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examples/aishell/asr0/conf/deepspeech2.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.0
-  max_input_len: 27.0 # second
-  min_output_len: 0.0
-  max_output_len: .inf
-  min_output_input_ratio: 0.00
-  max_output_input_ratio: .inf
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+min_input_len: 0.0
+max_input_len: 27.0 # second
+min_output_len: 0.0
+max_output_len: .inf
+min_output_input_ratio: 0.00
+max_output_input_ratio: .inf
 
-collator:
-  batch_size: 64 # one gpu
-  mean_std_filepath: data/mean_std.json
-  unit_type: char
-  vocab_filepath: data/lang_char/vocab.txt 
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  feat_dim: 
-  delta_delta: False
-  stride_ms: 10.0
-  window_ms: 20.0
-  n_fft: None
-  max_freq: None
-  target_sample_rate: 16000
-  use_dB_normalization: True
-  target_dB: -20
-  dither: 1.0
-  keep_transcription_text: False
-  sortagrad: True
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+batch_size: 64 # one gpu
+mean_std_filepath: data/mean_std.json
+unit_type: char
+vocab_filepath: data/lang_char/vocab.txt 
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+delta_delta: False
+stride_ms: 10.0
+window_ms: 20.0
+n_fft: None
+max_freq: None
+target_sample_rate: 16000
+use_dB_normalization: True
+target_dB: -20
+dither: 1.0
+keep_transcription_text: False
+sortagrad: True
+shuffle_method: batch_shuffle
+num_workers: 2
 
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 3
-  rnn_layer_size: 1024
-  use_gru: True 
-  share_rnn_weights: False
-  blank_id: 0
-  ctc_grad_norm_type: instance 
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 3
+rnn_layer_size: 1024
+use_gru: True 
+share_rnn_weights: False
+blank_id: 0
+ctc_grad_norm_type: instance 
 
-training:
-  n_epoch: 80
-  accum_grad: 1
-  lr: 2e-3
-  lr_decay: 0.83
-  weight_decay: 1e-06
-  global_grad_clip: 3.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-  
-decoding:
-  batch_size: 128
-  error_rate_type: cer 
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
-  alpha: 1.9
-  beta: 5.0
-  beam_size: 300
-  cutoff_prob: 0.99
-  cutoff_top_n: 40
-  num_proc_bsearch: 10
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 80
+accum_grad: 1
+lr: 2e-3
+lr_decay: 0.83
+weight_decay: 1e-06
+global_grad_clip: 3.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/aishell/asr0/conf/deepspeech2_online.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.0
-  max_input_len: 27.0 # second
-  min_output_len: 0.0
-  max_output_len: .inf
-  min_output_input_ratio: 0.00
-  max_output_input_ratio: .inf
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+min_input_len: 0.0
+max_input_len: 27.0 # second
+min_output_len: 0.0
+max_output_len: .inf
+min_output_input_ratio: 0.00
+max_output_input_ratio: .inf
 
-collator:
-  batch_size: 64 # one gpu
-  mean_std_filepath: data/mean_std.json
-  unit_type: char
-  vocab_filepath: data/lang_char/vocab.txt 
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear #linear, mfcc, fbank
-  feat_dim: 
-  delta_delta: False
-  stride_ms: 10.0
-  window_ms: 20.0
-  n_fft: None
-  max_freq: None
-  target_sample_rate: 16000
-  use_dB_normalization: True
-  target_dB: -20
-  dither: 1.0
-  keep_transcription_text: False
-  sortagrad: True
-  shuffle_method: batch_shuffle
-  num_workers: 0
+###########################################
+#              Dataloader                 #
+###########################################
+batch_size: 64 # one gpu
+mean_std_filepath: data/mean_std.json
+unit_type: char
+vocab_filepath: data/lang_char/vocab.txt 
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear #linear, mfcc, fbank
+feat_dim: 
+delta_delta: False
+stride_ms: 10.0
+window_ms: 20.0
+n_fft: None
+max_freq: None
+target_sample_rate: 16000
+use_dB_normalization: True
+target_dB: -20
+dither: 1.0
+keep_transcription_text: False
+sortagrad: True
+shuffle_method: batch_shuffle
+num_workers: 0
 
