mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
124 lines
5.2 KiB
Python
124 lines
5.2 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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Usage: Instruct TTS
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python3 infer.py \
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--token2wav-path /workspace/CosyVoice2-0.5B \
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--prompt-text "吃燕窝就选燕之屋，本节目由26年专注高品质燕窝的燕之屋冠名播出。豆奶牛奶换着喝，营养更均衡，本节目由豆本豆豆奶特约播出。" \
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--prompt-speech-path ./assets/prompt_audio.wav \
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--model-path ./transformers_cosyvoice2_llm \
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--input-text "用四川话说<|endofprompt|>扁担长，板凳宽，扁担绑在板凳上。吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。"
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"""
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from cosyvoice.cli.cosyvoice import CosyVoice2
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import sys
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from argparse import ArgumentParser
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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sys.path.append("/workspace/CosyVoice/third_party/Matcha-TTS")
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def get_args():
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parser = ArgumentParser()
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parser.add_argument(
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"--pretrained-cosyvoice2-path",
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type=str,
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default="/workspace/CosyVoice2-0.5B",
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help="Token2Wav path, default to %(default)r",
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)
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parser.add_argument(
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"--save-path",
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type=str,
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default='./transformers_cosyvoice2_llm',
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help="The path to save the model",
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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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cosy2_model = CosyVoice2(
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args.pretrained_cosyvoice2_path, load_jit=False, load_trt=False, fp16=False
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)
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llm = cosy2_model.model.llm.llm.model
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speech_embedding = cosy2_model.model.llm.speech_embedding
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llm_decoder = cosy2_model.model.llm.llm_decoder
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llm_embedding = cosy2_model.model.llm.llm_embedding
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tokenizer = AutoTokenizer.from_pretrained(f"{args.pretrained_cosyvoice2_path}/CosyVoice-BlankEN")
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special_tokens = {
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'eos_token': '<|endoftext|>',
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'pad_token': '<|endoftext|>',
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'additional_special_tokens': [
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'<|im_start|>', '<|im_end|>', '<|endofprompt|>',
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'[breath]', '<strong>', '</strong>', '[noise]',
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'[laughter]', '[cough]', '[clucking]', '[accent]',
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'[quick_breath]',
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"<laughter>", "</laughter>",
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"[hissing]", "[sigh]", "[vocalized-noise]",
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"[lipsmack]", "[mn]"
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]
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}
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tokenizer.add_special_tokens(special_tokens)
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original_tokenizer_vocab_size = len(tokenizer)
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cosyvoice2_token_size = 6561
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new_tokens = [f"<|s_{i}|>" for i in range(cosyvoice2_token_size)] + [
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"<|eos1|>", "<|eos2|>", "<|eos3|>", "<|sos|>", "<|task_id|>"
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]
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num_added_tokens = tokenizer.add_tokens(new_tokens)
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llm.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=128)
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vocab_size = llm.get_input_embeddings().weight.shape[0]
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feature_size = speech_embedding.embedding_dim
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new_lm_head = torch.nn.Linear(in_features=feature_size, out_features=vocab_size, bias=True)
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with torch.no_grad():
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# set the weight and bias of the new lm_head to 0
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new_lm_head.weight.data.zero_()
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new_lm_head.bias.data.zero_()
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new_lm_head.weight[original_tokenizer_vocab_size:original_tokenizer_vocab_size+cosyvoice2_token_size+3] = llm_decoder.weight
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new_lm_head.bias[original_tokenizer_vocab_size:original_tokenizer_vocab_size+cosyvoice2_token_size+3] = llm_decoder.bias
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llm.lm_head = new_lm_head
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input_embeddings = llm.get_input_embeddings()
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with torch.no_grad():
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input_embeddings.weight[original_tokenizer_vocab_size:original_tokenizer_vocab_size+cosyvoice2_token_size+3] = speech_embedding.weight
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input_embeddings.weight[original_tokenizer_vocab_size+cosyvoice2_token_size+3:original_tokenizer_vocab_size+cosyvoice2_token_size+3+2] = llm_embedding.weight
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eos_token_ids = [original_tokenizer_vocab_size + cosyvoice2_token_size, original_tokenizer_vocab_size + cosyvoice2_token_size + 1, original_tokenizer_vocab_size + cosyvoice2_token_size + 2]
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llm.generation_config.eos_token_id = eos_token_ids
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llm.generation_config.temperature = 1.0
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llm.generation_config.top_p = 0.8
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llm.generation_config.top_k = 25
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llm.config.eos_token_id = original_tokenizer_vocab_size + cosyvoice2_token_size
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llm.config.vocab_size = vocab_size
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llm.config.tie_word_embeddings = False
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llm.config.use_bias = True
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llm.to(torch.bfloat16)
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llm.save_pretrained(args.save_path)
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TEMPLATE = "{%- for message in messages %}{%- if message['role'] == 'user' %}{{- '<|sos|>' + message['content'] + '<|task_id|>' }}{%- elif message['role'] == 'assistant' %}{{- message['content']}}{%- endif %}{%- endfor %}"
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tokenizer.chat_template = TEMPLATE
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tokenizer.save_pretrained(args.save_path) |