| ---
|
| license: apache-2.0
|
| language:
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| - en
|
| tags:
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| - audio-classification
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| - pronunciation
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| - audio-quality
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| - whisper
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| - speech
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| library_name: transformers
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| base_model: openai/whisper-base
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| pipeline_tag: audio-classification
|
| ---
|
|
|
| # ReadAI - Pronunciation & Audio Quality Assessment Models
|
|
|
| This repository contains two models for audio assessment:
|
|
|
| ## 1. Pronunciation Assessment Model (`pronunciation_v3/`)
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|
|
| A fine-tuned **WhisperForAudioClassification** model (based on `openai/whisper-base`) for binary pronunciation quality classification.
|
|
|
| ### Labels
|
| | Label | ID |
|
| |-------|-----|
|
| | Bad | 0 |
|
| | Good | 1 |
|
|
|
| ### Usage
|
|
|
| ```python
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| from transformers import pipeline
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|
|
| classifier = pipeline(
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| task="audio-classification",
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| model="jecallora/readai",
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| subfolder="pronunciation_v3"
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| )
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|
|
| result = classifier("audio_sample.wav")
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| print(result)
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| # [{'label': 'Good', 'score': 0.95}, {'label': 'Bad', 'score': 0.05}]
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| ```
|
|
|
| ### Model Details
|
| - **Architecture:** WhisperForAudioClassification
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| - **Base Model:** openai/whisper-base
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| - **Sampling Rate:** 16,000 Hz
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| - **Input Format:** Audio (WAV, MP3, etc.)
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| - **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 |
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| | Bad | 50 |
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| | Very Bad | 25 |
|
|
|
| ### Files
|
| - `audio_classifier.joblib` — Trained classifier
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| - `scaler.joblib` — StandardScaler for feature normalization
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| - `label_encoder.joblib` — Label encoder
|
|
|
| ### Usage
|
|
|
| ```python
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| import joblib
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| import librosa
|
| import numpy as np
|
|
|
| # Load models
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| classifier = joblib.load("audio_quality/audio_classifier.joblib")
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| scaler = joblib.load("audio_quality/scaler.joblib")
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| label_encoder = joblib.load("audio_quality/label_encoder.joblib")
|
|
|
| # Extract features from audio (16kHz mono)
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| y, sr = librosa.load("audio_sample.wav", sr=16000, mono=True)
|
|
|
| # Your feature extraction pipeline here...
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| # features = extract_features(y)
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| # scaled = scaler.transform([features])
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| # prediction = classifier.predict(scaled)
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| # label = label_encoder.inverse_transform(prediction)
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| ```
|
|
|
| ### Dependencies
|
| - scikit-learn==1.5.0
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| - librosa==0.10.2.post1
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| - numpy==1.26.4
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| - joblib
|
|
|
| ---
|
|
|
| ## Requirements
|
|
|
| ```
|
| transformers>=4.41.2
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| torch>=2.3.1
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| torchaudio>=2.3.1
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| scikit-learn>=1.5.0
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| librosa>=0.10.2.post1
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| soundfile>=0.12.1
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| numpy>=1.26.4
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| ```
|
|
|
