| |
|
|
| import argparse |
| import json |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| import torch |
|
|
| REPO = Path(__file__).resolve().parent.parent |
| sys.path.insert(0, str(REPO / "scripts")) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--codes", default="data/_fish_fan_codes.jsonl") |
| ap.add_argument("--outdir", default="out_fish_fan") |
| ap.add_argument("--device", default="cuda") |
| args = ap.parse_args() |
|
|
| import soundfile as sf |
| from fish_codec_util import load_fish_codec |
| codec = load_fish_codec(args.device) |
| sr = int(codec.sample_rate) |
|
|
| out = Path(args.outdir) |
| out.mkdir(parents=True, exist_ok=True) |
| rows = [json.loads(l) for l in open(args.codes) if l.strip()] |
| manifest = [] |
| for i, r in enumerate(rows): |
| levels = [r["speech_tokens"]] + r["residual_codes"] |
| idx = torch.tensor(levels, dtype=torch.long, |
| device=args.device).unsqueeze(0) |
| with torch.no_grad(): |
| y = codec.from_indices(idx) |
| y = y[0] if isinstance(y, (tuple, list)) else y |
| w = np.clip(y.squeeze().float().cpu().numpy(), -1.0, 1.0) |
| p = out / f"sample_{i:02d}.wav" |
| sf.write(str(p), w, sr) |
| dur = len(w) / sr |
| manifest.append({"file": p.name, "prompt": r["prompt"], |
| "dur_s": round(dur, 2), "n_frames": r.get("n_frames")}) |
| print(f"[{i}] {p.name} {dur:.2f}s @ {sr}Hz " |
| f"rms={float(np.sqrt((w**2).mean())):.4f} {r['prompt'][:45]!r}", |
| flush=True) |
| (out / "manifest.json").write_text(json.dumps(manifest, indent=2)) |
| print(f"DECODE_FISH_CODES_DONE {len(manifest)} wavs -> {out}") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
