9 Commits

Author SHA1 Message Date
Triplecq
42c152f5cb decrease learning-rate to solve the error: RuntimeError: grad_scale is too small, exiting: 5.820766091346741e-11 2024-01-14 12:12:15 -05:00
Triplecq
dc2d531540 customized recipes for rs 2024-01-14 22:28:53 +09:00
Triplecq
b1de6f266c customized recipes for reazonspeech 2024-01-14 22:28:32 +09:00
Triplecq
1e6fe2eae1 restore 2024-01-14 08:05:49 -05:00
Triplecq
5e9a171b20 customize tranning script for rs 2024-01-14 07:45:33 -05:00
Triplecq
8eae6ec7d1 Add pruned_transducer_stateless2 from reazonspeech branch 2024-01-14 05:23:26 -05:00
Triplecq
af87726bf2 init zipformer recipe 2024-01-14 19:13:21 +09:00
Chen
2436597f7f Zipformer recipe 2023-12-28 05:37:40 +09:00
Fujimoto Seiji
c1ce7ca9e3 Add first cut at ReazonSpeech recipe
This recipe is mostly based on egs/csj, but tweaked to the point that
can be run with ReazonSpeech corpus.

That being said, there are some big caveats:

 * Currently the model quality is not very good. Actually, it is very
   bad. I trained a model with 1000h corpus, and it resulted in >80%
   CER on JSUT.

 * The core issue seems that Zipformer is prone to ignore untterances
   as sielent segments. It often produces an empty hypothesis despite
   that the audio actually contains human voice.

 * This issue is already reported in the upstream and not fully
   resolved yet as of Dec 2023.

Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net>
2023-12-18 16:12:11 +09:00