernel + + kernel, width = _get_sinc_resample_kernel( + 24000, 48000, math.gcd(24000, 48000), 6, 0.99, "sinc_interp_hann", None, + torch.device("cpu"), torch.float32, + ) + self.register_buffer("kernel", kernel) # (2, 1, taps) + self.width = int(width) + self.out_len = 2 * in_len + + def forward(self, wav: Tensor) -> Tensor: + # wav: (B, L) -> (B, 2L); mirrors _apply_sinc_resample_kernel, orig_freq=1 + x = F.pad(wav.unsqueeze(1), (self.width, self.width + 1)) + y = F.conv1d(x, self.kernel) # (B, 2, L+1) + y = y.transpose(1, 2).reshape(1, -1) + return y[:, : self.out_len] + + +class FirCrossover(nn.Module): + """FIR stand-in for linkwitz.py's whole-signal FFT brickwall at 12 kHz: + merged = low_path + hp(high_path - low_path). Linear-phase highpass by + spectral inversion keeps the branches exactly complementary.""" + + def __init__(self, taps: int = FIR_TAPS): + super().__init__() + from scipy.signal import firwin + + lp = firwin(taps, CUTOFF_HZ, fs=SR) + hp = -lp + hp[taps // 2] += 1.0 + self.taps = taps + self.register_buffer("weight", torch.from_numpy(hp[None, None, :]).float()) + + def forward(self, high: Tensor, low: Tensor) -> Tensor: + d = (high - low).unsqueeze(1) + hp = F.conv1d(d, self.weight, padding=self.taps // 2) + return low + hp.squeeze(1) + + +class CoreMLVocoder(nn.Module): + """Whole vocoder decode as one fixed-shape graph: mel (1,100,S) -> (1,(S-1)*512).""" + + def __init__(self, vocos: nn.Module, frames: int, fir_taps: int = FIR_TAPS): + super().__init__() + self.backbone = vocos.backbone + self.upsampler = vocos.upsampler + self.head_24k = DftIstftHead(vocos.head, frames) + self.head_48k = DftIstftHead(vocos.head_48k, 2 * frames + 1) # upsampler emits 2S+1 + self.resample = Resample2x(self.head_24k.out_len) + self.crossover = FirCrossover(fir_taps) + self.out_len = (frames - 1) * 2 * HOP + + def forward(self, mel: Tensor) -> Tensor: + features = self.backbone(mel) # (B, S, 512) + upsampled = self.upsampler(features.transpose(1, 2)) # (B, 512, 2S+1) + audio_hi = self.head_48k(upsampled.transpose(1, 2)) # (B, 2S*HOP) + audio_lo = self.resample(self.head_24k(features)) # (B, (S-1)*2*HOP) + return self.crossover(audio_hi[:, : self.out_len], audio_lo) + + +def snr_db(ref, test) -> float: + ref = np.asarray(ref, dtype=np.float64).ravel() + test = np.asarray(test, dtype=np.float64).ravel() + return float(10 * np.log10(np.sum(ref**2) / (np.sum((ref - test) ** 2) + 1e-30))) + + +def validate_istft(): + """Gate: DFT-matmul ISTFT vs torch.istft on random mag/phase.""" + torch.manual_seed(0) + for frames in (282, 565): + log_mag = torch.randn(1, frames, N_BINS).clamp(max=np.log(100.0)) + phase = torch.randn(1, frames, N_BINS) * 3.0 + mag = torch.exp(log_mag) + real, imag = mag * torch.cos(phase), mag * torch.sin(phase) + + spec = torch.complex(real, imag).transpose(1, 2) + ref = torch.istft(spec, N_FFT, HOP, N_FFT, torch.hann_window(N_FFT), center=True) + + basis_cos, basis_sin, env_inv = istft_constants(frames) + fr = real @ basis_cos + imag @ basis_sin + flat, full = frames * HOP, frames * HOP + (N_FFT - HOP) + y = sum( + F.pad(fr[:, :, c * HOP : (c + 1) * HOP].reshape(1, flat), (c * HOP, full - flat - c * HOP)) + for c in range(N_FFT // HOP) + ) + mine = y[:, N_FFT // 2 : full - N_FFT // 2] * env_inv + + s = snr_db(ref.numpy(), mine.numpy()) + print(f"[istft standalone] frames={frames}: SNR {s:.1f} dB") + assert s > 60, f"ISTFT reimplementation SNR {s:.1f} dB < 60" + + +def validate_wrapper(vocos, wrapper, frames: int, mel_path: str | None): + """Gate: eager fp32 wrapper vs vocos.decode (isolates the FIR crossover).""" + if mel_path: + mel = torch.from_numpy(np.load(mel_path)).float() + assert mel.shape == (1, FEAT_DIM, frames), mel.shape + else: + torch.manual_seed(1) + mel = torch.randn(1, FEAT_DIM, frames) * 