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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 tqdm import tqdm | |
| from hyperpyyaml import load_hyperpyyaml | |
| from modelscope import snapshot_download | |
| from cosyvoice.cli.frontend import CosyVoiceFrontEnd | |
| from cosyvoice.cli.model import CosyVoiceModel | |
| from cosyvoice.utils.file_utils import logging | |
| class CosyVoice: | |
| def __init__(self, model_dir, load_jit=True, load_onnx=False): | |
| instruct = True if '-Instruct' in model_dir else False | |
| self.model_dir = model_dir | |
| 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) | |
| 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), | |
| instruct, | |
| configs['allowed_special']) | |
| self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift']) | |
| 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.fp16.zip'.format(model_dir), | |
| '{}/llm.llm.fp16.zip'.format(model_dir), | |
| '{}/flow.encoder.fp32.zip'.format(model_dir)) | |
| if load_onnx: | |
| self.model.load_onnx('{}/flow.decoder.estimator.fp32.onnx'.format(model_dir)) | |
| del configs | |
| def list_avaliable_spks(self): | |
| spks = list(self.frontend.spk2info.keys()) | |
| return spks | |
| def inference_sft(self, tts_text, spk_id, stream=False): | |
| for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): | |
| 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.inference(**model_input, stream=stream): | |
| speech_len = model_output['tts_speech'].shape[1] / 22050 | |
| 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): | |
| prompt_text = self.frontend.text_normalize(prompt_text, split=False) | |
| for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): | |
| model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k) | |
| start_time = time.time() | |
| logging.info('synthesis text {}'.format(i)) | |
| for model_output in self.model.inference(**model_input, stream=stream): | |
| speech_len = model_output['tts_speech'].shape[1] / 22050 | |
| 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): | |
| if self.frontend.instruct is True: | |
| raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir)) | |
| for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): | |
| model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k) | |
| start_time = time.time() | |
| logging.info('synthesis text {}'.format(i)) | |
| for model_output in self.model.inference(**model_input, stream=stream): | |
| speech_len = model_output['tts_speech'].shape[1] / 22050 | |
| 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): | |
| if self.frontend.instruct is False: | |
| raise ValueError('{} do not support instruct inference'.format(self.model_dir)) | |
| instruct_text = self.frontend.text_normalize(instruct_text, split=False) | |
| for i in tqdm(self.frontend.text_normalize(tts_text, split=True)): | |
| 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.inference(**model_input, stream=stream): | |
| speech_len = model_output['tts_speech'].shape[1] / 22050 | |
| logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) | |
| yield model_output | |
| start_time = time.time() | |