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import librosa
import numpy as np
from sklearn.preprocessing import MinMaxScaler
uni_genres_list = ['House', 'Soundtrack', 'Composed Music', 'Drone', 'Instrumental', 'Ambient Electronic', 'Blues', 'Easy Listening', 'Classical', 'Jazz', 'Christmas', 'Electronic', 'Ambient', 'Lo-fi Instrumental', 'Lounge', 'Contemporary Classical', 'Indie-Rock', 'Dance', 'New Age', 'Halloween', 'Lo-fi Electronic', '20th Century Classical', 'Piano', 'Chill-out', 'Pop']
genres2idx = {genre: idx for idx, genre in enumerate(uni_genres_list)}
idx2genres = {idx: genre for genre, idx in genres2idx.items()}
def tokenize(genres):
return [genres2idx[genre] for genre in genres if genre in genres2idx]
def detokenize_tolist(tokens):
return [idx2genres[token] for token in tokens if token in idx2genres]
def onehot_encode(tokens, max_genres):
onehot = np.zeros(max_genres)
onehot[tokens] = 1
return onehot
def onehot_decode(onehot):
return [idx for idx, val in enumerate(onehot) if val == 1]
def load_and_resample_audio(file_path, target_sr=22050, max_duration=15):
audio, sr = librosa.load(file_path, sr=None)
if sr != target_sr:
audio = librosa.resample(audio, orig_sr=sr, target_sr=target_sr)
if len(audio) > target_sr * max_duration:
audio = audio[:target_sr * max_duration]
return audio, target_sr
def audio_to_melspec(audio, sr, n_mels=256, n_fft=2048, hop_length=512, to_db=False):
spec = librosa.feature.melspectrogram(y=audio,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
win_length=None,
window='hann',
center=True,
pad_mode='reflect',
power=2.0,
n_mels=n_mels)
if to_db:
spec = librosa.power_to_db(spec, ref=np.max)
return spec
# Normalize the Mel spectrogram
def normalize_melspec(melspec, norm_range=(0, 1)):
scaler = MinMaxScaler(feature_range=norm_range)
melspec = melspec.T
melspec_normalized = scaler.fit_transform(melspec)
return melspec_normalized.T
# Denormalize the Mel spectrogram
def denormalize_melspec(melspec_normalized, original_melspec, norm_range=(0, 1)):
scaler = MinMaxScaler(feature_range=norm_range)
melspec = original_melspec.T
scaler.fit(melspec)
melspec_denormalized = scaler.inverse_transform(melspec_normalized.T)
return melspec_denormalized.T
# Function to convert Mel spectrogram back to audio
def melspec_to_audio(melspec, sr=22050, n_fft=2048, hop_length=512, n_iter=64):
if np.any(melspec < 0):
melspec = librosa.db_to_power(melspec)
audio_reconstructed = librosa.feature.inverse.mel_to_audio(melspec,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
win_length=None,
window='hann',
center=True,
pad_mode='reflect',
power=2.0, # Ensure the correct inverse transformation
n_iter=n_iter)
return audio_reconstructed
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