mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
369f3c2c18
- Simplified estimator count retrieval in CosyVoice and CosyVoice2 classes to directly access the configs dictionary. - Adjusted memory pool limit in the ONNX to TensorRT conversion function from 8GB to 1GB for optimized resource management.
215 lines
13 KiB
Python
215 lines
13 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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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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import os
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import time
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from typing import Generator
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from tqdm import tqdm
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from hyperpyyaml import load_hyperpyyaml
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from modelscope import snapshot_download
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import torch
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from cosyvoice.cli.frontend import CosyVoiceFrontEnd
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from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, VllmCosyVoice2Model
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from cosyvoice.utils.file_utils import logging
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from cosyvoice.utils.class_utils import get_model_type
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class CosyVoice:
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def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
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self.instruct = True if '-Instruct' in model_dir else False
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self.model_dir = model_dir
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self.fp16 = fp16
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if not os.path.exists(model_dir):
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model_dir = snapshot_download(model_dir)
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with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
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configs = load_hyperpyyaml(f)
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assert get_model_type(configs) != CosyVoice2Model, 'do not use {} for CosyVoice initialization!'.format(model_dir)
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self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
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configs['feat_extractor'],
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'{}/campplus.onnx'.format(model_dir),
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'{}/speech_tokenizer_v1.onnx'.format(model_dir),
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'{}/spk2info.pt'.format(model_dir),
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configs['allowed_special'])
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self.sample_rate = configs['sample_rate']
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if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
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load_jit, load_trt, fp16 = False, False, False
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logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
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self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16)
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self.model.load('{}/llm.pt'.format(model_dir),
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'{}/flow.pt'.format(model_dir),
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'{}/hift.pt'.format(model_dir))
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if load_jit:
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self.model.load_jit('{}/llm.text_encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
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'{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
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'{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
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if load_trt:
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self.estimator_count = configs.get('estimator_count', 1)
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self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
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'{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
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self.fp16, self.estimator_count)
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del configs
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def list_available_spks(self):
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spks = list(self.frontend.spk2info.keys())
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return spks
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def add_spk_info(self, spk_id, spk_info):
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self.frontend.add_spk_info(spk_id, spk_info)
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def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
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for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
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model_input = self.frontend.frontend_sft(i, spk_id)
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start_time = time.time()
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logging.info('synthesis text {}'.format(i))
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for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
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yield model_output
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start_time = time.time()
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def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
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prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
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for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
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if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
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logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
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model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate)
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start_time = time.time()
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logging.info('synthesis text {}'.format(i))
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for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
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yield model_output
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start_time = time.time()
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def inference_zero_shot_by_spk_id(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
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"""使用预定义的说话人执行 zero_shot 推理"""
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for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
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model_input = self.frontend.frontend_zero_shot_by_spk_id(i, spk_id)
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start_time = time.time()
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last_time = start_time
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chunk_index = 0
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logging.info('synthesis text {}'.format(i))
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for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech index:{}, len {:.2f}, rtf {:.3f}, cost {:.3f}s, all cost time {:.3f}s'.format(
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chunk_index, speech_len, (time.time()-last_time)/speech_len, time.time()-last_time, time.time()-start_time))
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yield model_output
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last_time = time.time()
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chunk_index += 1
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def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
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for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
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model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate)
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start_time = time.time()
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logging.info('synthesis text {}'.format(i))
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for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
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yield model_output
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start_time = time.time()
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def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
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assert isinstance(self.model, CosyVoiceModel), 'inference_instruct is only implemented for CosyVoice!'
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if self.instruct is False:
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raise ValueError('{} do not support instruct inference'.format(self.model_dir))
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instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
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for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
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model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
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start_time = time.time()
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logging.info('synthesis text {}'.format(i))
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for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
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yield model_output
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start_time = time.time()
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def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
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model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
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start_time = time.time()
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for model_output in self.model.vc(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
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yield model_output
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start_time = time.time()
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class CosyVoice2(CosyVoice):
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def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, use_vllm=False):
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self.instruct = True if '-Instruct' in model_dir else False
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self.model_dir = model_dir
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self.fp16 = fp16
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if not os.path.exists(model_dir):
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model_dir = snapshot_download(model_dir)
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with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
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configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
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assert get_model_type(configs) == CosyVoice2Model, 'do not use {} for CosyVoice2 initialization!'.format(model_dir)
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self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
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configs['feat_extractor'],
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'{}/campplus.onnx'.format(model_dir),
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'{}/speech_tokenizer_v2.onnx'.format(model_dir),
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'{}/spk2info.pt'.format(model_dir),
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configs['allowed_special'])
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self.sample_rate = configs['sample_rate']
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if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
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load_jit, load_trt, fp16 = False, False, False
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logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
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if use_vllm:
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try:
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self.model = VllmCosyVoice2Model(model_dir, configs['flow'], configs['hift'], fp16)
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except Exception as e:
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logging.warning(f'use vllm inference failed. \n{e}')
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raise e
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else:
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self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16)
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self.model.load('{}/llm.pt'.format(model_dir),
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'{}/flow.pt'.format(model_dir),
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'{}/hift.pt'.format(model_dir))
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if load_jit:
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self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
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if load_trt:
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self.estimator_count = configs.get('estimator_count', 1)
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self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
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'{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
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self.fp16, self.estimator_count)
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del configs
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def inference_instruct(self, *args, **kwargs):
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raise NotImplementedError('inference_instruct is not implemented for CosyVoice2!')
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def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
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assert isinstance(self.model, CosyVoice2Model), 'inference_instruct2 is only implemented for CosyVoice2!'
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for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
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model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_speech_16k, self.sample_rate)
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start_time = time.time()
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logging.info('synthesis text {}'.format(i))
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for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
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yield model_output
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start_time = time.time()
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def inference_instruct2_by_spk_id(self, tts_text, instruct_text, spk_id, stream=False, speed=1.0, text_frontend=True):
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for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
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model_input = self.frontend.frontend_instruct2_by_spk_id(i, instruct_text, spk_id)
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start_time = time.time()
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logging.info('synthesis text {}'.format(i))
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for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
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speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
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logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
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yield model_output
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start_time = time.time()
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