# 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 torch.nn import functional as F 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, fp16: bool): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.llm = llm self.flow = flow self.hift = hift self.fp16 = fp16 self.token_min_hop_len = 2 * self.flow.input_frame_rate self.token_max_hop_len = 4 * self.flow.input_frame_rate self.token_overlap_len = 20 # mel fade in out self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256) self.mel_window = np.hamming(2 * self.mel_overlap_len) # hift cache self.mel_cache_len = 20 self.source_cache_len = int(self.mel_cache_len * 256) # speech fade in out self.speech_window = np.hamming(2 * self.source_cache_len) # rtf and decoding related 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.lock = threading.Lock() # dict used to store session related variable self.tts_speech_token_dict = {} self.llm_end_dict = {} self.mel_overlap_dict = {} self.flow_cache_dict = {} self.hift_cache_dict = {} def load(self, llm_model, flow_model, hift_model): self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True) self.llm.to(self.device).eval() if self.fp16 is True: self.llm.half() self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True) self.flow.to(self.device).eval() # in case hift_model is a hifigan model hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()} self.hift.load_state_dict(hift_state_dict, strict=True) self.hift.to(self.device).eval() def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model): assert self.fp16 is True, "we only provide fp16 jit model, set fp16=True if you want to use jit model" llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device) self.llm.text_encoder = llm_text_encoder llm_llm = torch.jit.load(llm_llm_model, map_location=self.device) self.llm.llm = llm_llm flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) self.flow.encoder = flow_encoder def load_onnx(self, flow_decoder_estimator_model): import onnxruntime option = onnxruntime.SessionOptions() option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL option.intra_op_num_threads = 1 providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider'] del self.flow.decoder.estimator self.flow.decoder.estimator = onnxruntime.InferenceSession(flow_decoder_estimator_model, sess_options=option, providers=providers) def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): if self.fp16 is True: llm_embedding = llm_embedding.half() with self.llm_context: for i in self.llm.inference(text=text.to(self.device), text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device), prompt_text=prompt_text.to(self.device), prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), prompt_speech_token=llm_prompt_speech_token.to(self.device), prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), embedding=llm_embedding.to(self.device)): self.tts_speech_token_dict[uuid].append(i) self.llm_end_dict[uuid] = True def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0): tts_mel, flow_cache = self.flow.inference(token=token.to(self.device), token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), prompt_token=prompt_token.to(self.device), prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), prompt_feat=prompt_feat.to(self.device), prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), embedding=embedding.to(self.device), flow_cache=self.flow_cache_dict[uuid]) self.flow_cache_dict[uuid] = flow_cache # mel overlap fade in out if self.mel_overlap_dict[uuid].shape[2] != 0: tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window) # append hift cache if self.hift_cache_dict[uuid] is not None: hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) else: hift_cache_source = torch.zeros(1, 1, 0) # keep overlap mel and hift cache if finalize is False: self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:] tts_mel = tts_mel[:, :, :-self.mel_overlap_len] tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) if self.hift_cache_dict[uuid] is not None: tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:], 'source': tts_source[:, :, -self.source_cache_len:], 'speech': tts_speech[:, -self.source_cache_len:]} tts_speech = tts_speech[:, :-self.source_cache_len] else: if speed != 1.0: assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode' tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) if self.hift_cache_dict[uuid] is not None: tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) return tts_speech def tts(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192), prompt_text=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs): # this_uuid is used to track variables related to this inference thread this_uuid = str(uuid.uuid1()) with self.lock: self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False self.hift_cache_dict[this_uuid] = None self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0) self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2) p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) p.start() if stream is True: token_hop_len = self.token_min_hop_len while True: time.sleep(0.1) if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len: this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \ .unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, finalize=False) yield {'tts_speech': this_tts_speech.cpu()} with self.lock: self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[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_dict[this_uuid] is True and len(self.tts_speech_token_dict[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.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, finalize=True) yield {'tts_speech': this_tts_speech.cpu()} else: # deal with all tokens p.join() this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, finalize=True, speed=speed) yield {'tts_speech': this_tts_speech.cpu()} with self.lock: self.tts_speech_token_dict.pop(this_uuid) self.llm_end_dict.pop(this_uuid) self.mel_overlap_dict.pop(this_uuid) self.hift_cache_dict.pop(this_uuid) def vc(self, source_speech_token, flow_prompt_speech_token, prompt_speech_feat, flow_embedding, stream=False, speed=1.0, **kwargs): # this_uuid is used to track variables related to this inference thread this_uuid = str(uuid.uuid1()) with self.lock: self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = source_speech_token.flatten().tolist(), True self.hift_cache_dict[this_uuid] = None self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0) self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2) if stream is True: token_hop_len = self.token_min_hop_len