File size: 10,843 Bytes
8ad4e11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
# 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 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()
|