Add task description

README.md

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+---
+language: en
+datasets:
+- superb
+tags:
+- speech
+- audio
+- hubert
+- s3prl
+license: apache-2.0
+---
+
+# Hubert-Base for Intent Classification
+
+## Model description
+
+This is a ported version of [S3PRL's Hubert for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands).
+
+The base model is [hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960). 
+It 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](https://arxiv.org/abs/2105.01051)
+
+## Task description
+
+Intent Classification (IC) classifies utterances into predefined classes to determine the intent of
+speakers. SUPERB uses the 
+[Fluent Speech Commands](https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/) 
+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](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#ic-intent-classification---fluent-speech-commands).
+
+
+## Usage examples
+
+You can use the model directly like so:
+```python
+import torch
+import numpy as np
+from datasets import load_dataset
+from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor
+
+# TODO: replace with the official superb dataset
+superb_ks = load_dataset("anton-l/superb_dummy", "ic", split="test")
+model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ic")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic")
+
+audio = np.array(superb_ks[0]["speech"])
+# compute attention masks and normalize the waveform if needed
+inputs = feature_extractor(audio, sampling_rate=16_000, return_tensors="pt")
+
+logits = model(**inputs).logits[0]
+
+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]
+```
+
+## Eval results
+
+The evaluation metric is accuracy.
+
+|      | `s3prl` | `transformers` |
+|------|---------|----------------|
+|`test`| TBA     | TBA            |
+
+### BibTeX entry and citation info
+
+```bibtex
+@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}
+}
+```