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 5
-  rnn_layer_size: 1024
-  rnn_direction: forward # [forward, bidirect]
-  num_fc_layers: 0
-  fc_layers_size_list: -1,
-  use_gru: False 
-  blank_id: 0
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 5
+rnn_layer_size: 1024
+rnn_direction: forward # [forward, bidirect]
+num_fc_layers: 0
+fc_layers_size_list: -1,
+use_gru: False 
+blank_id: 0
   
   
-training:
-  n_epoch: 65
-  accum_grad: 1
-  lr: 5e-4
-  lr_decay: 0.93
-  weight_decay: 1e-06
-  global_grad_clip: 3.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 65
+accum_grad: 1
+lr: 5e-4
+lr_decay: 0.93
+weight_decay: 1e-06
+global_grad_clip: 3.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
+
   
-decoding:
-  batch_size: 32
-  error_rate_type: cer 
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
-  alpha: 2.2 #1.9
-  beta: 4.3
-  beam_size: 300
-  cutoff_prob: 0.99
-  cutoff_top_n: 40
-  num_proc_bsearch: 10

examples/aishell/asr0/conf/tuning/chunk_decode.yaml

+chunk_batch_size: 32
+error_rate_type: cer 
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
+alpha: 2.2 #1.9
+beta: 4.3
+beam_size: 300
+cutoff_prob: 0.99
+cutoff_top_n: 40
+num_proc_bsearch: 10

examples/aishell/asr0/conf/tuning/decode.yaml

+decode_batch_size: 128
+error_rate_type: cer 
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
+alpha: 1.9
+beta: 5.0
+beam_size: 300
+cutoff_prob: 0.99
+cutoff_top_n: 40
+num_proc_bsearch: 10

examples/aishell/asr0/local/test.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
 
 # download language model
 bash local/download_lm_ch.sh
@@ -21,6 +22,7 @@ fi
 python3 -u ${BIN_DIR}/test.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type}

examples/aishell/asr0/local/test_export.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-jit_model_export_path=$2
-model_type=$3
+decode_config_path=$2
+jit_model_export_path=$3
+model_type=$4
 
 # download language model
 bash local/download_lm_ch.sh > /dev/null 2>&1
@@ -21,6 +22,7 @@ fi
 python3 -u ${BIN_DIR}/test_export.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${jit_model_export_path}.rsl \
 --export_path ${jit_model_export_path} \
 --model_type ${model_type}

examples/aishell/asr0/local/test_hub_ori

+#!/bin/bash
+
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix model_type audio_file"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_prefix=$2
+model_type=$3
+audio_file=$4
+
+mkdir -p data
+wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
+if [ $? -ne 0 ]; then
+   exit 1
+fi
+
+if [ ! -f ${audio_file} ]; then
+    echo "Plase input the right audio_file path"
+    exit 1
+fi
+
+# download language model
+bash local/download_lm_ch.sh
+if [ $? -ne 0 ]; then
+   exit 1
+fi
+
+python3 -u ${BIN_DIR}/test_hub.py \
+--nproc ${ngpu} \
+--config ${config_path} \
+--result_file ${ckpt_prefix}.rsl \
+--checkpoint_path ${ckpt_prefix} \
+--model_type ${model_type} \
+--audio_file ${audio_file}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in evaluation!"
+    exit 1
+fi
+
+
+exit 0

examples/aishell/asr0/local/test_wav.sh

 #!/bin/bash
 
-if [ $# != 4 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type audio_file"
+if [ $# != 5 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type audio_file"
     exit -1
 fi
 
@@ -9,9 +9,10 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
-audio_file=$4
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
+audio_file=$5
 
 mkdir -p data
 wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
@@ -33,6 +34,7 @@ fi
 python3 -u ${BIN_DIR}/test_wav.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type} \

examples/aishell/asr0/run.sh

@@ -6,6 +6,7 @@ gpus=0,1,2,3
 stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml    #conf/deepspeech2.yaml or conf/deepspeeech2_online.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=1
 model_type=offline    # offline or online
 audio_file=data/demo_01_03.wav
@@ -34,7 +35,7 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type}|| exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
@@ -44,11 +45,11 @@ fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test export ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}|| exit -1
 fi
 
 # Optionally, you can add LM and test it with runtime.
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
 fi

examples/aishell/asr1/conf/chunk_conformer.yaml

@@ -54,8 +54,9 @@ test_manifest: data/manifest.test
 ###########################################
 
 vocab_filepath: data/lang_char/vocab.txt 
+spm_model_prefix: ''
 unit_type: 'char'
-augmentation_config: conf/preprocess.yaml
+preprocess_config: conf/preprocess.yaml
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -74,7 +75,7 @@ subsampling_factor: 1
 num_encs: 1
 
