ASR Project

Custom Arabic Speech Recognition for Abserny

Introduction

The ASR (Automatic Speech Recognition) Project is a dedicated effort to develop a custom Arabic speech recognition model optimized specifically for Abserny's use case. This side project aims to improve recognition accuracy, reduce latency, and provide better performance in various acoustic conditions.

Status: In Development

Project Goals

The ASR project has several key objectives:

Primary Goals

  • Improved Accuracy: Better recognition of Arabic trigger words in various accents and dialects
  • Reduced Latency: Faster processing for more responsive voice activation
  • Noise Robustness: Better performance in noisy environments
  • Lower Resource Usage: More efficient model requiring less CPU/RAM

Secondary Goals

  • Support for additional Arabic dialects
  • Continuous learning from user interactions
  • Customizable wake word training
  • Integration with Abserny ecosystem

Current Status

The project is currently in the early development phase:

Completed

  • Project planning and requirements gathering
  • Initial dataset collection started
  • Architecture design completed
  • Development environment setup

In Progress

  • Dataset expansion and augmentation
  • Model architecture implementation
  • Training pipeline development

Planned

  • Initial model training
  • Evaluation and benchmarking
  • Integration testing with Abserny Core
  • Production deployment

Architecture

The ASR system is being developed with the following architecture:

Model Structure

  • Input Processing: Audio feature extraction using MFCC/Mel-spectrograms
  • Acoustic Model: Deep neural network for phoneme recognition
  • Language Model: N-gram model for Arabic trigger words
  • Decoder: Beam search decoder for final word prediction

Technology Stack

import tensorflow as tf
import librosa
import numpy as np

# Audio feature extraction
def extract_features(audio_path):
    audio, sr = librosa.load(audio_path, sr=16000)
    mfcc = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
    return mfcc

# Model architecture (simplified)
model = tf.keras.Sequential([
    tf.keras.layers.LSTM(256, return_sequences=True),
    tf.keras.layers.LSTM(256),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(num_classes, activation='softmax')
])

Dataset

We are building a custom dataset specifically for Arabic trigger words:

Dataset Composition

  • 10,000+ recordings of trigger words
  • Multiple speakers with various accents
  • Different acoustic conditions (quiet, noisy, reverberant)
  • Various recording devices (phone, laptop, headset)

Data Collection

Dataset is being collected through:

  • Crowdsourced recordings from volunteers
  • Synthetic data generation and augmentation
  • Existing Arabic speech corpora

Data Augmentation

To increase robustness, we apply:

  • Background noise addition
  • Speed and pitch variations
  • Room impulse response simulation
  • Volume normalization

Training

The model training process involves:

Training Configuration

# Training parameters
BATCH_SIZE = 32
LEARNING_RATE = 0.001
EPOCHS = 100
VALIDATION_SPLIT = 0.2

# Optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)

# Loss function
loss = tf.keras.losses.CategoricalCrossentropy()

# Metrics
metrics = ['accuracy', 'precision', 'recall']

Training Pipeline

  1. Data preprocessing and feature extraction
  2. Train/validation/test split
  3. Model training with early stopping
  4. Hyperparameter tuning
  5. Final model evaluation
  6. Model optimization for deployment

Development Roadmap

Phase 1: Foundation (Current)

  • Dataset collection and preparation
  • Model architecture implementation
  • Training infrastructure setup

Phase 2: Training

  • Initial model training
  • Evaluation and benchmarking
  • Model optimization

Phase 3: Integration

  • Integration with Abserny Core
  • End-to-end testing
  • Performance optimization

Phase 4: Deployment

  • Production release
  • User feedback collection
  • Continuous improvement

Contributing

We welcome contributions to the ASR project!

How to Contribute

  • Data Collection: Record trigger words in your voice
  • Code Contributions: Improve training pipeline or model architecture
  • Testing: Test models and provide feedback
  • Documentation: Help improve documentation

Recording Guidelines

If you want to contribute voice recordings:

  1. Record in a quiet environment
  2. Use a good quality microphone
  3. Speak naturally at normal pace
  4. Record each trigger word 10 times
  5. Save as WAV format, 16kHz sample rate

Development Setup

git clone https://github.com/yourusername/abserny-asr.git
cd abserny-asr
pip install -r requirements.txt
python train.py --config config.yaml

Note: Detailed documentation will be available as the project progresses.