diff --git a/cosyvoice/cli/frontend.py b/cosyvoice/cli/frontend.py index d614883..ae2b485 100644 --- a/cosyvoice/cli/frontend.py +++ b/cosyvoice/cli/frontend.py @@ -193,13 +193,13 @@ class CosyVoiceFrontEnd: model_input = self.frontend_sft(tts_text, spk_id) # in instruct mode, we remove spk_embedding in llm due to information leakage del model_input['llm_embedding'] - instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text + '') + instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text) model_input['prompt_text'] = instruct_text_token model_input['prompt_text_len'] = instruct_text_token_len return model_input def frontend_instruct2(self, tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id): - model_input = self.frontend_zero_shot(tts_text, instruct_text + '<|endofprompt|>', prompt_wav, resample_rate, zero_shot_spk_id) + model_input = self.frontend_zero_shot(tts_text, instruct_text, prompt_wav, resample_rate, zero_shot_spk_id) del model_input['llm_prompt_speech_token'] del model_input['llm_prompt_speech_token_len'] return model_input diff --git a/cosyvoice/cli/model.py b/cosyvoice/cli/model.py index b0fc120..85e63d4 100644 --- a/cosyvoice/cli/model.py +++ b/cosyvoice/cli/model.py @@ -129,7 +129,7 @@ class CosyVoiceModel: def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0): with torch.cuda.amp.autocast(self.fp16): - tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device), + tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device, dtype=torch.int32), token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), prompt_token=prompt_token.to(self.device), prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), @@ -284,7 +284,7 @@ class CosyVoice2Model(CosyVoiceModel): def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0): with torch.cuda.amp.autocast(self.fp16): - tts_mel, _ = self.flow.inference(token=token.to(self.device), + tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32), token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), prompt_token=prompt_token.to(self.device), prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), @@ -413,7 +413,7 @@ class CosyVoice3Model(CosyVoice2Model): def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0): with torch.cuda.amp.autocast(self.fp16): - tts_mel, _ = self.flow.inference(token=token.to(self.device), + tts_mel, _ = self.flow.inference(token=token.to(self.device, dtype=torch.int32), token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), prompt_token=prompt_token.to(self.device), prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), diff --git a/cosyvoice/llm/llm.py b/cosyvoice/llm/llm.py index cba0c57..559d49f 100644 --- a/cosyvoice/llm/llm.py +++ b/cosyvoice/llm/llm.py @@ -155,7 +155,7 @@ class TransformerLM(torch.nn.Module): num_trials, max_trials = 0, 100 while True: top_ids = self.sampling(weighted_scores, decoded_tokens, sampling) - if (not ignore_eos) or (self.speech_token_size not in top_ids): + if (not ignore_eos) or (top_ids < self.speech_token_size): break num_trials += 1 if num_trials > max_trials: @@ -506,7 +506,7 @@ class Qwen2LM(TransformerLM): masks=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool), cache=cache) logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) - top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False).item() + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False) if top_ids in self.stop_token_ids: break # in stream mode, yield token one by one @@ -654,7 +654,7 @@ class CosyVoice3LM(Qwen2LM): self.mix_ratio = mix_ratio # 5. vllm related - self.stop_token_ids = [speech_token_size + i for i in range(4)] + self.stop_token_ids = [speech_token_size + i for i in range(200)] self.vllm_output_queue = {} def forward( diff --git a/cosyvoice/utils/common.py b/cosyvoice/utils/common.py index 2b602fa..3c4cb44 100644 --- a/cosyvoice/utils/common.py +++ b/cosyvoice/utils/common.py @@ -25,32 +25,32 @@ import torch IGNORE_ID = -1 -instruct_list = ["You are a helpful assistant. 请用广东话表达。", - "You are a helpful assistant. 请用东北话表达。", - "You are a helpful assistant. 请用甘肃话表达。", - "You are a helpful assistant. 请用贵州话表达。", - "You are a helpful assistant. 请用河南话表达。", - "You are a helpful assistant. 请用湖北话表达。", - "You are a helpful assistant. 请用湖南话表达。", - "You are a helpful assistant. 