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