Wav2Vec2-Base情感识别模型

模型描述

这是 S3PRL's Wav2Vec2 for the SUPERB Emotion Recognition task 的移植版本。

基础模型是 wav2vec2-base ，在16kHz采样的语音音频上进行了预训练。使用此模型时，请确保您的语音输入也是以16kHz采样。

更多信息请参考 SUPERB: Speech processing Universal PERformance Benchmark

任务和数据集描述

情感识别（ER）为每个话语预测情感类别。采用最常用的ER数据集 IEMOCAP ，并遵循常规的评估协议：丢弃不平衡的情感类别，以保留最终的四个类别中具有相似数据点数量的数据，并在五个标准分割的交叉验证中进行评估。

有关原始模型的训练和评估说明，请参考 S3PRL downstream task README 。

用法示例

您可以通过音频分类流程使用该模型：

from datasets import load_dataset
from transformers import pipeline

dataset = load_dataset("anton-l/superb_demo", "er", split="session1")

classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er")
labels = classifier(dataset[0]["file"], top_k=5)

或者直接使用模型：

import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor

def map_to_array(example):
    speech, _ = librosa.load(example["file"], sr=16000, mono=True)
    example["speech"] = speech
    return example

# load a demo dataset and read audio files
dataset = load_dataset("anton-l/superb_demo", "er", split="session1")
dataset = dataset.map(map_to_array)

model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er")

# compute attention masks and normalize the waveform if needed
inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt")

logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]

评估结果

评估指标为准确度。

BibTeX条目和引文信息

@article{yang2021superb,
  title={SUPERB: Speech processing Universal PERformance Benchmark},
  author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others},
  journal={arXiv preprint arXiv:2105.01051},
  year={2021}
}