README.md

metadata

tags:
  - audio
  - automatic-speech-recognition
  - audio-classification

Music Genre Classification using Wav2Vec 2.0

How to use

Requirements

# 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

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

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)

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

path = "genres_original/disco/disco.00067.wav"
outputs = predict(path, sampling_rate)

[
{'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.