# 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 import uuid from cosyvoice.utils.common import fade_in_out 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.token_min_hop_len = 100 self.token_max_hop_len = 400 self.token_overlap_len = 20 self.speech_overlap_len = 34 * 256 self.window = np.hamming(2 * self.speech_overlap_len) self.stream_scale_factor = 1 assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf' 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() # dict used to store session related variable self.tts_speech_token = {} self.llm_end = {} 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.llm.half() 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 load_script(self, llm_text_encoder_model, llm_llm_model): llm_text_encoder = torch.jit.load(llm_text_encoder_model) self.llm.text_encoder = llm_text_encoder llm_llm = torch.jit.load(llm_llm_model) self.llm.llm = llm_llm def llm_job(self, text, text_len, prompt_text, prompt_text_len, llm_prompt_speech_token, llm_prompt_speech_token_len, llm_embedding, this_uuid): 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).half(), sampling=25, max_token_text_ratio=30, min_token_text_ratio=3): self.tts_speech_token[this_uuid].append(i) self.llm_end[this_uuid] = 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): # this_uuid is used to track variables related to this inference thread this_uuid = str(uuid.uuid1()) with self.lock: self.tts_speech_token[this_uuid], self.llm_end[this_uuid] = [], False 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), this_uuid)) p.start() if stream is True: cache_speech, cache_token, token_hop_len = None, None, self.token_min_hop_len while True: time.sleep(0.1) if len(self.tts_speech_token[this_uuid]) >= token_hop_len + self.token_overlap_len: this_tts_speech_token = torch.concat(self.tts_speech_token[this_uuid][:token_hop_len + self.token_overlap_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 = fade_in_out(this_tts_speech, cache_speech, self.window) yield {'tts_speech': this_tts_speech[:, :-self.speech_overlap_len]} cache_speech = this_tts_speech[:, -self.speech_overlap_len:] cache_token = self.tts_speech_token[this_uuid][:token_hop_len] with self.lock: self.tts_speech_token[this_uuid] = self.tts_speech_token[this_uuid][token_hop_len:] # increase token_hop_len for better speech quality token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor)) if self.llm_end[this_uuid] is True and len(self.tts_speech_token[this_uuid]) < token_hop_len + self.token_overlap_len: break p.join() # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None this_tts_speech_token = torch.concat(self.tts_speech_token[this_uuid], dim=1) if this_tts_speech_token.shape[1] < self.token_min_hop_len + self.token_overlap_len and cache_token is not None: cache_token_len = self.token_min_hop_len + self.token_overlap_len - this_tts_speech_token.shape[1] this_tts_speech_token = torch.concat([torch.concat(cache_token[-cache_token_len:], dim=1), this_tts_speech_token], dim=1) else: cache_token_len = 0 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)) this_tts_speech = this_tts_speech[:, int(cache_token_len / this_tts_speech_token.shape[1] * this_tts_speech.shape[1]):] if cache_speech is not None: this_tts_speech = fade_in_out(this_tts_speech, cache_speech, self.window) yield {'tts_speech': this_tts_speech} else: # deal with all tokens p.join() this_tts_speech_token = torch.concat(self.tts_speech_token[this_uuid], 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)) yield {'tts_speech': this_tts_speech} with self.lock: self.tts_speech_token.pop(this_uuid) self.llm_end.pop(this_uuid) torch.cuda.synchronize()