From 2bdf4c946af66cc173d638c072ba6435cd18a286 Mon Sep 17 00:00:00 2001 From: Hui Zhang Date: Fri, 14 May 2021 21:02:29 +0800 Subject: [PATCH] fix image link (#612) --- doc/src/benchmark.md | 2 +- doc/src/text_front_end.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/src/benchmark.md b/doc/src/benchmark.md index 3b5f8e95..1f78223c 100644 --- a/doc/src/benchmark.md +++ b/doc/src/benchmark.md @@ -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. -
+
| # of GPU | Acceleration Rate | | -------- | --------------: | diff --git a/doc/src/text_front_end.md b/doc/src/text_front_end.md index 01b60859..64d5cdb0 100644 --- a/doc/src/text_front_end.md +++ b/doc/src/text_front_end.md @@ -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 -
+
论文: 2015 .Ding Et al. - Automatic Prosody Prediction For Chinese Speech Synthesis Using BLSTM-RNN and Embedding Features