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RSK World
speech-recognition
RSK World
speech-recognition
Speech Recognition Dataset - Audio AI + Speech-to-Text + Voice Recognition
speech-recognition
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/**
 * ============================================================================
 * Speech Recognition Dataset - Demo Page
 * ============================================================================
 * 
 * Project: Speech Recognition Dataset
 * Description: Audio speech recognition dataset with labeled speech samples 
 *              for training speech-to-text and voice recognition models.
 * 
 * ============================================================================
 * DEVELOPER INFORMATION
 * ============================================================================
 * Website: https://rskworld.in
 * Founded by: Molla Samser
 * Designer & Tester: Rima Khatun
 * Email: help@rskworld.in
 * Support: support@rskworld.in
 * Phone: +91 93305 39277
 * 
 * ============================================================================
 * COPYRIGHT NOTICE
 * ============================================================================
 * © 2026 RSK World. All rights reserved.
 * This dataset is provided for educational and research purposes.
 * 
 * ============================================================================
 */
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                Speech Recognition
                <span class="gradient-text">Dataset</span>
            </h1>
            <p class="hero-description">
                A comprehensive audio speech recognition dataset with labeled speech samples 
                for training speech-to-text and voice recognition models. Perfect for deep 
                learning applications and audio AI research.
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                        <span class="stat-number" data-count="25">0</span>
                        <span class="stat-label">Speakers</span>
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                        <span class="stat-number" data-count="145">0</span>
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                        <input type="range" id="volumeSlider" min="0" max="100" value="70" class="volume-slider" title="Volume">
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                <div class="player-info">
                    <span class="speaker-tag">Speaker_001</span>
                    <span class="transcript">"Hello, how are you today?"</span>
                </div>
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                <h2 class="section-title">Dataset Capabilities</h2>
                <p class="section-subtitle">Everything you need for speech recognition research</p>
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                <div class="feature-card" data-aos="fade-up" data-aos-delay="0">
                    <div class="feature-icon">
                        <i class="fas fa-file-audio"></i>
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                    <h3>Audio Recordings with Transcripts</h3>
                    <p>High-quality audio files paired with accurate text transcriptions for supervised learning.</p>
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                    <h3>Multiple Speakers</h3>
                    <p>Diverse speaker dataset with various accents, ages, and genders for robust model training.</p>
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                    <h3>Various Audio Lengths</h3>
                    <p>Audio samples ranging from short commands to longer sentences for flexible model training.</p>
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                    <h3>Preprocessed Features</h3>
                    <p>Pre-extracted MFCC, spectrograms, and mel-frequency features ready for model input.</p>
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                    <h3>Ready for RNN/LSTM Models</h3>
                    <p>Formatted data structures optimized for recurrent neural networks and sequence models.</p>
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                    <h3>Python Ready</h3>
                    <p>Compatible with NumPy, Librosa, TensorFlow, and PyTorch for seamless integration.</p>
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                            <span class="speaker"><i class="fas fa-user"></i> Speaker_001</span>
                            <span class="duration"><i class="fas fa-clock"></i> 2.3s</span>
                        </div>
                        <p class="transcript">"Hello, how are you today?"</p>
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                            <i class="fas fa-play"></i>
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                            <span class="speaker"><i class="fas fa-user"></i> Speaker_015</span>
                            <span class="duration"><i class="fas fa-clock"></i> 3.1s</span>
                        </div>
                        <p class="transcript">"Please turn on the lights"</p>
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                        </button>
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                            <span class="speaker"><i class="fas fa-user"></i> Speaker_028</span>
                            <span class="duration"><i class="fas fa-clock"></i> 4.5s</span>
                        </div>
                        <p class="transcript">"What's the weather like outside?"</p>
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                            <span class="speaker"><i class="fas fa-user"></i> Speaker_042</span>
                            <span class="duration"><i class="fas fa-clock"></i> 2.8s</span>
                        </div>
                        <p class="transcript">"Set a timer for five minutes"</p>
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                            <i class="fas fa-play"></i>
                        </button>
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                    <h3><i class="fas fa-chart-pie"></i> Speaker Distribution</h3>
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                        <thead>
                            <tr>
                                <th>ID</th>
                                <th>File Name</th>
                                <th>Speaker</th>
                                <th>Duration (s)</th>
                                <th>Transcript</th>
                                <th>Category</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td>001</td>
                                <td>audio_001.wav</td>
                                <td>Speaker_001</td>
                                <td>2.34</td>
                                <td>"Hello, how are you today?"</td>
                                <td><span class="category-badge greeting">Greeting</span></td>
                            </tr>
                            <tr>
                                <td>002</td>
                                <td>audio_002.wav</td>
                                <td>Speaker_001</td>
                                <td>1.87</td>
                                <td>"Good morning"</td>
                                <td><span class="category-badge greeting">Greeting</span></td>
                            </tr>
                            <tr>
                                <td>003</td>
                                <td>audio_003.wav</td>
                                <td>Speaker_002</td>
                                <td>3.12</td>
                                <td>"Please turn on the lights"</td>
                                <td><span class="category-badge command">Command</span></td>
                            </tr>
                            <tr>
                                <td>004</td>
                                <td>audio_004.wav</td>
                                <td>Speaker_003</td>
                                <td>4.56</td>
                                <td>"What's the weather like outside?"</td>
                                <td><span class="category-badge question">Question</span></td>
                            </tr>
                            <tr>
                                <td>005</td>
                                <td>audio_005.wav</td>
                                <td>Speaker_004</td>
                                <td>2.89</td>
                                <td>"Set a timer for five minutes"</td>
                                <td><span class="category-badge command">Command</span></td>
                            </tr>
                            <tr>
                                <td>006</td>
                                <td>audio_006.wav</td>
                                <td>Speaker_002</td>
                                <td>2.15</td>
                                <td>"Thank you very much"</td>
                                <td><span class="category-badge greeting">Greeting</span></td>
                            </tr>
                            <tr>
                                <td>007</td>
                                <td>audio_007.wav</td>
                                <td>Speaker_005</td>
                                <td>3.45</td>
                                <td>"Can you play some music?"</td>
                                <td><span class="category-badge command">Command</span></td>
                            </tr>
                            <tr>
                                <td>008</td>
                                <td>audio_008.wav</td>
                                <td>Speaker_001</td>
                                <td>2.67</td>
                                <td>"How much does it cost?"</td>
                                <td><span class="category-badge question">Question</span></td>
                            </tr>
                        </tbody>
                    </table>
                </div>
            </div>
        </div>
    </section>

