Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import librosa
+import pickle
+import tensorflow as tf
+from sklearn.preprocessing import LabelEncoder
+
+# Paths to your models and label encoders
+lstm_speaker_model = '/content/lstm_speaker_model.h5'
+lstm_gender_model = '/content/lstm_gender_model.h5'
+lstm_speaker_label = '/content/lstm_speaker_label.pkl'
+lstm_gender_label = '/content/lstm_gender_label.pkl'
+
+# ------------------- Feature Extraction -------------------
+def extract_features(audio_data, max_len=34):
+    """Extract MFCC features from an audio file."""
+    audio, sr = librosa.load(audio_data, sr=None)
+
+    # Extract MFCC features (13 coefficients)
+    mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
+    mfccs_mean = np.mean(mfccs, axis=1)
+
+    # Spectral Features: Chroma
+    chroma = librosa.feature.chroma_stft(y=audio, sr=sr)
+    chroma_mean = np.mean(chroma, axis=1)
+
+    # Spectral Features: Spectral Contrast
+    spectral_contrast = librosa.feature.spectral_contrast(y=audio, sr=sr)
+    spectral_contrast_mean = np.mean(spectral_contrast, axis=1)
+
+    # Combine only a subset of features (to match the model's expected input size)
+    features = np.hstack([mfccs_mean[:13], chroma_mean[:13], spectral_contrast_mean[:8]])
+
+    # Padding or truncating to fixed length (max_len)
+    if features.shape[0] < max_len:
+        padding = np.zeros((max_len - features.shape[0],))
+        features = np.concatenate((features, padding))
+    elif features.shape[0] > max_len:
+        features = features[:max_len]
+
+    return features
+
+def preprocess_audio_for_model(audio_data, max_len=34):
+    """Preprocess audio file for model prediction."""
+    features = extract_features(audio_data, max_len=max_len)
+    features = features.reshape(1, 1, features.shape[0])  # Shape for LSTM: (samples, timesteps, features)
+    return features
+
+# ------------------- Load Pre-trained Models and Label Encoders -------------------
+def load_trained_model(model_path='/content/lstm_speaker_model.h5'):
+    """Load the trained speaker model."""
+    return tf.keras.models.load_model(model_path)
+
+def load_gender_model(model_path='/content/lstm_gender_model.h5'):
+    """Load the trained gender model."""
+    return tf.keras.models.load_model(model_path)
+
+def load_label_encoder(label_encoder_path='/content/lstm_speaker_label.pkl'):
+    """Load the label encoder for speaker labels."""
+    with open(label_encoder_path, 'rb') as f:
+        label_encoder = pickle.load(f)
+    return label_encoder
+
+def load_gender_label_encoder(label_encoder_path='/content/lstm_gender_label.pkl'):
+    """Load the label encoder for gender labels."""
+    with open(label_encoder_path, 'rb') as f:
+        label_encoder = pickle.load(f)
+    return label_encoder
+
+# ------------------- Predict Top 3 Speakers and Gender -------------------
+def predict_top_3_speakers_and_gender(audio_data, speaker_model, gender_model, speaker_encoder, gender_encoder, max_len=34):
+    """Predict the top 3 speakers and gender from an uploaded audio file."""
+    features = preprocess_audio_for_model(audio_data, max_len=max_len)
+
+    # Predict the speaker probabilities
+    speaker_pred = speaker_model.predict(features)
+
+    # Get top 3 speakers
+    top_3_speakers_idx = np.argsort(speaker_pred[0])[::-1][:3]
+    top_3_speakers_probs = speaker_pred[0][top_3_speakers_idx] * 100  # Convert to percentages
+    top_3_speakers = speaker_encoder.inverse_transform(top_3_speakers_idx)
+
+    # Predict the gender
+    gender_pred = gender_model.predict(features)  # Gender model needs 1D features
+    predicted_gender = gender_encoder.inverse_transform([np.argmax(gender_pred)])[0]
+
+    return top_3_speakers, top_3_speakers_probs, predicted_gender
+
+# ------------------- Gradio Interface -------------------
+def gradio_interface(audio):
+    # Load the trained models and label encoders
+    speaker_model = load_trained_model(lstm_speaker_model)  # Speaker model
+    gender_model = load_gender_model(lstm_gender_model)  # Gender model
+    speaker_encoder = load_label_encoder(lstm_speaker_label)  # Speaker label encoder
+    gender_encoder = load_gender_label_encoder(lstm_gender_label)  # Gender label encoder
+
+    # Predict the top 3 speakers and gender from the uploaded audio file
+    top_3_speakers, top_3_speakers_probs, predicted_gender = predict_top_3_speakers_and_gender(
+        audio, speaker_model, gender_model, speaker_encoder, gender_encoder
+    )
+
+    # Return results as a formatted string for Gradio output
+    result = f"The top 3 predicted speakers are:\n"
+    for speaker, prob in zip(top_3_speakers, top_3_speakers_probs):
+        result += f"{speaker}: {prob:.2f}%\n"
+
+    result += f"\nThe predicted gender is: {predicted_gender}"
+
+    return result
+
+# Gradio interface creation
+demo = gr.Interface(
+    fn=gradio_interface,  # The function to predict speaker and gender
+    inputs=gr.Audio(type="filepath"),  # Audio input (file upload)
+    outputs="text",  # Output the prediction result as text
+    live=False,  # Disable live feedback
+    title="Speaker and Gender Prediction",
+    description="Upload or record an audio file to predict the top 3 speakers and gender.",
+    allow_flagging="never",  # Disable flagging
+    theme="compact",  # Set the theme
+    css="""
+    body {
+        margin: 0;
+        padding: 0;
+        background-color: #f1f1f1;
+        font-family: 'Roboto', sans-serif;
+    }
+
+    .gradio-container {
+        background-color: #ffffff;
+        padding: 20px;
+        border-radius: 8px;
+        box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
+    }
+
+    h1, p {
+        color: #333;
+    }
+    """
+)
+
+# Launch Gradio app
+demo.launch()