Etherll / NoisySpeechDetection-v0.2

Speech Quality and Environmental Noise Classifier

This is a binary audio classification model that determines if a speech recording is clean or if it is degraded by environmental noise .

It is specifically trained to be robust and understand the difference between clean audio and audio that has actual background noise (like cars, music, or other people talking).

• LABEL_0: clean : The audio contains speech with no significant environmental noise. This includes high-quality recordings as well as recordings with source artifacts like hiss, clipping, or "bad microphone" quality.
• LABEL_1: noisy : The audio contains speech that is obscured by external, environmental background noise.

Intended Uses & Limitations

This model is ideal for:

• Pre-processing a large audio dataset to filter for clean samples.
• Automatically tagging audio clips for quality control.
• As a gate for ASR (Automatic Speech Recognition) systems that perform better on clean audio.

Limitations:

• This model is a classifier , not a noise-reduction tool. It only tells you if environmental noise is present.
• Its definition of "noisy" is based on environmental sounds. It is trained to classify audio with only source artifacts (like microphone hum or pure static) as clean .

How to Use

The easiest way to use this model is with a pipeline .

pip install transformers torch

from transformers import pipeline

classifier = pipeline("audio-classification", model="Etherll/NoisySpeechDetection-v0.2")

# Classify a local audio file (must be a WAV or other supported format)
# The pipeline automatically handles resampling to 16kHz.
results = classifier("path/to/your_audio_file.wav")

# The result is a list of dictionaries
# [{'score': 0.9979726672172546, 'label': 'clean'},
# {'score': 0.002027299487963319, 'label': 'noisy'}]
print(results)

Note: The model outputs a confidence score for each label. In my use case, I consider audio to be clean if the score for the clean label is greater than 0.7 .

Training Data

This model was trained on a sophisticated, custom-built dataset of ~55,000 audio clips, specifically designed to teach the nuances of audio quality.

This whisper model was trained 2x faster with Unsloth and Huggingface's TRL library.