# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) # 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou) # # 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 from typing import Generator import torch import numpy as np import threading import time from torch.nn import functional as F from contextlib import nullcontext import uuid from models.cosyvoice.utils.common import fade_in_out from models.cosyvoice.utils.file_utils import ( convert_onnx_to_trt, export_cosyvoice2_vllm, ) from models.cosyvoice.utils.common import TrtContextWrapper class CosyVoiceModel: def __init__( self, llm: torch.nn.Module, flow: torch.nn.Module, hift: torch.nn.Module, fp16: bool = False, ): 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 = {} self.silent_tokens = [] def load(self, llm_model, flow_model, hift_model): self.llm.load_state_dict( torch.load(llm_model, map_location=self.device, weights_only=True), strict=True, ) self.llm.to(self.device).eval() self.flow.load_state_dict( torch.load(flow_model, map_location=self.device, weights_only=True), 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, weights_only=True ).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): 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_trt( self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16, ): assert torch.cuda.is_available(), "tensorrt only supports gpu!" if ( not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0 ): convert_onnx_to_trt( flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16, ) del self.flow.decoder.estimator import tensorrt as trt with open(flow_decoder_estimator_model, "rb") as f: estimator_engine = trt.Runtime( trt.Logger(trt.Logger.INFO) ).deserialize_cuda_engine(f.read()) assert estimator_engine is not None, "failed to load trt {}".format( flow_decoder_estimator_model ) self.flow.decoder.estimator = TrtContextWrapper( estimator_engine, trt_concurrent=trt_concurrent, device=self.device ) def get_trt_kwargs(self): min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)] opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)] max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)] input_names = ["x", "mask", "mu", "cond"] return { "min_shape": min_shape, "opt_shape": opt_shape, "max_shape": max_shape, "input_names": input_names, } def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): cur_silent_token_num, max_silent_token_num = 0, 5 with self.llm_context, torch.cuda.amp.autocast( self.fp16 is True and hasattr(self.llm, "vllm") is False ): if isinstance(text, Generator): assert (self.__class__.__name__ != "CosyVoiceModel") and not hasattr( self.llm, "vllm" ), "streaming input text is only implemented for CosyVoice2/3 and do not support vllm!" token_generator = self.llm.inference_bistream( text=text, 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), ) else: token_generator = 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), uuid=uuid, ) for i in token_generator: if i in self.silent_tokens: cur_silent_token_num += 1 if cur_silent_token_num > max_silent_token_num: continue else: cur_silent_token_num = 0 self.tts_speech_token_dict[uuid].append(i) self.llm_end_dict[uuid] = True def vc_job(self, source_speech_token, uuid): self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist() self.llm_end_dict[uuid] = True def token2wav( self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0, ): with torch.cuda.amp.autocast(self.fp16): tts_mel, self.flow_cache_dict[uuid] = self.flow.inference( token=token.to(self.device, dtype=torch.int32), 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], ) # 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=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), 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), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), 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) if source_speech_token.shape[1] == 0: p = threading.Thread( target=self.llm_job, args=( text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid, ), ) else: p = threading.Thread( target=self.vc_job, args=(source_speech_token, 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) self.flow_cache_dict.pop(this_uuid) if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.current_stream().synchronize() class CosyVoice2Model(CosyVoiceModel): def __init__( self, llm: torch.nn.Module, flow: torch.nn.Module, hift: torch.nn.Module, fp16: bool = False, ): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.llm = llm self.flow = flow self.hift = hift self.fp16 = fp16 # NOTE must matching training static_chunk_size self.token_hop_len = 25 # 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.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 = {} self.silent_tokens = [] 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_vllm(self, model_dir): export_cosyvoice2_vllm(self.llm, model_dir, self.device) from vllm import EngineArgs, LLMEngine engine_args = EngineArgs( model=model_dir, skip_tokenizer_init=True, enable_prompt_embeds=True, gpu_memory_utilization=0.2, ) self.llm.vllm = LLMEngine.from_engine_args(engine_args) self.llm.lock = threading.Lock() del self.llm.llm.model.model.layers def token2wav( self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0, ): with torch.cuda.amp.autocast(self.fp16): tts_mel, _ = self.flow.inference( token=token.to(self.device, dtype=torch.int32), 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), streaming=stream, 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=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), 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), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), 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 if source_speech_token.shape[1] == 0: p = threading.Thread( target=self.llm_job, args=( text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid, ), ) else: p = threading.Thread( target=self.vc_job, args=(source_speech_token, this_uuid) ) p.start() if stream is True: token_offset = 0 prompt_token_pad = int( np.ceil(flow_prompt_speech_token.shape[1] / self.token_hop_len) * self.token_hop_len - flow_prompt_speech_token.shape[1] ) while True: time.sleep(0.1) this_token_hop_len = ( self.token_hop_len + prompt_token_pad if token_offset == 0 else self.token_hop_len ) if ( len(self.tts_speech_token_dict[this_uuid]) - token_offset >= this_token_hop_len + self.flow.pre_lookahead_len ): this_tts_speech_token = torch.tensor( self.tts_speech_token_dict[this_uuid][ : token_offset + this_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, token_offset=token_offset, uuid=this_uuid, stream=stream, finalize=False, ) token_offset += this_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 < this_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, token_offset=token_offset, 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, token_offset=0, 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.hift_cache_dict.pop(this_uuid) if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.current_stream().synchronize() def reconstruct( self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), 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), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs ): 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 this_tts_speech = self.token2wav( token=llm_prompt_speech_token, prompt_token=flow_prompt_speech_token, prompt_feat=prompt_speech_feat, embedding=flow_embedding, token_offset=0, uuid=this_uuid, finalize=True, speed=speed, ) yield {"tts_speech": this_tts_speech.cpu()} class CosyVoice3Model(CosyVoice2Model): def __init__( self, llm: torch.nn.Module, flow: torch.nn.Module, hift: torch.nn.Module, fp16: bool = False, ): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.llm = llm self.flow = flow self.hift = hift self.fp16 = fp16 # NOTE must matching training static_chunk_size self.token_hop_len = 25 # rtf and decoding related 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 = {} # FSQ silent and breath token self.silent_tokens = [1, 2, 28, 29, 55, 248, 494, 2241, 2242, 2322, 2323] def token2wav( self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0, ): with torch.cuda.amp.autocast(self.fp16): tts_mel, _ = self.flow.inference( token=token.to(self.device, dtype=torch.int32), 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), streaming=stream, finalize=finalize, ) tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio :] # append mel cache if self.hift_cache_dict[uuid] is not None: hift_cache_mel = self.hift_cache_dict[uuid]["mel"] tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) self.hift_cache_dict[uuid]["mel"] = tts_mel else: self.hift_cache_dict[uuid] = {"mel": tts_mel, "speech_offset": 0} if speed != 1.0: assert ( token_offset == 0 and finalize is True ), "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, _ = self.hift.inference(speech_feat=tts_mel, finalize=finalize) tts_speech = tts_speech[:, self.hift_cache_dict[uuid]["speech_offset"] :] self.hift_cache_dict[uuid]["speech_offset"] += tts_speech.shape[1] return tts_speech