请用江西话表达。", - "You are a helpful assistant. 请用闽南话表达。", - "You are a helpful assistant. 请用宁夏话表达。", - "You are a helpful assistant. 请用山西话表达。", - "You are a helpful assistant. 请用陕西话表达。", - "You are a helpful assistant. 请用山东话表达。", - "You are a helpful assistant. 请用上海话表达。", - "You are a helpful assistant. 请用四川话表达。", - "You are a helpful assistant. 请用天津话表达。", - "You are a helpful assistant. 请用云南话表达。", - "You are a helpful assistant. Please say a sentence as loudly as possible.", - "You are a helpful assistant. Please say a sentence in a very soft voice.", - "You are a helpful assistant. 请用尽可能慢地语速说一句话。", - "You are a helpful assistant. 请用尽可能快地语速说一句话。", - "You are a helpful assistant. 请非常开心地说一句话。", - "You are a helpful assistant. 请非常伤心地说一句话。", - "You are a helpful assistant. 请非常生气地说一句话。", - "You are a helpful assistant. 我想体验一下小猪佩奇风格，可以吗？", - "You are a helpful assistant. 你可以尝试用机器人的方式解答吗？"] +instruct_list = ["You are a helpful assistant. 请用广东话表达。", + "You are a helpful assistant. 请用东北话表达。", + "You are a helpful assistant. 请用甘肃话表达。", + "You are a helpful assistant. 请用贵州话表达。", + "You are a helpful assistant. 请用河南话表达。", + "You are a helpful assistant. 请用湖北话表达。", + "You are a helpful assistant. 请用湖南话表达。", + "You are a helpful assistant. 请用江西话表达。", + "You are a helpful assistant. 请用闽南话表达。", + "You are a helpful assistant. 请用宁夏话表达。", + "You are a helpful assistant. 请用山西话表达。", + "You are a helpful assistant. 请用陕西话表达。", + "You are a helpful assistant. 请用山东话表达。", + "You are a helpful assistant. 请用上海话表达。", + "You are a helpful assistant. 请用四川话表达。", + "You are a helpful assistant. 请用天津话表达。", + "You are a helpful assistant. 请用云南话表达。", + "You are a helpful assistant. Please say a sentence as loudly as possible.", + "You are a helpful assistant. Please say a sentence in a very soft voice.", + "You are a helpful assistant. 请用尽可能慢地语速说一句话。", + "You are a helpful assistant. 请用尽可能快地语速说一句话。", + "You are a helpful assistant. 请非常开心地说一句话。", + "You are a helpful assistant. 请非常伤心地说一句话。", + "You are a helpful assistant. 请非常生气地说一句话。", + "You are a helpful assistant. 我想体验一下小猪佩奇风格，可以吗？", + "You are a helpful assistant. 你可以尝试用机器人的方式解答吗？"] def pad_list(xs: List[torch.Tensor], pad_value: int): """Perform padding for the list of tensors. @@ -156,12 +156,12 @@ def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25): break prob = torch.tensor(prob).to(weighted_scores) indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device) - top_ids = indices[prob.multinomial(1, replacement=True)] + top_ids = indices[prob.multinomial(1, replacement=True)].item() return top_ids def random_sampling(weighted_scores, decoded_tokens, sampling): - top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True) + top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True).item() return top_ids diff --git a/example.py b/example.py index ba7c5a2..6216cf5 100644 --- a/example.py +++ b/example.py @@ -28,7 +28,7 @@ def cosyvoice_example(): cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-Instruct') # instruct usage, support [laughter][breath] - for i, j in enumerate(cosyvoice.inference_instruct('在面对挑战时，他展现了非凡的勇气与智慧。', '中文男', 'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.', stream=False)): + for i, j in enumerate(cosyvoice.inference_instruct('在面对挑战时，他展现了非凡的勇气与智慧。', '中文男', 'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.<|endofprompt|>', stream=False)): torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) def cosyvoice2_example(): @@ -52,7 +52,7 @@ def cosyvoice2_example(): torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) # instruct usage - for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '用四川话说这句话', './asset/zero_shot_prompt.wav', stream=False)): + for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '用四川话说这句话<|endofprompt|>', './asset/zero_shot_prompt.wav', stream=False)): torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) # bistream usage, you can use generator as input, this is useful when using text llm model as input @@ -70,21 +70,21 @@ def cosyvoice3_example(): """ cosyvoice = CosyVoice3('pretrained_models/CosyVoice3-0.5B', load_jit=False, load_trt=False, fp16=False) # zero_shot usage - for i, j in enumerate(cosyvoice.inference_zero_shot('八百标兵奔北坡，北坡炮兵并排跑，炮兵怕把标兵碰，标兵怕碰炮兵炮。