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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
import torch
from cosyvoice.cli.frontend import CosyVoiceFrontEnd
from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
from cosyvoice.utils.file_utils import logging
class CosyVoice:
def __init__(self, model_dir, load_jit=True, load_onnx=False, fp16=True):
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.sample_rate = configs['sample_rate']
if torch.cuda.is_available() is False and (fp16 is True or load_jit is True):
load_jit = False
fp16 = False
logging.warning('cpu do not support fp16 and jit, force set 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.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, speed=1.0):
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.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):
prompt_text = self.frontend.text_normalize(prompt_text, split=False)
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
if 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 {}, abs mean {}, std {}'.format(speech_len, (time.time() - start_time) / speech_len, model_output['tts_speech'].abs().mean(), model_output['tts_speech'].std()))
yield model_output
start_time = time.time()
def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0):
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, 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):
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.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_instruct2(self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
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 {}, abs mean {}, std {}'.format(speech_len, (time.time() - start_time) / speech_len, model_output['tts_speech'].abs().mean(), model_output['tts_speech'].std()))
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_onnx=False, load_trt=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, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
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),
instruct,
configs['allowed_special'])
self.sample_rate = configs['sample_rate']
if torch.cuda.is_available() is False and load_jit is True:
load_jit = False
logging.warning('cpu do not support jit, force set to False')
self.model = CosyVoice2Model(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('{}/flow.encoder.fp32.zip'.format(model_dir))
if load_trt is True and load_onnx is True:
load_onnx = False
logging.warning('can not set both load_trt and load_onnx to True, force set load_onnx to False')
if load_onnx:
self.model.load_onnx('{}/flow.decoder.estimator.fp32.onnx'.format(model_dir))
if load_trt:
self.model.load_trt('{}/flow.decoder.estimator.fp16.A10.plan'.format(model_dir))
del configs |