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--- |
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language: en |
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tags: |
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- audio |
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- noise-reduction |
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- speech-enhancement |
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- deep-learning |
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datasets: |
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- haydarkadioglu/speech-noise-dataset |
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license: mit |
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pipeline_tag: audio-to-audio |
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--- |
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# Nocle - Noise Cleaner Model |
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## Model Description |
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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. |
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## Intended Use |
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- Speech enhancement in noisy environments |
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- Audio cleanup for voice recordings |
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- Background noise reduction |
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- Audio quality improvement for voice applications |
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## Training Data |
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The model was trained using the [Speech Noise Dataset](https://huggingface.co/datasets/haydarkadioglu/speech-noise-dataset), which includes: |
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- Clean speech recordings |
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- Various environmental noise samples |
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- Mixed noisy speech samples for training |
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## Model Architecture |
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- Neural network optimized for audio processing |
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- Input: Noisy audio waveform |
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- Output: Enhanced clean audio waveform |
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- Processing: 16kHz sampling rate |
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## Usage |
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This model is integrated into the Nocle application. To use it: |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/haydarkadioglu/nocle-app.git |
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cd nocle-app |
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``` |
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2. Install dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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3. Run the application: |
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```bash |
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python main.py |
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``` |
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For programmatic usage: |
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```python |
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from setup import Setup |
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from model_handler import ModelHandler |
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# Model will be automatically downloaded |
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model = ModelHandler() |
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# Process audio |
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cleaned_audio = model.process_audio(noisy_audio) |
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``` |
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For more detailed instructions and source code, visit the [GitHub Repository](https://github.com/haydarkadioglu/nocle-app). |
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## Performance and Limitations |
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### Strengths |
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- Effective at removing common background noise |
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- Preserves speech clarity |
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- Real-time processing capability |
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### Limitations |
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- Optimized for 16kHz audio |
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- Best suited for speech enhancement |
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- May require adjustment for non-speech audio |
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## Training Procedure |
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### Training Data |
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- Dataset: [Speech Noise Dataset](https://huggingface.co/datasets/haydarkadioglu/speech-noise-dataset) |
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- Audio format: 16kHz WAV files |
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- Mixed with various noise types at different SNR levels |
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### Training Parameters |
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- Optimizer: Adam |
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- Loss function: Mean Squared Error (MSE) |
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- Batch size: 12000 samples |
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## Ethical Considerations |
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- The model is designed for general noise reduction and should be used responsibly |
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- Users should respect privacy when processing audio containing personal information |
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- The model should not be used for deceptive audio manipulation |
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## Links |
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- [GitHub Repository](https://github.com/haydarkadioglu/nocle-app) |
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- [Training Dataset](https://huggingface.co/datasets/haydarkadioglu/speech-noise-dataset) |
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- [Model on Hugging Face](https://huggingface.co/haydarkadioglu/nocle-app) |
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## Citation |
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```bibtex |
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@misc{nocle2025, |
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author = {Haydar Kadıoğlu}, |
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title = {Nocle: Noise Cleaner Model}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/haydarkadioglu/nocle-app}} |
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} |
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``` |
