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| import librosa | |
| import numpy as np | |
| import tensorflow as tf | |
| #import argparse | |
| import json | |
| import tensorflow.keras as keras | |
| # Input dimension for model = 259*13 | |
| mappings = ["blues","classical","country","disco","hip-hop","jazz","metal","pop","reggae","rock"] | |
| model_path = "Genre_classifier_model.h5" | |
| class _Genre_spotting_service(): | |
| instance = None | |
| model = None | |
| mappings = ["blues", "classical", "country", "disco", "hip-hop", "jazz", "metal", "pop", "reggae", "rock"] | |
| def __init__(self,model_path): | |
| self.model = tf.keras.models.load_model(model_path) | |
| def predict(self,file_path): | |
| input_data = self.preprocess_audio(file_path) | |
| input_data = input_data[np.newaxis, ...] | |
| predictions = self.model.predict(input_data) | |
| text_predictions = [mappings[np.argmax(predictions, axis=1)[0]]] | |
| return text_predictions | |
| def preprocess_audio(self,file_path, target_frames=259, n_mfcc=13, sr=22050, n_fft=2048, hop_length=512): | |
| # Load audio file | |
| y, sr = librosa.load(file_path, sr=sr) | |
| # Compute MFCCs | |
| mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=n_mfcc, n_fft=n_fft, hop_length=hop_length) | |
| # Pad or truncate MFCCs to ensure they have target_frames | |
| if mfccs.shape[1] < target_frames: | |
| pad_width = target_frames - mfccs.shape[1] | |
| mfccs = np.pad(mfccs, pad_width=((0, 0), (0, pad_width)), mode='constant') | |
| else: | |
| mfccs = mfccs[:, :target_frames] | |
| return mfccs.T | |
| def Genre_spotting_service(): | |
| if _Genre_spotting_service.instance == None: | |
| _Genre_spotting_service.instance = _Genre_spotting_service() | |
| _Genre_spotting_service.model = keras.models.load_model(model_path) | |
| return _Genre_spotting_service.instance | |
| # if __name__ == "__main__": | |
| # genre_service = _Genre_spotting_service("Genre_classifier_model.h5") | |
| # predicted_genre = genre_service.predict("10.mp3") | |
| # print(predicted_genre) | |
| # file_path = "10.mp3" | |
| # target_frames=259 | |
| # n_mfcc=13 | |
| # sr=22050 | |
| # n_fft=2048 | |
| # hop_length=512 | |
| # # Load audio file | |
| # y, sr = librosa.load(file_path, sr=sr) | |
| # | |
| # # Compute MFCCs | |
| # mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=n_mfcc, n_fft=n_fft, hop_length=hop_length) | |
| # print(str(sr)) | |
| # | |
| # # Pad or truncate MFCCs to ensure they have target_frames | |
| # if mfccs.shape[1] < target_frames: | |
| # pad_width = target_frames - mfccs.shape[1] | |
| # mfccs = np.pad(mfccs, pad_width=((0, 0), (0, pad_width)), mode='constant') | |
| # else: | |
| # mfccs = mfccs[:, :target_frames] | |
| # print(mfccs) | |