Spaces:
Build error
Build error
File size: 2,541 Bytes
1580527 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
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)
|