 ###########################################
-#                 training                #
+#                 Training                #
 ###########################################
 n_epoch: 240 
 accum_grad: 2
@@ -82,7 +83,7 @@ global_grad_clip: 5.0
 optim: adam
 optim_conf:
   lr: 0.002
-  weight_decay: 1e-6
+  weight_decay: 1.0e-6
 scheduler: warmuplr
 scheduler_conf:
   warmup_steps: 25000

examples/aishell/asr1/conf/conformer.yaml

@@ -49,8 +49,9 @@ test_manifest: data/manifest.test
 #              Dataloader                 #
 ###########################################
 vocab_filepath: data/lang_char/vocab.txt 
+spm_model_prefix: ''
 unit_type: 'char'
-augmentation_config: conf/preprocess.yaml
+preprocess_config: conf/preprocess.yaml
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -69,7 +70,7 @@ subsampling_factor: 1
 num_encs: 1
 
 ###########################################
-#                 training                #
+#                Training                 #
 ###########################################
 n_epoch: 240 
 accum_grad: 2

examples/aishell/asr1/conf/transformer.yaml

@@ -46,6 +46,7 @@ test_manifest: data/manifest.test
 ###########################################
 unit_type: 'char'
 vocab_filepath: data/lang_char/vocab.txt 
+spm_model_prefix: ''
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -59,13 +60,13 @@ batch_bins: 0
 batch_frames_in: 0
 batch_frames_out: 0
 batch_frames_inout: 0
-augmentation_config: conf/preprocess.yaml 
+preprocess_config: conf/preprocess.yaml 
 num_workers: 0
 subsampling_factor: 1
 num_encs: 1
 
 ###########################################
-#                 training                #
+#                 Training                #
 ###########################################
 n_epoch: 240 
 accum_grad: 2
@@ -73,7 +74,7 @@ global_grad_clip: 5.0
 optim: adam
 optim_conf:
   lr: 0.002
-  weight_decay: 1e-6
+  weight_decay: 1.0e-6
 scheduler: warmuplr     
 scheduler_conf:
   warmup_steps: 25000

examples/aishell/asr1/local/align.sh

@@ -21,7 +21,7 @@ mkdir -p ${output_dir}
 python3 -u ${BIN_DIR}/alignment.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
---decode_config ${decode_config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${output_dir}/${type}.align \
 --checkpoint_path ${ckpt_prefix} \
 --opts decode.decode_batch_size ${batch_size}

examples/aishell/asr1/local/test.sh

@@ -30,14 +30,14 @@ for type in attention ctc_greedy_search; do
         # stream decoding only support batchsize=1
         batch_size=1
     else
-        batch_size=1
+        batch_size=64
     fi
     output_dir=${ckpt_prefix}
     mkdir -p ${output_dir}
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
-    --decode_config ${decode_config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
     --opts decode.decoding_method ${type} \
@@ -57,7 +57,7 @@ for type in ctc_prefix_beam_search attention_rescoring; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
-    --decode_config ${decode_config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
     --opts decode.decoding_method ${type} \

examples/aishell/asr1/local/test_wav.sh

@@ -43,7 +43,7 @@ for type in  attention_rescoring; do
     python3 -u ${BIN_DIR}/test_wav.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
-    --decode_config ${decode_config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
     --opts decode.decoding_method ${type} \

examples/callcenter/asr1/conf/chunk_conformer.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.5
-  max_input_len: 20.0 # second
-  min_output_len: 0.0
-  max_output_len: 400.0
-  min_output_input_ratio: 0.05
-  max_output_input_ratio: 10.0
-  
-
-collator:
-  vocab_filepath: data/lang_char/vocab.txt 
-  unit_type: 'char'
-  spm_model_prefix: ''
-  augmentation_config: conf/preprocess.yaml
-  batch_size: 32
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 8000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True 
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
-
-
-# network architecture
-model:
-    cmvn_file: 
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: conformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: True
-        use_cnn_module: True
-        cnn_module_kernel: 15
-        activation_type: 'swish'
-        pos_enc_layer_type: 'rel_pos'
-        selfattention_layer_type: 'rel_selfattn'
-        causal: true
-        use_dynamic_chunk: true
-        cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
-        use_dynamic_left_chunk: false
-
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
-
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
 
