app.py

import streamlit as st
from transformers import pipeline
import torch
import numpy as np
from pydub import AudioSegment
import io

# Load the ASR pipeline with Whisper model
pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")

def audio_to_numpy(audio_file):
 # Load the audio file into an AudioSegment object
 audio = AudioSegment.from_file(io.BytesIO(audio_file.read()))
 
 # Convert audio to mono and set sample rate to 16000
 audio = audio.set_channels(1).set_frame_rate(16000)
 
 # Convert to numpy array
 samples = np.array(audio.get_array_of_samples())
 
 # Normalize the data
 samples = samples.astype(np.float32) / np.iinfo(audio.sample_width * 8).max
 
 return samples

def transcribe_audio(audio_file):
 # Convert audio bytes to numpy array
 audio_numpy = audio_to_numpy(audio_file)
 
 # Transcribe audio
 transcription = pipe(audio_numpy)
 return transcription['text']

# Streamlit UI
st.title("Speech-to-Text Transcription App")
st.write("Upload an audio file to transcribe its content into text.")

uploaded_file = st.file_uploader("Choose an audio file...", type=["wav", "mp3", "flac"])

if uploaded_file is not None:
 try:
 with st.spinner("Transcribing..."):
 text = transcribe_audio(uploaded_file)
 st.subheader("Transcription Result:")
 st.write(text)
 except Exception as e:
 st.error(f"An error occurred: {e}")