haydarkadioglu/nocle-app

Nocle - Noise Cleaner Model

Model Description

This is a deep learning model trained for audio noise reduction, specifically targeting speech enhancement. The model is designed to effectively remove background noise while preserving the quality and intelligibility of speech.

Intended Use

• Speech enhancement in noisy environments
• Audio cleanup for voice recordings
• Background noise reduction
• Audio quality improvement for voice applications

Training Data

The model was trained using the Speech Noise Dataset, which includes:

• Clean speech recordings
• Various environmental noise samples
• Mixed noisy speech samples for training

Model Architecture

• Neural network optimized for audio processing
• Input: Noisy audio waveform
• Output: Enhanced clean audio waveform
• Processing: 16kHz sampling rate

Usage

This model is integrated into the Nocle application. To use it:

1. Clone the repository:

git clone https://github.com/haydarkadioglu/nocle-app.git
cd nocle-app

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python main.py

For programmatic usage:

from setup import Setup
from model_handler import ModelHandler

# Model will be automatically downloaded
model = ModelHandler()
# Process audio
cleaned_audio = model.process_audio(noisy_audio)

For more detailed instructions and source code, visit the GitHub Repository.

Performance and Limitations

Strengths

• Effective at removing common background noise
• Preserves speech clarity
• Real-time processing capability

Limitations

• Optimized for 16kHz audio
• Best suited for speech enhancement
• May require adjustment for non-speech audio

Training Procedure

Training Data

• Dataset: Speech Noise Dataset
• Audio format: 16kHz WAV files
• Mixed with various noise types at different SNR levels

Training Parameters

• Optimizer: Adam
• Loss function: Mean Squared Error (MSE)
• Batch size: 12000 samples

Ethical Considerations

• The model is designed for general noise reduction and should be used responsibly
• Users should respect privacy when processing audio containing personal information
• The model should not be used for deceptive audio manipulation

Links

• GitHub Repository
• Training Dataset
• Model on Hugging Face

Citation

@misc{nocle2025,
  author = {Haydar Kadıoğlu},
  title = {Nocle: Noise Cleaner Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/haydarkadioglu/nocle-app}}
}