fix libri script

examples/librispeech/conf/deepspeech2.yaml

@@ -29,11 +29,11 @@ model:
   use_gru: False 
   share_rnn_weights: True 
 training:
-  n_epoch: 50
+  n_epoch: 20
   lr: 5e-4
   lr_decay: 0.83
   weight_decay: 1e-06
-  global_grad_clip: 5.0
+  global_grad_clip: 400.0
 decoding:
   batch_size: 128
   error_rate_type: wer

examples/librispeech/local/infer.sh

@@ -10,8 +10,7 @@ python3 -u ${BIN_DIR}/infer.py \
 --device 'gpu' \
 --nproc 1 \
 --config conf/deepspeech2.yaml \
---output ckpt
-
+--checkpoint_path ${1} 
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/librispeech/local/infer_golden.sh

@@ -1,46 +0,0 @@
-#! /usr/bin/env bash
-
-# download language model
-bash local/download_lm_en.sh
-if [ $? -ne 0 ]; then
-    exit 1
-fi
-
-# download well-trained model
-bash local/download_model.sh
-if [ $? -ne 0 ]; then
-    exit 1
-fi
-
-# infer
-CUDA_VISIBLE_DEVICES=0 \
-python3 -u ${MAIN_ROOT}/infer.py \
---num_samples=10 \
---beam_size=500 \
---num_proc_bsearch=8 \
---num_conv_layers=2 \
---num_rnn_layers=3 \
---rnn_layer_size=2048 \
---alpha=2.5 \
---beta=0.3 \
---cutoff_prob=1.0 \
---cutoff_top_n=40 \
---use_gru=False \
---use_gpu=True \
---share_rnn_weights=True \
---infer_manifest="data/manifest.test-clean" \
---mean_std_path="${MAIN_ROOT}/models/librispeech/mean_std.npz" \
---vocab_path="${MAIN_ROOT}/models/librispeech/vocab.txt" \
---model_path="${MAIN_ROOT}/models/librispeech" \
---lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
---decoding_method="ctc_beam_search" \
---error_rate_type="wer" \
---specgram_type="linear"
-
-if [ $? -ne 0 ]; then
-    echo "Failed in inference!"
-    exit 1
-fi
-
-
-exit 0

examples/librispeech/local/test_golden.sh

@@ -1,47 +0,0 @@
-#! /usr/bin/env bash
-
-# download language model
-bash local/download_lm_en.sh
-if [ $? -ne 0 ]; then
-    exit 1
-fi
-
-# download well-trained model
-bash local/download_model.sh
-if [ $? -ne 0 ]; then
-    exit 1
-fi
-
-
-# evaluate model
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u $MAIN_ROOT/test.py \
---batch_size=128 \
---beam_size=500 \
---num_proc_bsearch=8 \
---num_conv_layers=2 \
---num_rnn_layers=3 \
---rnn_layer_size=2048 \
---alpha=2.5 \
---beta=0.3 \
---cutoff_prob=1.0 \
---cutoff_top_n=40 \
---use_gru=False \
---use_gpu=True \
---share_rnn_weights=True \
---test_manifest="data/manifest.test-clean" \
---mean_std_path="$MAIN_ROOT/models/librispeech/mean_std.npz" \
---vocab_path="$MAIN_ROOT/models/librispeech/vocab.txt" \
---model_path="$MAIN_ROOT/models/librispeech" \
---lang_model_path="$MAIN_ROOT/models/lm/common_crawl_00.prune01111.trie.klm" \
---decoding_method="ctc_beam_search" \
---error_rate_type="wer" \
---specgram_type="linear"
-
-if [ $? -ne 0 ]; then
-    echo "Failed in evaluation!"
-    exit 1
-fi
-
-
-exit 0