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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import csv | |
| import librosa | |
| import tensorflow as tf | |
| #!gdown https://drive.google.com/uc?id=1hKQdsTZ35KQmNV9Zrqg-ksTLSmPapR53 | |
| model = tf.keras.models.load_model('TTM_model.h5') | |
| def config_audio(audio): | |
| print('enter2') | |
| header = 'ChromaSTFT RMS SpectralCentroid SpectralBandwidth Rolloff ZeroCrossingRate' | |
| for i in range(1, 21): | |
| header += f' mfcc{i}' | |
| header += ' label' | |
| header = header.split() | |
| print(1) | |
| file = open('predict_file.csv', 'w', newline='') | |
| with file: | |
| writer = csv.writer(file) | |
| writer.writerow(header) | |
| print(2) | |
| #taalfile = audio | |
| #print('stored in taalfile') | |
| y, sr = librosa.load(audio, mono=True, duration=30) | |
| print(3) | |
| rms = librosa.feature.rms(y=y) | |
| chroma = librosa.feature.chroma_stft(y=y, sr=sr) | |
| spec_centroid = librosa.feature.spectral_centroid(y=y, sr=sr) | |
| spec_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr) | |
| rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr) | |
| zcr = librosa.feature.zero_crossing_rate(y) | |
| mfcc = librosa.feature.mfcc(y=y, sr=sr) | |
| to_append = f' {np.mean(chroma)} {np.mean(rms)} {np.mean(spec_centroid)} {np.mean(spec_bandwidth)} {np.mean(rolloff)} {np.mean(zcr)} ' | |
| for e in mfcc: | |
| to_append += f' {np.mean(e)}' | |
| #to_append += f' {t}' | |
| file = open('predict_file.csv', 'a', newline='') | |
| with file: | |
| writer = csv.writer(file) | |
| writer.writerow(to_append.split()) | |
| predict_file = pd.read_csv("predict_file.csv") | |
| X_predict = predict_file.drop('label', axis=1) | |
| print('exit2') | |
| return X_predict | |
| def predict_audio(Audio_Input): | |
| audio=Audio_Input.name | |
| print('enter1') | |
| X_predict = config_audio(audio) | |
| taals = ['addhatrital','bhajani','dadra','deepchandi','ektal','jhaptal','rupak','trital'] | |
| pred = model.predict(X_predict).flatten() | |
| print('exit1') | |
| return {taals[i]: float(pred[i]) for i in range(8)},audio | |
| audio = gr.inputs.Audio(source="upload", optional=False) | |
| label = gr.outputs.Label() | |
| audio = gr.inputs.Audio(source="upload", optional=False) | |
| #label = gr.outputs.Label() | |
| gr.Interface(predict_audio, | |
| "file", | |
| [gr.outputs.Label(),gr.outputs.Audio()], | |
| description="", | |
| examples = [["Addhatrital_Sample1.wav"], ["Addhatrital_Sample2.wav"], ["Bhajani_Sample1.wav"], ["Bhajani_Sample2.wav"], | |
| ["Dadra_Sample1.wav"], ["Dadra_Sample2.wav"], ["Deepchandi_Sample1.wav"], ["Deepchandi_Sample2.wav"], | |
| ["Ektal_Sample1.wav"], ["Ektal_Sample2.wav"], ["Jhaptal_Sample1.wav"], ["Jhaptal_Sample2.wav"], | |
| ["Rupak_Sample1.wav"], ["Rupak_Sample2.wav"], ["Trital_Sample1.wav"], ["Trital_Sample2.wav"]]).launch() |