# 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 import numpy as np import threading import time from contextlib import nullcontext class CosyVoiceModel: def __init__(self, llm: torch.nn.Module, flow: torch.nn.Module, hift: torch.nn.Module): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.llm = llm self.flow = flow self.hift = hift self.stream_win_len = 60 * 4 self.stream_hop_len = 50 * 4 self.overlap = 4395 * 4 # 10 token equals 4395 sample point self.window = np.hamming(2 * self.overlap) self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() self.flow_hift_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() self.lock = threading.Lock() def load(self, llm_model, flow_model, hift_model): self.llm.load_state_dict(torch.load(llm_model, map_location=self.device)) self.llm.to(self.device).eval() self.flow.load_state_dict(torch.load(flow_model, map_location=self.device)) self.flow.to(self.device).eval() self.hift.load_state_dict(torch.load(hift_model, map_location=self.device)) self.hift.to(self.device).eval() def llm_job(self, text, text_len, prompt_text, prompt_text_len, llm_prompt_speech_token, llm_prompt_speech_token_len, llm_embedding): with self.llm_context: for i in 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, stream=True): self.tts_speech_token.append(i) self.llm_end = True def token2wav(self, token, prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, embedding): with self.flow_hift_context: tts_mel = self.flow.inference(token=token.to(self.device), token_len=torch.tensor([token.size(1)], dtype=torch.int32).to(self.device), prompt_token=prompt_token.to(self.device), prompt_token_len=prompt_token_len.to(self.device), prompt_feat=prompt_feat.to(self.device), prompt_feat_len=prompt_feat_len.to(self.device), embedding=embedding.to(self.device)) tts_speech = self.hift.inference(mel=tts_mel).cpu() return tts_speech 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), stream=False): if stream is True: self.tts_speech_token, self.llm_end, cache_speech = [], False, None p = threading.Thread(target=self.llm_job, args=(text.to(self.device), text_len.to(self.device), prompt_text.to(self.device), prompt_text_len.to(self.device), llm_prompt_speech_token.to(self.device), llm_prompt_speech_token_len.to(self.device), llm_embedding.to(self.device))) p.start() while True: time.sleep(0.1) if len(self.tts_speech_token) >= self.stream_win_len: this_tts_speech_token = torch.concat(self.tts_speech_token[:self.stream_win_len], dim=1) with self.flow_hift_context: this_tts_speech = self.token2wav(token=this_tts_speech_token, 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)) # fade in/out if necessary if cache_speech is not None: this_tts_speech[:, :self.overlap] = this_tts_speech[:, :self.overlap] * self.window[:self.overlap] + cache_speech * self.window[-self.overlap:] yield {'tts_speech': this_tts_speech[:, :-self.overlap]} cache_speech = this_tts_speech[:, -self.overlap:] with self.lock: self.tts_speech_token = self.tts_speech_token[self.stream_hop_len:] if self.llm_end is True: break # deal with remain tokens if cache_speech is None or len(self.tts_speech_token) > self.stream_win_len - self.stream_hop_len: this_tts_speech_token = torch.concat(self.tts_speech_token, dim=1) with self.flow_hift_context: this_tts_mel = self.flow.inference(token=this_tts_speech_token, token_len=torch.tensor([this_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)) this_tts_speech = self.hift.inference(mel=this_tts_mel).cpu() if cache_speech is not None: this_tts_speech[:, :self.overlap] = this_tts_speech[:, :self.overlap] * self.window[:self.overlap] + cache_speech * self.window[-self.overlap:] yield {'tts_speech': this_tts_speech} else: assert len(self.tts_speech_token) == self.stream_win_len - self.stream_hop_len, 'tts_speech_token not equal to {}'.format(self.stream_win_len - self.stream_hop_len) yield {'tts_speech': cache_speech} p.join() torch.cuda.synchronize() else: tts_speech_token = [] for i in 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, stream=stream): tts_speech_token.append(i) assert len(tts_speech_token) == 1, 'tts_speech_token len should be 1 when stream is {}'.format(stream) tts_speech_token = torch.concat(tts_speech_token, dim=1) 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() torch.cuda.empty_cache() yield {'tts_speech': tts_speech}