5.1 KiB
Hubert
Overview
Hubert was proposed in HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
The abstract from the paper is the following:
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets.
This model was contributed by patrickvonplaten.
Usage tips
- Hubert is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Hubert model was fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
using [
Wav2Vec2CTCTokenizer].
Using Flash Attention 2
Flash Attention 2 is an faster, optimized version of the model.
Installation
First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the official documentation. If your hardware is not compatible with Flash Attention 2, you can still benefit from attention kernel optimisations through Better Transformer support covered above.
Next, install the latest version of Flash Attention 2:
pip install -U flash-attn --no-build-isolation
Usage
Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of facebook/hubert-large-ls960-ft, the flash-attention-2 and the sdpa (scale-dot-product-attention) version. We show the average speedup obtained on the librispeech_asr clean validation split:
>>> from transformers import Wav2Vec2Model
model = Wav2Vec2Model.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to(device)
...
Expected speedups
Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of the facebook/hubert-large-ls960-ft model and the flash-attention-2 and sdpa (scale-dot-product-attention) versions. . We show the average speedup obtained on the librispeech_asr clean validation split:
Resources
HubertConfig
autodoc HubertConfig
HubertModel
autodoc HubertModel - forward
HubertForCTC
autodoc HubertForCTC - forward
HubertForSequenceClassification
autodoc HubertForSequenceClassification - forward
TFHubertModel
autodoc TFHubertModel - call
TFHubertForCTC
autodoc TFHubertForCTC - call