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Emotion Detection.py
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Emotion Detection.py
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from flask import Flask, request, jsonify
import librosa
import torch
import os
from speechbrain.pretrained import EncoderClassifier
from pydub import AudioSegment
app = Flask(__name__)
# Load the pre-trained model (using SpeechBrain pre-trained model)
classifier = EncoderClassifier.from_hparams(source="speechbrain/emotion-recognition", savedir="tmp")
# Function to process audio and extract emotion
def predict_emotion(file_path):
try:
# Load audio file
signal, sample_rate = librosa.load(file_path, sr=16000)
# Convert to a Torch tensor
signal_tensor = torch.tensor(signal).unsqueeze(0)
# Predict emotions
emotion_prediction = classifier.classify_batch(signal_tensor)
# Extract emotion label (predicted emotion class)
emotion = emotion_prediction[3][0]
return emotion
except Exception as e:
return str(e)
# Route to upload and detect emotion from voice file
@app.route('/upload', methods=['POST'])
def upload():
if 'file' not in request.files:
return jsonify({"error": "No file part"}), 400
file = request.files['file']
if file.filename == '':
return jsonify({"error": "No selected file"}), 400
# Save the file
filename = file.filename
file_path = os.path.join('uploads', filename)
# Convert file to WAV format if it's not in WAV format
if filename.endswith('.mp3'):
audio = AudioSegment.from_mp3(file)
file_path = os.path.join('uploads', filename.replace('.mp3', '.wav'))
audio.export(file_path, format='wav')
else:
file.save(file_path)
# Predict emotion from audio
emotion = predict_emotion(file_path)
return jsonify({"emotion": emotion})
if __name__ == '__main__':
os.makedirs('uploads', exist_ok=True)
app.run(debug=True)