while True: if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len: this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \ .unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, finalize=False) yield {'tts_speech': this_tts_speech.cpu()} with self.lock: self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[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_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len: break # 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.tensor(self.tts_speech_token_dict[this_uuid], dim=1).unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, finalize=True) yield {'tts_speech': this_tts_speech.cpu()} else: # deal with all tokens this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, finalize=True, speed=speed) yield {'tts_speech': this_tts_speech.cpu()} with self.lock: self.tts_speech_token_dict.pop(this_uuid) self.llm_end_dict.pop(this_uuid) self.mel_overlap_dict.pop(this_uuid) self.hift_cache_dict.pop(this_uuid) class CosyVoice2Model: 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_hop_len = 2 * self.flow.input_frame_rate # here we fix flow encoder/decoder decoding_chunk_size, in the future we will send it as arguments, or use cache self.flow.encoder.static_chunk_size = 2 * self.flow.input_frame_rate self.flow.decoder.estimator.static_chunk_size = 2 * self.flow.input_frame_rate * self.flow.token_mel_ratio # hift cache self.mel_cache_len = 8 self.source_cache_len = int(self.mel_cache_len * 480) # speech fade in out self.speech_window = np.hamming(2 * self.source_cache_len) # rtf and decoding related self.stream_scale_factor = 1 self.llm_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_dict = {} self.llm_end_dict = {} self.hift_cache_dict = {} def load(self, llm_model, flow_model, hift_model): self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True) self.llm.to(self.device).eval() self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True) self.flow.to(self.device).eval() self.flow.decoder.fp16 = False # in case hift_model is a hifigan model hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()} self.hift.load_state_dict(hift_state_dict, strict=True) self.hift.to(self.device).eval() def load_jit(self, flow_encoder_model): flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) self.flow.encoder = flow_encoder def load_onnx(self, flow_decoder_estimator_model): import onnxruntime option = onnxruntime.SessionOptions() option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL option.intra_op_num_threads = 1 providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider'] del self.flow.decoder.estimator self.flow.decoder.estimator = onnxruntime.InferenceSession(flow_decoder_estimator_model, sess_options=option, providers=providers) def load_trt(self, flow_decoder_estimator_model): del self.flow.decoder.estimator import tensorrt as trt with open(flow_decoder_estimator_model, 'rb') as f: self.flow.decoder.estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read()) self.flow.decoder.estimator = self.flow.decoder.estimator_engine.create_execution_context() self.flow.decoder.fp16 = True def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): with self.llm_context: for i in self.llm.inference(text=text.to(self.device), text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device), prompt_text=prompt_text.to(self.device), prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), prompt_speech_token=llm_prompt_speech_token.to(self.device), prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), embedding=llm_embedding.to(self.device)): self.tts_speech_token_dict[uuid].append(i) self.llm_end_dict[uuid] = True def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0): tts_mel, _ = self.flow.inference(token=token.to(self.device), token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), prompt_token=prompt_token.to(self.device), prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), prompt_feat=prompt_feat.to(self.device), prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), embedding=embedding.to(self.device), finalize=finalize) tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:] # append hift cache if self.hift_cache_dict[uuid] is not None: hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) else: hift_cache_source = torch.zeros(1, 1, 0) # keep overlap mel and hift cache if finalize is False: tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) if self.hift_cache_dict[uuid] is not None: tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:], 'source': tts_source[:, :, -self.source_cache_len:], 'speech': tts_speech[:, -self.source_cache_len:]} tts_speech = tts_speech[:, :-self.source_cache_len] else: if speed != 1.0: assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode' tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear') tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source) if self.hift_cache_dict[uuid] is not None: tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window) return tts_speech def tts(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192), prompt_text=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs): # this_uuid is used to track variables related to this inference thread this_uuid = str(uuid.uuid1()) with self.lock: self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False self.hift_cache_dict[this_uuid] = None p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) p.start() if stream is True: token_offset = 0 while True: time.sleep(0.1) if len(self.tts_speech_token_dict[this_uuid]) - token_offset >= self.token_hop_len + self.flow.pre_lookahead_len: this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_offset + self.token_hop_len + self.flow.pre_lookahead_len]) \ .unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, token_offset=token_offset, finalize=False) token_offset += self.token_hop_len yield {'tts_speech': this_tts_speech.cpu()} if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) - token_offset < self.token_hop_len + self.flow.pre_lookahead_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.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, token_offset=token_offset, finalize=True) yield {'tts_speech': this_tts_speech.cpu()} else: # deal with all tokens p.join() this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0) this_tts_speech = self.token2wav(token=this_tts_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, token_offset=0, finalize=True, speed=speed) yield {'tts_speech': this_tts_speech.cpu()} with self.lock: self.tts_speech_token_dict.pop(this_uuid) self.llm_end_dict.pop(this_uuid)