readme: ---
  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).