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Browse files- app.py +60 -0
- requirements.txt +3 -0
app.py
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!pip install opensmile
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!pip install scikit-learn==1.3.2
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!pip install gradio
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import opensmile
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import joblib
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import wave
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import datetime
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import os
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from IPython.display import Javascript, Audio
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from base64 import b64decode
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import gradio as gr
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model_path = "RF_emobase_20_model_top1_score0.6863_20231207_1537.joblib"
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model = joblib.load(model_path)
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def extract_features(audio_path):
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smile = opensmile.Smile(
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#feature_set=opensmile.FeatureSet.GeMAPSv01b,
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feature_set=opensmile.FeatureSet.emobase,
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feature_level=opensmile.FeatureLevel.Functionals,
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)
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feature_df = smile.process_files(audio_path)
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output_features = ['F0env_sma_de_amean', 'lspFreq_sma_de[5]_linregc1', 'mfcc_sma[3]_linregc1', 'lspFreq_sma[6]_quartile1', 'lspFreq_sma_de[6]_linregerrQ', 'lspFreq_sma_de[6]_maxPos', 'lspFreq_sma_de[6]_iqr2-3', 'lspFreq_sma_de[7]_minPos', 'lspFreq_sma_de[4]_linregc1', 'lspFreq_sma_de[6]_linregerrA', 'lspFreq_sma_de[6]_linregc2', 'lspFreq_sma[5]_amean', 'lspFreq_sma_de[6]_iqr1-2', 'mfcc_sma[1]_minPos', 'mfcc_sma[4]_linregc1', 'mfcc_sma[9]_iqr2-3', 'lspFreq_sma[5]_kurtosis', 'lspFreq_sma_de[3]_skewness', 'mfcc_sma[3]_minPos', 'mfcc_sma[12]_linregc1']
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df = pd.DataFrame(feature_df.values[0], index=feature_df.columns)
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df = df[df.index.isin(output_features)]
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df = df.T
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scaler = StandardScaler()
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feature = scaler.fit_transform(df)
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print(df.shape)
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return feature
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def main(input):
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# openSMILEで特徴量抽出
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feature_vector = extract_features([input])
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# ロードしたモデルで推論
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prediction = model.predict(feature_vector)
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#print(f"Prediction: {prediction}")
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return prediction
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gr.Interface(
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title = 'Question Classifier Model',
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fn = main,
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inputs=[
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gr.Audio(sources=["microphone","upload"], type="filepath")
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],
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outputs=[
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"textbox"
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],
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live=True
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).launch(debug=True)
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requirements.txt
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opensmile
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scikit-learn==1.3.2
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gradio
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