+  
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: 'char'
+spm_model_prefix: ''
+preprocess_config: conf/preprocess.yaml
+batch_size: 32
+raw_wav: True  # use raw_wav or kaldi feature
+spectrum_type: fbank #linear, mfcc, fbank
+feat_dim: 80
+delta_delta: False
+dither: 1.0
+target_sample_rate: 8000
+max_freq: None
+n_fft: None
+stride_ms: 10.0
+window_ms: 25.0
+use_dB_normalization: True 
+target_dB: -20
+random_seed: 0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 2
 
-training:
-  n_epoch: 240
-  accum_grad: 4
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 0.001
-    weight_decay: 1e-6
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
 
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: 
+cmvn_file_type: "json"
+# encoder related
+encoder: conformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: True
+    use_cnn_module: True
+    cnn_module_kernel: 15
+    activation_type: 'swish'
+    pos_enc_layer_type: 'rel_pos'
+    selfattention_layer_type: 'rel_selfattn'
+    causal: true
+    use_dynamic_chunk: true
+    cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
+    use_dynamic_left_chunk: false
 
-decoding:
-  batch_size: 128
-  error_rate_type: cer 
-  decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: true  # simulate streaming inference. Defaults to False.
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 240
+accum_grad: 4
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 0.001
+  weight_decay: 1.0e-6
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/callcenter/asr1/conf/conformer.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.5
-  max_input_len: 20.0 # second
-  min_output_len: 0.0
-  max_output_len: 400.0
-  min_output_input_ratio: 0.0
-  max_output_input_ratio: .inf 
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
 
 
-collator:
-  vocab_filepath: data/lang_char/vocab.txt 
-  unit_type: 'char'
-  spm_model_prefix: ''
-  augmentation_config: conf/preprocess.yaml
-  batch_size: 32
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 8000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True 
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: 'char'
+spm_model_prefix: ''
+preprocess_config: conf/preprocess.yaml
+feat_dim: 80
+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: 64
+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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
 
-# network architecture
-model:
-    cmvn_file: 
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: conformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: True
-        use_cnn_module: True
-        cnn_module_kernel: 15
-        activation_type: 'swish'
-        pos_enc_layer_type: 'rel_pos'
-        selfattention_layer_type: 'rel_selfattn'
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: 
+cmvn_file_type: "json"
+# encoder related
+encoder: conformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: True
+    use_cnn_module: True
+    cnn_module_kernel: 15
+    activation_type: 'swish'
+    pos_enc_layer_type: 'rel_pos'
+    selfattention_layer_type: 'rel_selfattn'
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
-
-
-training:
-  n_epoch: 100 # 50 will be lowest 
-  accum_grad: 4
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 0.002
-    weight_decay: 1e-6
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-
-
-
-
-decoding:
-  batch_size: 128
-  error_rate_type: cer 
-  decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
 
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 100 # 50 will be lowest 
+accum_grad: 4
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 0.002
+  weight_decay: 1.0e-6
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/callcenter/asr1/conf/preprocess.yaml

 process:
   # extract kaldi fbank from PCM
   - type: fbank_kaldi
-    fs: 16000
+    fs: 8000
     n_mels: 80
     n_shift: 160
     win_length: 400

examples/callcenter/asr1/conf/tuning/chunk_decode.yaml

+decode_batch_size: 128
+error_rate_type: cer 
+decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+beam_size: 10
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: true  # simulate streaming inference. Defaults to False.
\ No newline at end of file

examples/callcenter/asr1/conf/tuning/decode.yaml

+decode_batch_size: 128
+error_rate_type: cer 
+decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+beam_size: 10
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.
+
+

examples/callcenter/asr1/local/align.sh

 #! /usr/bin/env bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 ckpt_name=$(basename ${ckpt_prefxi})
 
@@ -25,9 +26,10 @@ mkdir -p ${output_dir}
 python3 -u ${BIN_DIR}/alignment.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${output_dir}/${type}.align \
 --checkpoint_path ${ckpt_prefix} \
---opts decoding.batch_size ${batch_size}
+--opts decode.decode_batch_size ${batch_size}
 
 if [ $? -ne 0 ]; then
     echo "Failed in ctc alignment!"

examples/callcenter/asr1/local/test.sh

 #! /usr/bin/env bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
+
 
 ckpt_name=$(basename ${ckpt_prefxi})
 
