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RSK World
voice-cloning
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data
RSK World
voice-cloning
Voice Cloning Dataset - Text-to-Speech + Voice Synthesis + TTS Models + Tacotron + WaveNet
data
  • audio_statistics.json1.4 KB
  • code_examples.json2.5 KB
  • dataset_manifest.json5 KB
  • dataset_statistics.json2.7 KB
  • dataset_summary.json1.6 KB
  • feature_statistics.json1.4 KB
  • sample_dataset.json2.3 KB
  • sample_features.csv889 B
  • sample_features.json908 B
  • sample_metadata.json1 KB
  • sample_processed_metadata.json1.4 KB
  • sample_speakers.json2.8 KB
  • sample_transcripts.json1.3 KB
  • speakers_extended.json3.5 KB
  • transcript_samples.json2.6 KB
  • usage_examples.json2.1 KB
index.html
index.html
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    <!--
        Voice Cloning Dataset Project
        Developer: Molla Samser
        Email: help@rskworld.in
        Phone: +91 93305 39277
        Website: https://rskworld.in
        Year: 2026
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            <a class="navbar-brand" href="#home">
                <i class="fas fa-music"></i>
                <span class="brand-text">Voice Cloning Dataset</span>
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                            <span class="nav-text">Statistics</span>
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                <h1 class="display-4 animate__animated animate__fadeInDown">Voice Cloning Dataset</h1>
                <p class="lead text-muted animate__animated animate__fadeInUp">High-quality voice recordings for text-to-speech and voice synthesis applications</p>
                <span class="difficulty-badge animate__animated animate__pulse">Advanced</span>
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                    <strong><i class="fas fa-phone-alt"></i> Contact:</strong> 
                    <a href="tel:+919330539277" class="alert-link">+91 93305 39277</a> | 
                    <a href="mailto:help@rskworld.in" class="alert-link">help@rskworld.in</a> | 
                    <a href="https://rskworld.in" target="_blank" class="alert-link">rskworld.in</a>
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                <div>
                    <strong>Developer:</strong> Molla Samser
                </div>
            </div>

            <div class="row mb-4">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-info-circle text-primary"></i> Description</h3>
                    <p class="text-justify">
                        This dataset contains high-quality voice recordings from multiple speakers with voice characteristics and metadata. 
                        Perfect for voice cloning, text-to-speech synthesis, and voice conversion applications. The dataset includes 
                        professionally recorded audio samples in WAV and FLAC formats, along with text transcripts and speaker metadata.
                    </p>
                </div>
            </div>

            <!-- Statistics Section -->
            <div class="row mb-4" id="stats">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-chart-bar text-primary"></i> Dataset Statistics</h3>
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                <div class="col-md-3 col-sm-6">
                    <div class="stats-card">
                        <i class="fas fa-users fa-2x mb-2"></i>
                        <div class="stats-number" id="speakerCount">50+</div>
                        <div>Speakers</div>
                    </div>
                </div>
                <div class="col-md-3 col-sm-6">
                    <div class="stats-card">
                        <i class="fas fa-file-audio fa-2x mb-2"></i>
                        <div class="stats-number" id="audioCount">1000+</div>
                        <div>Audio Files</div>
                    </div>
                </div>
                <div class="col-md-3 col-sm-6">
                    <div class="stats-card">
                        <i class="fas fa-clock fa-2x mb-2"></i>
                        <div class="stats-number" id="durationCount">10+</div>
                        <div>Hours</div>
                    </div>
                </div>
                <div class="col-md-3 col-sm-6">
                    <div class="stats-card">
                        <i class="fas fa-language fa-2x mb-2"></i>
                        <div class="stats-number" id="languageCount">5+</div>
                        <div>Languages</div>
                    </div>
                </div>
            </div>

            <div class="row mb-4">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-tags text-primary"></i> Category</h3>
                    <p class="h5"><span class="badge bg-primary fs-6">Audio Data</span></p>
                </div>
            </div>

            <div class="row mb-4">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-code text-primary"></i> Technologies</h3>
                    <div>
                        <span class="tech-badge"><i class="fas fa-file-audio"></i> WAV</span>
                        <span class="tech-badge"><i class="fas fa-file-audio"></i> FLAC</span>
                        <span class="tech-badge"><i class="fab fa-python"></i> Librosa</span>
                        <span class="tech-badge"><i class="fas fa-brain"></i> Tacotron</span>
                        <span class="tech-badge"><i class="fas fa-wave-square"></i> WaveNet</span>
                        <span class="tech-badge"><i class="fas fa-microchip"></i> TensorFlow</span>
                        <span class="tech-badge"><i class="fas fa-fire"></i> PyTorch</span>
                        <span class="tech-badge"><i class="fas fa-chart-line"></i> MFCC</span>
                    </div>
                </div>
            </div>

