Create app.py

app.py

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+import os
+import numpy as np
+import librosa
+import sounddevice as sd
+import soundfile as sf
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+import torch
+
+# Load Hugging Face Wav2Vec2 Model
+model_name = "facebook/wav2vec2-large-xlsr-53"
+tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+def extract_features(audio, sample_rate=16000):
+    mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)  # Extract MFCCs
+    mfccs_scaled = np.mean(mfccs.T, axis=0)  # Scale the MFCCs
+    return mfccs_scaled
+
+# Function to predict emotion based on audio input
+def predict_emotion(audio):
+    # Extract features from audio
+    features = extract_features(audio).reshape(1, -1)  # Reshape for classifier input
+    predicted_emotion = model_rf.predict(features)
+    return predicted_emotion[0]
+
+# Prepare your emotion classification model
+# Replace this section with your own training procedures as necessary
+
+# Assume we have trained 'model_rf', for example purposes
+# Here you can load a trained model or define how to train it
+emotions = ['happy', 'sad', 'angry', 'fear', 'surprise']  # Example emotion categories
+# For demonstration purposes, we are creating a dummy classifier.
+# Replace this with the actual model training as demonstrated previously.
+model_rf = RandomForestClassifier(n_estimators=100, random_state=42)  # Dummy model for demo
+
+# This is a placeholder training step; you would train your model on actual data.
+features_dummy = np.random.rand(100, 40)  # Dummy feature data
+labels_dummy = np.random.choice(emotions, 100)  # Random dummy labels
+model_rf.fit(features_dummy, labels_dummy)  # Dummy fit
+
+# Function to record audio and analyze emotion
+def record_and_predict():
+    print("Recording... Please speak with emotion...")
+    duration = 5  # Duration of recording in seconds
+    sample_rate = 16000  # Sample rate for audio recording
+    
+    # Record audio
+    audio = sd.rec(int(duration * sample_rate), samplerate=sample_rate, channels=1, dtype='float32')
+    sd.wait()  # Wait until recording is finished
+    print("Recording finished.")
+
+    # Predict emotion from the recorded audio
+    emotion = predict_emotion(audio.flatten())
+    print(f'Predicted Emotion: {emotion}')
+
+if __name__ == "__main__":
+    record_and_predict()