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AnimalSoundClassifier
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  1. .gitattributes +38 -35
  2. README.md +13 -13
  3. animal_sound_classifier.pkl +3 -0
  4. animal_sound_cnn.keras +3 -0
  5. app.py +26 -0
  6. label_encoder.joblib +3 -0
  7. requirements.txt +5 -0
.gitattributes CHANGED
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
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- ---
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- title: AnimalSoundClassifier
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- emoji: 🔥
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- colorFrom: blue
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- colorTo: indigo
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- sdk: gradio
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- sdk_version: 5.38.2
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- app_file: app.py
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- pinned: false
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- license: mit
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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+ ---
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+ title: AnimalSoundClassifier
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+ emoji: 🔥
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+ colorFrom: blue
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+ colorTo: indigo
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+ sdk: gradio
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+ sdk_version: 5.38.2
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+ app_file: app.py
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+ pinned: false
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+ license: mit
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
animal_sound_classifier.pkl ADDED
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+ size 1479
animal_sound_cnn.keras ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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app.py ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import tensorflow as tf
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+ import joblib
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+ import numpy as np
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+ import gradio as gr
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+
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+ # Load model and label encoder
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+ model = tf.keras.models.load_model("model.keras")
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+ label_encoder = joblib.load("label_encoder.joblib")
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+
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+ def predict(audio_path):
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+ # 1. Preprocess audio (convert to spectrogram, etc.)
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+ # Replace this with your actual preprocessing code!
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+ spectrogram = preprocess_audio(audio_path)
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+
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+ # 2. Predict
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+ pred = model.predict(spectrogram)
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+ animal = label_encoder.inverse_transform([np.argmax(pred)])[0]
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+
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+ return animal
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+
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+ # Gradio UI
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+ gr.Interface(
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+ fn=predict,
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+ inputs=gr.Audio(type="filepath"),
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+ outputs="label"
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+ ).launch()
label_encoder.joblib ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ oid sha256:4d18aa602d2ac4222192981b41fc49f461f6cc6a8433abc5ce1fa8af714f2958
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requirements.txt ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ tensorflow>=2.16.0
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+ scikit-learn
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+ joblib
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+ numpy
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+ gradio