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            <div class="section-header">
                <span class="section-tag">Technologies</span>
                <h2 class="section-title">Compatible Technologies</h2>
                <p class="section-subtitle">Works seamlessly with popular audio processing tools</p>
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                <div class="tech-item">
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                    <span>MP3</span>
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                    <div class="tech-icon numpy">
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                    <span>NumPy</span>
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                    <span>Librosa</span>
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                    <div class="tech-icon tensorflow">
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                    <span>TensorFlow</span>
                </div>
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                    <span>PyTorch</span>
                </div>
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    </section>

    <!-- Code Preview Section -->
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                <span class="section-tag">Quick Start</span>
                <h2 class="section-title">Get Started in Minutes</h2>
                <p class="section-subtitle">Simple code to load and use the dataset</p>
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                <pre class="code-block active" id="python"><code><span class="comment"># Load the Speech Recognition Dataset</span>
<span class="comment"># Author: Molla Samser - RSK World</span>
<span class="comment"># Website: https://rskworld.in</span>

<span class="keyword">import</span> pandas <span class="keyword">as</span> pd
<span class="keyword">import</span> numpy <span class="keyword">as</span> np

<span class="comment"># Load metadata</span>
metadata = pd.read_csv(<span class="string">'data/metadata.csv'</span>)

<span class="comment"># Display dataset info</span>
<span class="function">print</span>(f<span class="string">"Total samples: {len(metadata)}"</span>)
<span class="function">print</span>(f<span class="string">"Unique speakers: {metadata['speaker'].nunique()}"</span>)
<span class="function">print</span>(f<span class="string">"Average duration: {metadata['duration'].mean():.2f}s"</span>)

<span class="comment"># Preview data</span>
metadata.head()</code></pre>
                <pre class="code-block" id="librosa"><code><span class="comment"># Audio Processing with Librosa</span>
<span class="comment"># Author: Molla Samser - RSK World</span>
<span class="comment"># Website: https://rskworld.in</span>

<span class="keyword">import</span> librosa
<span class="keyword">import</span> librosa.display
<span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt

<span class="comment"># Load audio file</span>
audio_path = <span class="string">'data/audio/audio_001.wav'</span>
y, sr = librosa.load(audio_path, sr=<span class="number">16000</span>)

<span class="comment"># Extract MFCC features</span>
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=<span class="number">13</span>)

<span class="comment"># Display spectrogram</span>
plt.figure(figsize=(<span class="number">10</span>, <span class="number">4</span>))
librosa.display.specshow(mfcc, x_axis=<span class="string">'time'</span>)
plt.colorbar()
plt.title(<span class="string">'MFCC Features'</span>)
plt.show()</code></pre>
                <pre class="code-block" id="tensorflow"><code><span class="comment"># TensorFlow Model Training</span>
<span class="comment"># Author: Molla Samser - RSK World</span>
<span class="comment"># Website: https://rskworld.in</span>

