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import librosa
import numpy as np
import joblib
import soundfile as sf
scaler = joblib.load("./models/std_scaler(1).pkl")
def load_audio_from_uploaded_file(uploaded_file):
# Use the soundfile library to read the audio data and sample rate
audio_data, sample_rate = sf.read(uploaded_file)
return audio_data, sample_rate
# sample_audio,sr = librosa.load(r"classical.00000.wav",sr = 44100)
Fields = ['name', 'length', 'chroma_stft_mean', 'chroma_stft_var', 'rms_mean', 'rms_var',
'spectral_centroid_mean', 'spectral_centroid_var', 'spectral_bandwidth_mean', 'spectral_bandwidth_var',
'rolloff_mean', 'rolloff_var', 'zero_crossing_rate_mean', 'zero_crossing_rate_var',
'harmony_mean', 'harmony_var', 'percussive_mean', 'percussive_var', 'tempo',
'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean', 'mfcc3_var', 'mfcc4_mean', 'mfcc4_var',
'mfcc5_mean', 'mfcc5_var', 'mfcc6_mean', 'mfcc6_var', 'mfcc7_mean', 'mfcc7_var', 'mfcc8_mean', 'mfcc8_var',
'mfcc9_mean', 'mfcc9_var', 'mfcc10_mean', 'mfcc10_var', 'mfcc11_mean', 'mfcc11_var', 'mfcc12_mean',
'mfcc12_var',
'mfcc13_mean', 'mfcc13_var', 'mfcc14_mean', 'mfcc14_var', 'mfcc15_mean', 'mfcc15_var', 'mfcc16_mean',
'mfcc16_var',
'mfcc17_mean', 'mfcc17_var', 'mfcc18_mean', 'mfcc18_var', 'mfcc19_mean', 'mfcc19_var', 'mfcc20_mean',
'mfcc20_var']
short_field = Fields[2:]
def all_feature_extraction(audio_path, sample_rate=22050):
data_list = []
val_field = []
audio_df, sr = librosa.load(audio_path, sr=22050)
data_list.append(audio_path)
data_list.append(len(audio_df))
# 1. Chroma STFT
chroma_stft = librosa.feature.chroma_stft(y=audio_df, hop_length=512)
chroma_stft_mean = np.mean(chroma_stft)
chroma_stft_var = np.var(chroma_stft)
val_field.append(chroma_stft)
data_list.append(chroma_stft_mean)
data_list.append(chroma_stft_var)
# 2. RMS
rms = librosa.feature.rms(y=audio_df)
rms_mean = np.mean(rms)
rms_var = np.var(rms)
data_list.append(rms_mean)
data_list.append(rms_var)
spectral_centroid = librosa.feature.spectral_centroid(y=audio_df)
spectral_centroid_mean = np.mean(spectral_centroid)
spectral_centroid_var = np.var(spectral_centroid)
data_list.append(spectral_centroid_mean)
data_list.append(spectral_centroid_var)
spectral_bandwidth = librosa.feature.spectral_bandwidth(y=audio_df)
spectral_bandwidth_mean = np.mean(spectral_bandwidth)
spectral_bandwidth_var = np.var(spectral_bandwidth)
data_list.append(spectral_bandwidth_mean)
data_list.append(spectral_bandwidth_var)
spectral_rolloff = librosa.feature.spectral_rolloff(y=audio_df)
spectral_rolloff_mean = np.mean(spectral_rolloff)
spectral_rolloff_var = np.var(spectral_rolloff)
data_list.append(spectral_rolloff_mean)
data_list.append(spectral_rolloff_var)
zcr = librosa.feature.zero_crossing_rate(y=audio_df)
zcr_mean = np.mean(zcr)
zcr_var = np.var(zcr)
data_list.append(zcr_mean)
data_list.append(zcr_var)
harmonic, percussive = librosa.effects.hpss(y=audio_df)
harmonic_mean = np.mean(harmonic)
harmonic_var = np.var(harmonic)
percussive_mean = np.mean(percussive)
percussive_var = np.var(percussive)
data_list.append(harmonic_mean)
data_list.append(harmonic_var)
data_list.append(percussive_mean)
data_list.append(percussive_var)
tempo = librosa.feature.tempo(y=audio_df)
tempo = np.mean(tempo)
data_list.append(tempo)
mfccs = librosa.feature.mfcc(y=audio_df, sr=sr)
row_means = np.mean(mfccs, axis=1)
row_vars = np.var(mfccs, axis=1)
mfcc_means = {}
mfcc_vars = {}
for i in range(1, 21):
variable_name = f'mfcc{i}'
mfcc_means[variable_name] = row_means[i - 1] # You can initialize with values if needed
mfcc_vars[variable_name] = row_vars[i - 1]
# Convert the dictionary values to a list
mfcc_list = [value for value in zip(mfcc_means.values(), mfcc_vars.values())]
for mean, var in mfcc_list:
data_list.append(mean)
data_list.append(var)
return [data_list,val_field]
def scale(initial_features):
final_features = initial_features[2:]
final_features = np.array(final_features)
# Apply the loaded scaler to your single data point
scaled_data_point = scaler.transform([final_features])
return scaled_data_point
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