Datasets:
Tasks:
Automatic Speech Recognition
Modalities:
Text
Formats:
json
Languages:
Japanese
Size:
10K - 100K
ArXiv:
License:
File size: 9,037 Bytes
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language:
- ja
task_categories:
- automatic-speech-recognition
pretty_name: asr-ja-bench
license: other
license_name: per-subset
license_link: LICENSE.md
configs:
- config_name: jsut
default: true
data_files:
- split: test
path: data/jsut/samples.jsonl
- config_name: jvnv
data_files:
- split: test
path: data/jvnv/samples.jsonl
- config_name: tedxjp10k
data_files:
- split: test
path: data/tedxjp10k/samples.jsonl
- config_name: fleurs
data_files:
- split: test
path: data/fleurs/samples.jsonl
- config_name: speech-bsd
data_files:
- split: test
path: data/speech-bsd/samples.jsonl
---
# asr-ja-bench
A Japanese speech recognition benchmark built from five public test sets: [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut), [JVNV](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus), [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K), [FLEURS](https://huggingface.co/datasets/google/fleurs) and [SpeechBSD](https://github.com/ku-nlp/speechBSD), with known label defects corrected or the samples removed. The [Haas-ja-v1.0](https://huggingface.co/third-intelligence/Haas-ja-v1.0) results are reported on it.
This repository contains **no audio and no transcripts**: the upstream corpora have different licences, and several do not allow redistribution. It lists which upstream samples the benchmark uses, the corrections made to their transcripts, and hashes to verify them. [`rebuild.py`](rebuild.py) recreates the benchmark from the upstream corpora and checks every transcript against its hash.
**Version: v1.1.** Report results with the version they were measured on.
| subset | upstream | samples | hours |
|---|---|---:|---:|
| `jsut` | JSUT basic5000 | 5,000 | 6.78 |
| `jvnv` | JVNV | 1,375 | 3.33 |
| `tedxjp10k` | TEDxJP-10K | 9,200 | 8.21 |
| `fleurs` | FLEURS (ja_jp, test) | 650 | 2.36 |
| `speech-bsd` | SpeechBSD (ja, test) | 2,093 | 2.70 |
| **total** | | **18,318** | **23.38** |
## Differences from the upstream test sets
| subset | upstream samples | not included (scope rule) | removed (label defect) | transcripts corrected | samples |
|---|---:|---:|---:|---:|---:|
| `jsut` | 5,000 | 0 | 0 | 3 | 5,000 |
| `jvnv` | 1,615 | 240 | 0 | 0 | 1,375 |
| `tedxjp10k` | 10,000 (9,233 reconstructed¹) | 10 | 23 | 13 | 9,200 |
| `fleurs` | 650 | 0 | 0 | 2 | 650 |
| `speech-bsd` | 2,120 | 1 | 26 | 23 | 2,093 |
¹ TEDxJP-10K is reconstructed from YouTube. When this benchmark was built, 767 of its 10,000 utterances could no longer be recovered (videos or subtitles removed), so it uses the remaining ones.
Why samples were removed (`excluded.jsonl`, field `reason`):
- `out_of_scope`: JVNV's phrase-free session (`*_free_*`): its non-verbal vocalizations are improvised and have no transcript.
- `label_too_long`: the transcript is longer than 15 characters per second of audio.
- `wrong_audio`: the audio does not contain the transcribed utterance.
- `no_speech`: the audio contains no speech.
Corrected transcripts fix errors heard in the audio. In JSUT, two transcripts belonged to the neighbouring file, and one repeated a word.
SpeechBSD transcripts were also normalized when the benchmark was built: full-width characters folded to half-width (NFKC), Latin letters lowercased, and 「」, ・ and spaces removed (540 samples). These changes are in the patches too, so a rebuild reproduces the benchmark exactly.
## Files
```
data/<subset>/samples.jsonl the benchmark's samples, in order
data/<subset>/excluded.jsonl upstream samples removed, with the reason
rebuild.py upstream corpora -> the benchmark
LICENSE.md which licence applies where
```
Each line of `samples.jsonl`:
| field | |
|---|---|
| `id` | the upstream sample id: JSUT `BASIC5000_0001`, JVNV `F1_anger_regular_01`, the TEDxJP-10K utterance id, the FLEURS recording's file name without `.wav`, the SpeechBSD `ja_wav` stem |
| `duration` | seconds of 16 kHz audio |
| `upstream_sha256` | SHA-256 of the upstream transcript (UTF-8) |
| `text_sha256` | SHA-256 of the benchmark transcript |
| `patch` | only if the transcript was changed: `[[start, end, replacement], ...]`, code-point offsets into the upstream transcript, applied from the right. It holds only the replacement characters, never the upstream text. |
## Rebuilding
Get the upstream corpora:
- **JSUT basic5000**: `jsut_ver1.1.zip` from the [JSUT page](https://sites.google.com/site/shinnosuketakamichi/publication/jsut) (pass the zip or its extracted directory as `--jsut`).
