jaedukseo/wav2seq

Official code for Wav2Seq

Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages

Usage

Dependency

The code is tested with fairseq commit bba000d.

torch==1.9.0+cu111
torchaudio==0.9.0
tqdm==4.62.3
hydra-core==1.0.7
omegaconf==2.0.6
einops==0.3.0
fire==0.4.0
fairseq==1.0.0a0+bba000d

Installation

git clone [email protected]:asappresearch/wav2seq.git
cd wav2seq
pip install -e .

Creatining Psuedo Subword Tokens

1. Create wav2vec style manifest files Please set LIBRISPEECH_PATH to your librispeech folder which contains three subfolders train-clean-100, train-clean-360, train-other-500.

mkdir -p manifest/librispeech/train-960
python -m examples.wav2vec.wav2vec_manifest LIBRISPEECH_PATH  --dest manifest/librispeech/train-960 --ext flac --valid-percent 0.01 --path-must-contain train

2. Train k-means model and get cluster indices Please make sure that you have download pre-trained hubert-base checkpoint at HUBERT_PATH. Notably, this step requires a GPU for feature extraction and 64GB main memory for k-means training. Extracting HuBERT features takes about 15 minutes, training k-means may take about an hour, dumping the cluster ids of the whole Librispeech 960h data takes more than two hours.

HUBERT_PATH="save/pretrained/hubert_base_ls960.pt"
mkdir -p save/pretrained
if ! [ -f $HUBERT_PATH ]; then
    wget https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt -O $HUBERT_PATH
fi
bash scripts/pl/extract-hubert-features.sh $HUBERT_PATH 9 2 2 500

where 9, 2, 2, 500 means that we use the 9-th layer of HuBERT, kernel size 2 and stride size 2 for average pooling, and 500 custers in k-means.

3. Training BPE model and create pseudo subword tokens

bash scripts/pl/create-hubert-pseudo-language.sh labels/hubert_base-l9-k2s2-fp16-ls0.1/c500 30000

Pre-training Wav2Seq

bash scripts/wav2seq-pt.sh wav2seq-hubert-base-ls960

Fine-tuning Wav2Seq

Pre-trained Models

Our pretrained models will be released.