Zipformer recipe for ReazonSpeech

[Triplecq]

ReazonSpeech is an open-source dataset that contains a diverse set of natural Japanese speech, collected from terrestrial television streams. It contains more than 35,000 hours of audio.

The dataset is available on Hugging Face. For more details, please visit:

• Dataset: https://huggingface.co/datasets/reazon-research/reazonspeech
• Paper: https://research.reazon.jp/_static/reazonspeech_nlp2023.pdf

[danpovey]

There are quite a few changes not in the directory you are adding. You might want to remove those as they are potential barriers to merging it. If there's anything outside that directory you believe we should change , it can be a separate PR.

[Triplecq]

There are quite a few changes not in the directory you are adding. You might want to remove those as they are potential barriers to merging it. If there's anything outside that directory you believe we should change , it can be a separate PR.

Thanks for your quick feedback during the holiday! I will remove unrelated changes and get back to you soon.

[Triplecq]

I've already removed those unrelated changes. It's ready for review now. Please let me know if you have any questions or comments. Thank you!

[pzelasko]

I noticed that you have „lhotse prepare reazonspeech” command in data prep, do you intend to submit a PR to Lhotse as well?

[Triplecq]

I noticed that you have „lhotse prepare reazonspeech” command in data prep, do you intend to submit a PR to Lhotse as well?

Thanks for the note. Sure, we're cleaning up the scripts and will submit a PR to Lhotse soon. :)

[Triplecq]

I just submitted a PR to Lhotse as well: lhotse-speech/lhotse#1330
Both PR are ready for review. Thank you!

[yfyeung]

Hi, May I kindly ask for some questions?

What are the main differences in quality and coverage between the small, medium, large, and all sets?
Which configuration (large, all, or small+medium+large) yields the best performance?

Thanks for your assistance.

[Triplecq]

Hi @yfyeung

Thank you for your interest and questions.

As far as I know, the various partitions only differ in their size and hours, as listed in the table on the Hugging Face page. (@fujimotos san could you please confirm this or correct me if I am wrong? Thank you!)

Here is a comparison of different partitions:

Model Name	Model Size	In-Distribution CER	JSUT CER	CommonVoice CER	TEDx CER
zipformer-L (medium)	155.92 M	10.31	16.52	12.8	28.8
zipformer-L (large)	157.24 M	6.19	10.35	9.36	24.23
zipformer-L (all)	159.34 M	4.2 (epoch 39 avg 7)	6.62 (epoch 39 avg 2)	7.76 (epoch 39 avg 2)	17.81 (epoch 39 avg 10)

P.S., With this larger setting of the zipformer model, we suggest using data with more than 300 hours. We have not tried the combination of small + medium + large together, but I assume the performance is basically determined by the hours of your data.

I hope this helps. Feel free to let me know if you have any other questions. Good luck and have fun with this recipe. :)

[fujimotos]

What are the main differences in quality and coverage between the small, medium, large, and all sets?

The only difference is their dataset sizes. Check out this table:

Name	Size	Hours
tiny	600MB	8.5 hours
small	6GB	100 hours
medium	65GB	1000 hours
large	330GB	5000 hours
all	2.3TB	35000 hours

Which configuration (large, all, or small+medium+large) yields the best performance?

Use all for the best performance. Other splits (tiny/small/medium/large)
are subsets of the all set.

Note: In case there is some confusion, the relationship of those sets is:

tiny ⊆small ⊆ medium ⊆ large ⊆ all

[JinZr]

I think this one is ready to be merged, thank you for your PR!

[csukuangfj]

Please upload links to pre-trained models in a separate PR.

[yujinqiu]

@Triplecq @fujimotos
Is the model ready to share ?

Please upload links to pre-trained models in a separate PR.

[Triplecq]

@yujinqiu Thanks for your patience! We just completed another validation test on JSUT-book before the release. I will submit a separate PR and get you updated once we open the model.

[yuyun2000]

This may be the world's number one Japanese language recognition model. If you could create a medium stream version of the model, it would be the number one in the universe!