- **JVNV**: `jvnv_ver1.zip` from the [JVNV page](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus) (`--jvnv`).
- **TEDxJP-10K**: Reconstruct TEDxJP-10K v1.1 with the [TEDxJP-10K repository](https://github.com/laboroai/TEDxJP-10K)'s instructions (download the listed YouTube videos and subtitles, run `compose_tedxjp10k.py`) and pass the resulting `TEDxJP-10K_v1.1` directory as `--tedxjp10k`. Videos and subtitles removed from YouTube cannot be recovered, so a reconstruction can have fewer utterances; `rebuild.py` lists any sample it cannot find.
- **FLEURS (ja_jp, test)**: Read from the Hub (`google/fleurs`, `ja_jp`, pinned revision) by `rebuild.py`.
- **SpeechBSD (ja, test)**: Read from the Hub (`ku-nlp/speech-bsd-hf`, pinned revision) by `rebuild.py`. The repository is gated: accept its terms on the Hub and log in (`hf auth login`) first.
Then:
```bash
pip install numpy soundfile pyarrow huggingface_hub # and ffmpeg on PATH
python rebuild.py --jsut jsut_ver1.1.zip --jvnv jvnv_ver1.zip \
--tedxjp10k TEDxJP-10K_v1.1 --out asr-ja-bench-v1.1
```
This writes the benchmark data directory `asr-ja-bench-v1.1/`: `<subset>.jsonl` (`id`, `audio`, `text`, `duration`) and the audio as 16 kHz mono 16-bit FLAC. It is separate from this repository's own folder, which holds only the sample lists. `--subset` limits it to some subsets, and `--text-only` checks the transcripts without writing audio. It exits with an error if a transcript does not match its hash or a sample is missing.
Transcripts are reproduced exactly. JSUT and JVNV are recorded at 48 kHz and are resampled to 16 kHz with ffmpeg's default resampler, which reproduces the benchmark audio bit for bit (tested with ffmpeg 7.1). FLEURS and SpeechBSD audio is used as is. TEDxJP-10K audio is whatever its reconstruction produces; reconstructions can differ by a few least-significant bits, because the upstream script converts the audio with sox, which dithers.
## Licences
Each `data/<subset>/` folder is derived from its source dataset and licensed under that dataset's licence, listed below; `rebuild.py` and this card are Apache-2.0 (see [`LICENSE.md`](LICENSE.md)). The audio and transcripts are not included: obtaining and using them is subject to each corpus's own terms.
| subset | licence of `data/<subset>/` |
|---|---|
| `jsut` | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
| `jvnv` | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
| `tedxjp10k` | [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) |
| `fleurs` | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
| `speech-bsd` | [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
## Acknowledgements
We thank the authors of JSUT, JVNV, TEDxJP-10K, FLEURS and SpeechBSD for creating these corpora and making them openly available. If you use asr-ja-bench, please also cite them:
- **JSUT** ([page](https://sites.google.com/site/shinnosuketakamichi/publication/jsut)): Sonobe, R., Takamichi, S., & Saruwatari, H. (2017). JSUT corpus: Free large-scale Japanese speech corpus for end-to-end speech synthesis. *arXiv preprint arXiv:1711.00354*.
- **JVNV** ([page](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus)): Xin, D., Jiang, J., Takamichi, S., Saito, Y., Aizawa, A., & Saruwatari, H. (2023). JVNV: A corpus of Japanese emotional speech with verbal content and nonverbal expressions. *arXiv preprint arXiv:2310.06072*.
- **TEDxJP-10K** ([repository](https://github.com/laboroai/TEDxJP-10K)): 安藤慎太郎, 藤原弘将 (2020). テレビ録画とその字幕を利用した大規模日本語音声コーパスの構築. *情報処理学会研究報告*, Vol.2020-SLP-134, No.8.
- **FLEURS** ([dataset](https://huggingface.co/datasets/google/fleurs)): Conneau, A., Ma, M., Khanuja, S., Zhang, Y., Axelrod, V., Dalmia, S., Riesa, J., Rivera, C., & Bapna, A. (2022). FLEURS: Few-shot learning evaluation of universal representations of speech. *arXiv preprint arXiv:2205.12446*.
- **SpeechBSD** ([repository](https://github.com/ku-nlp/speechBSD)): Shimizu, S., Chu, C., Li, S., & Kurohashi, S. (2023). Towards speech dialogue translation mediating speakers of different languages. In *Findings of the Association for Computational Linguistics: ACL 2023* (pp. 1122–1134). Association for Computational Linguistics. https://aclanthology.org/2023.findings-acl.72
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