<span class="keyword">import</span> tensorflow <span class="keyword">as</span> tf
<span class="keyword">from</span> tensorflow.keras.models <span class="keyword">import</span> Sequential
<span class="keyword">from</span> tensorflow.keras.layers <span class="keyword">import</span> LSTM, Dense, Dropout

<span class="comment"># Build LSTM model for speech recognition</span>
model = Sequential([
    LSTM(<span class="number">128</span>, return_sequences=<span class="keyword">True</span>, input_shape=(None, <span class="number">13</span>)),
    Dropout(<span class="number">0.3</span>),
    LSTM(<span class="number">64</span>),
    Dropout(<span class="number">0.3</span>),
    Dense(<span class="number">32</span>, activation=<span class="string">'relu'</span>),
    Dense(num_classes, activation=<span class="string">'softmax'</span>)
])

model.compile(optimizer=<span class="string">'adam'</span>,
              loss=<span class="string">'categorical_crossentropy'</span>,
              metrics=[<span class="string">'accuracy'</span>])</code></pre>
                <pre class="code-block" id="pytorch"><code><span class="comment"># PyTorch Model Training</span>
<span class="comment"># Author: Molla Samser - RSK World</span>
<span class="comment"># Website: https://rskworld.in</span>

<span class="keyword">import</span> torch
<span class="keyword">import</span> torch.nn <span class="keyword">as</span> nn

<span class="keyword">class</span> <span class="function">SpeechRecognitionModel</span>(nn.Module):
    <span class="keyword">def</span> <span class="function">__init__</span>(self, input_size, hidden_size, num_classes):
        <span class="function">super</span>().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, 
                           num_layers=<span class="number">2</span>, batch_first=<span class="keyword">True</span>,
                           bidirectional=<span class="keyword">True</span>, dropout=<span class="number">0.3</span>)
        self.fc = nn.Linear(hidden_size * <span class="number">2</span>, num_classes)
    
    <span class="keyword">def</span> <span class="function">forward</span>(self, x):
        out, _ = self.lstm(x)
        out = self.fc(out[:, -<span class="number">1</span>, :])
        <span class="keyword">return</span> out

<span class="comment"># Initialize model</span>
model = SpeechRecognitionModel(<span class="number">13</span>, <span class="number">128</span>, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=<span class="number">0.001</span>)</code></pre>
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                <span class="section-tag">Guide</span>
                <h2 class="section-title">How to Use This Project</h2>
                <p class="section-subtitle">Step-by-step guide to get started with the Speech Recognition Dataset</p>
            </div>

            <div class="how-to-steps">
                <!-- Step 1: Installation -->
                <div class="step-card glass-card" data-aos="fade-up" data-aos-delay="0">
                    <div class="step-number">1</div>
                    <div class="step-content">
                        <h3><i class="fas fa-download"></i> Installation</h3>
                        <p>Install all required Python dependencies</p>
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                                <span class="code-lang">Terminal</span>
                                <button class="copy-code-btn" data-code="install-code">
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                            <pre id="install-code"><code># Clone or download the project
cd speech-recognition

# Install dependencies
pip install -r requirements.txt

# Or install individually
pip install numpy pandas librosa tensorflow scikit-learn matplotlib seaborn tqdm soundfile scipy jupyter</code></pre>
                        </div>
                    </div>
                </div>

                <!-- Step 2: Load Dataset -->
                <div class="step-card glass-card" data-aos="fade-up" data-aos-delay="100">
                    <div class="step-number">2</div>
                    <div class="step-content">
                        <h3><i class="fas fa-database"></i> Load the Dataset</h3>
                        <p>Load and explore the dataset using the provided loader</p>
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                                <button class="copy-code-btn" data-code="load-code">
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                                </button>
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                            <pre id="load-code"><code>from scripts.load_dataset import SpeechRecognitionDataset

# Initialize dataset
dataset = SpeechRecognitionDataset(data_dir='data')

# Get statistics
stats = dataset.get_statistics()
print(stats)

# Load an audio file
audio, sr = dataset.load_audio(file_id=1)
transcript = dataset.get_transcript(file_id=1)
print(f"Transcript: {transcript}")

# Get files by speaker
speaker_files = dataset.get_files_by_speaker('Speaker_001')
print(f"Found {len(speaker_files)} files for Speaker_001")</code></pre>
                        </div>
                    </div>
                </div>

