Wav2vec2 Xlsr Greek Speech Emotion Recognition

🚀 使用Wav2Vec 2.0进行希腊语（el）语音情感识别

本项目利用Wav2Vec 2.0模型实现希腊语语音的情感识别，能够准确判断语音中的情感类别，如愤怒、厌恶、恐惧等。

🚀 快速开始

🔧 环境要求

运行此项目前，你需要安装以下依赖包：

# 依赖包安装命令
!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

高级用法

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = "m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition"
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 = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
    return outputs

path = "/path/to/disgust.wav"
outputs = predict(path, sampling_rate)

[
{'Emotion': 'anger', 'Score': '0.0%'},
{'Emotion': 'disgust', 'Score': '99.2%'},
{'Emotion': 'fear', 'Score': '0.1%'},
{'Emotion': 'happiness', 'Score': '0.3%'},
{'Emotion': 'sadness', 'Score': '0.5%'}
]

📊 评估结果

以下表格总结了模型在整体和各个类别上的得分情况：

❓ 常见问题

如果你有任何疑问，可以点击此处在GitHub上提交问题。

📄 许可证

本项目采用Apache 2.0许可证。

信息表格