82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
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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from musetalk.whisper.audio2feature import Audio2Feature
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class MuseASR:
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def __init__(self, opt, audio_processor:Audio2Feature):
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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 = 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.stride_left_size = self.stride_right_size = 6
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self.audio_feats = []
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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=0.02)
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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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frames = []
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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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frames.append(audio_frame)
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self.output_queue.put((audio_frame,type))
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inputs = np.concatenate(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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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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start_time = time.time()
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frames = []
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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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frames.append(audio_frame)
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self.output_queue.put((audio_frame,type))
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inputs = np.concatenate(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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def get_next_feat(self):
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whisper_chunks = self.audio_processor.feature2chunks(feature_array=self.audio_feats,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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return whisper_chunks |