deepgeek/CosyVoice
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fix export_jit.py

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lyuxiang.lx
2025-12-23 17:23:23 +08:00
parent 7538c6a73d
commit 2db78e7058
3 changed files with 4 additions and 5 deletions
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5
cosyvoice/bin/export_jit.py
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@@ -24,7 +24,6 @@ ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/../..'.format(ROOT_DIR)) sys.path.append('{}/../..'.format(ROOT_DIR))
sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
from cosyvoice.cli.cosyvoice import AutoModel from cosyvoice.cli.cosyvoice import AutoModel
from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
from cosyvoice.utils.file_utils import logging from cosyvoice.utils.file_utils import logging
@@ -60,7 +59,7 @@ def main():
model = AutoModel(model_dir=args.model_dir) model = AutoModel(model_dir=args.model_dir)
if isinstance(model.model, CosyVoiceModel): if model.__class__.__name__ == 'CosyVoice':
# 1. export llm text_encoder # 1. export llm text_encoder
llm_text_encoder = model.model.llm.text_encoder llm_text_encoder = model.model.llm.text_encoder
script = get_optimized_script(llm_text_encoder) script = get_optimized_script(llm_text_encoder)
@@ -84,7 +83,7 @@ def main():
script = get_optimized_script(flow_encoder.half()) script = get_optimized_script(flow_encoder.half())
script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir)) script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
logging.info('successfully export flow_encoder') logging.info('successfully export flow_encoder')
elif isinstance(model.model, CosyVoice2Model): elif model.__class__.__name__ == 'CosyVoice2':
# 1. export flow encoder # 1. export flow encoder
flow_encoder = model.model.flow.encoder flow_encoder = model.model.flow.encoder
script = get_optimized_script(flow_encoder) script = get_optimized_script(flow_encoder)

2
cosyvoice/cli/cosyvoice.py
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@@ -114,7 +114,7 @@ class CosyVoice:
start_time = time.time() start_time = time.time()
def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True): def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
assert isinstance(self.model, CosyVoiceModel), 'inference_instruct is only implemented for CosyVoice!' assert self.__class__.__name__ == 'CosyVoice', 'inference_instruct is only implemented for CosyVoice!'
instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend) instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)): for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text) model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)

2
cosyvoice/cli/model.py
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@@ -100,7 +100,7 @@ class CosyVoiceModel:
def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False): with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False):
if isinstance(text, Generator): if isinstance(text, Generator):
assert isinstance(self, CosyVoice2Model) and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2 and do not support vllm!' assert (self.__class__.__name__ != 'CosyVoiceModel') and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2/3 and do not support vllm!'
for i in self.llm.inference_bistream(text=text, for i in self.llm.inference_bistream(text=text,
prompt_text=prompt_text.to(self.device), prompt_text=prompt_text.to(self.device),
prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),