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Update app.py
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app.py
CHANGED
@@ -22,7 +22,7 @@ button_click = st.button("Run Analysis", type="primary")
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# load song
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input_file = "test1.mp3"
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output_file =
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# preprocess and crop audio file
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def audio_preprocess():
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@@ -34,15 +34,16 @@ def audio_preprocess():
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start_time = 60000 # e.g. 30 seconds, 30000
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end_time = 110000 # e.g. 40 seconds, 40000
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audio = AudioSegment.from_file('
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cropped_audio = audio[start_time:end_time]
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cropped_audio.export('cropped_vocals.wav', format='wav') # save vocal audio file
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# ASR transcription
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def asr_model():
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# load audio file
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y, sr = librosa.load('cropped_vocals.wav', sr=16000)
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# ASR model
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MODEL_NAME = "RexChan/ISOM5240-whisper-small-zhhk_1"
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# load song
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input_file = "test1.mp3"
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output_file = os.path.join(os.path.dirname(__file__)
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# preprocess and crop audio file
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def audio_preprocess():
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start_time = 60000 # e.g. 30 seconds, 30000
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end_time = 110000 # e.g. 40 seconds, 40000
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audio = AudioSegment.from_file(os.path.join(os.path.dirname(__file__), 'vocals.wav'))
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cropped_audio = audio[start_time:end_time]
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cropped_audio.export(os.path.join(os.path.dirname(__file__), 'cropped_vocals.wav'), format='wav') # save vocal audio file
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# ASR transcription
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def asr_model():
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# load audio file
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#y, sr = librosa.load('cropped_vocals.wav', sr=16000)
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y, sr = librosa.load(os.path.join(os.path.dirname(__file__), 'cropped_vocals.wav'), sr=16000)
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# ASR model
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MODEL_NAME = "RexChan/ISOM5240-whisper-small-zhhk_1"
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