            <!-- Audio Preview Section -->
            <div class="row mb-4">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-headphones text-primary"></i> Audio Preview</h3>
                    <div class="audio-preview">
                        <p class="mb-3"><strong>Sample Audio Recording:</strong></p>
                        <audio controls class="w-100" style="border-radius: 10px;">
                            <source src="audio/sample.wav" type="audio/wav">
                            <source src="audio/sample.mp3" type="audio/mpeg">
                            Your browser does not support the audio element.
                        </audio>
                        <p class="mt-3 text-muted small">
                            <i class="fas fa-info-circle"></i> This is a sample from the dataset. Full dataset includes multiple speakers and formats.
                        </p>
                    </div>
                </div>
            </div>

            <!-- Features Section -->
            <div class="row mb-4" id="features">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-star text-primary"></i> Features</h3>
                    <div class="row">
                        <div class="col-md-6 col-lg-4">
                            <div class="feature-card">
                                <i class="fas fa-microphone-alt feature-icon"></i>
                                <h5>High-quality recordings</h5>
                                <p>Professional studio-quality audio recordings with optimal sampling rates (16kHz, 22kHz, 44.1kHz)</p>
                            </div>
                        </div>
                        <div class="col-md-6 col-lg-4">
                            <div class="feature-card">
                                <i class="fas fa-users feature-icon"></i>
                                <h5>Multiple speakers</h5>
                                <p>Diverse speaker voices with different accents, ages, and vocal characteristics</p>
                            </div>
                        </div>
                        <div class="col-md-6 col-lg-4">
                            <div class="feature-card">
                                <i class="fas fa-chart-line feature-icon"></i>
                                <h5>Voice characteristics</h5>
                                <p>Detailed metadata including pitch, tone, timbre, and spectral features</p>
                            </div>
                        </div>
                        <div class="col-md-6 col-lg-4">
                            <div class="feature-card">
                                <i class="fas fa-file-alt feature-icon"></i>
                                <h5>Text transcripts</h5>
                                <p>Accurate text transcripts aligned with audio recordings for training</p>
                            </div>
                        </div>
                        <div class="col-md-6 col-lg-4">
                            <div class="feature-card">
                                <i class="fas fa-robot feature-icon"></i>
                                <h5>Ready for TTS models</h5>
                                <p>Pre-processed and formatted for direct use with Tacotron, WaveNet, and other TTS models</p>
                            </div>
                        </div>
                        <div class="col-md-6 col-lg-4">
                            <div class="feature-card">
                                <i class="fas fa-database feature-icon"></i>
                                <h5>Structured format</h5>
                                <p>Well-organized directory structure with metadata and configuration files</p>
                            </div>
                        </div>
                    </div>
                </div>
            </div>

            <!-- Advanced Features Section -->
            <div class="row mb-4" id="advanced">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-rocket text-primary"></i> Advanced Features</h3>
                    <div class="row">
                        <div class="col-md-6">
                            <div class="advanced-feature">
                                <h5><i class="fas fa-cogs"></i> Automated Processing</h5>
                                <p>Python scripts for automated audio processing, feature extraction, and dataset preparation</p>
                            </div>
                        </div>
                        <div class="col-md-6">
                            <div class="advanced-feature">
                                <h5><i class="fas fa-chart-bar"></i> Feature Extraction</h5>
                                <p>Automatic extraction of MFCC, mel spectrograms, pitch, and spectral features</p>
                            </div>
                        </div>
                        <div class="col-md-6">
                            <div class="advanced-feature">
                                <h5><i class="fas fa-network-wired"></i> Model Compatibility</h5>
                                <p>Compatible with Tacotron, Tacotron2, WaveNet, WaveGlow, FastSpeech, and more</p>
                            </div>
                        </div>
                        <div class="col-md-6">
                            <div class="advanced-feature">
                                <h5><i class="fas fa-sliders-h"></i> Configurable Settings</h5>
                                <p>JSON configuration files for easy customization of processing parameters</p>
                            </div>
                        </div>
                        <div class="col-md-6">
                            <div class="advanced-feature">
                                <h5><i class="fas fa-code-branch"></i> Train/Test Split</h5>
                                <p>Automatic dataset splitting for training, validation, and testing</p>
                            </div>
                        </div>
                        <div class="col-md-6">
                            <div class="advanced-feature">
                                <h5><i class="fas fa-file-export"></i> Multiple Formats</h5>
                                <p>Support for WAV, FLAC, MP3 with automatic format conversion</p>
                            </div>
                        </div>
                    </div>
                </div>
            </div>

