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jecallora
/
readai

Audio Classification
Transformers
Joblib
Safetensors
English
pronunciation
audio-quality
whisper
speech
Model card Files
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readai
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---

license: apache-2.0
language:
  - en
tags:
  - audio-classification
  - pronunciation
  - audio-quality
  - whisper
  - speech
library_name: transformers
base_model: openai/whisper-base
pipeline_tag: audio-classification
---


# ReadAI - Pronunciation & Audio Quality Assessment Models

This repository contains two models for audio assessment:

## 1. Pronunciation Assessment Model (`pronunciation_v3/`)



A fine-tuned **WhisperForAudioClassification** model (based on `openai/whisper-base`) for binary pronunciation quality classification.



### Labels

| Label | ID |

|-------|-----|

| Bad   | 0   |

| Good  | 1   |



### Usage



```python

from transformers import pipeline



classifier = pipeline(

    task="audio-classification",

    model="jecallora/readai",

    subfolder="pronunciation_v3"
)

result = classifier("audio_sample.wav")

print(result)

# [{'label': 'Good', 'score': 0.95}, {'label': 'Bad', 'score': 0.05}]

```



### Model Details

- **Architecture:** WhisperForAudioClassification

- **Base Model:** openai/whisper-base

- **Sampling Rate:** 16,000 Hz

- **Input Format:** Audio (WAV, MP3, etc.)

- **Framework:** PyTorch (safetensors)



---



## 2. Audio Quality Classifier (`audio_quality/`)

A scikit-learn classifier for audio quality assessment.

### Labels
| Quality   | Score |
|-----------|-------|
| Very Good | 100   |
| Good      | 75    |
| Bad       | 50    |
| Very Bad  | 25    |

### Files
- `audio_classifier.joblib` — Trained classifier
- `scaler.joblib` — StandardScaler for feature normalization
- `label_encoder.joblib` — Label encoder

### Usage

```python

import joblib

import librosa

import numpy as np



# Load models

classifier = joblib.load("audio_quality/audio_classifier.joblib")

scaler = joblib.load("audio_quality/scaler.joblib")

label_encoder = joblib.load("audio_quality/label_encoder.joblib")



# Extract features from audio (16kHz mono)

y, sr = librosa.load("audio_sample.wav", sr=16000, mono=True)



# Your feature extraction pipeline here...

# features = extract_features(y)

# scaled = scaler.transform([features])

# prediction = classifier.predict(scaled)

# label = label_encoder.inverse_transform(prediction)

```

### Dependencies
- scikit-learn==1.5.0
- librosa==0.10.2.post1
- numpy==1.26.4
- joblib

---

## Requirements

```

transformers>=4.41.2

torch>=2.3.1

torchaudio>=2.3.1

scikit-learn>=1.5.0

librosa>=0.10.2.post1

soundfile>=0.12.1

numpy>=1.26.4

```