| """Initialize the decoder student by channel-slicing a frozen pretrained vocoder.
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|
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| Training a decoder this small from scratch does not converge to intelligible
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| audio; slicing the top-left corner of a pretrained neural vocoder's decoder
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| weights does. Pass any ConvNeXt-style vocoder state dict with --vocoder-ckpt;
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| this takes the first `--dim` channels, the first `--pw` hidden units and the
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| first `--blocks` blocks (dim=40 / pw=120 / blocks=3 for the shipped model).
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|
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| The sliced checkpoint still needs the recovery stage (train_decoder.py) before
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| it is usable -- a fresh slice renders noise, and only fine-tuning brings it
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| back.
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| """
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| import sys
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| from pathlib import Path
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|
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| import torch
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|
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| sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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| from microtts.models import DecoderStudent
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|
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| def main():
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| import argparse
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| ap = argparse.ArgumentParser()
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| ap.add_argument("--dim", type=int, default=40)
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| ap.add_argument("--pw", type=int, default=120)
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| ap.add_argument("--blocks", type=int, default=3)
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| ap.add_argument("--vocoder-ckpt", default=None,
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| help="state dict / checkpoint of a ConvNeXt-style neural vocoder")
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| ap.add_argument("--out", default="decoder_init.pt")
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| args = ap.parse_args()
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|
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| if args.vocoder_ckpt:
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|
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| obj = torch.load(args.vocoder_ckpt, map_location="cpu", weights_only=False)
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| sd = obj.state_dict() if hasattr(obj, "state_dict") else obj
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| else:
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|
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| raise ImportError("pass --vocoder-ckpt pointing at a ConvNeXt-style vocoder state dict")
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|
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| student = DecoderStudent(dim=args.dim, pw_hidden=args.pw, blocks=args.blocks)
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| ssd = student.state_dict()
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|
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| ssd["embed.weight"] = sd["backbone.embed.weight"][:args.dim].contiguous()
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| ssd["embed.bias"] = sd["backbone.embed.bias"][:args.dim].contiguous()
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| ssd["norm.weight"] = sd["backbone.norm.weight"][:args.dim].contiguous()
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| ssd["norm.bias"] = sd["backbone.norm.bias"][:args.dim].contiguous()
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| for i in range(args.blocks):
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| src = f"backbone.convnext.{i}"
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| dst = f"blocks.{i}"
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|
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| ssd[f"{dst}.dw.weight"] = sd[f"{src}.dwconv.weight"][:args.dim].contiguous()
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| ssd[f"{dst}.dw.bias"] = sd[f"{src}.dwconv.bias"][:args.dim].contiguous()
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| ssd[f"{dst}.norm.weight"] = sd[f"{src}.norm.weight"][:args.dim].contiguous()
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| ssd[f"{dst}.norm.bias"] = sd[f"{src}.norm.bias"][:args.dim].contiguous()
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|
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| ssd[f"{dst}.pw0.weight"] = sd[f"{src}.pwconv1.weight"][:args.pw, :args.dim].contiguous()
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| ssd[f"{dst}.pw0.bias"] = sd[f"{src}.pwconv1.bias"][:args.pw].contiguous()
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| ssd[f"{dst}.pw1.weight"] = sd[f"{src}.pwconv2.weight"][:args.dim, :args.pw].contiguous()
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| ssd[f"{dst}.pw1.bias"] = sd[f"{src}.pwconv2.bias"][:args.dim].contiguous()
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| ssd[f"{dst}.gamma"] = sd[f"{src}.gamma"][:args.dim].contiguous()
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|
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| ssd["fnorm.weight"] = sd["backbone.final_layer_norm.weight"][:args.dim].contiguous()
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| ssd["fnorm.bias"] = sd["backbone.final_layer_norm.bias"][:args.dim].contiguous()
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|
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| ssd["head.weight"] = sd["head.out.weight"][:, :args.dim].contiguous()
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| ssd["head.bias"] = sd["head.out.bias"].contiguous()
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|
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| student.load_state_dict(ssd)
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| torch.save(student.state_dict(), args.out)
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| n = sum(p.numel() for p in student.parameters())
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| print(f"saved {args.out} | student params: {n:,}")
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|
|
|
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| if __name__ == "__main__":
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| main()
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|
|
