fix espnet kaldi libri s2 config

examples/librispeech/s2/conf/transformer.yaml

@@ -12,7 +12,7 @@ collator:
   stride_ms: 10.0
   window_ms: 25.0
   sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs 
-  batch_size: 32 
+  batch_size: 30 
   maxlen_in: 512  # if input length  > maxlen-in, batchsize is automatically reduced
   maxlen_out: 150  # if output length > maxlen-out, batchsize is automatically reduced
   minibatches: 0 # for debug
@@ -59,7 +59,7 @@ model:
     model_conf:
         ctc_weight: 0.3
         ctc_dropoutrate: 0.0
-        ctc_grad_norm_type: instance
+        ctc_grad_norm_type: batch
         lsm_weight: 0.1     # label smoothing option
         length_normalized_loss: false
 
@@ -83,7 +83,7 @@ scheduler_conf:
   lr_decay: 1.0
 
 decoding:
-  batch_size: 64
+  batch_size: 1
   error_rate_type: wer
   decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
   lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm

examples/librispeech/s2/local/test.sh

@@ -36,7 +36,7 @@ for type in attention ctc_greedy_search; do
         # stream decoding only support batchsize=1
         batch_size=1
     else
-        batch_size=64
+        batch_size=1
     fi
     python3 -u ${BIN_DIR}/test.py \
     --model-name u2_kaldi \

examples/librispeech/s2/run.sh

@@ -6,7 +6,7 @@ stage=0
 stop_stage=100
 conf_path=conf/transformer.yaml
 dict_path=data/train_960_unigram5000_units.txt
-avg_num=5
+avg_num=10
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
 avg_ckpt=avg_${avg_num}
@@ -20,12 +20,12 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # train model, all `ckpt` under `exp` dir
-    CUDA_VISIBLE_DEVICES=0,1,2,3 ./local/train.sh ${conf_path}  ${ckpt}
+    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./local/train.sh ${conf_path}  ${ckpt}
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # avg n best model
-    avg.sh best exp/${ckpt}/checkpoints ${avg_num}
+    avg.sh latest exp/${ckpt}/checkpoints ${avg_num}
 fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then

utils/avg_model.py

@@ -80,8 +80,8 @@ def main(args):
         data = json.dumps({
             "avg_ckpt": args.dst_model,
             "ckpt": path_list,
-            "epoch": selected_epochs.tolist(),
-            "val_loss": beat_val_scores.tolist(),
+            "epoch": selected_epochs,
+            "val_loss": beat_val_scores,
         })
         f.write(data + "\n")