|
--- |
|
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). |
