Turkish Speech Recognition Model
This project is a deep learning-based speech recognition system trained on the Mozilla Common Voice Turkish dataset. The model can convert audio recordings into text.
Dataset
The project uses the Mozilla Common Voice Turkish dataset:
Source: https://datacollective.mozillafoundation.org/datasets/cmj8u3px500s1nxxb4qh79iqr
Dataset structure: clips/ directory and TSV files under tr/ folder
Training: train.tsv
Testing: test.tsv
Model Architecture
The model has a hybrid CNN-RNN architecture:
CNN Layers : Residual CNN blocks for feature extraction from Mel-spectrogramsRNN Layers : 4-layer bidirectional LSTM for temporal contextOutput : Character-level prediction with CTC (Connectionist Temporal Classification) loss
Technical Details
Input: 128-dimensional Mel-spectrogram (16kHz, 1024 FFT, 256 hop)
CNN: 32-64 channel residual blocks with GELU activation
LSTM: 512 hidden units, 4 layers, bidirectional
Alphabet: 37 characters (Turkish letters + space)
Optimization: AdamW + OneCycleLR scheduler
File Descriptions
1. data.py
Data loading and preprocessing module:
Reading data from TSV files
Converting audio files to Mel-spectrograms
Text normalization and character encoding
Data augmentation for training (optional noise injection)
2. train_pro.py
Initial training script:
40 epochs of training
Batch size: 16
Learning rate: 0.0003
Data augmentation with SpecAugment
Model saved after each epoch
3. resume.py
Resume training script:
Continue training from a saved model
Lower learning rate (0.00005)
Increased regularization
Designed for epochs 41-75
4. check_voca.py
Helper script for alphabet verification. Displays the character set used by the model.
5. count.py
Dataset statistics:
Total number of recordings
Total duration calculation
Fast calculation if clip_durations.tsv exists, otherwise scans audio files
Installation
Requirements
pip install torch torchaudio pandas Levenshtein sounddevice scipy numpyPreparing the Dataset
Download the Mozilla Common Voice Turkish dataset
Extract to tr/ folder
Structure should be:
tr/
├── clips/
│ ├── common_voice_tr_*.mp3
│ └── ...
├── train.tsv
├── test.tsv
└── clip_durations.tsv (optional)Training
Initial Training
Trains for 40 epochs
Saves model_advanced_epoch_X.pth after each epoch
Terminal output shows loss, CER score, and sample predictions
Resume Training
Starts from model_advanced_epoch_40.pth
Trains epochs 41-75
Uses lower learning rate for fine-tuning
Data Augmentation
The model uses two types of data augmentation during training:
Waveform Noise (data.py): Random Gaussian noise in training modeSpecAugment (train_pro.py, resume.py): Frequency and time masking
Performance Metrics
Model performance is measured with CER (Character Error Rate):
CER: Character-level error rate
Evaluated on test set after each epoch
Sample predictions printed to console
Model Outputs
After training, model files are created for each epoch:
model_advanced_epoch_1.pth - model_advanced_epoch_75.pthThe best performing model can be selected for use
Dataset Analysis
To get information about the dataset:
This script displays the total number of recordings and duration.
Notes
GPU usage is automatically detected
Gradient clipping is applied during training
All parameters are saved when the model is stored
Alphabet: _abcçdefgğhıijklmnoöprsştuüvyzqwx (37 characters)
License
Code
MIT License - Feel free to use, modify, and distribute this code.
Dataset
The Mozilla Common Voice Turkish dataset is licensed under
CC0 1.0 Universal . The dataset is in the public domain and free to use for any purpose.
