2023-10-04 15:18:18 +01:00
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import speech_recognition as sr
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import os
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from pydub import AudioSegment
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from pydub.silence import split_on_silence
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# sound = AudioSegment.from_mp3("test.mp3")
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# sound.export("test.wav", format="wav")
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fname = "ciberseguretat.wav"
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keyWords = ['ciberseguretat', 'hacker', 'atac', 'pentesting']
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r = sr.Recognizer()
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def transcript_audio(audio):
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with sr.AudioFile(fname) as source:
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audio_data = r.record(source)
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text = r.recognize_whisper(audio_data, language='ca')
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return(text)
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def large_audio(path, minutes=5):
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"""Splitting the large audio file into fixed interval chunks
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and apply speech recognition on each of these chunks"""
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print("Loading file")
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sound = AudioSegment.from_file(path)
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print(len(sound))
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print("Splitting file")
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chunk_length_ms = int(1000 * 60 * minutes) # convert to milliseconds
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chunks = [sound[i:i + chunk_length_ms] for i in range(0, len(sound), chunk_length_ms)]
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folder_name = "audio-fixed-chunks"
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if not os.path.isdir(folder_name):
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os.mkdir(folder_name)
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whole_text = ""
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print("Starting transcription")
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2023-10-04 15:48:18 +01:00
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total_chunks = len(chunks)
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2023-10-04 15:18:18 +01:00
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for i, audio_chunk in enumerate(chunks, start=1):
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2023-10-04 15:48:18 +01:00
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print(f"Chunk {i} of {total_chunks}")
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2023-10-04 15:18:18 +01:00
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# export audio chunk and save it in
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# the `folder_name` directory.
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chunk_filename = os.path.join(folder_name, f"chunk{i}.wav")
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audio_chunk.export(chunk_filename, format="wav")
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# recognize the chunk
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try:
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text = transcript_audio(chunk_filename)
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except sr.UnknownValueError as e:
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print("Error:", str(e))
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else:
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text = f"{text.capitalize()}. "
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2023-10-04 15:48:18 +01:00
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# print(chunk_filename, ":", text)
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2023-10-04 15:18:18 +01:00
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whole_text += text
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# return the text for all chunks detected
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return whole_text
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if __name__=="__main__":
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2023-10-04 15:48:18 +01:00
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text = large_audio(fname)
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fname = "transcript.txt"
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with open(fname, 'w') as f:
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f.writeline(text)
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