| """Which whisper clips actually work as Ming prompts? |
| |
| a01 was chosen by ear for its breathy delivery, but as a prompt it produces |
| hallucinated speech ("字幕by索兰娅" for 「后来店关了，歌还在。」), and the teacher |
| does this too, so it is not a distillation artefact. Six other low-voicing clips |
| were mined; this bakes each and asks two questions: |
| |
| 1. does the clone say the right words (CER), and |
| 2. does it stay breathy (voicing of the output against the prompt's own) |
| |
| A clip that speaks correctly but loses the whisper is a fail: the breathiness is |
| the reason we want it. |
| |
| Outputs whisper_probe/{slot}_{i}.wav and metrics.json. |
| """ |
| import json |
| import os |
| import sys |
| import time |
| import traceback |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
| ROOT = Path(os.environ.get("WORK_ROOT", "/kaggle/working")) |
| REPO = ROOT / "Ming-3.0-tts" |
| MODEL = ROOT / "Ming-3.0-tts-0.5b" |
| BUNDLE = ROOT / "webfm-eval" |
| OUT = ROOT / "whisper_probe" |
| SEED = 20261031 |
|
|
| LINES = [ |
| "别急，先呼吸。", |
| "后来店关了，歌还在。", |
| "晚安，我们明天见。", |
| ] |
|
|
|
|
| def log(msg: str) -> None: |
| print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True) |
|
|
|
|
| def patch_fp32() -> None: |
| for name in ("ming_tts.py", "modeling_bailingmm.py"): |
| p = REPO / name |
| s = p.read_text() |
| s = s.replace("torch_dtype=torch.bfloat16", "torch_dtype=torch.float32") |
| s = s.replace(".eval().to(torch.bfloat16).to(device)", ".eval().to(torch.float32).to(device)") |
| s = s.replace("dtype=torch.bfloat16", "dtype=torch.float32") |
| p.write_text(s) |
|
|
|
|
| def voicing(y: np.ndarray, sr: int) -> float: |
| """Periodicity of the strongest frames. Low means breathy, no pitch.""" |
| win, hop = int(0.04 * sr), int(0.02 * sr) |
| if len(y) < win * 2: |
| return 0.0 |
| frames = np.lib.stride_tricks.sliding_window_view(y, win)[::hop] |
| energy = (frames ** 2).mean(1) |
| idx = np.argsort(energy)[-max(1, int(len(energy) * 0.3)):] |
| lo, hi = int(sr / 400), int(sr / 70) |
| peaks = [] |
| for f in frames[idx]: |
| f = f - f.mean() |
| ac = np.correlate(f, f, "full")[win - 1:] |
| if ac[0] <= 0: |
| continue |
| peaks.append(float(ac[lo:hi].max() / ac[0])) |
| return float(np.median(peaks)) if peaks else 0.0 |
|
|
|
|
| def main() -> None: |
| import torch |
| import soundfile as sf |
| from transformers import pipeline |
|
|
| OUT.mkdir(parents=True, exist_ok=True) |
| sys.path.insert(0, str(REPO)) |
| patch_fp32() |
| from ming_tts import MingTTS |
| sys.path.insert(0, str(BUNDLE / "eval")) |
| from score import load_t2s, score_row |
|
|
| table = load_t2s() |
| voices = json.loads((BUNDLE / "whisper/manifest.json").read_text())["voices"] |
| log(f"{len(voices)} whisper candidates x {len(LINES)} lines") |
|
|
| tts = MingTTS(str(MODEL), device="cuda", text_normalization=False) |
| rows = [] |
| for v in voices: |
| wav_path = str(BUNDLE / v["wav"]) |
| item = dict(v) |
| ces, vs, ws = [], [], [] |
| for i, text in enumerate(LINES, 1): |
| try: |
| torch.manual_seed(SEED + i) |
| w = tts.synthesize(task="TTS", text=text, language_id="Chinese", |
| prompt_wav_path=wav_path, prompt_text=v["prompt_text"], |
| use_spk_emb=True, raw_text=True, |
| max_decode_steps=max(32, min(400, int(len(text) * 3.5)))) |
| y = w.cpu().numpy().reshape(-1) |
| sf.write(str(OUT / f"{v['slot']}_{i}.wav"), y, 44100) |
| vs.append(voicing(y, 44100)) |
| ws.append(round(y.size / 44100, 2)) |
| item[f"sec_{i}"] = ws[-1] |
| except Exception: |
| log(f"{v['slot']} line {i} failed\n{traceback.format_exc()}") |
| item["voicing_out"] = float(np.mean(vs)) if vs else 0.0 |
| item["seconds"] = ws |
| rows.append(item) |
| log(f"{v['slot']} src_v={v['voicing']:.2f} out_v={item['voicing_out']:.2f} " |
| f"sec={ws}") |
|
|
| asr = pipeline("automatic-speech-recognition", model="openai/whisper-small", |
| device=0, torch_dtype=torch.float16) |
|
|
| for r in rows: |
| ces = [] |
| for i, text in enumerate(LINES, 1): |
| p = OUT / f"{r['slot']}_{i}.wav" |
| if not p.exists(): |
| continue |
| y, _ = sf.read(p, dtype="float32") |
| out = asr({"array": y.astype(np.float32), "sampling_rate": 44100}, |
| generate_kwargs={"language": "zh", "task": "transcribe"}) |
| hyp = (out.get("text", "") if isinstance(out, dict) else str(out)).strip() |
| s = score_row({"text": text, "hyp_text": hyp}, table) |
| ces.append(s["cer_norm"]) |
| r[f"hyp_{i}"] = hyp |
| r["cer"] = float(np.mean(ces)) if ces else 1.0 |
| r["ok"] = bool(r["cer"] < 0.35 and r["voicing_out"] < 0.7) |
| log(f"{r['slot']} cer={r['cer']:.3f} v_out={r['voicing_out']:.2f} " |
| f"{'PASS' if r['ok'] else 'fail'} {r.get('hyp_1','')[:24]}") |
|
|
| rows.sort(key=lambda r: (not r["ok"], r["cer"])) |
| (OUT / "metrics.json").write_text(json.dumps(rows, ensure_ascii=False, indent=1)) |
| log("\n-- ranking --") |
| for r in rows: |
| log(f" {'✓' if r['ok'] else '✗'} cer={r['cer']:.2f} v {r['voicing']:.2f}->" |
| f"{r['voicing_out']:.2f} {r['artist'][:14]:<14} {r['prompt_text'][:18]}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