@@ -30,10 +32,11 @@ for type in attention ctc_greedy_search; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"
@@ -49,10 +52,11 @@ for type in ctc_prefix_beam_search attention_rescoring; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"

examples/callcenter/asr1/run.sh

@@ -6,6 +6,7 @@ gpus=0,1,2,3
 stage=0
 stop_stage=100
 conf_path=conf/conformer.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=20
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
@@ -31,12 +32,12 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # ctc alignment of test data
-    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then

examples/librispeech/asr0/conf/deepspeech2.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev-clean
-  test_manifest: data/manifest.test-clean
-  min_input_len: 0.0
-  max_input_len: 30.0 # second
-  min_output_len: 0.0
-  max_output_len: .inf
-  min_output_input_ratio: 0.00
-  max_output_input_ratio: .inf
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev-clean
+test_manifest: data/manifest.test-clean
+min_input_len: 0.0
+max_input_len: 30.0 # second
+min_output_len: 0.0
+max_output_len: .inf
+min_output_input_ratio: 0.00
+max_output_input_ratio: .inf
 
-collator:
-  batch_size: 20
-  mean_std_filepath: data/mean_std.json
-  unit_type: char
-  vocab_filepath: data/lang_char/vocab.txt
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 20.0
-  delta_delta: False
-  dither: 1.0
-  use_dB_normalization: True 
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+batch_size: 20
+mean_std_filepath: data/mean_std.json
+unit_type: char
+vocab_filepath: data/lang_char/vocab.txt
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+target_sample_rate: 16000
+max_freq: None
+n_fft: None
+stride_ms: 10.0
+window_ms: 20.0
+delta_delta: False
+dither: 1.0
+use_dB_normalization: True 
+target_dB: -20
+random_seed: 0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 2
 
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 3
-  rnn_layer_size: 2048
-  use_gru: False 
-  share_rnn_weights: True
-  blank_id: 0
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 3
+rnn_layer_size: 2048
+use_gru: False 
+share_rnn_weights: True
+blank_id: 0
   
 
-training:
-  n_epoch: 50
-  accum_grad: 1
-  lr: 1e-3
-  lr_decay: 0.83
-  weight_decay: 1e-06
-  global_grad_clip: 5.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-
-decoding:
-  batch_size: 128
-  error_rate_type: wer
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 1.9
-  beta: 0.3
-  beam_size: 500
-  cutoff_prob: 1.0
-  cutoff_top_n: 40
-  num_proc_bsearch: 8
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 50
+accum_grad: 1
+lr: 1e-3
+lr_decay: 0.83
+weight_decay: 1e-06
+global_grad_clip: 5.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/librispeech/asr0/conf/deepspeech2_online.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev-clean
-  test_manifest: data/manifest.test-clean
-  min_input_len: 0.0
-  max_input_len: 30.0 # second
-  min_output_len: 0.0
-  max_output_len: .inf
-  min_output_input_ratio: 0.00
-  max_output_input_ratio: .inf
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev-clean
+test_manifest: data/manifest.test-clean
+min_input_len: 0.0
+max_input_len: 30.0 # second
+min_output_len: 0.0
+max_output_len: .inf
+min_output_input_ratio: 0.00
+max_output_input_ratio: .inf
 
-collator:
-  batch_size: 15
-  mean_std_filepath: data/mean_std.json
-  unit_type: char
-  vocab_filepath: data/lang_char/vocab.txt
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 20.0
-  delta_delta: False
-  dither: 1.0
-  use_dB_normalization: True 
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
+###########################################
+#              Dataloader                 #
+###########################################
+batch_size: 15
+mean_std_filepath: data/mean_std.json
+unit_type: char
+vocab_filepath: data/lang_char/vocab.txt
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+target_sample_rate: 16000
+max_freq: None
+n_fft: None
+stride_ms: 10.0
+window_ms: 20.0
+delta_delta: False
+dither: 1.0
+use_dB_normalization: True 
+target_dB: -20
+random_seed: 0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 0
 
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 3
-  rnn_layer_size: 2048
-  rnn_direction: forward
-  num_fc_layers: 2
-  fc_layers_size_list: 512, 256
-  use_gru: False 
-  blank_id: 0
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 3
+rnn_layer_size: 2048
+rnn_direction: forward
+num_fc_layers: 2
+fc_layers_size_list: 512, 256
+use_gru: False 
+blank_id: 0
   
