{
  "_comment": "Voice Cloning Dataset - Code Examples - Developer: Molla Samser, Email: help@rskworld.in, Phone: +91 93305 39277, Website: https://rskworld.in, Year: 2026",
  "examples": [
    {
      "title": "Load and Process Audio",
      "language": "python",
      "code": "import librosa\nimport soundfile as sf\n\n# Load audio file\naudio, sr = librosa.load('audio/speaker_001/sample.wav', sr=22050)\n\n# Normalize audio\naudio = librosa.util.normalize(audio)\n\n# Save processed audio\nsf.write('processed/sample.wav', audio, sr)\nprint(f'Audio processed: {len(audio)/sr:.2f} seconds')"
    },
    {
      "title": "Extract MFCC Features",
      "language": "python",
      "code": "import librosa\nimport numpy as np\n\n# Load audio\naudio, sr = librosa.load('audio/sample.wav', sr=22050)\n\n# Extract MFCC features\nmfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)\n\n# Get mean MFCC\nmean_mfcc = np.mean(mfccs, axis=1)\nprint(f'MFCC Features: {mean_mfcc.shape}')"
    },
    {
      "title": "Extract Mel Spectrogram",
      "language": "python",
      "code": "import librosa\nimport numpy as np\n\n# Load audio\naudio, sr = librosa.load('audio/sample.wav', sr=22050)\n\n# Extract mel spectrogram\nmel_spec = librosa.feature.melspectrogram(y=audio, sr=sr, n_mels=80)\nmel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)\n\nprint(f'Mel Spectrogram Shape: {mel_spec_db.shape}')"
    },
    {
      "title": "Extract Pitch",
      "language": "python",
      "code": "import librosa\nimport numpy as np\n\n# Load audio\naudio, sr = librosa.load('audio/sample.wav', sr=22050)\n\n# Extract pitch\npitches, magnitudes = librosa.piptrack(y=audio, sr=sr)\npitch_values = []\nfor t in range(pitches.shape[1]):\n    index = magnitudes[:, t].argmax()\n    pitch = pitches[index, t]\n    if pitch > 0:\n        pitch_values.append(pitch)\n\naverage_pitch = np.mean(pitch_values) if pitch_values else 0\nprint(f'Average Pitch: {average_pitch:.2f} Hz')"
    },
    {
      "title": "Process Multiple Files",
      "language": "python",
      "code": "from pathlib import Path\nimport librosa\nimport soundfile as sf\n\n# Process all audio files in directory\naudio_dir = Path('audio')\nfor audio_file in audio_dir.rglob('*.wav'):\n    audio, sr = librosa.load(str(audio_file), sr=22050)\n    audio = librosa.util.normalize(audio)\n    \n    output_path = f'processed/{audio_file.name}'\n    sf.write(output_path, audio, sr)\n    print(f'Processed: {audio_file.name}')"
    }
  ]
}