SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms.
Terms of Access:
The "Researcher" has requested permission to use the GigaSpeech 2 database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:

Researcher shall use the Database only for non-commercial research and educational purposes.
The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.

!!! Please also fill out the Google Form TODO: to request access to the Gigaspeech 2 dataset.

Dataset Card for Gigaspeech 2

Dataset Description

GigaSpeech 2 is an evolving, large-scale, multi-domain, and multilingual ASR corpus focusing on low-resource languages. GigaSpeech 2 raw comprises about 30,000 hours of automatically transcribed speech, across Thai, Indonesian, and Vietnamese. GigaSpeech 2 refine consists of 10,000 hours of Thai, 6,000 hours each for Indonesian and Vietnamese.

Homepage: https://github.com/SpeechColab/GigaSpeech2?tab=readme-ov-file#gigaspeech-2
Repository: https://github.com/SpeechColab/GigaSpeech2
Paper: https://export.arxiv.org/pdf/2406.11546
Leaderboard: https://github.com/SpeechColab/GigaSpeech2#leaderboard
ModelScope: https://modelscope.cn/datasets/AI-ModelScope/gigaspeech2
Point of Contact: [email protected]

Example Usage

TODO:

Supported Tasks and Leaderboards

automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. Evaluation metrics includes Character Error Rate (CER) for Thai, and Word Error Rate (WER) for Indonesian and Vietnamese. The task has an active leaderboard which can be found at https://github.com/SpeechColab/GigaSpeech2#leaderboard and ranks models based on their WER.

Languages

Gigaspeech 2 contains audio and transcription data in Thai, Indonesian, Vietnamese.

Dataset Structure

GigaSpeech 2
├── data
│   ├── id
│   │   ├── md5
│   │   ├── dev.tar.gz
│   │   ├── dev.tsv
│   │   ├── test.tar.gz
│   │   ├── test.tsv
│   │   ├── train
│   │   │   ├── 0.tar.gz
│   │   │   ├── 1.tar.gz
│   │   │   └── ...
│   │   ├── train_raw.tsv
│   │   └── train_refined.tsv
│   ├── th
│   │   ├── md5
│   │   ├── dev.tar.gz
│   │   ├── dev.tsv
│   │   ├── test.tar.gz
│   │   ├── test.tsv
│   │   ├── train
│   │   │   ├── 0.tar.gz
│   │   │   ├── 1.tar.gz
│   │   │   └── ...
│   │   ├── train_raw.tsv
│   │   └── train_refined.tsv
│   └── vi
│       ├── md5
│       ├── dev.tar.gz
│       ├── dev.tsv
│       ├── test.tar.gz
│       ├── test.tsv
│       ├── train
│       │   ├── 0.tar.gz
│       │   ├── 1.tar.gz
│       │   └── ...
│       ├── train_raw.tsv
│       └── train_refined.tsv
├── metadata.json
└── README.md

Data Instances

Audio file (.wav):
    Channels: 1
    Sample Rate: 16000
    Sample Encoding: 16-bit Signed Integer PCM

Transcript file (.tsv):
    <segment_id>\t<text>\n

Data Fields

segment_id (string) - string id of the segment.
text (string) - transcription of the segment.

Data Splits

The dataset has three subsets for each language: train, dev and test. The train set has two configurations: raw, refined. train_raw contains all the data from train_refined.

Transcribed Training Subsets Size

	Thai (hours)	Indonesian (hours)	Vietnamese (hours)
GigaSpeech 2 raw	12901.8	8112.9	7324.0
GigaSpeech 2 refined	10262.0	5714.0	6039.0

GigaSpeech 2 raw contains all the data from GigaSpeech 2 refined.

Transcribed Evaluation Subsets

	Thai (hours)	Indonesian (hours)	Vietnamese (hours)
GigaSpeech 2 dev	10.0	10.0	10.2
GigaSpeech 2 test	10.0	10.0	11.0

Dataset Creation

Source Data

GigaSpeech 2 raw: 30,000 hours of automatically transcribed speech across Thai, Indonesian, and Vietnamese.
GigaSpeech 2 refined: 10,000 hours of Thai, 6,000 hours each for Indonesian and Vietnamese.
GigaSpeech 2 DEV & TEST: 10 hours for DEV and 10 hours for TEST per language, transcribed by professional human annotators, challenging and realistic.

Annotations

Who are the annotators?

Development (DEV) and test (TEST) subsets are annotated by professional human annotators.

Licensing Information

In general, when training a machine learning model on a given dataset, the license of the model is independent to that of the dataset. That is to say, speech recognition models trained on the GigaSpeech 2 dataset may be eligible for commercial license, provided they abide to the 'Fair Use' terms of the underlying data and do not violate any explicit copyright restrictions. This is likely to be true in most use-cases. However, it is your responsiblity to verify the appropriate model license for your specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible for the license of any machine learning model trained on the GigaSpeech 2 dataset.

Citation Information

Please cite this paper if you find this work useful:

@article{gigaspeech2,
  title={GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement},
  author={Yifan Yang and Zheshu Song and Jianheng Zhuo and Mingyu Cui and Jinpeng Li and Bo Yang and Yexing Du and Ziyang Ma and Xunying Liu and Ziyuan Wang and Ke Li and Shuai Fan and Kai Yu and Wei-Qiang Zhang and Guoguo Chen and Xie Chen},
  journal={arXiv preprint arXiv:2406.11546},
  year={2024},
}

Terms of Access

The "Researcher" has requested permission to use the GigaSpeech 2 database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:

Researcher shall use the Database only for non-commercial research and educational purposes.
The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.

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