# 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 import torch from tts.cosyvoice.cli.frontend import CosyVoiceFrontEnd from tts.cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model from tts.cosyvoice.utils.file_utils import logging from tts.cosyvoice.utils.class_utils import get_model_type class CosyVoice(torch.nn.Module): def __init__(self, model_dir,gpu_id=-1, load_jit=False, load_trt=False, fp16=False): super(CosyVoice, self).__init__() 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'], # gpu_id=gpu_id) self.sample_rate = configs['sample_rate'] self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16, gpu_id=gpu_id) self.model.load('{}/flow.pt'.format(model_dir), '{}/hift.pt'.format(model_dir)) 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, token_list=None): 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)) # import pdb;pdb.set_trace() for model_output in self.model.tts(**model_input, stream=stream, speed=speed, token_list=token_list): 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)) return model_output def inference_zero_shot_gxl(self,tts_text, prompt_text,prompt_speech_16k, stream=False, speed=1.0, text_frontend=True, token_list=None): prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend) input_text = self.frontend.text_normalize(tts_text, split=False, text_frontend=text_frontend) model_input = self.frontend.frontend_zero_shot(input_text, prompt_text, prompt_speech_16k, self.sample_rate) start_time = time.time() model_output = self.model.tts_gxl(**model_input, stream=stream, speed=speed, token_list=token_list) 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)) return model_output def inference_zero_shot_gz_22k(self,tts_text, prompt_text,prompt_speech_22k, stream=False, speed=1.0, text_frontend=True, token_list=None): prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend) input_text = self.frontend.text_normalize(tts_text, split=False, text_frontend=text_frontend) model_input = self.frontend.frontend_zero_shot_22k(input_text, prompt_text, prompt_speech_22k, self.sample_rate) start_time = time.time() model_output = self.model.tts_gxl(**model_input, stream=stream, speed=speed, token_list=token_list) 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)) return model_output 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()