| ---
|
| library_name: keras
|
| tags:
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| - audio-classification
|
| - cnn
|
| - cebuano
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| - sinama
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| - mel-spectrogram
|
| pipeline_tag: audio-classification
|
| ---
|
|
|
| # Sinama Audio Classifier
|
|
|
| A CNN-based audio classification model trained to recognise spoken
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| Cebuano / Sinama words from short audio clips.
|
|
|
| ## Usage
|
|
|
| ### Via Inference API
|
|
|
| ```python
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| import requests
|
|
|
| API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/sinama-translator"
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| headers = {"Authorization": "Bearer hf_YOUR_TOKEN"}
|
|
|
| with open("audio.wav", "rb") as f:
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| response = requests.post(API_URL, headers=headers, data=f.read())
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|
|
| print(response.json())
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| # [{"label": "ako", "score": 0.95}, ...]
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| ```
|
|
|
| ### Local inference
|
|
|
| ```python
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| import tensorflow as tf, json, librosa, numpy as np
|
|
|
| model = tf.keras.models.load_model("best_model.keras")
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| with open("label_map.json") as f:
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| label_map = {int(k): v for k, v in json.load(f).items()}
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|
|
| # preprocess your audio the same way as training …
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| pred = model.predict(features)
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| print(label_map[pred.argmax()])
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| ```
|
|
|
| ## Training details
|
|
|
| - **Architecture:** 3-block CNN (Conv2D → BN → ReLU → MaxPool → Dropout)
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| - **Features:** 128-bin Mel Spectrogram, 4 s clips, 22 050 Hz
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| - **Optimiser:** Adam
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| - **Loss:** Categorical cross-entropy
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|
|
