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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 torch
from hyperpyyaml import load_hyperpyyaml
from huggingface_hub import snapshot_download
from cosyvoice.cli.frontend import CosyVoiceFrontEnd
from cosyvoice.cli.model import CosyVoiceModel
class CosyVoice:
def __init__(self, model_dir):
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, local_dir=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))
del configs
def list_avaliable_spks(self):
spks = list(self.frontend.spk2info.keys())
return spks
def inference_sft(self, tts_text, spk_id):
tts_speeches = []
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_sft(i, spk_id)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k):
prompt_text = self.frontend.text_normalize(prompt_text, split=False)
tts_speeches = []
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
def inference_cross_lingual(self, tts_text, prompt_speech_16k):
if self.frontend.instruct is True:
raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir))
tts_speeches = []
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
def inference_instruct(self, tts_text, spk_id, instruct_text):
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)
tts_speeches = []
for i in self.frontend.text_normalize(tts_text, split=True):
model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
model_output = self.model.inference(**model_input)
tts_speeches.append(model_output['tts_speech'])
return {'tts_speech': torch.concat(tts_speeches, dim=1)}
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