Fast-HuBERT

Paper

Intro

Existing speech-based SSL models face a common dilemma in terms of computational cost, which might hinder their potential application and in-depth academic research. Fast-HuBERT is proposed to improve the pretraining efficiency.

Fast-HuBERT optimizes the front-end inputs, loss computation and also aggregates other SOTA techniques, including ILS and MonoBERT.

Fast-HuBERT can be trained in 1.1 days with 8 V100 GPUs on Librispeech benchmark, without performance drop compared to the original implementation, resulting in 5.2X speedup.

Model

Requirements and Installation

git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./
git clone https://github.com/yanghaha0908/FastHuBERT

Data preparation

Refer to this to prepare data.

Pre-train a Fast-HuBERT model

$ python fairseq_cli/hydra_train.py \
    --config-dir /path/to/FastHuBERT/config/pretrain \
    --config-name fasthubert_base_lirbispeech \
    common.user_dir=/path/to/FastHuBERT \
    task.data=/path/to/data \
    task.label_dir=/path/to/labels \
    task.labels=["phn"] \
    model.label_rate=50 \

Fine-tune a Fast-HuBERT model with a CTC loss

$ python fairseq_cli/hydra_train.py \
    --config-dir /path/to/FastHuBERT/config/finetune \
    --config-name base_100h \
    common.user_dir=/path/to/FastHuBERT \
    task.data=/path/to/data 
    task.label_dir=/path/to/transcriptions \
    model.w2v_path=/path/to/checkpoint \

Decode a FastHuBERT model

Viterbi decoding

$ python examples/speech_recognition/new/infer.py \
    --config-dir examples/hubert/config/decode \
    --config-name infer_viterbi \
    common.user_dir=/path/to/FastHuBERT \
    common_eval.path=/path/to/checkpoint \
    task.data=/path/to/data \
    task.normalize=[true|false] \
    task._name=fasthubert_pretraining \
    dataset.gen_subset=test \

KenLM / Fairseq-LM decoding

$ python examples/speech_recognition/new/infer.py \
    --config-dir examples/hubert/config/decode \
    --config-name infer_kenlm \
    common.user_dir=/path/to/FastHuBERT \
    common_eval.path=/path/to/checkpoint \
    task.data=/path/to/data \
    task.normalize=[true|false] \
    task._name=fasthubert_pretraining \
    dataset.gen_subset=test \
    decoding.lmweight=2 decoding.wordscore=-1 decoding.silweight=0 \
    decoding.beam=1500 \
    decoding.lexicon=/path/to/lexicon \
    decoding.lmpath=/path/to/arpa \

ddlBoJack / FastHuBERT