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37 lines
1.8 KiB
Python
37 lines
1.8 KiB
Python
import time
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import numpy as np
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import queue
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from queue import Queue
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import multiprocessing as mp
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from baseasr import BaseASR
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from musetalk.whisper.audio2feature import Audio2Feature
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class MuseASR(BaseASR):
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def __init__(self, opt, parent,audio_processor:Audio2Feature):
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super().__init__(opt,parent)
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self.audio_processor = audio_processor
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def run_step(self):
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############################################## extract audio feature ##############################################
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start_time = time.time()
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for _ in range(self.batch_size*2):
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audio_frame,type=self.get_audio_frame()
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self.frames.append(audio_frame)
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self.output_queue.put((audio_frame,type))
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if len(self.frames) <= self.stride_left_size + self.stride_right_size:
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return
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inputs = np.concatenate(self.frames) # [N * chunk]
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whisper_feature = self.audio_processor.audio2feat(inputs)
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# for feature in whisper_feature:
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# self.audio_feats.append(feature)
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#print(f"processing audio costs {(time.time() - start_time) * 1000}ms, inputs shape:{inputs.shape} whisper_feature len:{len(whisper_feature)}")
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whisper_chunks = self.audio_processor.feature2chunks(feature_array=whisper_feature,fps=self.fps/2,batch_size=self.batch_size,start=self.stride_left_size/2 )
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#print(f"whisper_chunks len:{len(whisper_chunks)},self.audio_feats len:{len(self.audio_feats)},self.output_queue len:{self.output_queue.qsize()}")
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#self.audio_feats = self.audio_feats[-(self.stride_left_size + self.stride_right_size):]
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self.feat_queue.put(whisper_chunks)
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# discard the old part to save memory
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self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]
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