Create README.md

README.md

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+---
+language: en
+datasets:
+- librispeech_asr
+tags:
+- speech
+license: apache-2.0
+---
+
+# DistilHuBERT
+
+[DistilHuBERT by NTU Speech Processing & Machine Learning Lab](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
+
+The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
+
+Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900)
+
+Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee
+
+**Abstract**
+Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech.
+
+The original model can be found under https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller .
+
+# Usage
+
+See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `HubertForCTC`.