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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 torch
class CosyVoiceModel:
def __init__(self,
llm: torch.nn.Module,
flow: torch.nn.Module,
hift: torch.nn.Module):
self.device = torch.device('cuda')
self.llm = llm
self.flow = flow
self.hift = hift
def load(self, llm_model, flow_model, hift_model):
self.llm.load_state_dict(torch.load(llm_model, map_location='cpu'))
self.llm.to(self.device).eval()
self.flow.load_state_dict(torch.load(flow_model, map_location='cpu'))
self.flow.to(self.device).eval()
self.hift.load_state_dict(torch.load(hift_model, map_location='cpu'))
self.hift.to(self.device).eval()
def inference(self, text, text_len, flow_embedding, llm_embedding=torch.zeros(0, 192),
prompt_text=torch.zeros(1, 0, dtype=torch.int32), prompt_text_len=torch.zeros(1, dtype=torch.int32),
llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32)):
tts_speech_token = self.llm.inference(text=text.to(self.device),
text_len=text_len.to(self.device),
prompt_text=prompt_text.to(self.device),
prompt_text_len=prompt_text_len.to(self.device),
prompt_speech_token=llm_prompt_speech_token.to(self.device),
prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
embedding=llm_embedding.to(self.device),
beam_size=1,
sampling=25,
max_token_text_ratio=30,
min_token_text_ratio=3)
tts_mel = self.flow.inference(token=tts_speech_token,
token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(self.device),
prompt_token=flow_prompt_speech_token.to(self.device),
prompt_token_len=flow_prompt_speech_token_len.to(self.device),
prompt_feat=prompt_speech_feat.to(self.device),
prompt_feat_len=prompt_speech_feat_len.to(self.device),
embedding=flow_embedding.to(self.device))
tts_speech = self.hift.inference(mel=tts_mel).cpu()
return {'tts_speech': tts_speech}
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