app.py

import gradio as gr
import torchaudio
from transformers import pipeline
from datasets import load_dataset, Audio

# Load your model
classifier = pipeline("audio-classification", model="candenizkocak/wav2vec2_turkish_gender_classification")

# Function to resample audio to 16kHz
def resample_audio(audio_file, target_sampling_rate=16000):
 waveform, original_sample_rate = torchaudio.load(audio_file)
 if original_sample_rate != target_sampling_rate:
 resampler = torchaudio.transforms.Resample(orig_freq=original_sample_rate, new_freq=target_sampling_rate)
 waveform = resampler(waveform)
 return waveform.squeeze().numpy(), target_sampling_rate

# Define the prediction function
def classify_audio(audio_file):
 # Resample the audio to 16kHz
 resampled_audio, _ = resample_audio(audio_file)
 
 # Classify the audio
 prediction = classifier(resampled_audio)
 
 # Return predictions as a dictionary
 return {entry['label']: entry['score'] for entry in prediction}

# Define Gradio interface
def demo():
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
 gr.Markdown("## Turkish Gender Audio Classification")
 
 # Input Audio
 with gr.Row():
 audio_input = gr.Audio(type="filepath", label="Input Audio")
 
 # Output Labels
 with gr.Row():
 label_output = gr.Label(label="Prediction")
 
 # Predict Button
 classify_btn = gr.Button("Classify")

 # Define the interaction
 classify_btn.click(fn=classify_audio, inputs=audio_input, outputs=label_output)
 
 return demo

# Launch the demo
demo().launch()