 
-training:
-  n_epoch: 50
-  accum_grad: 4
-  lr: 1e-3
-  lr_decay: 0.83
-  weight_decay: 1e-06
-  global_grad_clip: 5.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-
-decoding:
-  batch_size: 128
-  error_rate_type: wer
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 1.9
-  beta: 0.3
-  beam_size: 500
-  cutoff_prob: 1.0
-  cutoff_top_n: 40
-  num_proc_bsearch: 8
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 50
+accum_grad: 4
+lr: 1e-3
+lr_decay: 0.83
+weight_decay: 1e-06
+global_grad_clip: 5.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/librispeech/asr0/conf/tuning/chunk_decode.yaml

+decode_batch_size: 128
+error_rate_type: wer
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+alpha: 1.9
+beta: 0.3
+beam_size: 500
+cutoff_prob: 1.0
+cutoff_top_n: 40
+num_proc_bsearch: 8
\ No newline at end of file

examples/librispeech/asr0/conf/tuning/decode.yaml

+decode_batch_size: 128
+error_rate_type: wer
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+alpha: 1.9
+beta: 0.3
+beam_size: 500
+cutoff_prob: 1.0
+cutoff_top_n: 40
+num_proc_bsearch: 8
\ No newline at end of file

examples/librispeech/asr0/local/test.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
 
 # download language model
 bash local/download_lm_en.sh
@@ -21,6 +22,7 @@ fi
 python3 -u ${BIN_DIR}/test.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type}

examples/librispeech/asr0/local/test_wav.sh

 #!/bin/bash
 
-if [ $# != 4 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type audio_file"
+if [ $# != 5 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type audio_file"
     exit -1
 fi
 
@@ -9,9 +9,10 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
-audio_file=$4
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
+audio_file=$5
 
 mkdir -p data
 wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/
@@ -33,6 +34,7 @@ fi
 python3 -u ${BIN_DIR}/test_wav.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type} \

examples/librispeech/asr0/run.sh

@@ -6,6 +6,7 @@ gpus=0,1,2,3,4,5,6,7
 stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=30
 model_type=offline
 audio_file=data/demo_002_en.wav
@@ -33,7 +34,7 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
@@ -43,5 +44,5 @@ fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} ${audio_file} || exit -1
 fi

examples/librispeech/asr1/conf/chunk_conformer.yaml

@@ -57,7 +57,7 @@ vocab_filepath: data/lang_char/vocab.txt
 unit_type: 'spm'
 spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
 mean_std_filepath: ""
-augmentation_config: conf/preprocess.yaml
+preprocess_config: conf/preprocess.yaml
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -70,8 +70,7 @@ batch_count: auto
 batch_bins: 0 
 batch_frames_in: 0
 batch_frames_out: 0
-batch_frames_inout: 0
-augmentation_config: conf/preprocess.yaml 
+batch_frames_inout: 0 
 num_workers: 0
 subsampling_factor: 1
 num_encs: 1
@@ -85,10 +84,11 @@ global_grad_clip: 5.0
 optim: adam
 optim_conf:
   lr: 0.001
-  weight_decay: 1e-06 
+  weight_decay: 1.0e-06 
 scheduler: warmuplr     
 scheduler_conf:
   warmup_steps: 25000
+  lr_decay: 1.0
 log_interval: 100
 checkpoint:
   kbest_n: 50

examples/librispeech/asr1/conf/chunk_transformer.yaml

@@ -50,7 +50,7 @@ vocab_filepath: data/lang_char/vocab.txt
 unit_type: 'spm'
 spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
 mean_std_filepath: ""
-augmentation_config: conf/preprocess.yaml
+preprocess_config: conf/preprocess.yaml
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -64,7 +64,6 @@ batch_bins: 0
 batch_frames_in: 0
 batch_frames_out: 0
 batch_frames_inout: 0
-augmentation_config: conf/preprocess.yaml 
 num_workers: 0
 subsampling_factor: 1
 num_encs: 1
@@ -79,7 +78,7 @@ global_grad_clip: 5.0
 optim: adam
 optim_conf:
   lr: 0.001
-  weight_decay: 1e-06
+  weight_decay: 1.0e-06
 scheduler: warmuplr     
 scheduler_conf:
   warmup_steps: 25000

