48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import time
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import torch
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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 wav2lip import audio
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class LipASR(BaseASR):
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def run_step(self):
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############################################## extract audio feature ##############################################
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# get a frame of audio
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for _ in range(self.batch_size*2):
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frame,type = self.get_audio_frame()
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self.frames.append(frame)
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# put to output
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self.output_queue.put((frame,type))
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# context not enough, do not run network.
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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 = audio.melspectrogram(inputs)
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#print(mel.shape[0],mel.shape,len(mel[0]),len(self.frames))
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# cut off stride
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left = max(0, self.stride_left_size*80/50)
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right = min(len(mel[0]), len(mel[0]) - self.stride_right_size*80/50)
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mel_idx_multiplier = 80.*2/self.fps
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mel_step_size = 16
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i = 0
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mel_chunks = []
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while i < (len(self.frames)-self.stride_left_size-self.stride_right_size)/2:
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start_idx = int(left + i * mel_idx_multiplier)
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#print(start_idx)
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if start_idx + mel_step_size > len(mel[0]):
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mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
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else:
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mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
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i += 1
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self.feat_queue.put(mel_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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