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import librosa, joblib, numpy as np, gradio as gr |
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from scipy.interpolate import interp1d |
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from pyAudioAnalysis import ShortTermFeatures |
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from pydub.silence import detect_nonsilent |
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from pydub import AudioSegment |
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def smart_resize(arr, target_size): |
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current_size = arr.shape[1] |
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current_idx = np.linspace(0, current_size - 1, current_size) |
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target_idx = np.linspace(0, current_size - 1, target_size) |
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interp_func = interp1d(current_idx, arr.squeeze(), kind='linear', fill_value="extrapolate") |
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resized_arr = interp_func(target_idx) |
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return resized_arr.reshape(1, target_size) |
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def remove_silence(wav_file): |
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audSeg = AudioSegment.from_wav(wav_file) |
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non_silence_ranges = detect_nonsilent(audSeg, min_silence_len=5, silence_thresh=-30) |
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if not non_silence_ranges: |
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sound = audSeg |
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else: |
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start = non_silence_ranges[0][0] |
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end = non_silence_ranges[-1][1] |
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trimmed_sound = audSeg[start:end] |
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sound = trimmed_sound |
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sound.export('audio.wav', format="wav") |
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def transform_data(audio): |
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remove_silence(audio) |
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x, sr = librosa.load('audio.wav') |
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result, f_names = ShortTermFeatures.feature_extraction(x, sr, 0.050*sr, 0.025*sr) |
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resize_features = smart_resize(result.reshape(1,-1), 20) |
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return resize_features |
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def predict(newdf, loaded_model): |
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prediction = loaded_model.predict(newdf) |
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proba = loaded_model.predict_proba(newdf) |
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return prediction, proba[0] |
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def get_label(newpred): |
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if newpred == 0: |
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return 'No' |
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else: |
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return 'Si' |
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def load_model(): |
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ram_for = joblib.load('models/sgd_90.pkl') |
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return ram_for |
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def main(audio): |
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newdf = transform_data(audio) |
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loaded_model = load_model() |
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newpred, proba = predict(newdf, loaded_model) |
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final = get_label(newpred) |
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return final, {'Si probability': proba[1], |
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'No probability': proba[0]} |
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demo = gr.Interface( |
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title = "Autoagent | YES or NO Classification - Layer7", |
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description = """<h3>This model is useful to classify if the user says 'Si' or 'No'. 🎙️ </h3> |
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<img src="https://huggingface.co/spaces/Adrian8as/imagen/resolve/main/output.png" width="350" height="350"/> <br> |
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<b>Record your voice:</b>""", |
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allow_flagging = "never", |
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fn = main, |
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inputs = gr.Audio( |
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sources=["microphone"], |
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type="filepath", |
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), |
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outputs = [gr.Textbox(label="Clasification"),"label"] |
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) |
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if __name__ == "__main__": |
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demo.launch(show_api=False) |