            <!-- Progress Indicators -->
            <div class="row mb-4">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-tasks text-primary"></i> Dataset Quality Metrics</h3>
                    <div class="mb-3">
                        <label class="form-label">Audio Quality: <strong>95%</strong></label>
                        <div class="progress" style="height: 25px;">
                            <div class="progress-bar progress-bar-custom" role="progressbar" style="width: 95%"></div>
                        </div>
                    </div>
                    <div class="mb-3">
                        <label class="form-label">Transcript Accuracy: <strong>98%</strong></label>
                        <div class="progress" style="height: 25px;">
                            <div class="progress-bar progress-bar-custom" role="progressbar" style="width: 98%"></div>
                        </div>
                    </div>
                    <div class="mb-3">
                        <label class="form-label">Speaker Diversity: <strong>92%</strong></label>
                        <div class="progress" style="height: 25px;">
                            <div class="progress-bar progress-bar-custom" role="progressbar" style="width: 92%"></div>
                        </div>
                    </div>
                    <div class="mb-3">
                        <label class="form-label">Processing Speed: <strong>88%</strong></label>
                        <div class="progress" style="height: 25px;">
                            <div class="progress-bar progress-bar-custom" role="progressbar" style="width: 88%"></div>
                        </div>
                    </div>
                </div>
            </div>

            <!-- Dataset Preview Table -->
            <div class="row mb-4" id="dataset-preview">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-table text-primary"></i> Dataset Preview</h3>
                    <div class="mb-3">
                        <div class="row">
                            <div class="col-md-6">
                                <input type="text" class="form-control" id="searchTable" placeholder="Search speakers, gender, language...">
                            </div>
                            <div class="col-md-3">
                                <select class="form-select" id="filterGender">
                                    <option value="">All Genders</option>
                                    <option value="Male">Male</option>
                                    <option value="Female">Female</option>
                                </select>
                            </div>
                            <div class="col-md-3">
                                <select class="form-select" id="filterLanguage">
                                    <option value="">All Languages</option>
                                    <option value="English">English</option>
                                    <option value="Spanish">Spanish</option>
                                    <option value="Hindi">Hindi</option>
                                </select>
                            </div>
                        </div>
                    </div>
                    <div class="table-responsive">
                        <table class="table table-striped table-hover">
                            <thead class="table-dark">
                                <tr>
                                    <th>Speaker ID</th>
                                    <th>Gender</th>
                                    <th>Age</th>
                                    <th>Language</th>
                                    <th>Recordings</th>
                                    <th>Duration</th>
                                    <th>Format</th>
                                </tr>
                            </thead>
                            <tbody id="datasetTable">
                                <tr>
                                    <td>speaker_001</td>
                                    <td>Male</td>
                                    <td>28</td>
                                    <td>English</td>
                                    <td>25</td>
                                    <td>12.5 min</td>
                                    <td>WAV, FLAC</td>
                                </tr>
                                <tr>
                                    <td>speaker_002</td>
                                    <td>Female</td>
                                    <td>32</td>
                                    <td>English</td>
                                    <td>30</td>
                                    <td>15.2 min</td>
                                    <td>WAV, FLAC</td>
                                </tr>
                                <tr>
                                    <td>speaker_003</td>
                                    <td>Male</td>
                                    <td>45</td>
                                    <td>English</td>
                                    <td>22</td>
                                    <td>11.8 min</td>
                                    <td>WAV</td>
                                </tr>
                                <tr>
                                    <td>speaker_004</td>
                                    <td>Female</td>
                                    <td>26</td>
                                    <td>English</td>
                                    <td>28</td>
                                    <td>14.5 min</td>
                                    <td>WAV, FLAC</td>
                                </tr>
                                <tr>
                                    <td>speaker_005</td>
                                    <td>Male</td>
                                    <td>35</td>
                                    <td>English</td>
                                    <td>27</td>
                                    <td>13.7 min</td>
                                    <td>WAV</td>
                                </tr>
                            </tbody>
                        </table>
                    </div>
                    <p class="text-muted small mt-2"><i class="fas fa-info-circle"></i> Showing sample data. Full dataset includes 50+ speakers.</p>
                </div>
            </div>

