8 mined voices + clone_voices.py

kaggle/clone_voices.py

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+"""Clone the eight mined voices with Ming and render a sample for each.
+
+The point is comparison: the listening page already has the raw 6 s crop, this
+adds what the model actually produces from it, on the same two sentences, so
+timbre can be judged before any voice joins the roster.
+
+Reads webfm-eval/mined/manifest.json, writes voices_clone/{slot}_1.wav,
+voices_clone/{slot}_2.wav and voices_clone/metrics.json.
+
+Note the prompt transcripts come from adult-roleplay ASMR clips. Only the timbre
+is cloned; the prompt text is conditioning, never spoken in the product. Source
+provenance still needs to hold up in the licence audit before any of these ship.
+"""
+import json
+import os
+import sys
+import time
+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 / "voices_clone"
+
+TEXTS = [
+    "灯关小一点，我们慢慢来。",
+    "睡不着的夜里，时间会走得特别慢。",
+]
+
+
+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"):
+        path = REPO / name
+        s = path.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")
+        path.write_text(s)
+
+
+def hf_ratio(y: np.ndarray, sr: int, cut: int = 8000) -> float:
+    spec = np.abs(np.fft.rfft(y)) ** 2
+    freqs = np.fft.rfftfreq(len(y), 1 / sr)
+    return float(spec[freqs > cut].sum() / (spec.sum() + 1e-12))
+
+
+def main() -> None:
+    import torch
+    import torchaudio
+
+    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()
+
+    from transformers import pipeline
+    asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small",
+                        device=0, torch_dtype=torch.float16)
+
+    def asr(y: np.ndarray) -> str:
+        out = asr_pipe({"array": y.astype(np.float32), "sampling_rate": 44100},
+                       generate_kwargs={"language": "zh", "task": "transcribe"})
+        return (out.get("text", "") if isinstance(out, dict) else str(out)).strip()
+
+    tts = MingTTS(str(MODEL), device="cuda", text_normalization=False)
+    voices = json.loads((BUNDLE / "mined/manifest.json").read_text())["voices"]
+    log(f"{len(voices)} voices, {len(TEXTS)} sentences each")
+
+    rows = []
+    for v in voices:
+        wav_path = BUNDLE / v["wav"]
+        entry = dict(v)
+        for i, text in enumerate(TEXTS, 1):
+            try:
+                t0 = time.time()
+                torch.manual_seed(1234)
+                wav = tts.synthesize(
+                    task="TTS", text=text, language_id="Chinese",
+                    prompt_wav_path=str(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))),
+                )
+                arr = wav.cpu().numpy().reshape(-1)
+                path = OUT / f"{v['slot']}_{i}.wav"
+                torchaudio.save(str(path), torch.from_numpy(arr).unsqueeze(0), 44100)
+                s = score_row({"text": text, "hyp_text": asr(arr)}, table)
+                entry[f"cer_{i}"] = s["cer_norm"]
+                entry[f"asr_{i}"] = s["hyp_text"]
+                entry[f"hf_{i}"] = hf_ratio(arr, 44100)
+                entry[f"sec_{i}"] = round(arr.size / 44100, 2)
+                log(f"{v['slot']} #{i} cer={s['cer_norm']:.3f} hf={entry[f'hf_{i}']:.4f} "
+                    f"{arr.size/44100:.1f}s {time.time()-t0:.0f}s")
+            except Exception as exc:
+                entry[f"error_{i}"] = f"{type(exc).__name__}: {exc}"
+                log(f"{v['slot']} #{i} failed: {exc}")
+        entry["cer"] = float(np.mean([entry[k] for k in entry if k.startswith("cer_")] or [9]))
+        entry["hf"] = float(np.mean([entry[k] for k in entry if k.startswith("hf_")] or [0]))
+        rows.append(entry)
+
+    rows.sort(key=lambda r: r["cer"])
+    (OUT / "metrics.json").write_text(json.dumps(rows, ensure_ascii=False, indent=1))
+    log("\n-- by CER --")
+    for r in rows:
+        log(f"  {r['cer']:.3f} hf={r['hf']:.4f}  {r['slot']} {r['artist'][:16]:<16} "
+            f"mine_cer={r['mine_cer']:.3f}")
+
+
+if __name__ == "__main__":
+    main()

mined/a01.wav

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+oid sha256:603c440f73dc386af85fcf0d8ddbbddf9769ca30792258b66ce51ddf0e6d285b
+size 159724

mined/a02.wav

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+oid sha256:36ac323b01904e6e8b16b4a84fbb57e138bcb5e4a585e6bf2ae98af708188633
+size 192044

mined/a03.wav

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+size 177644

mined/a04.wav

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+size 156524

mined/a05.wav

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+size 191724

mined/a06.wav

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+size 192044

mined/a07.wav

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+oid sha256:35be36f5cdb3b2223348772e9ebc19591565d8b578406758be5421bfd2072e19
+size 186924

mined/a08.wav

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+oid sha256:3fb35eba47b304c7950a3286393cec7da76deb03c7770ff21094b172a94d813c
+size 192044

mined/manifest.json

@@ -0,0 +1,68 @@
+{
+ "voices": [
+  {
+   "slot": "a01",
+   "artist": "千千",
+   "wav": "mined/a01.wav",
+   "prompt_text": "感覺那個地方有個變形風的感覺是",
+   "mine_cer": 0.4101449275362319,
+   "voicing": 0.721
+  },
+  {
+   "slot": "a02",
+   "artist": "Miss哈尼",
+   "wav": "mined/a02.wav",
+   "prompt_text": "隨時隨地抱抱開心也要抱抱難過也要抱",
+   "mine_cer": 0.22939958592132503,
+   "voicing": 0.833
+  },
+  {
+   "slot": "a03",
+   "artist": "优优",
+   "wav": "mined/a03.wav",
+   "prompt_text": "你好非常感謝先生購買這次的教練課",
+   "mine_cer": 0.2101449275362319,
+   "voicing": 0.706
+  },
+  {
+   "slot": "a04",
+   "artist": "atinybear",
+   "wav": "mined/a04.wav",
+   "prompt_text": "好的好的好的 我出去我出去教授我马上就走我马上滚蛋",
+   "mine_cer": 0.13395445134575568,
+   "voicing": 0.734
+  },
+  {
+   "slot": "a05",
+   "artist": "buy1take2",
+   "wav": "mined/a05.wav",
+   "prompt_text": "但是你還是要像往常一樣以通關為目的的進行遊戲哦",
+   "mine_cer": 0.11014492753623188,
+   "voicing": 0.859
+  },
+  {
+   "slot": "a06",
+   "artist": "vivian",
+   "wav": "mined/a06.wav",
+   "prompt_text": "你現在老二跟丹丹應該都撞得很難受吧",
+   "mine_cer": 0.07681159420289856,
+   "voicing": 0.732
+  },
+  {
+   "slot": "a07",
+   "artist": "伊芙琳",
+   "wav": "mined/a07.wav",
+   "prompt_text": "嘿嘿竟然那么老实的尊仗我的吩咐主人果然是无可救药的M呢",
+   "mine_cer": 0.07163561076604555,
+   "voicing": 0.85
+  },
+  {
+   "slot": "a08",
+   "artist": "Rainnight雨",
+   "wav": "mined/a08.wav",
+   "prompt_text": "先生您是第一次点我们上门按摩的服务吗?",
+   "mine_cer": 0.014492753623188406,
+   "voicing": 0.888
+  }
+ ]
+}