                <!-- Step 3: Preprocess -->
                <div class="step-card glass-card" data-aos="fade-up" data-aos-delay="200">
                    <div class="step-number">3</div>
                    <div class="step-content">
                        <h3><i class="fas fa-cogs"></i> Extract Features</h3>
                        <p>Extract audio features (MFCC, Mel Spectrogram, etc.) from audio files</p>
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                                <span class="code-lang">Python</span>
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                            </div>
                            <pre id="preprocess-code"><code>from scripts.preprocess import SpeechRecognitionPreprocessor

# Initialize preprocessor
preprocessor = SpeechRecognitionPreprocessor(
    audio_dir='data/audio',
    output_dir='data/features',
    sr=16000
)

# Process entire dataset
preprocessor.process_dataset(metadata_path='data/metadata.csv')

# Or extract features from a single file
mfcc = preprocessor.extract_mfcc('data/audio/audio_001.wav')
mel_spec = preprocessor.extract_mel_spectrogram('data/audio/audio_001.wav')
chroma = preprocessor.extract_chroma('data/audio/audio_001.wav')</code></pre>
                        </div>
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                </div>

                <!-- Step 4: Train Model -->
                <div class="step-card glass-card" data-aos="fade-up" data-aos-delay="300">
                    <div class="step-number">4</div>
                    <div class="step-content">
                        <h3><i class="fas fa-brain"></i> Train a Model</h3>
                        <p>Train an LSTM or Transformer model for speech recognition</p>
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                                <span class="code-lang">Python</span>
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                            <pre id="train-code"><code>from scripts.train_model import SpeechRecognitionModel

# Initialize model trainer
trainer = SpeechRecognitionModel(
    feature_dir='data/features',
    model_dir='models'
)

# Load features
X, y, metadata = trainer.load_features(feature_name='mfcc')

# Train model
history = trainer.train(
    X, y, 
    epochs=50, 
    batch_size=32,
    validation_split=0.2
)

# Save model
trainer.save_model('models/speech_model.h5')

# Evaluate model
results = trainer.evaluate(X, y)
print(f"Accuracy: {results['accuracy']:.2%}")</code></pre>
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                    </div>
                </div>

                <!-- Step 5: Data Augmentation -->
                <div class="step-card glass-card" data-aos="fade-up" data-aos-delay="400">
                    <div class="step-number">5</div>
                    <div class="step-content">
                        <h3><i class="fas fa-expand-arrows-alt"></i> Data Augmentation (Optional)</h3>
                        <p>Augment your dataset to improve model performance</p>
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                            </div>
                            <pre id="augment-code"><code>from scripts.augmentation import BatchAudioAugmentor

# Initialize augmentor
augmentor = BatchAudioAugmentor(
    audio_dir='data/audio',
    output_dir='data/audio_augmented'
)

# Apply augmentations
augmentor.augment_dataset(
    metadata_path='data/metadata.csv',
    augmentations=['time_stretch', 'pitch_shift', 'add_noise']
)

# Or augment a single file
augmented = augmentor.augment_file(
    'data/audio/audio_001.wav',
    time_stretch_factor=1.1,
    pitch_shift_semitones=2,
    noise_factor=0.01
)</code></pre>
                        </div>
                    </div>
                </div>

                <!-- Step 6: Evaluate Model -->
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                    <div class="step-number">6</div>
                    <div class="step-content">
                        <h3><i class="fas fa-chart-line"></i> Evaluate Model</h3>
                        <p>Comprehensive model evaluation with metrics and visualizations</p>
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                            <pre id="evaluate-code"><code>from scripts.evaluate_model import ModelEvaluator

# Initialize evaluator
evaluator = ModelEvaluator(
    model_path='models/speech_model.h5',
    output_dir='results'
)

# Evaluate model
evaluator.evaluate(
    X_test, y_test,
    class_names=['Greeting', 'Command', 'Question']
)

# This generates:
# - Confusion matrix
# - ROC curves
# - Precision-Recall curves
# - Classification report
# - Error analysis</code></pre>
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                            <span>FFT Size: 2048</span>
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                    <p>Implement secure voice-based biometric authentication systems.</p>
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                    <p>Create voice-controlled interfaces for automotive applications.</p>
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                        <p>The dataset primarily uses WAV format at 16kHz sample rate for high-quality audio. MP3 versions are also available for reduced file size. The Python scripts support loading both formats using Librosa.</p>
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                        <p>We recommend an 80-10-10 split for training, validation, and testing. The provided scripts handle this automatically with stratified sampling to ensure balanced class distribution across splits.</p>
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                        <p>The training scripts include automatic padding and truncation. You can also use bucketing for efficient batching of similar-length sequences, which is implemented in the advanced training script.</p>
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About RSK World

Founded by Molla Samser, with Designer & Tester Rima Khatun, RSK World is your one-stop destination for free programming resources, source code, and development tools.

Founder: Molla Samser
Designer & Tester: Rima Khatun

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