            <!-- Data Visualization -->
            <div class="row mb-4" id="visualization">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-chart-pie text-primary"></i> Data Visualization</h3>
                    <div class="row">
                        <div class="col-md-6 mb-4">
                            <div class="card chart-card">
                                <div class="card-body">
                                    <h5 class="card-title mb-3">
                                        <i class="fas fa-users text-primary me-2"></i>Speaker Distribution by Gender
                                    </h5>
                                    <div class="chart-container">
                                        <canvas id="genderChart"></canvas>
                                    </div>
                                </div>
                            </div>
                        </div>
                        <div class="col-md-6 mb-4">
                            <div class="card chart-card">
                                <div class="card-body">
                                    <h5 class="card-title mb-3">
                                        <i class="fas fa-file-audio text-primary me-2"></i>Audio Format Distribution
                                    </h5>
                                    <div class="chart-container">
                                        <canvas id="formatChart"></canvas>
                                    </div>
                                </div>
                            </div>
                        </div>
                    </div>
                    <div class="row">
                        <div class="col-md-6 mb-4">
                            <div class="card chart-card">
                                <div class="card-body">
                                    <h5 class="card-title mb-3">
                                        <i class="fas fa-clock text-primary me-2"></i>Recording Duration Distribution
                                    </h5>
                                    <div class="chart-container">
                                        <canvas id="durationChart"></canvas>
                                    </div>
                                </div>
                            </div>
                        </div>
                        <div class="col-md-6 mb-4">
                            <div class="card chart-card">
                                <div class="card-body">
                                    <h5 class="card-title mb-3">
                                        <i class="fas fa-birthday-cake text-primary me-2"></i>Age Group Distribution
                                    </h5>
                                    <div class="chart-container">
                                        <canvas id="ageChart"></canvas>
                                    </div>
                                </div>
                            </div>
                        </div>
                    </div>
                    <div class="row">
                        <div class="col-md-12">
                            <div class="card chart-card">
                                <div class="card-body">
                                    <h5 class="card-title mb-3">
                                        <i class="fas fa-language text-primary me-2"></i>Language Distribution
                                    </h5>
                                    <div class="chart-container-large">
                                        <canvas id="languageChart"></canvas>
                                    </div>
                                </div>
                            </div>
                        </div>
                    </div>
                </div>
            </div>

            <!-- Code Examples Section -->
            <div class="row mb-4" id="code-examples">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-code text-primary"></i> Code Examples</h3>
                    <ul class="nav nav-tabs" id="codeTabs" role="tablist">
                        <li class="nav-item" role="presentation">
                            <button class="nav-link active" id="python-tab" data-bs-toggle="tab" data-bs-target="#python" type="button">
                                <i class="fab fa-python"></i> Python
                            </button>
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                            <button class="nav-link" id="processing-tab" data-bs-toggle="tab" data-bs-target="#processing" type="button">
                                <i class="fas fa-cogs"></i> Processing
                            </button>
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                        <li class="nav-item" role="presentation">
                            <button class="nav-link" id="features-tab" data-bs-toggle="tab" data-bs-target="#features-code" type="button">
                                <i class="fas fa-chart-line"></i> Features
                            </button>
                        </li>
                    </ul>
                    <div class="tab-content" id="codeTabContent">
                        <div class="tab-pane fade show active" id="python" role="tabpanel">
                            <pre class="bg-dark text-light p-3 rounded"><code># Load and process audio file
import librosa
import soundfile as sf

# Load audio
audio, sr = librosa.load('audio/speaker_001/recordings/sample_001.wav', 
                         sr=22050, mono=True)

# Normalize audio
audio = librosa.util.normalize(audio)

# Save processed audio
sf.write('processed/sample_001.wav', audio, sr)

print(f"Audio loaded: {len(audio)/sr:.2f} seconds")
print(f"Sample rate: {sr} Hz")</code></pre>
                        </div>
                        <div class="tab-pane fade" id="processing" role="tabpanel">
                            <pre class="bg-dark text-light p-3 rounded"><code># Process entire dataset
python scripts/process_audio.py \
    --input audio/ \
    --output processed/ \
    --sample-rate 22050

