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34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
import numpy as np
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import whisper
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import torch
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import wave
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import os
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class AudioTranscriber:
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def __init__(self):
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self.transcript = []
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self.audio_model = whisper.load_model(os.getcwd() + r'\tiny.en' + '.pt')
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def transcribe(self, audio_data):
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with wave.open(f'temp_{id(self)}.wav', 'wb') as wav_file:
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wav_file.setnchannels(audio_data.shape[1])
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wav_file.setsampwidth(2)
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wav_file.setframerate(16000)
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audio_data = (audio_data * (2**15 - 1)).astype(np.int16)
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wav_file.writeframes(audio_data.tobytes())
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result = self.audio_model.transcribe(f'temp_{id(self)}.wav', fp16=torch.cuda.is_available())
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text = result['text'].strip()
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return text
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def create_transcription_from_queue(self, audio_queue):
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while True:
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top_of_queue = audio_queue.get()
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source = top_of_queue[0]
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audio_data = top_of_queue[1]
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audio_data_transcription = self.transcribe(audio_data)
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# whisper gives "you" on many null inputs
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if audio_data_transcription != '' and audio_data_transcription.lower() != 'you':
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self.transcript = [source + ": [" + audio_data_transcription + ']\n\n'] + self.transcript
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def get_transcript(self):
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return "".join(self.transcript) |