Release BaiduEn8k model and prepare demo example.

examples/mandarin/run_demo_server.sh → examples/deploy_demo/run_english_demo_server.sh

@@ -13,7 +13,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/librispeech > /dev/null
+cd models/baidu_en8k > /dev/null
 sh download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -28,19 +28,19 @@ python -u deploy/demo_server.py \
 --host_port=8086 \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
---rnn_layer_size=2048 \
---alpha=0.36 \
---beta=0.25 \
---cutoff_prob=0.99 \
+--rnn_layer_size=1024 \
+--alpha=1.15 \
+--beta=0.15 \
+--cutoff_prob=1.0 \
 --cutoff_top_n=40 \
---use_gru=False \
+--use_gru=True \
 --use_gpu=True \
---share_rnn_weights=True \
+--share_rnn_weights=False \
 --speech_save_dir='demo_cache' \
 --warmup_manifest='data/tiny/manifest.test-clean' \
---mean_std_path='models/librispeech/mean_std.npz' \
---vocab_path='models/librispeech/vocab.txt' \
---model_path='models/librispeech/params.tar.gz' \
+--mean_std_path='models/baidu_en8k/mean_std.npz' \
+--vocab_path='models/baidu_en8k/vocab.txt' \
+--model_path='models/baidu_en8k/params.tar.gz' \
 --lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
 --decoding_method='ctc_beam_search' \
 --specgram_type='linear'

models/baidu_en8k/download_model.sh

+#! /usr/bin/env bash
+
+. ../../utils/utility.sh
+
+URL='To-be-added'
+MD5=a19d40cb3b558eb696c44d883f32cfda
+TARGET=./baidu_en8k_model.tar.gz
+
+
+echo "Download BaiduEn8k model ..."
+download $URL $MD5 $TARGET
+if [ $? -ne 0 ]; then
+    echo "Fail to download BaiduEn8k model!"
+    exit 1
+fi
+tar -zxvf $TARGET
+
+
+exit 0