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README.md

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+---
+language: en
+datasets:
+- superb
+tags:
+- speech
+- audio
+- wav2vec2
+license: apache-2.0
+---
+
+# Wav2Vec2-Base for Intent Classification
+
+## Model description
+
+This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands).
+
+The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), 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](https://arxiv.org/abs/2105.01051)
+
+## Task and dataset 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 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", "ic", split="test")
+dataset = dataset.map(map_to_array)
+
+model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ic")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-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]
+```
+
+## Eval results
+
+The evaluation metric is accuracy.
+
+|        | **s3prl** | **transformers** |
+|--------|-----------|------------------|
+|**test**| `0.9235`  | `N/A`         |
+
+### 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}
+}
+```