mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
212 lines
12 KiB
Python
212 lines
12 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import torch
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import numpy as np
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import threading
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import time
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from contextlib import nullcontext
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import uuid
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from cosyvoice.utils.common import fade_in_out
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import numpy as np
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import onnxruntime as ort
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class CosyVoiceModel:
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def __init__(self,
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llm: torch.nn.Module,
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flow: torch.nn.Module,
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hift: torch.nn.Module):
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.llm = llm
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self.flow = flow
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self.hift = hift
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self.token_min_hop_len = 100
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self.token_max_hop_len = 200
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self.token_overlap_len = 20
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# mel fade in out
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self.mel_overlap_len = 34
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self.mel_window = np.hamming(2 * self.mel_overlap_len)
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# hift cache
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self.mel_cache_len = 20
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self.source_cache_len = int(self.mel_cache_len * 256)
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# rtf and decoding related
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self.stream_scale_factor = 1
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assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
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self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
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self.flow_hift_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
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self.lock = threading.Lock()
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# dict used to store session related variable
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self.tts_speech_token_dict = {}
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self.llm_end_dict = {}
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self.mel_overlap_dict = {}
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self.hift_cache_dict = {}
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def load(self, llm_model, flow_model, hift_model):
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self.llm.load_state_dict(torch.load(llm_model, map_location=self.device))
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self.llm.to(self.device).eval()
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self.llm.half()
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self.flow.load_state_dict(torch.load(flow_model, map_location=self.device))
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self.flow.to(self.device).eval()
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self.hift.load_state_dict(torch.load(hift_model, map_location=self.device))
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self.hift.to(self.device).eval()
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def load_jit(self, llm_text_encoder_model, llm_llm_model):
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llm_text_encoder = torch.jit.load(llm_text_encoder_model)
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self.llm.text_encoder = llm_text_encoder
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llm_llm = torch.jit.load(llm_llm_model)
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self.llm.llm = llm_llm
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# def load_trt(self, model_dir, use_fp16):
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# import tensorrt as trt
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# trt_file_name = 'estimator_fp16.plan' if use_fp16 else 'estimator_fp32.plan'
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# trt_file_path = os.path.join(model_dir, trt_file_name)
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# if not os.path.isfile(trt_file_path):
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# raise f"{trt_file_path} does not exist. Please use bin/export_trt.py to generate .plan file"
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# trt.init_libnvinfer_plugins(None, "")
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# logger = trt.Logger(trt.Logger.WARNING)
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# runtime = trt.Runtime(logger)
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# with open(trt_file_path, 'rb') as f:
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# serialized_engine = f.read()
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# engine = runtime.deserialize_cuda_engine(serialized_engine)
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# self.flow.decoder.estimator_context = engine.create_execution_context()
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# self.flow.decoder.estimator = None
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def load_onnx(self, model_dir, use_fp16):
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onnx_file_name = 'estimator_fp16.onnx' if use_fp16 else 'estimator_fp32.onnx'
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onnx_file_path = os.path.join(model_dir, onnx_file_name)
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if not os.path.isfile(onnx_file_path):
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raise f"{onnx_file_path} does not exist. Please use bin/export_trt.py to generate .onnx file"
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providers = ['CUDAExecutionProvider']
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sess_options = ort.SessionOptions()
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# Add TensorRT Execution Provider
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providers = [
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'CUDAExecutionProvider'
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]
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# Load the ONNX model
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self.flow.decoder.session = ort.InferenceSession(onnx_file_path, sess_options=sess_options, providers=providers)
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# self.flow.decoder.estimator_context = None
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self.flow.decoder.estimator = None
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def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
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with self.llm_context:
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for i in self.llm.inference(text=text.to(self.device),
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text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
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prompt_text=prompt_text.to(self.device),
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prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
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prompt_speech_token=llm_prompt_speech_token.to(self.device),
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prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
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embedding=llm_embedding.to(self.device).half(),
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sampling=25,
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max_token_text_ratio=30,
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min_token_text_ratio=3):
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self.tts_speech_token_dict[uuid].append(i)
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self.llm_end_dict[uuid] = True
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def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False):
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with self.flow_hift_context:
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tts_mel = self.flow.inference(token=token.to(self.device),
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token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
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prompt_token=prompt_token.to(self.device),
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prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
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prompt_feat=prompt_feat.to(self.device),
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prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
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embedding=embedding.to(self.device))
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# mel overlap fade in out
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if self.mel_overlap_dict[uuid] is not None:
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tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
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# append hift cache
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if self.hift_cache_dict[uuid] is not None:
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hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
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tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
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else:
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hift_cache_source = torch.zeros(1, 1, 0)
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# keep overlap mel and hift cache
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if finalize is False:
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self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
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tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
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tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
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self.hift_cache_dict[uuid] = {'source': tts_source[:, :, -self.source_cache_len:], 'mel': tts_mel[:, :, -self.mel_cache_len:]}
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tts_speech = tts_speech[:, :-self.source_cache_len]
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else:
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tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
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return tts_speech
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def inference(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
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prompt_text=torch.zeros(1, 0, dtype=torch.int32),
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llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, **kwargs):
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# this_uuid is used to track variables related to this inference thread
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this_uuid = str(uuid.uuid1())
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with self.lock:
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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
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p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
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p.start()
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p.join()
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if stream is True:
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token_hop_len = self.token_min_hop_len
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while True:
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time.sleep(0.1)
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if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
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this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len], dim=1)
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with self.flow_hift_context:
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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uuid=this_uuid,
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finalize=False)
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yield {'tts_speech': this_tts_speech.cpu()}
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with self.lock:
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self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
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# increase token_hop_len for better speech quality
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token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
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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:
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break
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# p.join()
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# deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
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this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
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with self.flow_hift_context:
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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uuid=this_uuid,
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finalize=True)
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yield {'tts_speech': this_tts_speech.cpu()}
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else:
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# deal with all tokens
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# p.join()
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this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1)
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with self.flow_hift_context:
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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uuid=this_uuid,
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finalize=True)
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yield {'tts_speech': this_tts_speech.cpu()}
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with self.lock:
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self.tts_speech_token_dict.pop(this_uuid)
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self.llm_end_dict.pop(this_uuid)
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self.mel_overlap_dict.pop(this_uuid)
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self.hift_cache_dict.pop(this_uuid)
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torch.cuda.synchronize()
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