deepgeek/CosyVoice
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Merge branch 'main' into dev/lyuxiang.lx

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lyuxiang.lx
2025-01-26 16:56:18 +08:00
parent fd1a951a6c 86e26f54c7
commit 24f796a2b1
3 changed files with 9 additions and 2 deletions
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2
README.md
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@@ -151,7 +151,7 @@ def text_generator():
yield '那份意外的惊喜与深深的祝福' yield '那份意外的惊喜与深深的祝福'
yield '让我心中充满了甜蜜的快乐，' yield '让我心中充满了甜蜜的快乐，'
yield '笑容如花儿般绽放。' yield '笑容如花儿般绽放。'
for i, j in enumerate(cosyvoice.inference_zero_shot(text_generator, '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)): for i, j in enumerate(cosyvoice.inference_zero_shot(text_generator(), '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)):
torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate)
``` ```

5
cosyvoice/llm/llm.py
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@@ -382,7 +382,10 @@ class Qwen2LM(TransformerLM):
if text_cache.size(1) >= self.mix_ratio[0]: if text_cache.size(1) >= self.mix_ratio[0]:
lm_input_text = text_cache[:, :self.mix_ratio[0]] lm_input_text = text_cache[:, :self.mix_ratio[0]]
logging.info('append {} text token'.format(lm_input_text.size(1))) logging.info('append {} text token'.format(lm_input_text.size(1)))
lm_input = torch.concat([lm_input, lm_input_text], dim=1) if len(out_tokens) != 0 and out_tokens[-1] == self.speech_token_size + 2:
lm_input = lm_input_text
else:
lm_input = torch.concat([lm_input, lm_input_text], dim=1)
text_cache = text_cache[:, self.mix_ratio[0]:] text_cache = text_cache[:, self.mix_ratio[0]:]
else: else:
logging.info('not enough text token to decode, wait for more') logging.info('not enough text token to decode, wait for more')

4
runtime/python/fastapi/server.py
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@@ -44,12 +44,14 @@ def generate_data(model_output):
@app.get("/inference_sft") @app.get("/inference_sft")
@app.post("/inference_sft")
async def inference_sft(tts_text: str = Form(), spk_id: str = Form()): async def inference_sft(tts_text: str = Form(), spk_id: str = Form()):
model_output = cosyvoice.inference_sft(tts_text, spk_id) model_output = cosyvoice.inference_sft(tts_text, spk_id)
return StreamingResponse(generate_data(model_output)) return StreamingResponse(generate_data(model_output))
@app.get("/inference_zero_shot") @app.get("/inference_zero_shot")
@app.post("/inference_zero_shot")
async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File()): async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File()):
prompt_speech_16k = load_wav(prompt_wav.file, 16000) prompt_speech_16k = load_wav(prompt_wav.file, 16000)
model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k) model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k)
@@ -57,6 +59,7 @@ async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(),
@app.get("/inference_cross_lingual") @app.get("/inference_cross_lingual")
@app.post("/inference_cross_lingual")
async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File()): async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File()):
prompt_speech_16k = load_wav(prompt_wav.file, 16000) prompt_speech_16k = load_wav(prompt_wav.file, 16000)
model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k) model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k)
@@ -64,6 +67,7 @@ async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile
@app.get("/inference_instruct") @app.get("/inference_instruct")
@app.post("/inference_instruct")
async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form()): async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form()):
model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text) model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text)
return StreamingResponse(generate_data(model_output)) return StreamingResponse(generate_data(model_output))