examples/librispeech/asr1/conf/conformer.yaml

@@ -55,7 +55,7 @@ vocab_filepath: data/lang_char/vocab.txt
 unit_type: 'spm'
 spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
 mean_std_filepath: ""
-augmentation_config: conf/preprocess.yaml
+preprocess_config: conf/preprocess.yaml
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -69,7 +69,6 @@ batch_bins: 0
 batch_frames_in: 0
 batch_frames_out: 0
 batch_frames_inout: 0
-augmentation_config: conf/preprocess.yaml 
 num_workers: 0
 subsampling_factor: 1
 num_encs: 1
@@ -84,7 +83,7 @@ global_grad_clip: 3.0
 optim: adam
 optim_conf:
   lr: 0.004
-  weight_decay: 1e-06
+  weight_decay: 1.0e-06
 scheduler: warmuplr     
 scheduler_conf:
   warmup_steps: 25000

examples/librispeech/asr1/conf/transformer.yaml

@@ -49,7 +49,7 @@ vocab_filepath: data/lang_char/vocab.txt
 unit_type: 'spm'
 spm_model_prefix: 'data/lang_char/bpe_unigram_5000'
 mean_std_filepath: ""
-augmentation_config: conf/preprocess.yaml
+preprocess_config: conf/preprocess.yaml
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -63,7 +63,6 @@ batch_bins: 0
 batch_frames_in: 0
 batch_frames_out: 0
 batch_frames_inout: 0
-augmentation_config: conf/preprocess.yaml 
 num_workers: 0
 subsampling_factor: 1
 num_encs: 1
@@ -78,7 +77,7 @@ global_grad_clip: 5.0
 optim: adam
 optim_conf:
   lr: 0.004
-  weight_decay: 1e-06
+  weight_decay: 1.0e-06
 scheduler: warmuplr     
 scheduler_conf:
   warmup_steps: 25000

examples/librispeech/asr1/local/align.sh

@@ -21,7 +21,7 @@ mkdir -p ${output_dir}
 python3 -u ${BIN_DIR}/alignment.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
---decode_config ${decode_config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${output_dir}/${type}.align \
 --checkpoint_path ${ckpt_prefix} \
 --opts decode.decode_batch_size ${batch_size}

examples/librispeech/asr1/local/test.sh

@@ -53,7 +53,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
         python3 -u ${BIN_DIR}/test.py \
             --ngpu ${ngpu} \
             --config ${config_path} \
-            --decode_config ${decode_config_path} \
+            --decode_cfg ${decode_config_path} \
             --result_file ${ckpt_prefix}.${type}.rsl \
             --checkpoint_path ${ckpt_prefix} \
             --opts decode.decoding_method ${type} \
@@ -78,7 +78,7 @@ for type in ctc_greedy_search; do
     python3 -u ${BIN_DIR}/test.py \
         --ngpu ${ngpu} \
         --config ${config_path} \
-        --decode_config ${decode_config_path} \
+        --decode_cfg ${decode_config_path} \
         --result_file ${ckpt_prefix}.${type}.rsl \
         --checkpoint_path ${ckpt_prefix} \
         --opts decode.decoding_method ${type} \
@@ -99,7 +99,7 @@ for type in ctc_prefix_beam_search attention_rescoring; do
     python3 -u ${BIN_DIR}/test.py \
         --ngpu ${ngpu} \
         --config ${config_path} \
-        --decode_config ${decode_config_path} \
+        --decode_cfg ${decode_config_path} \
         --result_file ${ckpt_prefix}.${type}.rsl \
         --checkpoint_path ${ckpt_prefix} \
         --opts decode.decoding_method ${type} \

examples/librispeech/asr1/local/test_wav.sh

@@ -50,7 +50,7 @@ for type in attention_rescoring; do
     python3 -u ${BIN_DIR}/test_wav.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
-    --decode_config ${decode_config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
     --opts decode.decoding_method ${type} \

examples/librispeech/asr2/conf/decode/decode_base.yaml

+decode_batch_size: 1
+error_rate_type: wer
+decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+beam_size: 10
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.

examples/librispeech/asr2/conf/transformer.yaml

 # https://yaml.org/type/float.html
-# network architecture
-model:
-    cmvn_file:  
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: transformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: true
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file:  
+cmvn_file_type: "json"
+# encoder related
+encoder: transformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test-clean
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test-clean
 