# Extract features
python scripts/extract_features.py \
    --audio audio/ \
    --output features/

# Prepare dataset
python scripts/prepare_dataset.py \
    --config config/dataset_config.json</code></pre>
                        </div>
                        <div class="tab-pane fade" id="features-code" role="tabpanel">
                            <pre class="bg-dark text-light p-3 rounded"><code># Extract MFCC features
import librosa

audio, sr = librosa.load('audio/sample.wav', sr=22050)
mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)

# Extract mel spectrogram
mel_spec = librosa.feature.melspectrogram(
    y=audio, sr=sr, n_mels=80
)
mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)

print(f"MFCC shape: {mfccs.shape}")
print(f"Mel spectrogram shape: {mel_spec_db.shape}")</code></pre>
                        </div>
                    </div>
                </div>
            </div>

            <!-- Installation & Step-by-Step Guide -->
            <div class="row mb-4" id="installation">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-download text-primary"></i> Installation & Step-by-Step Guide</h3>
                    
                    <div class="card mb-4">
                        <div class="card-header bg-primary text-white">
                            <h5 class="mb-0"><i class="fas fa-list-ol"></i> Step 1: Prerequisites</h5>
                        </div>
                        <div class="card-body">
                            <p>Before starting, ensure you have the following installed:</p>
                            <ul class="list-group list-group-flush">
                                <li class="list-group-item">
                                    <i class="fas fa-check-circle text-success me-2"></i>
                                    <strong>Python 3.8 or higher</strong> - Check version: <code>python --version</code>
                                </li>
                                <li class="list-group-item">
                                    <i class="fas fa-check-circle text-success me-2"></i>
                                    <strong>pip package manager</strong> - Usually comes with Python
                                </li>
                                <li class="list-group-item">
                                    <i class="fas fa-check-circle text-success me-2"></i>
                                    <strong>Git</strong> (optional) - For cloning the repository
                                </li>
                            </ul>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-success text-white">
                            <h5 class="mb-0"><i class="fas fa-download"></i> Step 2: Download/Clone Project</h5>
                        </div>
                        <div class="card-body">
                            <p><strong>Option A: Clone from GitHub</strong></p>
                            <pre class="bg-dark text-light p-3 rounded"><code>git clone https://github.com/rskworld/voice-cloning.git
cd voice-cloning</code></pre>
                            
                            <p class="mt-3"><strong>Option B: Download ZIP</strong></p>
                            <p>Download the project ZIP file from: <a href="https://github.com/rskworld/voice-cloning" target="_blank">GitHub Repository</a></p>
                            <p>Extract the ZIP file and navigate to the project directory.</p>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-info text-white">
                            <h5 class="mb-0"><i class="fas fa-cog"></i> Step 3: Install Dependencies</h5>
                        </div>
                        <div class="card-body">
                            <p>Install all required Python packages using one of the following methods:</p>
                            
                            <p><strong>Method 1: Using setup.py (Recommended)</strong></p>
                            <pre class="bg-dark text-light p-3 rounded"><code>python setup.py</code></pre>
                            <p class="small text-muted">This will automatically check Python version, create directories, and install packages.</p>
                            
                            <p class="mt-3"><strong>Method 2: Using requirements.txt</strong></p>
                            <pre class="bg-dark text-light p-3 rounded"><code>pip install -r requirements.txt</code></pre>
                            
                            <p class="mt-3"><strong>Required packages (from requirements.txt):</strong></p>
                            <ul>
                                <li><code>librosa</code> - Audio processing and analysis</li>
                                <li><code>soundfile</code> - Audio file I/O</li>
                                <li><code>numpy</code> - Numerical computing</li>
                                <li><code>pandas</code> - Data manipulation</li>
                                <li><code>tqdm</code> - Progress bars</li>
                                <li>And more (see <code>requirements.txt</code> file)</li>
                            </ul>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-warning text-dark">
                            <h5 class="mb-0"><i class="fas fa-folder-open"></i> Step 4: Prepare Audio Files</h5>
                        </div>
                        <div class="card-body">
                            <p>Organize your audio files in the following structure:</p>
                            <pre class="bg-dark text-light p-3 rounded"><code>audio/
├── speaker_001/
│   ├── recordings/
│   │   ├── sample_001.wav
│   │   ├── sample_002.wav
│   │   └── ...
│   ├── metadata.json
│   └── transcripts.txt
├── speaker_002/
│   └── ...
└── ...</code></pre>
                            
