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| # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| import time | |
| from typing import Generator | |
| from tqdm import tqdm | |
| from hyperpyyaml import load_hyperpyyaml | |
| from modelscope import snapshot_download | |
| # from huggingface_hub import snapshot_download | |
| import torch | |
| from cosyvoice.cli.frontend import CosyVoiceFrontEnd | |
| from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model | |
| 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) | |
| with open('{}/cosyvoice.yaml'.format(model_dir), '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 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 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_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, 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) | |
| 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, 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) | |
| 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.vc(**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): | |
| 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) | |
| with open('{}/cosyvoice.yaml'.format(model_dir), '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) | |
| 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) | |
| 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('{}/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, 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) | |
| 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() | |