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Call on more volunteers to add recipes with new datasets or new models #394
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I suggest you can have look at this concrete tutorial https://icefall.readthedocs.io/en/latest/contributing/how-to-create-a-recipe.html Here, I want to provide a simple tutorial about how to add a recipe quickly and easily. Before you build a recipe, I strongly suggest you look at our other existing recipes https://github.com/k2-fsa/icefall/tree/master/egs and this tutorial https://icefall.readthedocs.io/en/latest/contributing/how-to-create-a-recipe.html. You will find that there is not much you need to modify or add. There are some steps about how to add a recipe for icefall (You don't have to be afraid to make mistakes, because many people will help you complete it together as long as you submit your PR): if I build a pruned_transducer_stateless2 recipe for an English dataset, such as tedlium:
if I build a pruned_transducer_stateless2 recipe for a Chinese dataset, such as thchs30:
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Could you add the tutorial to https://github.com/k2-fsa/icefall/tree/master/docs ? |
Oh, I find there are very concrete tutorial in https://github.com/k2-fsa/icefall/tree/master/docs. I just write a simple tutorial here. I think https://icefall.readthedocs.io/en/latest/contributing/index.html is enough. |
fisher-swbd recipe coming soon. |
I would like try Pruned_Stateless_Transducer_2 on aishell2 if no one is doing it. |
@yuekaizhang You are very welcome. PS: Please use |
Mentioning here to avoid recipe duplication. I will work on recipes for AMI and AliMeeting this fall. For both these datasets, there are close-talk and far-field recordings available. The idea would be to train a single model that can handle both settings. Additionally, we can also use GSS-enhanced multi-channel data for training, although this is optional. (We found during the CHiME-6 challenge that it helps significantly for overlapped speech.) |
I'm working on the Tedlium |
I am working on a Japanese CSJ recipe. So far I have managed a working lang_char model using the conv_emformer_transducer_stateless2 setup, yielding the preliminary results below at 28 epochs.
In the spirit of pythonising the recipe, I have rewritten the bash and perl data preparation scripts from kaldi's recipe. However, this yielded a somewhat different transcript than Kaldi, so my results are not directly comparable with espnet and kaldi. I will send in a pull request once a version comparable to espnet and kaldi is up. |
Thanks! |
AMI recipe is now available: #698 |
MGB2 is also available: #396 |
ASR recipes often require some form of corpus-specific text normalization. We are trying to make such normalizations available in the manifest preparation stage in Lhotse (e.g., see AMI, CHiME-6, AliMeeting recipes in Lhotse). The specific implementations are done in the lhotse.recipes.utils and called using an additional |
AliMeeting multi-condition training recipe is merged: #751 |
The next generation Kaldi is developing rapidly. We have gotten some competitive results in some large and popular datasets based on k2, icefall, and Lhotse. Now, we want to apply it to many datasets. We welcome more volunteers to the work of adding recipes with new datasets or new models. You can comment on this issue if you want to add some dataset or model. So I can put your name on the appropriate volunteer place. This can avoid the overlap of everyone's work. (Note: You can choose some dataset by yourself even if it doesn't appear in the following form.)
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