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35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
7 months ago
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import time
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import torch
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import numpy as np
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from baseasr import BaseASR
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class LightASR(BaseASR):
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def __init__(self, opt, parent, audio_processor):
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super().__init__(opt, parent)
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self.audio_processor = audio_processor
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self.stride_left_size = 32
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self.stride_right_size = 32
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def run_step(self):
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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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mel = self.audio_processor.get_hubert_from_16k_speech(inputs)
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mel_chunks=self.audio_processor.feature2chunks(feature_array=mel,fps=self.fps/2,batch_size=self.batch_size,start=self.stride_left_size/2)
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self.feat_queue.put(mel_chunks)
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self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]
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print(f"Processing audio costs {(time.time() - start_time) * 1000}ms")
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