app.py

import gradio as gr
import torch
import librosa
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor

# Load the model and feature extractor
model_name = "r-f/wav2vec-english-speech-emotion-recognition"
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)

# Define the emotion labels
labels = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']

def predict_emotion(audio):
    # Load and preprocess the audio
    audio, rate = librosa.load(audio, sr=16000)
    inputs = feature_extractor(audio, sampling_rate=rate, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits).item()
    return labels[predicted_class_id]

# Create the Gradio interface
interface = gr.Interface(fn=predict_emotion, inputs=gr.Audio(type="filepath"), outputs="text")
interface.launch()