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fix image link (#612)

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Hui Zhang 4 years ago committed by GitHub
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doc/src/benchmark.md
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@ -4,7 +4,7 @@
We compare the training time with 1, 2, 4, 8 Tesla V100 GPUs (with a subset of LibriSpeech samples whose audio durations are between 6.0 and 7.0 seconds). And it shows that a **near-linear** acceleration with multiple GPUs has been achieved. In the following figure, the time (in seconds) cost for training is printed on the blue bars. We compare the training time with 1, 2, 4, 8 Tesla V100 GPUs (with a subset of LibriSpeech samples whose audio durations are between 6.0 and 7.0 seconds). And it shows that a **near-linear** acceleration with multiple GPUs has been achieved. In the following figure, the time (in seconds) cost for training is printed on the blue bars.
<img src="images/multi_gpu_speedup.png" width=450><br/> <img src="../images/multi_gpu_speedup.png" width=450><br/>
| # of GPU | Acceleration Rate | | # of GPU | Acceleration Rate |
| -------- | --------------: | | -------- | --------------: |

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doc/src/text_front_end.md
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@ -101,7 +101,7 @@ LP -> LO -> L1(#1) -> L2(#2) -> L3(#3) -> L4(#4) -> L5 -> L6 -> L7
常用方法使用的是级联CRF，首先预测如果是PW，再继续预测是否是PPH，再预测是否是IPH 常用方法使用的是级联CRF，首先预测如果是PW，再继续预测是否是PPH，再预测是否是IPH
<img src="images/prosody.jpeg" width=450><br/> <img src="../images/prosody.jpeg" width=450><br/>
论文: 2015 .Ding Et al. - Automatic Prosody Prediction For Chinese Speech Synthesis Using BLSTM-RNN and Embedding Features 论文: 2015 .Ding Et al. - Automatic Prosody Prediction For Chinese Speech Synthesis Using BLSTM-RNN and Embedding Features

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