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import gradio as gr
from transformers import pipeline, AutoFeatureExtractor, AutoModelForAudioClassification
import torch
import librosa
import numpy as np
# Load model and feature extractor
model_id = "your-username/speech-emotion-recognition-model"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = AutoModelForAudioClassification.from_pretrained(model_id)
# Define emotions
emotions = ["neutral", "happy", "sad", "angry", "fearful", "disgust", "surprised"]
def predict_emotion(audio_path):
# Load audio
audio, sampling_rate = librosa.load(audio_path, sr=16000)
# Process through feature extractor
inputs = feature_extractor(audio, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
# Get prediction
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1)
predicted_class_id = torch.argmax(probs, dim=1).item()
predicted_label = emotions[predicted_class_id]
confidence = probs[0][predicted_class_id].item()
# Return result
result = {emotion: float(probs[0][i].item()) for i, emotion in enumerate(emotions)}
return result
# Create Gradio interface
demo = gr.Interface(
fn=predict_emotion,
inputs=gr.Audio(source="microphone", type="filepath"),
outputs=gr.Label(num_top_classes=7),
title="Speech Emotion Recognition",
description="Upload audio or record your voice to identify the emotion. This model can detect neutral, happy, sad, angry, fearful, disgust, and surprised emotions."
)
demo.launch() |