# 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 os import torch import numpy as np import threading import time from contextlib import nullcontext import uuid from cosyvoice.utils.common import fade_in_out import numpy as np import onnxruntime as ort 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 = 200 self.token_overlap_len = 20 # mel fade in out self.mel_overlap_len = 34 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) # 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.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_dict = {} self.llm_end_dict = {} self.mel_overlap_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)) 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_jit(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 load_trt(self, model_dir, use_fp16): # import tensorrt as trt # trt_file_name = 'estimator_fp16.plan' if use_fp16 else 'estimator_fp32.plan' # trt_file_path = os.path.join(model_dir, trt_file_name) # if not os.path.isfile(trt_file_path): # raise f"{trt_file_path} does not exist. Please use bin/export_trt.py to generate .plan file" # trt.init_libnvinfer_plugins(None, "") # logger = trt.Logger(trt.Logger.WARNING) # runtime = trt.Runtime(logger) # with open(trt_file_path, 'rb') as f: # serialized_engine = f.read() # engine = runtime.deserialize_cuda_engine(serialized_engine) # self.flow.decoder.estimator_context = engine.create_execution_context() # self.flow.decoder.estimator = None def load_onnx(self, model_dir, use_fp16): onnx_file_name = 'estimator_fp16.onnx' if use_fp16 else 'estimator_fp32.onnx' onnx_file_path = os.path.join(model_dir, onnx_file_name) if not os.path.isfile(onnx_file_path): raise f"{onnx_file_path} does not exist. Please use bin/export_trt.py to generate .onnx file" providers = ['CUDAExecutionProvider'] sess_options = ort.SessionOptions() # Add TensorRT Execution Provider providers = [ 'CUDAExecutionProvider' ] # Load the ONNX model self.flow.decoder.session = ort.InferenceSession(onnx_file_path, sess_options=sess_options, providers=providers) # self.flow.decoder.estimator_context = None self.flow.decoder.estimator = None 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).half(), sampling=25, max_token_text_ratio=30, min_token_text_ratio=3): 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): with self.flow_hift_context: 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)) # mel overlap fade in out if self.mel_overlap_dict[uuid] is not None: 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(mel=tts_mel, cache_source=hift_cache_source) self.hift_cache_dict[uuid] = {'source': tts_source[:, :, -self.source_cache_len:], 'mel': tts_mel[:, :, -self.mel_cache_len:]} tts_speech = tts_speech[:, :-self.source_cache_len] else: tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source) return tts_speech def inference(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, **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], self.mel_overlap_dict[this_uuid], self.hift_cache_dict[this_uuid] = [], False, None, None p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) p.start() p.join() 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.concat(self.tts_speech_token_dict[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, 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.concat(self.tts_speech_token_dict[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, 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.concat(self.tts_speech_token_dict[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, prompt_feat=prompt_speech_feat, embedding=flow_embedding, uuid=this_uuid, finalize=True) 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) torch.cuda.synchronize()