3e4da5f781
* Replace deprecated pytorch methods
- torch.cuda.amp.GradScaler(...) => torch.amp.GradScaler("cuda", ...)
- torch.cuda.amp.autocast(...) => torch.amp.autocast("cuda", ...)
* Replace `with autocast(...)` with `with autocast("cuda", ...)`
Co-authored-by: Li Peng <lipeng@unisound.ai>
Introduction
This recipe trains multi-domain ASR models for AliMeeting. By multi-domain, we mean that we train a single model on close-talk and far-field conditions. This recipe optionally uses [GSS]-based enhancement for far-field array microphone. We pool data in the following 4 ways and train a single model on the pooled data:
(i) individual headset microphone (IHM) (ii) IHM with simulated reverb (iii) Single distant microphone (SDM) (iv) GSS-enhanced array microphones
This is different from alimeeting/ASR since that recipe trains a model only on the
far-field audio. Additionally, we use text normalization here similar to the original
M2MeT challenge, so the results should be more comparable to those from Table 4 of
the paper.
The following additional packages need to be installed to run this recipe:
pip install jiebapip install paddlepaddlepip install git+https://github.com/desh2608/gss.git
./RESULTS.md contains the latest results.
Performance Record
pruned_transducer_stateless7
The following are decoded using modified_beam_search:
| Evaluation set | eval WER | test WER |
|---|---|---|
| IHM | 9.58 | 11.53 |
| SDM | 23.37 | 25.85 |
| MDM (GSS-enhanced) | 11.82 | 14.22 |
See RESULTS for details.