Alibaba-MIT-Speech

This is a PATCH file with the DFSMN related codes and example scripts for LibriSpeech task.

Apply Patch

The patch is built based on the Kaldi speech recognition toolkit with commit "04b1f7d6658bc035df93d53cb424edc127fab819".

You can apply this patch to your own kaldi branch by using the following commands: (Instead of applying the PATCH file, one can also directly clone the project at "https://github.com/tramphero/kaldi")

##Take a look at what changes are in the patch

git apply --stat Alibaba_MIT_Speech_DFSMN.patch

##Test the patch before you actually apply it

git apply --check Alibaba_MIT_Speech_DFSMN.patch

##If you don’t get any errors, the patch can be applied cleanly.

git am --signoff < Alibaba_MIT_Speech_DFSMN.patch

Run Example Scripts:

The training scripts and experimental results for the LibriSpeech task is available at kaldi/egs/librispeech/s5.

There are three DFSMN configurations with different model size: DFSMN_S, DFSMN_M, DFSMN_L.

#Training FSMN models on the cleaned-up data

#Three configurations of DFSMN with different model size: DFSMN_S, DFSMN_M, DFSMN_L

local/nnet/run_fsmn_ivector.sh DFSMN_S

local/nnet/run_fsmn_ivector.sh DFSMN_M

local/nnet/run_fsmn_ivector.sh DFSMN_L

The DFSMN_S is a small DFSMN with six DFSMN-components while DFSMN_L is a large DFSMN consist of 10 DFSMN-components.

For the 960-hours-setting, it takes about 2-3 days to train DFSMN_S only using one M40 GPU.

And the detailed experimental results are listed in the RESULTS file.