                            <p class="mt-3"><strong>File Requirements:</strong></p>
                            <ul>
                                <li><strong>metadata.json</strong> - Speaker metadata (see example in <code>audio/speaker_001/metadata.json</code>)</li>
                                <li><strong>transcripts.txt</strong> - Text transcripts, one per line (see example in <code>audio/speaker_001/transcripts.txt</code>)</li>
                                <li><strong>recordings/</strong> - Directory containing audio files (WAV, FLAC, or MP3)</li>
                            </ul>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-danger text-white">
                            <h5 class="mb-0"><i class="fas fa-play-circle"></i> Step 5: Process Audio Files</h5>
                        </div>
                        <div class="card-body">
                            <p>Process your audio files using the processing script:</p>
                            <pre class="bg-dark text-light p-3 rounded"><code>python scripts/process_audio.py --input audio/ --output processed/ --sample-rate 22050</code></pre>
                            
                            <p class="mt-3"><strong>What this does:</strong></p>
                            <ul>
                                <li>Reads audio files from <code>audio/</code> directory</li>
                                <li>Resamples to 22050 Hz (or specified rate)</li>
                                <li>Converts to mono channel</li>
                                <li>Normalizes audio levels</li>
                                <li>Saves processed files to <code>processed/</code> directory</li>
                            </ul>
                            
                            <p class="mt-3"><strong>Output:</strong> Processed WAV files in <code>processed/</code> directory</p>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-secondary text-white">
                            <h5 class="mb-0"><i class="fas fa-chart-line"></i> Step 6: Extract Features</h5>
                        </div>
                        <div class="card-body">
                            <p>Extract audio features from processed files:</p>
                            <pre class="bg-dark text-light p-3 rounded"><code>python scripts/extract_features.py --audio audio/ --output features/</code></pre>
                            
                            <p class="mt-3"><strong>What this does:</strong></p>
                            <ul>
                                <li>Extracts MFCC features (13 coefficients)</li>
                                <li>Extracts mel spectrogram (80 mels)</li>
                                <li>Calculates pitch (F0)</li>
                                <li>Computes spectral features (centroid, rolloff, zero-crossing rate)</li>
                                <li>Saves features as JSON files</li>
                            </ul>
                            
                            <p class="mt-3"><strong>Output Files:</strong></p>
                            <ul>
                                <li>Individual feature JSON files in <code>features/</code> directory</li>
                                <li><code>features/features_summary.json</code> - Summary of all features</li>
                            </ul>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-dark text-white">
                            <h5 class="mb-0"><i class="fas fa-database"></i> Step 7: Prepare Dataset for Training</h5>
                        </div>
                        <div class="card-body">
                            <p>Prepare the dataset with metadata and train/test split:</p>
                            <pre class="bg-dark text-light p-3 rounded"><code>python scripts/prepare_dataset.py --config config/dataset_config.json</code></pre>
                            
                            <p class="mt-3"><strong>What this does:</strong></p>
                            <ul>
                                <li>Scans all audio files and organizes by speaker</li>
                                <li>Creates <code>prepared/metadata.json</code> with complete dataset information</li>
                                <li>Generates train/test/validation split (80%/10%/10% by default)</li>
                                <li>Creates <code>prepared/train_test_split.json</code> with split information</li>
                            </ul>
                            
                            <p class="mt-3"><strong>Configuration File:</strong> <code>config/dataset_config.json</code></p>
                            <p>You can modify this file to change:</p>
                            <ul>
                                <li>Sample rate settings</li>
                                <li>Train/test split ratios</li>
                                <li>Feature extraction settings</li>
                            </ul>
                            
                            <p class="mt-3"><strong>Output Files:</strong></p>
                            <ul>
                                <li><code>prepared/metadata.json</code> - Complete dataset metadata</li>
                                <li><code>prepared/train_test_split.json</code> - Train/test split information</li>
                            </ul>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-primary text-white">
                            <h5 class="mb-0"><i class="fas fa-check-circle"></i> Step 8: Validate Dataset (Optional)</h5>
                        </div>
                        <div class="card-body">
                            <p>Validate your dataset to ensure everything is correct:</p>
                            <pre class="bg-dark text-light p-3 rounded"><code>python scripts/validate_dataset.py --dataset audio/ --output validation_results.json</code></pre>
                            
