RepCodec: A Speech Representation Codec for Speech Tokenization

RepCodec is a speech tokenization method for converting a speech waveform into a sequence of discrete semantic tokens. The main idea is to train a representation codec which learns a vector quantization codebook through reconstructing the input speech representations from speech encoders like HuBERT or data2vec. Extensive experiments show that RepCodec significantly outperforms the widely used k-means clustering approach in both speech understanding and generation. Also, RepCodec generalizes well across various speech encoders and languages.

Please first install RepCodec by

git clone https://github.com/mct10/RepCodec.git
cd RepCodec
pip install .

We used Python 3.9.18 and PyTorch 1.12.1 to test the usage, but the code should be compatible with other recent Python and PyTorch versions.

We adapt the dump_hubert_feature.py script from fairseq to support dumping representations from data2vec, HuBERT, or Whisper encoders.

If you use our script (examples/dump_feature.py), please also install the following packages:

pip install npy_append_array soundfile 

Additionally, if you want to dump representations from

• data2vec or HuBERT: please follow fairseq's instruction to install the latest fairseq.

• Whisper: please follow Whispers'instruction to install the latest Whisper.

Then, you can follow the given examples to dump representations:

# Example 1: dump from HuBERT base layer 9 
# (for data2vec, simply change "model_type" to data2vec and "ckpt_path" to the path of data2vec model)

layer=9

python3 examples/dump_feature.py \
    --model_type hubert \
    --tsv_path /path/to/tsv/file \
    --ckpt_path /path/to/HuBERT/model  \
    --layer ${layer} \
    --feat_dir /dir/to/save/representations


# Example 2: dump from Whisper medium layer 24

layer=24

python3 examples/dump_feature.py \
    --model_type whisper \
    --tsv_path /path/to/tsv/file \
    --whisper_root /directory/to/save/whisper/model \
    --whisper_name medium \
    --layer ${layer} \
    --feat_dir /dir/to/save/representations

Explanations about the args:

• model_type: choose from data2vec, hubert, and whisper.

• tsv_path: path of the tsv file. Should have the format of

/dir/to/dataset
path_of_utterance_1 number_of_frames
path_of_utterance_2 number_of_frames

You can follow this script to generate the tsv file.

For example, by running

python wav2vec_manifest.py \
  /dir/to/LibriSpeech/dev-clean \
  --dest /dir/to/manifest \
  --ext flac \
  --valid-percent 0

you can obtain the dev-clean.tsv in /dir/to/manifest for LibriSpeech. (By default, the output file name is train.tsv. Remember to rename the file.)

It should be similar to:

/dir/to/LibriSpeech/dev-clean
2277/149896/2277-149896-0026.flac	78720
2277/149896/2277-149896-0005.flac	89600
2277/149896/2277-149896-0033.flac	45520

• ckpt_path: must provide for data2vec and HuBERT. You need to download the model from data2vec website or HuBERT website yourself. --ckpt_path is the path of the data2vec/HuBERT model.

• whisper_root and whisper_name: must provide BOTH --whisper_root and --whisper_name for Whisper. If there is no corresponding model in --whisper_root, the script will download for you.

• layer: which Transformer encoder layer of the model should the representations be extracted from. It is 1-based. For example, if layer=9, then the outputs from the 9^th Transformer encoder layer are dumped. Range: [1, number of Transformer encoder layers]

• feat_dir: The output representations will be saved to ${feat_dir}/0_1.npy and ${feat_dir}/0_1.len.

For other useful functionalities (e.g., sharding), please check the argument list in examples/dump_feature.py.

We expect to have ${feat_dir}/0_1.npy and ${feat_dir}/0_1.len in the provided directory /dir/to/representaitons.

Also, the tsv file should be the same as the one used in Representation Preparation.

repcodec /dir/to/representaitons \
    --model /path/to/repcodec/model \
    --tsv_path /path/to/tsv/file \
    [--use_gpu] \
    [--out_dir /path/to/output]

This command will tokenize the representations and the output discrete tokens will be saved to ${out_dir}/tokens. The tokens are in the same order as the provided tsv file.

An example of the output file:

/dir/to/LibriSpeech/dev-clean
2277/149896/2277-149896-0026.flac	696 696 198 198 198 498 ...
2277/149896/2277-149896-0005.flac	696 696 198 198 198 907 ...
2277/149896/2277-149896-0033.flac	696 696 198 198 198 696 ...

Under examples/tokens, we provide some token files as references. They are obtained from LibriSpeech dev-clean subset using the 6 types of representations and corresponding RepCodec Models. Your results should be very similar to ours.

import torch
import yaml

from repcodec.RepCodec import RepCodec

# for feature types of HubERT base & data2vec base, please use repcodec_dim768.yaml;
# for feature types of HuBERT large & data2vec large & Whisper medium, please use repcodec_dim1024.yaml;
# for feature types of Whisper large-v2, please use repcodec_dim1280.yaml
config = "repcodec/configs/repcodec_dim768.yaml"
with open(config) as fp:
    conf = yaml.load(fp, Loader=yaml.FullLoader)

model = RepCodec(**conf)
model.load_state_dict(torch.load("./hubert_base_l9.pkl", map_location="cpu")["model"]["repcodec"])
model.quantizer.initial()
model.eval()

# input shape: (batch size, hidden dim, sequence length)
random_features = torch.randn(size=(1, 768, 100))
with torch.no_grad():
    x = model.encoder(random_features)
    z = model.projector(x)
    _, idx = model.quantizer.codebook.forward_index(z.transpose(2, 1))
    tokens = idx.cpu().data.numpy().tolist()[0]

Our implementation is based on facebookresearch/AudioDec. We thank them for open-sourcing their code!

If you find our work useful, please cite the following article.

@misc{huang2023repcodec,
      title={RepCodec: A Speech Representation Codec for Speech Tokenization}, 
      author={Zhichao Huang and Chutong Meng and Tom Ko},
      year={2023},
      eprint={2309.00169},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}