mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
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# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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python3 hf2pretrained.py --hf-cosyvoice2-llm-path /workspace/rl-exp/checkpoint-400 --output-path /workspace/CosyVoice2-0.5B/llm-new.pt
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"""
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from argparse import ArgumentParser
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import torch
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from safetensors import safe_open
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from transformers import AutoTokenizer
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def get_args():
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parser = ArgumentParser()
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parser.add_argument(
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"--hf-cosyvoice2-llm-path",
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type=str,
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default=None,
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help="The RL trained CosyVoice2 model path in HuggingFace format",
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)
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parser.add_argument(
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"--output-path",
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type=str,
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default="./llm.pt",
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help="The path to save the llm.pt",
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)
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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args = get_args()
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tokenizer = AutoTokenizer.from_pretrained(args.hf_cosyvoice2_llm_path)
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speech_start_idx = tokenizer.convert_tokens_to_ids("<|s_0|>")
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cosyvoice2_token_size = 6561 + 3
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llm_embedding_vocab_size = 2
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hf_tensors = {}
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with safe_open(f"{args.hf_cosyvoice2_llm_path}/model.safetensors", framework="pt", device="cpu") as f:
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for k in f.keys():
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if k.startswith("lm_head.bias"):
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# RL trained model disable bias for lm_head
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continue
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new_k = "llm.model." + k
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hf_tensors[new_k] = f.get_tensor(k)
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if k.startswith("lm_head"):
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hf_tensors["llm_decoder.weight"] = f.get_tensor(k)[speech_start_idx:speech_start_idx + cosyvoice2_token_size]
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hf_tensors["llm_decoder.bias"] = torch.zeros_like(hf_tensors["llm_decoder.weight"][:, 0])
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if k.startswith("model.embed_tokens"):
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hf_tensors["speech_embedding.weight"] = f.get_tensor(k)[speech_start_idx:speech_start_idx + cosyvoice2_token_size]
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hf_tensors["llm_embedding.weight"] = f.get_tensor(k)[speech_start_idx + cosyvoice2_token_size:speech_start_idx + cosyvoice2_token_size + llm_embedding_vocab_size]
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# use tie_word_embeddings=True
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hf_tensors["llm.model.model.embed_tokens.weight"] = hf_tensors["llm.model.model.embed_tokens.weight"][:151936]
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hf_tensors["llm.model.lm_head.weight"] = hf_tensors["llm.model.model.embed_tokens.weight"]
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torch.save(hf_tensors, args.output_path)
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