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): + for i, j in enumerate(cosyvoice.inference_zero_shot('八百标兵奔北坡，北坡炮兵并排跑，炮兵怕把标兵碰，标兵怕碰炮兵炮。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) # fine grained control, for supported control, check cosyvoice/tokenizer/tokenizer.py#L280 - for i, j in enumerate(cosyvoice.inference_cross_lingual('[breath]因为他们那一辈人[breath]在乡里面住的要习惯一点，[breath]邻居都很活络，[breath]嗯，都很熟悉。[breath]', './asset/zero_shot_prompt.wav', stream=False)): + for i, j in enumerate(cosyvoice.inference_cross_lingual('You are a helpful assistant.<|endofprompt|>[breath]因为他们那一辈人[breath]在乡里面住的要习惯一点，[breath]邻居都很活络，[breath]嗯，都很熟悉。[breath]', './asset/zero_shot_prompt.wav', stream=False)): torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) # instruct usage - for i, j in enumerate(cosyvoice.inference_instruct2('好少咯，一般系放嗰啲国庆啊，中秋嗰啲可能会咯。', 'You are a helpful assistant. 请用广东话表达。', './asset/zero_shot_prompt.wav', stream=False)): + for i, j in enumerate(cosyvoice.inference_instruct2('好少咯，一般系放嗰啲国庆啊，中秋嗰啲可能会咯。', 'You are a helpful assistant. 请用广东话表达。<|endofprompt|>', './asset/zero_shot_prompt.wav', stream=False)): torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) - for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', 'You are a helpful assistant. 请用尽可能快地语速说一句话。', './asset/zero_shot_prompt.wav', stream=False)): + for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', 'You are a helpful assistant. 请用尽可能快地语速说一句话。<|endofprompt|>', './asset/zero_shot_prompt.wav', stream=False)): torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) # hotfix usage - for i, j in enumerate(cosyvoice.inference_zero_shot('高管也通过电话、短信、微信等方式对报道[j][ǐ]予好评。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): + for i, j in enumerate(cosyvoice.inference_zero_shot('高管也通过电话、短信、微信等方式对报道[j][ǐ]予好评。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): torchaudio.save('hotfix_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) def main(): diff --git a/examples/libritts/cosyvoice3/conf/cosyvoice2.yaml b/examples/libritts/cosyvoice3/conf/cosyvoice3.yaml similarity index 100% rename from examples/libritts/cosyvoice3/conf/cosyvoice2.yaml rename to examples/libritts/cosyvoice3/conf/cosyvoice3.yaml diff --git a/examples/libritts/cosyvoice3/run.sh b/examples/libritts/cosyvoice3/run.sh index ad59c0a..ce20043 100644 --- a/examples/libritts/cosyvoice3/run.sh +++ b/examples/libritts/cosyvoice3/run.sh @@ -7,7 +7,7 @@ stop_stage=3 data_url=www.openslr.org/resources/60 data_dir=/mnt/lyuxiang.lx/data/tts/openslr/libritts -pretrained_model_dir=../../../pretrained_models/CosyVoice2-0.5B +pretrained_model_dir=../../../pretrained_models/CosyVoice3-0.5B if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then echo "Data Download" @@ -36,7 +36,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir" for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do tools/extract_speech_token.py --dir data/$x \ - --onnx_path $pretrained_model_dir/speech_tokenizer_v2.onnx + --onnx_path $pretrained_model_dir/speech_tokenizer_v3.onnx done fi @@ -72,14 +72,14 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \ cosyvoice/bin/train.py \ --train_engine $train_engine \ - --config conf/cosyvoice2.yaml \ + --config conf/cosyvoice3.yaml \ --train_data data/train.data.list \ --cv_data data/dev.data.list \ --qwen_pretrain_path $pretrained_model_dir/CosyVoice-BlankEN \ --model $model \ --checkpoint $pretrained_model_dir/$model.pt \ - --model_dir `pwd`/exp/cosyvoice2/$model/$train_engine \ - --tensorboard_dir `pwd`/tensorboard/cosyvoice2/$model/$train_engine \ + --model_dir `pwd`/exp/cosyvoice3/$model/$train_engine \ + --tensorboard_dir `pwd`/tensorboard/cosyvoice3/$model/$train_engine \ --ddp.dist_backend $dist_backend \ --num_workers ${num_workers} \ --prefetch ${prefetch} \ diff --git a/vllm_example.py b/vllm_example.py index f8bc01b..e4222af 100644 --- a/vllm_example.py +++ b/vllm_example.py @@ -24,7 +24,7 @@ def cosyvoice3_example(): cosyvoice = CosyVoice3('pretrained_models/CosyVoice3-0.5B', load_trt=True, load_vllm=True, fp16=True) for i in tqdm(range(100)): set_all_random_seed(i) - for _, _ in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): + for _, _ in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', 'You are a helpful assistant.<|endofprompt|>希望你以后能够做的比我还好呦。', './asset/zero_shot_prompt.wav', stream=False)): continue def main():