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import librosa |
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import numpy as np |
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from sklearn.preprocessing import MinMaxScaler |
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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'] |
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genres2idx = {genre: idx for idx, genre in enumerate(uni_genres_list)} |
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idx2genres = {idx: genre for genre, idx in genres2idx.items()} |
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def tokenize(genres): |
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return [genres2idx[genre] for genre in genres if genre in genres2idx] |
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def detokenize_tolist(tokens): |
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return [idx2genres[token] for token in tokens if token in idx2genres] |
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def onehot_encode(tokens, max_genres): |
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onehot = np.zeros(max_genres) |
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onehot[tokens] = 1 |
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return onehot |
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def onehot_decode(onehot): |
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return [idx for idx, val in enumerate(onehot) if val == 1] |
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def load_and_resample_audio(file_path, target_sr=22050, max_duration=15): |
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audio, sr = librosa.load(file_path, sr=None) |
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if sr != target_sr: |
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audio = librosa.resample(audio, orig_sr=sr, target_sr=target_sr) |
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if len(audio) > target_sr * max_duration: |
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audio = audio[:target_sr * max_duration] |
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return audio, target_sr |
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def audio_to_melspec(audio, sr, n_mels=256, n_fft=2048, hop_length=512, to_db=False): |
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spec = librosa.feature.melspectrogram(y=audio, |
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sr=sr, |
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n_fft=n_fft, |
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hop_length=hop_length, |
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win_length=None, |
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window='hann', |
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center=True, |
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pad_mode='reflect', |
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power=2.0, |
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n_mels=n_mels) |
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if to_db: |
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spec = librosa.power_to_db(spec, ref=np.max) |
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return spec |
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def normalize_melspec(melspec, norm_range=(0, 1)): |
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scaler = MinMaxScaler(feature_range=norm_range) |
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melspec = melspec.T |
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melspec_normalized = scaler.fit_transform(melspec) |
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return melspec_normalized.T |
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def denormalize_melspec(melspec_normalized, original_melspec, norm_range=(0, 1)): |
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scaler = MinMaxScaler(feature_range=norm_range) |
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melspec = original_melspec.T |
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scaler.fit(melspec) |
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melspec_denormalized = scaler.inverse_transform(melspec_normalized.T) |
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return melspec_denormalized.T |
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def melspec_to_audio(melspec, sr=22050, n_fft=2048, hop_length=512, n_iter=64): |
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if np.any(melspec < 0): |
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melspec = librosa.db_to_power(melspec) |
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audio_reconstructed = librosa.feature.inverse.mel_to_audio(melspec, |
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sr=sr, |
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n_fft=n_fft, |
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hop_length=hop_length, |
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win_length=None, |
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window='hann', |
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center=True, |
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pad_mode='reflect', |
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power=2.0, |
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n_iter=n_iter) |
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return audio_reconstructed |
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