Cannot reproduce the RESULTS(wer:43.25%) of S5b_track1 in Chime6

[dospeech]

When I run the run.sh baseline of S5b_track1 in Chime6, I can't get the wer:43.25% of RESULTS,and I get the best result is wer:45.48% . wer-details: %WER 45.48 [ 26779 / 58881, 2332 ins, 11138 del, 13309 sub ] exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_cnn_sp/decode_dev_gss_multiarray_2stage/wer_9_0.0

is there something wrong with my result？

[danpovey]

@desh2608 might be able to help with this.

[desh2608]

It's hard to say what may be going wrong just based on WER. Did you change any hyperparameters? Did you look at the intermediate results (e.g. from GMM decoding) to see if they match those in the recipe?

[dospeech]

It's hard to say what may be going wrong just based on WER. Did you change any hyperparameters? Did you look at the intermediate results (e.g. from GMM decoding) to see if they match those in the recipe?

THE wer of GMM decoding (tri3 )is 83.75%(baseline is 85.72 %), I just changed the number of gpu (gpu_num=4）and didn't change any hyperparameters.

Whether the result of baseline (wer:43.25%) is generated by using RNNLM-rescore？

[jtrmal]

just a note -- it might be just a difference caused by different random initialization. y.

On Wed, Feb 23, 2022 at 2:15 PM dospeech @.***> wrote:

It's hard to say what may be going wrong just based on WER. Did you change any hyperparameters? Did you look at the intermediate results (e.g. from GMM decoding) to see if they match those in the recipe?

THE wer of GMM decoding (tri3 )is 83.75%(baseline is 85.72 %), I just changed the number of gpu (gpu_num=4）and didn't change any hyperparameters.

Whether the result of baseline (wer:43.25%) is generated by using RNNLM-rescore？

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[desh2608]

I think so, yeah. The WER without RNNLM rescoring should be closer to 46%. See this at the top of the run_cnn_tdnn_1b.sh: %WER 46.07 [ 27124 / 58881, 2905 ins, 9682 del, 14537 sub ] exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_cnn_sp/decode_dev_gss_multiarray_2stage/wer_10_0.0

Could you try with the RNNLM rescoring?

[dospeech]

I think so, yeah. The WER without RNNLM rescoring should be closer to 46%. See this at the top of the run_cnn_tdnn_1b.sh: %WER 46.07 [ 27124 / 58881, 2905 ins, 9682 del, 14537 sub ] exp/chain_train_worn_simu_u400k_cleaned_rvb/tdnn1b_cnn_sp/decode_dev_gss_multiarray_2stage/wer_10_0.0

Could you try with the RNNLM rescoring?

I‘ve tried with the RNNLM rescoring, and the WER is 43.72%,it looks closer to 43.25(baseline). Is there any other optimizations?

[desh2608]

You can try tuning some of the hyperparameters (esp. learning rate) since you changed the number of training jobs (GPUs). But I think at this point you're close enough that the difference is statistically insignificant.

[dospeech]

You can try tuning some of the hyperparameters (esp. learning rate) since you changed the number of training jobs (GPUs). But I think at this point you're close enough that the difference is statistically insignificant.

OK,thank you for your answer. Besides,is there the WER ( baseline )on the evaluation set ?

[desh2608]

Sorry, I don't think I have the eval numbers for that exact recipe on hand. We tried several systems during the challenge (see Table 7 in https://arxiv.org/pdf/2006.07898.pdf) and it seems the eval WER tracks dev. It would be great if you could make a PR with the number if you run it.

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