利用vllm加速llm模型但是生成的是无意义的噪音
Author: G1017Created May 23, 2025Updated Sep 13, 2026
Labelsstale
复现步骤：
git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git && cd CosyVoice
&&git submodule update --init --recursive
model：
from modelscope import snapshot_download
snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
/CosyVoice/cosyvoice/cli/cosyvoice.py
import os
import time
from typing import Generator
from tqdm import tqdm
from hyperpyyaml import load_hyperpyyaml
from modelscope import snapshot_download
import torch
from cosyvoice.cli.frontend import CosyVoiceFrontEnd
###修改处
from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model ,VllmCosyVoice2Model
###修改处
from cosyvoice.utils.file_utils import logging
from cosyvoice.utils.class_utils import get_model_type
class CosyVoice:
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
self.instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir
self.fp16 = fp16
if not os.path.exists(model_dir):
model_dir = snapshot_download(model_dir)
hyper_yaml_path = '{}/cosyvoice.yaml'.format(model_dir)
if not os.path.exists(hyper_yaml_path):
raise ValueError('{} not found!'.format(hyper_yaml_path))
with open(hyper_yaml_path, 'r') as f:
configs = load_hyperpyyaml(f)
assert get_model_type(configs) != CosyVoice2Model, 'do not use {} for CosyVoice initialization!'.format(model_dir)
self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
configs['feat_extractor'],
'{}/campplus.onnx'.format(model_dir),
'{}/speech_tokenizer_v1.onnx'.format(model_dir),
'{}/spk2info.pt'.format(model_dir),
configs['allowed_special'])
self.sample_rate = configs['sample_rate']
if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
load_jit, load_trt, fp16 = False, False, False
logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16)
self.model.load('{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir),
'{}/hift.pt'.format(model_dir))
if load_jit:
self.model.load_jit('{}/llm.text_encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
if load_trt:
self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
self.fp16)
del configs
def list_available_spks(self):
spks = list(self.frontend.spk2info.keys())
return spks
def add_zero_shot_spk(self, prompt_text, prompt_speech_16k, zero_shot_spk_id):
assert zero_shot_spk_id != '', 'do not use empty zero_shot_spk_id'
model_input = self.frontend.frontend_zero_shot('', prompt_text, prompt_speech_16k, self.sample_rate, '')
del model_input['text']
del model_input['text_len']
self.frontend.spk2info[zero_shot_spk_id] = model_input
return True
def save_spkinfo(self):
torch.save(self.frontend.spk2info, '{}/spk2info.pt'.format(self.model_dir))
def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_sft(i, spk_id)
start_time = time.time()
logging.info('synthesis sft text {}'.format(i))
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
#计算转化时间
speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate, zero_shot_spk_id)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
def inference_cross_lingual(self, tts_text, prompt_speech_16k, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate, zero_shot_spk_id)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
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!'
if self.instruct is False:
raise ValueError('{} do not support instruct inference'.format(self.model_dir))
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)):
model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
start_time = time.time()
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
class CosyVoice2(CosyVoice):
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, use_flow_cache=False):
self.instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir
self.fp16 = fp16
if not os.path.exists(model_dir):
model_dir = snapshot_download(model_dir)
hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir)
if not os.path.exists(hyper_yaml_path):
raise ValueError('{} not found!'.format(hyper_yaml_path))
with open(hyper_yaml_path, 'r') as f:
configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
assert get_model_type(configs) == CosyVoice2Model, 'do not use {} for CosyVoice2 initialization!'.format(model_dir)
#原始语音信号转化为后续模块（如 LLM）可处理的格式
self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
configs['feat_extractor'],
'{}/campplus.onnx'.format(model_dir),
'{}/speech_tokenizer_v2.onnx'.format(model_dir),
'{}/spk2info.pt'.format(model_dir),
configs['allowed_special'])
self.sample_rate = configs['sample_rate']
if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
load_jit, load_trt, fp16 = False, False, False
logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
# self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16, use_flow_cache)
###修改处
self.model = VllmCosyVoice2Model(model_dir, configs['flow'], configs['hift'], fp16)
###修改处
self.model.load('{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir) if use_flow_cache is False else '{}/flow.cache.pt'.format(model_dir),
'{}/hift.pt'.format(model_dir))
if load_jit:
self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
if load_trt:
self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
self.fp16)
del configs
def inference_instruct(self, *args, **kwargs):
raise NotImplementedError('inference_instruct is not implemented for CosyVoice2!')
def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, zero_shot_spk_id='', stream=False, speed=1.0, text_frontend=True):
assert isinstance(self.model, CosyVoice2Model), 'inference_instruct2 is only implemented for CosyVoice2!'
for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_speech_16k, self.sample_rate, zero_shot_spk_id)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
yield model_output
start_time = time.time()
Source: QwenAudio/CosyVoice