0.5 + with torch.no_grad(): + ref = vocos.decode(mel) + mine = wrapper(mel) + s = snr_db(ref.numpy(), mine.numpy()) + print(f"[wrapper eager fp32] frames={frames} src={'oracle mel' if mel_path else 'random mel'}: " + f"SNR {s:.1f} dB (vs torch decode; residual = FIR-vs-FFT crossover)") + assert s > 40, f"eager wrapper SNR {s:.1f} dB < 40" + + +def convert(vocos, frames: int, out_dir: Path, fir_taps: int, validate: bool, mel_path: str | None): + wrapper = CoreMLVocoder(vocos, frames, fir_taps).eval() + if validate: + validate_istft() + validate_wrapper(vocos, wrapper, frames, mel_path) + + mel = torch.randn(1, FEAT_DIM, frames) * 0.5 + with torch.no_grad(): + traced = torch.jit.trace(wrapper, (mel,)) + + t0 = time.perf_counter() + mlmodel = ct.convert( + traced, + inputs=[ct.TensorType(name="mel", shape=(1, FEAT_DIM, frames), dtype=np.float32)], + outputs=[ct.TensorType(name="audio", dtype=np.float32)], + minimum_deployment_target=ct.target.iOS17, + compute_precision=ct.precision.FLOAT16, + convert_to="mlprogram", + ) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / "Vocoder.mlpackage" + mlmodel.save(str(path)) + print(f"saved {path} ({(time.perf_counter() - t0):.1f} s) — " + f"mel (1,{FEAT_DIM},{frames}) -> audio (1,{(frames - 1) * 2 * HOP}) @48k") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--frames", type=int, default=282, help="fixed mel frame count S (gen region)") + parser.add_argument("--output-dir", default="build/coreml-vocoder") + parser.add_argument("--fir-taps", type=int, default=FIR_TAPS) + parser.add_argument("--validate", action="store_true", help="run eager ISTFT + wrapper gates first") + parser.add_argument("--mel", default=None, help="optional .npy mel (1,100,frames) for the eager gate") + args = parser.parse_args() + + patch_coremltools_int() + patch_snake() + vocos = load_vocos() + convert(vocos, args.frames, Path(args.output_dir), args.fir_taps, args.validate, args.mel) + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/coreml/vocoder/parity_vocoder.py b/models/tts/zipvoice/coreml/vocoder/parity_vocoder.py new file mode 100644 index 0000000..e257ddb --- /dev/null +++ b/models/tts/zipvoice/coreml/vocoder/parity_vocoder.py @@ -0,0 +1,129 @@ +"""Parity: CoreML Vocoder vs torch vocos.decode on the oracle generated mel. + +Regenerates the oracle mel deterministically through the pure-torch reference +path (same as coreml/parity.py's torch branch: forward_text_inference + +4-step anchor-Euler with the oracle seed), then feeds it to torch +vocos.decode and the CoreML Vocoder and compares wavs: sample-domain SNR, +log-mel cos, RMS delta, whisper-base transcripts. + +Usage: + .venv/bin/python -m coreml.vocoder.parity_vocoder --coreml-dir build/coreml-vocoder +""" + +import argparse +import json +from pathlib import Path + +import coremltools as ct +import numpy as np +import soundfile as sf +import torch +import torch.nn.functional as F + +from coreml.convert_coreml import load_model +from coreml.parity import cos +from coreml.vocoder.convert_vocoder import load_vocos, snr_db +from zipvoice.models.modules.solver import get_time_steps + + +def regenerate_oracle_mel(oracle: Path): + """Final mel via the pure-torch reference (deterministic, oracle seed).""" + meta = json.loads((oracle / "meta.json").read_text()) + prompt_features = torch.from_numpy(np.load(oracle / "prompt_features.npy")) + prompt_tokens = np.load(oracle / "prompt_tokens.npy").tolist() + text_tokens = np.load(oracle / "text_tokens.npy").tolist() + prompt_len = meta["prompt_features_lens"] + speed = 1.0 * 1.3 # generate() multiplies speed by 1.3 + + model, _ = load_model() + with torch.no_grad(): + text_condition, _ = model.forward_text_inference_ratio_duration( + tokens=[text_tokens], prompt_tokens=[prompt_tokens], + prompt_features_lens=torch.tensor([prompt_len]), speed=speed, + ) + features_len = text_condition.shape[1] + speech_condition = F.pad(prompt_features, (0, 0, 0, features_len - prompt_features.size(1))) + pad_mask = torch.zeros(1, features_len, dtype=torch.bool) + + timesteps = get_time_steps(num_step=meta["num_steps"], t_shift=0.5) + torch.manual_seed(meta["seed"]) + x = torch.randn(1, features_len, 100) + for step in range(meta["num_steps"]): + t_cur, t_next = float(timesteps[step]), float(timesteps[step + 1]) + with torch.no_grad(): + v = model.forward_fm_decoder( + t=torch.tensor(t_cur), xt=x, text_condition=text_condition, + speech_condition=speech_condition, padding_mask=pad_mask, + guidance_scale=torch.tensor(meta["guidance_scale"]), + ) + x1p = x + (1.0 - t_cur) * v + x0p = x - t_cur * v + x = (1.0 - t_next) * x0p + t_next * x1p if step < meta["num_steps"] - 1 else x1p + + mel = x[:, prompt_len:features_len, :].permute(0, 2, 1) / 0.1 # (1, 100, gen) + return mel, meta + + +def log_mel_cos(ref, test, sr=48000): + import librosa + + def lm(x): + m = librosa.feature.melspectrogram(y=x, sr=sr, n_fft=2048, hop_length=512, n_mels=128) + return np.log(np.maximum(m, 1e-10)) + + return cos(lm(ref), lm(test)) + + +def rms_db(x): + return 20 * np.log10(np.sqrt(np.mean(np.square(x))) + 1e-12) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--oracle-dir", default="build/oracle") + parser.add_argument("--coreml-dir", default="build/coreml-vocoder") + parser.add_argument("--compute-units", default="CPU_AND_GPU", + choices=["ALL", "CPU_ONLY", "CPU_AND_NE", "CPU_AND_GPU"]) + parser.add_argument("--transcribe", action="store_true", help="whisper-base both wavs") + args = parser.parse_args() + + mel, meta = regenerate_oracle_mel(Path(args.oracle_dir)) + print(f"[mel] regenerated oracle gen-region mel: {tuple(mel.shape)}") + + vocos = load_vocos() + with torch.no_grad(): + ref_wav = vocos.decode(mel).squeeze(0).clamp(-1, 1).numpy() + + cu = getattr(ct.ComputeUnit, args.compute_units) + voc_ml = ct.models.MLModel(str(Path(args.coreml_dir) / "Vocoder.mlpackage"), compute_units=cu) + cm_wav = voc_ml.predict({"mel": mel.numpy().astype(np.float32)})["audio"] + cm_wav = np.clip(cm_wav.squeeze(0).astype(np.float32), -1, 1) + + n = min(len(ref_wav), len(cm_wav)) + ref_wav, cm_wav = ref_wav[:n], cm_wav[:n] + print(f"[wav {args.compute_units}] len={n} " + f"SNR={snr_db(ref_wav, cm_wav):.1f} dB " + f"cos={cos(ref_wav, cm_wav):.6f} " + f"log-mel cos={log_mel_cos(ref_wav, cm_wav):.5f} " + f"RMS delta={rms_db(cm_wav) - rms_db(ref_wav):+.3f} dB") + + # host-side prompt-level scaling, as the pipeline does + scale = meta["prompt_rms"] / 0.1 if meta["prompt_rms"] < 0.1 else 1.0 + out = Path(args.coreml_dir) + sf.write(out / "parity_torch_48k.wav", ref_wav * scale, 48000) + sf.write(out / "parity_coreml_48k.wav", cm_wav * scale, 48000) + print(f"wrote {out}/parity_torch_48k.wav, parity_coreml_48k.wav") + + if args.transcribe: + from transformers import pipeline + + asr = pipeline("automatic-speech-recognition", model="openai/whisper-base", device="cpu") + t_ref = asr(str(out / "parity_torch_48k.wav"))["text"].strip() + t_cm = asr(str(out / "parity_coreml_48k.wav"))["text"].strip() + print(f"[whisper torch ] {t_ref}") + print(f"[whisper coreml] {t_cm}") + print(f"[transcript match] {t_ref == t_cm}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/pyproject.toml b/models/tts/zipvoice/pyproject.toml new file mode 100644 index 0000000..bb74d2b --- /dev/null +++ b/models/tts/zipvoice/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "zipvoice-coreml" +version = "0.1.0" +description = "CoreML conversion for ZipVoice / LuxTTS (flow-matching zero-shot TTS)" +requires-python = ">=3.10,<3.13" +dependencies = [ + "zipvoice", + "coremltools>=8.1", + "soundfile", + "onnx", + "onnxruntime", + "setuptools<81", +] + +[tool.uv] +find-links = ["https://k2-fsa.github.io/icefall/piper_phonemize.html"] +required-environments = ["sys_platform == 'darwin' and platform_machine == 'arm64'"] + +[tool.uv.sources] +zipvoice = { path = "upstream/luxtts", editable = true } diff --git a/models/tts/zipvoice/scripts/__init__.py b/models/tts/zipvoice/scripts/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/tts/zipvoice/scripts/reference_infer.py b/models/tts/zipvoice/scripts/reference_infer.py new file mode 100644 index 0000000..88c466f --- /dev/null +++ b/models/tts/zipvoice/scripts/reference_infer.py @@ -0,0 +1,119 @@ +"""LuxTTS PyTorch reference inference — parity oracle for the CoreML conversion. + +Runs the upstream torch path (not ONNX), saves the generated wav plus +intermediate tensors (prompt features, text tokens, fm_decoder output) +for per-component parity checks. + +Usage: + uv run --no-sync python scripts/reference_infer.py \ + --prompt-audio --text "..." --output-dir build/oracle +""" + +import argparse +import json +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch + +from huggingface_hub import snapshot_download +from torch.nn.utils import parametrize +from transformers import pipeline + +from linacodec.vocoder.vocos import Vocos +from zipvoice.modeling_utils import generate, process_audio +from zipvoice.models.zipvoice_distill import ZipVoiceDistill +from zipvoice.tokenizer.tokenizer import EmiliaTokenizer +from zipvoice.utils.checkpoint import load_checkpoint +from zipvoice.utils.feature import VocosFbank + + +def load_models_cpu_torch(): + """Upstream load_models_gpu, minus the torch.device(device, 0) breakage on CPU.""" + model_path = snapshot_download("YatharthS/LuxTTS") + + tokenizer = EmiliaTokenizer(token_file=f"{model_path}/tokens.txt") + with open(f"{model_path}/config.json") as f: + model_config = json.load(f) + + model = ZipVoiceDistill( + **model_config["model"], + vocab_size=tokenizer.vocab_size, + pad_id=tokenizer.pad_id, + ) + load_checkpoint(filename=f"{model_path}/model.pt", model=model, strict=True) + model = model.eval() + + vocos = Vocos.from_hparams(f"{model_path}/vocoder/config.yaml") + parametrize.remove_parametrizations(vocos.upsampler.upsample_layers[0], "weight") + parametrize.remove_parametrizations(vocos.upsampler.upsample_layers[1], "weight") + vocos.load_state_dict(torch.load(f"{model_path}/vocoder/vocos.bin", map_location="cpu")) + vocos = vocos.eval() + + transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-tiny", device="cpu") + return model, VocosFbank(), vocos, tokenizer, transcriber + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--prompt-audio", required=True) + parser.add_argument("--text", default="The quick brown fox jumps over the lazy dog, and honestly, it felt great.") + parser.add_argument("--output-dir", default="build/oracle") + parser.add_argument("--num-steps", type=int, default=4) + parser.add_argument("--guidance-scale", type=float, default=3.0) + parser.add_argument("--seed", type=int, default=42) + args = parser.parse_args() + + out = Path(args.output_dir) + out.mkdir(parents=True, exist_ok=True) + + torch.manual_seed(args.seed) + + model, feature_extractor, vocos, tokenizer, transcriber = load_models_cpu_torch() + vocos.freq_range = 12000 + vocos.return_48k = True + + prompt_tokens, prompt_features_lens, prompt_features, prompt_rms = process_audio( + args.prompt_audio, transcriber, tokenizer, feature_extractor, "cpu", target_rms=0.01, duration=5 + ) + + torch.manual_seed(args.seed) # reseed so sampling noise is reproducible + wav = generate( + prompt_tokens, + prompt_features_lens, + prompt_features, + prompt_rms, + args.text, + model, + vocos, + tokenizer, + num_step=args.num_steps, + guidance_scale=args.guidance_scale, + ) + + wav_np = wav.numpy().squeeze() + sf.write(out / "reference_48k.wav", wav_np, 48000) + + # Persist oracle tensors for parity checks + np.save(out / "prompt_features.npy", prompt_features.numpy()) + np.save(out / "prompt_tokens.npy", np.asarray(prompt_tokens[0], dtype=np.int64)) + text_tokens = tokenizer.texts_to_token_ids([args.text]) + np.save(out / "text_tokens.npy", np.asarray(text_tokens[0], dtype=np.int64)) + meta = { + "text": args.text, + "num_steps": args.num_steps, + "guidance_scale": args.guidance_scale, + "seed": args.seed, + "prompt_rms": float(prompt_rms), + "prompt_features_lens": int(prompt_features_lens[0]), + "wav_samples": int(wav_np.shape[-1]), + "wav_seconds_48k": float(wav_np.shape[-1] / 48000.0), + } + (out / "meta.json").write_text(json.dumps(meta, indent=2)) + print(json.dumps(meta, indent=2)) + print(f"wrote {out / 'reference_48k.wav'}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/zipvoice/swift/RssBench.swift b/models/tts/zipvoice/swift/RssBench.swift new file mode 100644 index 0000000..31973f1 --- /dev/null +++ b/models/tts/zipvoice/swift/RssBench.swift @@ -0,0 +1,128 @@ +// Swift CoreML harness: latency + memory (phys_footprint, the jetsam metric) +// for the LuxTTS TextEncoder/FmDecoder at each pipeline stage. +// +// Build: swiftc -O swift/RssBench.swift -o build/rss_bench +// Run: build/rss_bench [steps] + +import CoreML +import Foundation + +func memMB() -> (footprint: Double, resident: Double) { + var info = task_vm_info_data_t() + var count = mach_msg_type_number_t( + MemoryLayout.size / MemoryLayout.size) + let kr = withUnsafeMutablePointer(to: &info) { + $0.withMemoryRebound(to: integer_t.self, capacity: Int(count)) { + task_info(mach_task_self_, task_flavor_t(TASK_VM_INFO), $0, &count) + } + } + guard kr == KERN_SUCCESS else { return (-1, -1) } + return (Double(info.phys_footprint) / 1_048_576, Double(info.resident_size) / 1_048_576) +} + +func report(_ stage: String) { + let m = memMB() + let pad = stage.padding(toLength: 28, withPad: " ", startingAt: 0) + print(pad + String(format: "footprint %7.1f MB resident %7.1f MB", m.footprint, m.resident)) + fflush(stdout) +} + +func randArray(_ shape: [NSNumber]) throws -> MLMultiArray { + let a = try MLMultiArray(shape: shape, dataType: .float32) + let p = a.dataPointer.bindMemory(to: Float32.self, capacity: a.count) + var seed: UInt64 = 0x9E3779B97F4A7C15 + for i in 0..> 11) / Double(UInt64.max >> 11)) * 2 - 1 + } + return a +} + +let args = CommandLine.arguments +guard args.count >= 5 else { + print("usage: rss_bench [steps]") + exit(1) +} +let dir = args[1] +let frames = Int(args[2])! +let tokens = Int(args[3])! +let steps = args.count > 5 ? Int(args[5])! : 4 + +let config = MLModelConfiguration() +switch args[4] { +case "cpu": config.computeUnits = .cpuOnly +case "gpu": config.computeUnits = .cpuAndGPU +case "ane": config.computeUnits = .cpuAndNeuralEngine +default: config.computeUnits = .all +} + +print("== \(dir) frames=\(frames) tokens=\(tokens) cu=\(args[4]) steps=\(steps)") +report("baseline") + +var t0 = Date() +let te = try MLModel( + contentsOf: URL(fileURLWithPath: "\(dir)/TextEncoder.mlmodelc"), configuration: config) +let fm = try MLModel( + contentsOf: URL(fileURLWithPath: "\(dir)/FmDecoder.mlmodelc"), configuration: config) +print(String(format: "model load: %.0f ms", -t0.timeIntervalSinceNow * 1000)) +report("after load") + +// text encoder inputs +let tokArr = try MLMultiArray(shape: [1, NSNumber(value: tokens)], dataType: .int32) +let tokMask = try MLMultiArray(shape: [1, NSNumber(value: tokens)], dataType: .float32) +for i in 0.. core RTFx %.1fx (for %.1fs gen)", + steps, coreMs, audioSec * 1000 / coreMs, audioSec)) +report("steady state")