đ Hubert-Large for Emotion Recognition
This is a Hubert-Large model designed for emotion recognition, leveraging pre - trained features to classify emotions in speech.
đ Quick Start
This model can be used for emotion recognition in speech. You can utilize it through the Audio Classification pipeline or use it directly.
⨠Features
đĻ Installation
No specific installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
Basic Usage
You can use the model via the Audio Classification pipeline:
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/hubert-large-superb-er")
labels = classifier(dataset[0]["file"], top_k=5)
Advanced Usage
Or use the model directly:
import torch
import librosa
from datasets import load_dataset
from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor
def map_to_array(example):
speech, _ = librosa.load(example["file"], sr=16000, mono=True)
example["speech"] = speech
return example
dataset = load_dataset("anton-l/superb_demo", "er", split="session1")
dataset = dataset.map(map_to_array)
model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-er")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-er")
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()]
đ Documentation
Model description
This is a ported version of S3PRL's Hubert for the SUPERB Emotion Recognition task. The base model is hubert - large - ll60k, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
For more information refer to SUPERB: Speech processing Universal PERformance Benchmark
Task and dataset description
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion classes to leave the final four classes with a similar amount of data points and cross - validate on five folds of the standard splits.
For the original model's training and evaluation instructions refer to the S3PRL downstream task README.
đ§ Technical Details
No detailed technical implementation details are provided in the original document, so this section is skipped.
đ License
This project is licensed under the Apache 2.0 license.
Eval results
The evaluation metric is accuracy.
Property |
Details |
Model Type |
Emotion Recognition Model |
Training Data |
IEMOCAP |
|
s3prl |
transformers |
session1 |
0.6762 |
N/A |
BibTeX entry and citation info
@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}
}