                            <p class="mt-3"><strong>What this checks:</strong></p>
                            <ul>
                                <li>Audio file integrity</li>
                                <li>Metadata.json files</li>
                                <li>Transcripts.txt files</li>
                                <li>Sample rates and durations</li>
                                <li>Silent or corrupted files</li>
                            </ul>
                            
                            <p class="mt-3"><strong>Output:</strong> <code>validation_results.json</code> with validation report</p>
                        </div>
                    </div>

                    <div class="card mb-4">
                        <div class="card-header bg-success text-white">
                            <h5 class="mb-0"><i class="fas fa-chart-bar"></i> Step 9: Analyze Dataset (Optional)</h5>
                        </div>
                        <div class="card-body">
                            <p>Generate comprehensive statistics about your dataset:</p>
                            <pre class="bg-dark text-light p-3 rounded"><code>python scripts/analyze_dataset.py --dataset audio/ --output dataset_analysis.json</code></pre>
                            
                            <p class="mt-3"><strong>What this generates:</strong></p>
                            <ul>
                                <li>Duration statistics (min, max, mean, median)</li>
                                <li>Sample rate distribution</li>
                                <li>Format distribution</li>
                                <li>Per-speaker statistics</li>
                                <li>Overall dataset summary</li>
                            </ul>
                            
                            <p class="mt-3"><strong>Output:</strong> <code>dataset_analysis.json</code> with complete analysis</p>
                        </div>
                    </div>

                    <div class="alert alert-success mt-4" role="alert">
                        <h5><i class="fas fa-check-circle"></i> Dataset Ready!</h5>
                        <p class="mb-0">Your dataset is now processed and ready for training TTS models like Tacotron, WaveNet, FastSpeech, and more!</p>
                    </div>

                    <div class="card mt-4">
                        <div class="card-header bg-info text-white">
                            <h5 class="mb-0"><i class="fas fa-file-code"></i> Important Files Reference</h5>
                        </div>
                        <div class="card-body">
                            <div class="row">
                                <div class="col-md-6">
                                    <h6>Configuration Files:</h6>
                                    <ul>
                                        <li><code>config/dataset_config.json</code> - Main configuration</li>
                                        <li><code>requirements.txt</code> - Python dependencies</li>
                                        <li><code>setup.py</code> - Setup script</li>
                                    </ul>
                                    
                                    <h6 class="mt-3">Script Files:</h6>
                                    <ul>
                                        <li><code>scripts/process_audio.py</code> - Audio processing</li>
                                        <li><code>scripts/extract_features.py</code> - Feature extraction</li>
                                        <li><code>scripts/prepare_dataset.py</code> - Dataset preparation</li>
                                        <li><code>scripts/validate_dataset.py</code> - Validation</li>
                                        <li><code>scripts/analyze_dataset.py</code> - Analysis</li>
                                        <li><code>scripts/convert_format.py</code> - Format conversion</li>
                                    </ul>
                                </div>
                                <div class="col-md-6">
                                    <h6>Documentation Files:</h6>
                                    <ul>
                                        <li><code>README.md</code> - Quick start guide</li>
                                        <li><code>COMPLETE_DOCUMENTATION.md</code> - Full documentation</li>
                                        <li><code>RELEASE_NOTES.md</code> - Release information</li>
                                    </ul>
                                    
                                    <h6 class="mt-3">Example Files:</h6>
                                    <ul>
                                        <li><code>example_usage.py</code> - Usage examples</li>
                                        <li><code>audio/speaker_001/metadata.json</code> - Metadata example</li>
                                        <li><code>audio/speaker_001/transcripts.txt</code> - Transcripts example</li>
                                    </ul>
                                    
                                    <h6 class="mt-3">Data Files:</h6>
                                    <ul>
                                        <li><code>data/dataset_manifest.json</code> - Dataset manifest</li>
                                        <li><code>data/audio_statistics.json</code> - Audio stats</li>
                                        <li><code>data/feature_statistics.json</code> - Feature stats</li>
                                    </ul>
                                </div>
                            </div>
                        </div>
                    </div>

                    <div class="card mt-4">
                        <div class="card-header bg-warning text-dark">
                            <h5 class="mb-0"><i class="fas fa-exclamation-triangle"></i> Troubleshooting</h5>
                        </div>
                        <div class="card-body">
                            <h6>Common Issues:</h6>
                            <ul>
                                <li><strong>Import Error:</strong> Make sure all dependencies are installed: <code>pip install -r requirements.txt</code></li>
                                <li><strong>Audio File Not Found:</strong> Check file paths and ensure audio files are in <code>audio/speaker_XXX/recordings/</code></li>
                                <li><strong>Permission Error:</strong> Ensure you have read/write permissions in the project directory</li>
                                <li><strong>Memory Error:</strong> Process files in smaller batches if dataset is very large</li>
                            </ul>
                            
