Hubert Base Superb Ic

🚀 Hubert-Base for Intent Classification

A ported model for intent classification based on Hubert, leveraging the SUPERB dataset.

🚀 Quick Start

This README provides a detailed introduction to the Hubert-Base model for intent classification, including its description, task and dataset details, usage examples, evaluation results, and citation information.

✨ Features

• Ported from S3PRL's Hubert for the SUPERB Intent Classification task.
• Based on the hubert-base-ls960 model, pretrained on 16kHz sampled speech audio.
• Applicable to the Intent Classification task using the Fluent Speech Commands dataset.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

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

model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ic")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic")

# 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

action_ids = torch.argmax(logits[:, :6], dim=-1).tolist()
action_labels = [model.config.id2label[_id] for _id in action_ids]

object_ids = torch.argmax(logits[:, 6:20], dim=-1).tolist()
object_labels = [model.config.id2label[_id + 6] for _id in object_ids]

location_ids = torch.argmax(logits[:, 20:24], dim=-1).tolist()
location_labels = [model.config.id2label[_id + 20] for _id in location_ids]

📚 Documentation

Model description

This is a ported version of S3PRL's Hubert for the SUPERB Intent Classification task.

The base model is hubert-base-ls960, 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

Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent labels: action, object, and location.

For the original model's training and evaluation instructions refer to the S3PRL downstream task README.

📄 License

This project is licensed under the Apache-2.0 license.

📊 Eval results

The evaluation metric is accuracy.

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}
}