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import streamlit as st |
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import logging |
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from transformers import pipeline |
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logging.basicConfig(level=logging.INFO) |
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asr = pipeline(task="automatic-speech-recognition", |
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model="distil-whisper/distil-small.en") |
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def transcribe_speech(audio_file): |
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if not audio_file: |
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logging.error("No audio file provided.") |
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return "No audio found, please retry." |
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try: |
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logging.info(f"Processing file: {audio_file.name}") |
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output = asr(audio_file.name) |
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return output[0]["transcription"] |
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except Exception as e: |
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logging.error(f"Error during transcription: {str(e)}") |
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return f"Error processing the audio file: {str(e)}" |
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st.title("Speech Recognition") |
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uploaded_file = st.file_uploader("Upload audio file", type=["wav", "mp3"]) |
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if uploaded_file: |
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st.audio(uploaded_file, format='audio/wav') |
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if st.button("Transcribe Audio"): |
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transcription = transcribe_speech(uploaded_file) |
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st.write("Transcription:") |
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st.write(transcription) |
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