app.py

import streamlit as st
import sounddevice as sd
import soundfile as sf
from transformers import pipeline

# Load the model pipeline
model = pipeline("audio-classification", model="HareemFatima/distilhubert-finetuned-stutterdetection")

# Define a function to map predicted labels to types of stuttering
def map_label_to_stutter_type(label):
    if label == 0:
        return "nonstutter"
    elif label == 1:
        return "prolongation"
    elif label == 2:
        return "repetition"
    elif label == 3:
        return "blocks"
    else:
        return "Unknown"

# Function to classify audio input and return the stutter type
def classify_audio(audio_input):
    # Call your model pipeline to classify the audio
    prediction = model(audio_input)
    # Get the predicted label
    predicted_label = prediction[0]["label"]
    # Map the label to the corresponding stutter type
    stutter_type = map_label_to_stutter_type(predicted_label)
    return stutter_type

# Streamlit app
def main():
    st.title("Stutter Classification App")
    audio_input = st.audio("Capture Audio", format="audio/wav", start_recording=True, channels=1)
    if st.button("Stop Recording"):
        sd.stop()
        with st.spinner("Classifying..."):
            # Read the recorded audio file
            recording_path = "recording.wav"
            audio_data, sampling_rate = sf.read(recording_path)
            # Classify the audio
            stutter_type = classify_audio(audio_data)
            st.write("Predicted Stutter Type:", stutter_type)

if __name__ == "__main__":
    main()