feat: CosyVoice 한국어 엔진 통합 (Apache-2.0, GPU)
- vendor/CosyVoice: 패치된 CosyVoice 코드 + Matcha-TTS 벤더링 - cosyvoice_worker: 전용 venv에서 상주하는 합성 워커(FastAPI, 모델 1회 로드) - app/engines/cosyvoice_engine: HTTP 프록시 엔진(속도/피치) - Dockerfile: 전용 venv(torch cu128) + 모델 베이킹 + 빌드 워밍업 - entrypoint.sh: 워커(8001) + 메인앱(8788) 동시 기동
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vendor/CosyVoice/cosyvoice/cli/cosyvoice.py
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vendor/CosyVoice/cosyvoice/cli/cosyvoice.py
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# 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, CosyVoice3Model
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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, trt_concurrent=1):
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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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hyper_yaml_path = '{}/cosyvoice.yaml'.format(model_dir)
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if not os.path.exists(hyper_yaml_path):
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raise ValueError('{} not found!'.format(hyper_yaml_path))
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with open(hyper_yaml_path, 'r') as f:
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configs = load_hyperpyyaml(f)
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assert get_model_type(configs) == CosyVoiceModel, '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.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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trt_concurrent,
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self.fp16)
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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_zero_shot_spk(self, prompt_text, prompt_wav, zero_shot_spk_id):
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assert zero_shot_spk_id != '', 'do not use empty zero_shot_spk_id'
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model_input = self.frontend.frontend_zero_shot('', prompt_text, prompt_wav, self.sample_rate, '')
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del model_input['text']
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del model_input['text_len']
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self.frontend.spk2info[zero_shot_spk_id] = model_input
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return True
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def save_spkinfo(self):
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torch.save(self.frontend.spk2info, '{}/spk2info.pt'.format(self.model_dir))
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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_wav, zero_shot_spk_id='', 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_wav, self.sample_rate, zero_shot_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_cross_lingual(self, tts_text, prompt_wav, zero_shot_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_cross_lingual(i, prompt_wav, self.sample_rate, zero_shot_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_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
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assert self.__class__.__name__ == 'CosyVoice', 'inference_instruct is only implemented for CosyVoice!'
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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_wav, prompt_wav, stream=False, speed=1.0):
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model_input = self.frontend.frontend_vc(source_wav, prompt_wav, self.sample_rate)
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start_time = time.time()
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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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class CosyVoice2(CosyVoice):
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def __init__(self, model_dir, load_jit=False, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1):
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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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hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir)
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if not os.path.exists(hyper_yaml_path):
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raise ValueError('{} not found!'.format(hyper_yaml_path))
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with open(hyper_yaml_path, '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 load_vllm is True or fp16 is True):
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load_jit, load_trt, load_vllm, fp16 = False, False, False, False
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logging.warning('no cuda device, set load_jit/load_trt/load_vllm/fp16 to False')
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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_vllm:
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self.model.load_vllm('{}/vllm'.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.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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trt_concurrent,
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self.fp16)
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del configs
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def inference_instruct2(self, tts_text, instruct_text, prompt_wav, zero_shot_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(i, instruct_text, prompt_wav, self.sample_rate, zero_shot_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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class CosyVoice3(CosyVoice2):
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def __init__(self, model_dir, load_trt=False, load_vllm=False, fp16=False, trt_concurrent=1):
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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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hyper_yaml_path = '{}/cosyvoice3.yaml'.format(model_dir)
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if not os.path.exists(hyper_yaml_path):
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raise ValueError('{} not found!'.format(hyper_yaml_path))
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with open(hyper_yaml_path, '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) == CosyVoice3Model, 'do not use {} for CosyVoice3 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_v3.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_trt is True or fp16 is True):
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load_trt, fp16 = False, False
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logging.warning('no cuda device, set load_trt/fp16 to False')
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self.model = CosyVoice3Model(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_vllm:
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self.model.load_vllm('{}/vllm'.format(model_dir))
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if load_trt:
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if self.fp16 is True:
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logging.warning('DiT tensorRT fp16 engine have some performance issue, use at caution!')
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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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trt_concurrent,
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self.fp16)
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del configs
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def AutoModel(**kwargs):
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if not os.path.exists(kwargs['model_dir']):
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kwargs['model_dir'] = snapshot_download(kwargs['model_dir'])
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if os.path.exists('{}/cosyvoice.yaml'.format(kwargs['model_dir'])):
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return CosyVoice(**kwargs)
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elif os.path.exists('{}/cosyvoice2.yaml'.format(kwargs['model_dir'])):
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return CosyVoice2(**kwargs)
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elif os.path.exists('{}/cosyvoice3.yaml'.format(kwargs['model_dir'])):
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return CosyVoice3(**kwargs)
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else:
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raise TypeError('No valid model type found!')
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