From 7addb847f87c49da94887d89ea6cd2a713a1a39e Mon Sep 17 00:00:00 2001 From: Yibing Liu Date: Tue, 19 Sep 2017 12:19:06 +0800 Subject: [PATCH] add figure for tuning & enrich the tuning section in doc --- deep_speech_2/README.md | 26 ++++++++++++------ .../docs/images/tuning_error_surface.png | Bin 0 -> 110461 bytes 2 files changed, 17 insertions(+), 9 deletions(-) create mode 100644 deep_speech_2/docs/images/tuning_error_surface.png diff --git a/deep_speech_2/README.md b/deep_speech_2/README.md index 9e9113d8..537eec0b 100644 --- a/deep_speech_2/README.md +++ b/deep_speech_2/README.md @@ -273,7 +273,7 @@ or refer to `example/librispeech/run_test.sh`. ## Hyper-parameters Tuning -The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta$ (coefficient for word count scorer) for the [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) often have a significant impact on the decoder's performance. It would be better to re-tune them on a validation set when the acoustic model is renewed. +The hyper-parameters $\alpha$ (language model weight) and $\beta$ (word insertion weight) for the [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) often have a significant impact on the decoder's performance. It would be better to re-tune them on the validation set when the acoustic model is renewed. `tools/tune.py` performs a 2-D grid search over the hyper-parameter $\alpha$ and $\beta$. You must provide the range of $\alpha$ and $\beta$, as well as the number of their attempts. @@ -283,12 +283,12 @@ The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ python tools/tune.py \ --trainer_count 8 \ - --alpha_from 0.1 \ - --alpha_to 0.36 \ - --num_alphas 14 \ - --beta_from 0.05 \ - --beta_to 1.0 \ - --num_betas 20 + --alpha_from 1.0 \ + --alpha_to 3.2 \ + --num_alphas 45 \ + --beta_from 0.1 \ + --beta_to 0.45 \ + --num_betas 8 ``` - Tuning with CPU: @@ -296,15 +296,23 @@ The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta ```bash python tools/tune.py --use_gpu False ``` + The grid search will log the WER (word error rate) or CER (character error rate) at each point in the hyper-parameter space and their minima, and draw the error surface optionally. A proper hyper-parameters range should include the global minima of the error surface for WER/CER, as illustrated in the following figure. -After tuning, you can reset $\alpha$ and $\beta$ in the inference and evaluation modules to see if they really help improve the ASR performance. +

+ +
An example error surface for tuning on the dev-clean set of LibriSpeech +

+ +Usually, as the figure shows the variation of language model weight ($alpha$) mainly affect the performance of CTC beam search decoder. And a better procedure is first tuning on serveral data batches (the number can be specified) to find out the proper range of hyper-parameters, then change to the whole validataion set to carray out an accurate tuning. + +After tuning, you can reset $\alpha$ and $\beta$ in the inference and evaluation modules to see if they really help improve the ASR performance. For more help ```bash python tune.py --help ``` or refer to `example/librispeech/run_tune.sh`. -TODO: add figure. + ## Distributed Cloud Training diff --git a/deep_speech_2/docs/images/tuning_error_surface.png b/deep_speech_2/docs/images/tuning_error_surface.png new file mode 100644 index 0000000000000000000000000000000000000000..2204cee2f5204d1d2d2e53fab8cdd0a1cb9ac47d GIT binary patch literal 110461 zcmeFZ^;ebM7d?7JN6BTMM8xZ5C}QsgSd#Ad-_4USG>-qN9DD* z7qsQ9U7PV-eUXvU`S}NAc+uDw?$q3_5pnArugN|td_34puZ|1#iA~ljd*18%{)0FF zE8}32^jBy&=~sj);iNd$1BBmS&XRW-II)oje7~V~DTU6o9O&Mr36;1Nx3nDT{@wf7 zKK7}-q}6h!@v>s?=Av@x(#x-M@{<+32qyUc7S_$Zj6{z7<>|YOfdYc?-^=u9SpNUL zgp3!2Ed0NBqKLs~|My1Gz&CJ8PZz`I^k6=t{qI^O!w?AWf43}R5r?4scdZ)X|9`vx z56<1bH<>><@}`JY_K3T-|M+S`-X%eP=T)zHDht-^^}$ur>pbReml z25%!7(4stTTaH7>xs0R7jPoM^i_L@KJ=9F8Pk8^ZYzMX7Rc>(0VV~Vs2C*of{$!u7ibxh}8VrR3XcNFDriQ9~)lk zEIOerwFA5MKbsIAaKMjT51rgFmQ&y`li@2o(gvYQOA1D07`Y;`%D;M9n{^*iTG0|# 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