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--- |
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base_model: unsloth/whisper-small |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- whisper |
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- trl |
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- audio |
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- audio-classification |
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- speech-processing |
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- noise-detection |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# Speech Quality and Environmental Noise Classifier |
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This is a binary audio classification model that determines if a speech recording is **clean** or if it is degraded by **environmental noise**. |
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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). |
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- **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. |
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- **LABEL_1: `noisy`**: The audio contains speech that is obscured by external, environmental background noise. |
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## Intended Uses & Limitations |
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This model is ideal for: |
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- Pre-processing a large audio dataset to filter for clean samples. |
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- Automatically tagging audio clips for quality control. |
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- As a gate for ASR (Automatic Speech Recognition) systems that perform better on clean audio. |
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**Limitations:** |
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- This model is a **classifier**, not a noise-reduction tool. It only tells you *if* environmental noise is present. |
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- 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`. |
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## How to Use |
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The easiest way to use this model is with a `pipeline`. |
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```bash |
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pip install transformers torch |
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``` |
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```python |
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from transformers import pipeline |
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classifier = pipeline("audio-classification", model="Etherll/NoisySpeechDetection-v0.2") |
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# Classify a local audio file (must be a WAV or other supported format) |
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# The pipeline automatically handles resampling to 16kHz. |
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results = classifier("path/to/your_audio_file.wav") |
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# The result is a list of dictionaries |
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# [{'score': 0.9979726672172546, 'label': 'clean'}, |
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# {'score': 0.002027299487963319, 'label': 'noisy'}] |
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print(results) |
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``` |
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> **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**. |
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## Training Data |
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This model was trained on a sophisticated, custom-built dataset of ~55,000 audio clips, specifically designed to teach the nuances of audio quality. |
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This whisper model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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