Spaces:
Sleeping
Sleeping
feat: incluye spinner loading icon y output block para los resultados
Browse files
app.py
CHANGED
@@ -27,33 +27,37 @@ def convert_mp3_to_wav(mp3_path):
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audio.export(wav_path, format="wav")
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return wav_path
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st.title('Transcriptor de Audio')
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uploaded_file = st.file_uploader("Sube tu archivo de audio para transcribir", type=['wav', 'mp3'])
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if uploaded_file is not None:
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# Save uploaded file to temp directory
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file_path = os.path.join("temp", uploaded_file.name)
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with open(file_path, "wb") as f:
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f.write(uploaded_file.getbuffer())
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st.write("Archivo de audio cargado correctamente. Por favor, selecciona el método de transcripción.")
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transcription_method = st.selectbox('Escoge el método de transcripción', ('OpenAI Whisper', 'Google Speech API'))
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if transcription_method == 'OpenAI Whisper':
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model_name = st.selectbox('Escoge el modelo de Whisper', ('base', 'small', 'medium', 'large', 'tiny'))
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elif transcription_method == 'Google Speech API' and file_path.endswith('.mp3'):
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# Convert mp3 to wav if Google Speech API is selected and file is in mp3 format
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file_path = convert_mp3_to_wav(file_path)
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if st.button('Transcribir'):
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if transcription_method == 'OpenAI Whisper':
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transcript = transcribe_whisper(model_name, file_path)
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else:
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transcript = transcribe_speech_recognition(file_path)
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audio.export(wav_path, format="wav")
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return wav_path
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def main():
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st.title('Transcriptor de Audio')
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uploaded_file = st.file_uploader("Sube tu archivo de audio para transcribir", type=['wav', 'mp3'])
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if uploaded_file is not None:
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file_details = {"FileName":uploaded_file.name, "FileType":uploaded_file.type, "FileSize":uploaded_file.size}
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st.write(file_details)
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# Save uploaded file to temp directory
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file_path = os.path.join("temp", uploaded_file.name)
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with open(file_path, "wb") as f:
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f.write(uploaded_file.getbuffer())
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st.write("Archivo de audio cargado correctamente. Por favor, selecciona el método de transcripción.")
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transcription_method = st.selectbox('Escoge el método de transcripción', ('OpenAI Whisper', 'Google Speech API'))
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if transcription_method == 'OpenAI Whisper':
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model_name = st.selectbox('Escoge el modelo de Whisper', ('base', 'small', 'medium', 'large', 'tiny'))
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elif transcription_method == 'Google Speech API' and file_path.endswith('.mp3'):
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# Convert mp3 to wav if Google Speech API is selected and file is in mp3 format
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file_path = convert_mp3_to_wav(file_path)
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if st.button('Transcribir'):
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with st.spinner('Transcribiendo...'):
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if transcription_method == 'OpenAI Whisper':
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transcript = transcribe_whisper(model_name, file_path)
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else:
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transcript = transcribe_speech_recognition(file_path)
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st.text_area('Resultado de la Transcripción:', transcript, height=200)
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if __name__ == "__main__":
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main()
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