Add figure for tuning & enrich the tuning section in doc #284
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kuke
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xinghai-sun and
pkuyym
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xinghai-sun
xinghai-sun
requested changes
Sep 19, 2017
deep_speech_2/README.md
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| ``` | ||
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| - Tuning with CPU: | ||
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| ```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. |
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log-->print ?
remove and their minima.
deep_speech_2/README.md
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| <br/>An example error surface for tuning on the dev-clean set of LibriSpeech | ||
| </p> | ||
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| 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. |
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as the figure shows --> as the figure shows,
is first tuning --> is to first tune
mainly affect --> significantly
alpha --> $\alpha$
deep_speech_2/README.md
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| 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. | ||
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| 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 |
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--> `$\alpha$ and
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kuke
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Sep 19, 2017
deep_speech_2/README.md
Outdated
| ``` | ||
|
|
||
| - Tuning with CPU: | ||
|
|
||
| ```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. |
deep_speech_2/README.md
Outdated
| <br/>An example error surface for tuning on the dev-clean set of LibriSpeech | ||
| </p> | ||
|
|
||
| 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. |
deep_speech_2/README.md
Outdated
|
|
||
| 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. | ||
|
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| 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 |
xinghai-sun
approved these changes
Sep 19, 2017
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Resolve #283