You can deploy this model and start earning money today!

haydarkadioglu/
nocle-app

Name: nocle-app
Availability: InStock

haydarkadioglu/speech-noise-dataset

mit

Views

No views yet

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:

Clone the repository:

bash

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

Install dependencies:

pip install -r requirements.txt

Run the application:

python main.py

For programmatic usage:

python

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

Citation

bibtex

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

haydarkadioglu/nocle-app

Nocle - Noise Cleaner Model

Model Description

Intended Use

Training Data

Model Architecture

Usage

Performance and Limitations

Strengths

Limitations

Training Procedure

Training Data

Training Parameters

Ethical Considerations

Links

Citation

haydarkadioglu/
nocle-app