| """How weights/*.fp16.pt were made from the training checkpoints (kept for provenance; not needed at runtime). |
| v0.8-en (run on the training machine in the tts-sv container, paths relative to /w): |
| python tools/export_weights.py --out weights \ |
| --hugin hugin-v0.8-b1=elev/runs/hugin-v0.8-en/ckpt_176000.pt:w:1:vocos-b1-40k:en_v2 |
| (vocos-b1-40k.fp16.pt and g2p.fp16.pt are unchanged since v0.5: --vocos elev/runs/vob1/vocos_40000.pt --g2p novel/g2p/g2p.pt; |
| hift-s3gopen.fp16.pt (the vocoder for own voices from hugin-finetune) and g2p_en.fp16.pt since v0.7: --hift out/runs/s3gopen/s3gen_8000.pt --g2p-en novel/g2p/g2p_en.pt) |
| The optional 5th field FRONT is stored as info["front"]; "en_v2" switches on the v0.8-en English text path in hugin/synth.py. |
| Only inference weights are kept (no optimiser state, no GAN discriminators). Floating tensors are stored as fp16 and loaded |
| back to fp32 at runtime; tiny vectors (< 4096 values: norms, biases, mel statistics, the iSTFT window) stay fp32.""" |
| import argparse, os, torch |
|
|
|
|
| def half(sd, small=4096): |
| return {k: (v.half() if v.is_floating_point() and v.numel() >= small else v.clone()) for k, v in sd.items()} |
|
|
|
|
| def g2p(src, dst): |
| g = torch.load(src, map_location="cpu", weights_only=False) |
| torch.save({"model": half(g["model"]), "vocab": {"in": list(g["vocab"]["in"]), "out": list(g["vocab"]["out"])}}, dst) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--hugin", action="append", default=[], help="NAME=CKPT:VOICE:TAG:VOCODER[:FRONT] (repeatable)") |
| ap.add_argument("--vocos"); ap.add_argument("--hift"); ap.add_argument("--g2p"); ap.add_argument("--g2p-en") |
| ap.add_argument("--out", default="weights") |
| a = ap.parse_args(); os.makedirs(a.out, exist_ok=True) |
| for spec in a.hugin: |
| name, rest = spec.split("=", 1); f = rest.split(":"); ck, voice, tag, voc = f[:4]; front = f[4] if len(f) > 4 else None |
| s = torch.load(ck, map_location="cpu", weights_only=False) |
| torch.save({"model": half(s["model"]), "vocab": list(s["vocab"]), "arch": dict(s.get("arch", {})), |
| "info": {"name": "futhark-hugin", "version": name.replace("hugin-", ""), "step": int(s.get("step", 0)), |
| "prominence": True, "voice": voice, "tag": int(tag), "vocoder": voc, **({"front": front} if front else {})}}, |
| os.path.join(a.out, f"{name}.fp16.pt")) |
| if a.vocos: |
| v = torch.load(a.vocos, map_location="cpu", weights_only=False) |
| torch.save({"V": half(v["V"]), "info": {"name": "vocos-b1", "step": int(v.get("step", 0))}}, os.path.join(a.out, "vocos-b1-40k.fp16.pt")) |
| if a.hift: |
| g = torch.load(a.hift, map_location="cpu", weights_only=True) |
| h = {k[len("mel2wav."):]: v for k, v in g.items() if k.startswith("mel2wav.")} |
| torch.save({"hift": half(h), "info": {"name": "hift-s3gopen", "source": "Chatterbox S3Gen HiFT (MIT), fine-tuned in s3gopen (licence-clean Swedish data), step 8000"}}, |
| os.path.join(a.out, "hift-s3gopen.fp16.pt")) |
| if a.g2p: g2p(a.g2p, os.path.join(a.out, "g2p.fp16.pt")) |
| if a.g2p_en: g2p(a.g2p_en, os.path.join(a.out, "g2p_en.fp16.pt")) |
| for f in sorted(os.listdir(a.out)): |
| if f.endswith(".pt"): print(f, f"{os.path.getsize(os.path.join(a.out, f)) / 1e6:.1f} MB") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
