🚀 使用Wav2Vec 2.0进行音乐流派分类
本项目利用Wav2Vec 2.0实现音乐流派分类,能够准确识别不同音乐的流派,为音乐分析和推荐等领域提供支持。
🚀 快速开始
🔧 环境准备
运行此项目需要安装以下依赖包,可使用以下命令进行安装:
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
💻 使用示例
基础用法
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%'}
]
📊 评估结果
以下表格总结了模型在整体和每个类别上的得分:
标签 |
精确率 |
召回率 |
F1分数 |
样本数 |
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 |
❓ 遇到问题?
如果您在使用过程中遇到问题,可以点击此处在GitHub上提交问题。