98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
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import time
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import torch
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import numpy as np
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import soundfile as sf
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import resampy
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import queue
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from queue import Queue
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from io import BytesIO
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import multiprocessing as mp
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from wav2lip import audio
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class LipASR:
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def __init__(self, opt):
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self.opt = opt
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self.fps = opt.fps # 20 ms per frame
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self.sample_rate = 16000
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self.chunk = self.sample_rate // self.fps # 320 samples per chunk (20ms * 16000 / 1000)
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self.queue = Queue()
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# self.input_stream = BytesIO()
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self.output_queue = mp.Queue()
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#self.audio_processor = audio_processor
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self.batch_size = opt.batch_size
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self.frames = []
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self.stride_left_size = self.stride_right_size = 10
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self.context_size = 10
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self.audio_feats = []
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self.feat_queue = mp.Queue(5)
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self.warm_up()
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def put_audio_frame(self,audio_chunk): #16khz 20ms pcm
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self.queue.put(audio_chunk)
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def __get_audio_frame(self):
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try:
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frame = self.queue.get(block=True,timeout=0.018)
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type = 0
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#print(f'[INFO] get frame {frame.shape}')
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except queue.Empty:
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frame = np.zeros(self.chunk, dtype=np.float32)
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type = 1
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return frame,type
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def get_audio_out(self): #get origin audio pcm to nerf
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return self.output_queue.get()
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def warm_up(self):
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for _ in range(self.stride_left_size + self.stride_right_size):
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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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for _ in range(self.stride_left_size):
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self.output_queue.get()
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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.context_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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def get_next_feat(self,block,timeout):
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return self.feat_queue.get(block,timeout)
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