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---
tags:
- audio
- automatic-speech-recognition
- audio-classification
---
# Music Genre Classification using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
### Prediction
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
```
```python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = "m3hrdadfi/wav2vec2-base-100k-voxpopuli-gtzan-music"
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
```
```python
def speech_file_to_array_fn(path, sampling_rate):
speech_array, _sampling_rate = torchaudio.load(path)
resampler = torchaudio.transforms.Resample(_sampling_rate)
speech = resampler(speech_array).squeeze().numpy()
return speech
def predict(path, sampling_rate):
speech = speech_file_to_array_fn(path, sampling_rate)
inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits = model(**inputs).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs
```
```python
path = "genres_original/disco/disco.00067.wav"
outputs = predict(path, sampling_rate)
```
```bash
[
{'Label': 'blues', 'Score': '0.0%'},
{'Label': 'classical', 'Score': '0.0%'},
{'Label': 'country', 'Score': '0.0%'},
{'Label': 'disco', 'Score': '99.8%'},
{'Label': 'hiphop', 'Score': '0.0%'},
{'Label': 'jazz', 'Score': '0.0%'},
{'Label': 'metal', 'Score': '0.0%'},
{'Label': 'pop', 'Score': '0.0%'},
{'Label': 'reggae', 'Score': '0.0%'},
{'Label': 'rock', 'Score': '0.0%'}
]
```
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
| label | precision | recall | f1-score | support |
|:------------:|:---------:|:------:|:--------:|:-------:|
| blues | 0.792 | 0.950 | 0.864 | 20 |
| classical | 0.864 | 0.950 | 0.905 | 20 |
| country | 0.812 | 0.650 | 0.722 | 20 |
| disco | 0.778 | 0.700 | 0.737 | 20 |
| hiphop | 0.933 | 0.700 | 0.800 | 20 |
| jazz | 1.000 | 0.850 | 0.919 | 20 |
| metal | 0.783 | 0.900 | 0.837 | 20 |
| pop | 0.917 | 0.550 | 0.687 | 20 |
| reggae | 0.543 | 0.950 | 0.691 | 20 |
| rock | 0.611 | 0.550 | 0.579 | 20 |
| accuracy | 0.775 | 0.775 | 0.775 | 0 |
| macro avg | 0.803 | 0.775 | 0.774 | 200 |
| weighted avg | 0.803 | 0.775 | 0.774 | 200 |
## Questions?
Post a Github issue from [HERE](https://github.com/m3hrdadfi/soxan/issues). |