Datasets:

ilyakam
/

librispeech-long

license: cc-by-4.0
dataset_info:
  - config_name: librispeech_long
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: dataset
        dtype: string
      - name: text
        dtype: string
      - name: id
        dtype: string
      - name: audio_length_s
        dtype: float64
    splits:
      - name: dev_clean
        num_bytes: 1809224578
        num_examples: 295
      - name: dev_other
        num_bytes: 1070577217
        num_examples: 188
      - name: test_clean
        num_bytes: 1677717130
        num_examples: 270
      - name: test_other
        num_bytes: 1195726954
        num_examples: 207
    download_size: 5753245879
    dataset_size: 5753245879
configs:
  - config_name: librispeech_long
    data_files:
      - split: dev_clean
        path: librispeech_long/dev_clean-*
      - split: dev_other
        path: librispeech_long/dev_other-*
      - split: test_clean
        path: librispeech_long/test_clean-*
      - split: test_other
        path: librispeech_long/test_other-*

LibriSpeech-Long

LibriSpeech-Long is a benchmark dataset for long-form speech generation and processing. Released as part of "Long-Form Speech Generation with Spoken Language Models" (arXiv 2024).

Prerequisites

Development

Clone the repo

git clone git@hf.co:datasets/ilyakam/librispeech-long && cd librispeech-long

Activate the virtual environment:
```
source .venv/bin/activate
```
Install dependencies:
```
uv pip install -r requirements.txt
```
Process the dataset:
```
python process.py [SOURCE_ROOT] . --limit-speakers
```
Notes:
- The SOURCE_ROOT is a folder where you've extracted the dataset containing the splits dev-clean, dev-other, test-clean, and test-other.
- The optional --limit-speakers flag will only process the first speaker directory in each split for quick testing.
Copy the metadata to the top of this README.md file.
Commit and push the generated *.parquet files and the updated README.md file.

Purpose

To migrate the google-deepmind/librispeech-long dataset to Hugging Face datasets.

LibriSpeech-Long (Original)

Download audio, ground-truth transcripts, and per-file durations for all splits (3GB).

This is a benchmark dataset for evaluating long-form variants of speech processing tasks such as speech continuation, speech recognition, and text-to-speech synthesis. It is derived from the LibriSpeech dev and test sets, whose utterances are reprocessed into contiguous examples of up to 4 minutes in length (in the manner of LibriLight's cut_by_vad.py script).

For more details: see "Long-Form Speech Generation with Spoken Language Models" (paper, website).

This is part of a preprint that is work-in-progress; dataset may be subject to change.

Citation

When using this dataset, please cite the associated paper:

@article{park2024long,
  author       = {Se Jin Park and
                  Julian Salazar and
                  Aren Jansen and
                  Keisuke Kinoshita and
                  Yong Man Ro and
                  R. J. Skerry{-}Ryan},
  title        = {Long-Form Speech Generation with Spoken Language Models},
  journal      = {CoRR},
  volume       = {abs/2412.18603},
  year         = {2024}
}

License and disclaimer

The software and materials, except for the underlying LibriSpeech data, are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode, or in the LICENSE file.

The materials contain adapted material from the LibriSpeech dataset. LibriSpeech is also licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode, or in the LICENSE file. LibriSpeech is available at https://www.openslr.org/12 and created by Vassil Panayotov, Guoguo Chen, Daniel Povey and Sanjeev Khudanpur, pursuant to the paper “LibriSpeech: an ASR corpus based on public domain audio books", ICASSP 2015 (https://ieeexplore.ieee.org/document/7178964).

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

This is not an official Google product.