KaushalB/ViTForMusicClassification
Image Classification
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class label 12
classes |
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8pop
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8pop
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5jazz
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1classical
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8pop
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0afro
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6latin
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1classical
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11rock
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4electro
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5jazz
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4electro
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11rock
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0afro
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9rap
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6latin
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0afro
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6latin
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9rap
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4electro
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2country
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8pop
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2country
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7metal
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6latin
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7metal
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8pop
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4electro
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2country
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3disco
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9rap
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6latin
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3disco
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7metal
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0afro
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6latin
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3disco
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8pop
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6latin
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4electro
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2country
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9rap
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8pop
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0afro
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0afro
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4electro
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2country
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6latin
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4electro
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5jazz
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6latin
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6latin
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1classical
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1classical
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11rock
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5jazz
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0afro
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7metal
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0afro
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8pop
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7metal
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3disco
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5jazz
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6latin
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6latin
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11rock
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0afro
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0afro
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6latin
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2country
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6latin
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7metal
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4electro
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9rap
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11rock
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10reggae
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7metal
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0afro
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11rock
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4electro
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3disco
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0afro
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8pop
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8pop
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0afro
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3disco
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9rap
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0afro
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8pop
|
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11rock
|
|
8pop
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7metal
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9rap
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11rock
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0afro
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3disco
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9rap
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6latin
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11rock
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11rock
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Dataset Card
The egtzan_plus dataset is an GTZAN like dataset for musical genre classification in the vision domain. In egtzan_plus, new classes such as Electro and Afro have been added to the original GTZAN dataset. Each audio track (30s) is transformed into a Mel-frequency spectrogram using Librosa:
# Mel-frequency spectrogram generation
y, sr = librosa.load(audio_file)
ms = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000)
log_ms = librosa.power_to_db(ms, ref=np.max)
librosa.display.specshow(log_ms)
The dataset contains the following classes:
- Afro
- Classical
- Country
- Disco
- Electro
- Jazz
- Latin
- Metal
- Pop
- Rap
- Reggae
- Rock
The dataset is split into train and test sets as follows:
- Train: 1697 examples
- Test: 189 examples
- Downloads last month
- 146
Size of downloaded dataset files:
143 MB
Size of the auto-converted Parquet files:
143 MB
Number of rows:
1,886