                            <p class="mt-3"><strong>Need Help?</strong></p>
                            <p>Contact us for support:</p>
                            <ul>
                                <li>Email: <a href="mailto:help@rskworld.in">help@rskworld.in</a></li>
                                <li>Phone: <a href="tel:+919330539277">+91 93305 39277</a></li>
                                <li>Website: <a href="https://rskworld.in" target="_blank">rskworld.in</a></li>
                            </ul>
                        </div>
                    </div>
                </div>
            </div>

            <!-- API Documentation -->
            <div class="row mb-4" id="api-docs">
                <div class="col-md-12">
                    <h3 class="section-title"><i class="fas fa-book text-primary"></i> API Documentation</h3>
                    <div class="accordion" id="apiAccordion">
                        <div class="accordion-item">
                            <h2 class="accordion-header">
                                <button class="accordion-button" type="button" data-bs-toggle="collapse" data-bs-target="#api1">
                                    <i class="fas fa-file-audio me-2"></i> process_audio.py
                                </button>
                            </h2>
                            <div id="api1" class="accordion-collapse collapse show" data-bs-parent="#apiAccordion">
                                <div class="accordion-body">
                                    <strong>Purpose:</strong> Process audio files (resampling, normalization)<br>
                                    <strong>Usage:</strong> <code>python scripts/process_audio.py --input DIR --output DIR [--sample-rate RATE]</code><br>
                                    <strong>Parameters:</strong>
                                    <ul>
                                        <li><code>--input</code>: Input directory containing audio files</li>
                                        <li><code>--output</code>: Output directory for processed files</li>
                                        <li><code>--sample-rate</code>: Target sample rate (default: 22050)</li>
                                        <li><code>--no-normalize</code>: Disable audio normalization</li>
                                    </ul>
                                </div>
                            </div>
                        </div>
                        <div class="accordion-item">
                            <h2 class="accordion-header">
                                <button class="accordion-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#api2">
                                    <i class="fas fa-chart-line me-2"></i> extract_features.py
                                </button>
                            </h2>
                            <div id="api2" class="accordion-collapse collapse" data-bs-parent="#apiAccordion">
                                <div class="accordion-body">
                                    <strong>Purpose:</strong> Extract audio features (MFCC, mel spectrogram, pitch)<br>
                                    <strong>Usage:</strong> <code>python scripts/extract_features.py --audio DIR --output DIR</code><br>
                                    <strong>Output:</strong> JSON files with extracted features for each audio file
                                </div>
                            </div>
                        </div>
                        <div class="accordion-item">
                            <h2 class="accordion-header">
                                <button class="accordion-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#api3">
                                    <i class="fas fa-database me-2"></i> prepare_dataset.py
                                </button>
                            </h2>
                            <div id="api3" class="accordion-collapse collapse" data-bs-parent="#apiAccordion">
                                <div class="accordion-body">
                                    <strong>Purpose:</strong> Prepare dataset for training (metadata, train/test split)<br>
                                    <strong>Usage:</strong> <code>python scripts/prepare_dataset.py --config FILE</code><br>
                                    <strong>Output:</strong> Metadata JSON and train/test split files
                                </div>
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                        <h5><i class="fas fa-question text-primary"></i> What audio formats are supported?</h5>
                        <p>The dataset supports WAV, FLAC, and MP3 formats. Processing scripts can handle format conversion automatically.</p>
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                        <h5><i class="fas fa-question text-primary"></i> How many speakers are included?</h5>
                        <p>The dataset includes 50+ speakers with diverse voices, accents, and characteristics for comprehensive training.</p>
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                        <h5><i class="fas fa-question text-primary"></i> What TTS models are compatible?</h5>
                        <p>This dataset is compatible with Tacotron, Tacotron2, WaveNet, WaveGlow, FastSpeech, and other popular TTS architectures.</p>
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                        <p>Use the provided Python scripts: <code>process_audio.py</code> for processing, <code>extract_features.py</code> for features, and <code>prepare_dataset.py</code> for preparation.</p>
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                    <strong>Developer:</strong> Molla Samser | 
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markup

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