-collator:
-  vocab_filepath: data/lang_char/train_960_unigram5000_units.txt
-  unit_type: spm
-  spm_model_prefix: data/lang_char/train_960_unigram5000
-  feat_dim: 83
-  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: 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
-  batch_count: auto
-  batch_bins: 0 
-  batch_frames_in: 0
-  batch_frames_out: 0
-  batch_frames_inout: 0
-  augmentation_config: conf/preprocess.yaml 
-  num_workers: 0
-  subsampling_factor: 1
-  num_encs: 1
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/train_960_unigram5000_units.txt
+unit_type: spm
+spm_model_prefix: data/lang_char/train_960_unigram5000
+feat_dim: 83
+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: 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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+preprocess_config: conf/preprocess.yaml 
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
 
 
-training:
-  n_epoch: 120
-  accum_grad: 2
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 120
+accum_grad: 2
+log_interval: 1
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
 
 optim: adam
 optim_conf:
@@ -79,23 +86,5 @@ scheduler_conf:
   warmup_steps: 25000
   lr_decay: 1.0
 
-decoding:
-  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
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
 
 

examples/librispeech/asr2/local/align.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path dict_path ckpt_path_prefix"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path dict_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-dict_path=$2
-ckpt_prefix=$3
+decode_config_path=$2
+dict_path=$3
+ckpt_prefix=$4
 
 batch_size=1
 output_dir=${ckpt_prefix}
@@ -24,9 +25,10 @@ python3 -u ${BIN_DIR}/test.py \
 --dict-path ${dict_path} \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result-file ${output_dir}/${type}.align \
 --checkpoint_path ${ckpt_prefix} \
---opts decoding.batch_size ${batch_size}
+--opts decode.decode_batch_size ${batch_size}
 
 if [ $? -ne 0 ]; then
     echo "Failed in ctc alignment!"

examples/librispeech/asr2/local/test.sh

@@ -19,8 +19,9 @@ bpeprefix=data/lang_char/${train_set}_${bpemode}${nbpe}
 bpemodel=${bpeprefix}.model
 
 config_path=conf/transformer.yaml
+decode_config_path=conf/decode/decode_base.yaml
 dict=data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
-ckpt_prefix=
+ckpt_prefix=exp/transformer/checkpoints/init
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
@@ -79,11 +80,12 @@ for dmethd in attention ctc_greedy_search ctc_prefix_beam_search attention_resco
             --ngpu ${ngpu} \
             --dict-path ${dict} \
             --config ${config_path} \
+            --decode_cfg ${decode_config_path} \
             --checkpoint_path ${ckpt_prefix} \
             --result-file ${decode_dir}/data.JOB.json \
-            --opts decoding.decoding_method ${dmethd} \
-            --opts decoding.batch_size ${batch_size} \
-            --opts data.test_manifest ${feat_recog_dir}/split${nj}/JOB/manifest.${rtask}
+            --opts decode.decoding_method ${dmethd} \
+            --opts decode.decode_batch_size ${batch_size} \
+            --opts test_manifest ${feat_recog_dir}/split${nj}/JOB/manifest.${rtask}
 
         score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel} --wer false ${decode_dir} ${dict}
 

examples/librispeech/asr2/run.sh

@@ -9,12 +9,14 @@ gpus=0,1,2,3,4,5,6,7
 stage=0
 stop_stage=50
 conf_path=conf/transformer.yaml
-dict_path=lang_char/train_960_unigram5000_units.txt
+decode_conf_path=conf/decode/decode_base.yaml
+dict_path=data/lang_char/train_960_unigram5000_units.txt
 avg_num=10
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
 avg_ckpt=avg_${avg_num}
+avg_ckpt=init
 ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
 echo "checkpoint name ${ckpt}"
 
@@ -35,7 +37,7 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # attetion resocre decoder
-    ./local/test.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    ./local/test.sh ${conf_path} ${decode_conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
@@ -45,7 +47,7 @@ fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # ctc alignment of test data
-    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${decode_conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then

examples/other/1xt2x/aishell/conf/deepspeech2.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.0
-  max_input_len: 27.0 # second
-  min_output_len: 0.0
-  max_output_len: .inf
-  min_output_input_ratio: 0.00
-  max_output_input_ratio: .inf
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+min_input_len: 0.0
+max_input_len: 27.0 # second
+min_output_len: 0.0
+max_output_len: .inf
+min_output_input_ratio: 0.00
+max_output_input_ratio: .inf
 
-collator:
-  batch_size: 64 # one gpu
-  mean_std_filepath: data/mean_std.npz
-  unit_type: char
-  vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  feat_dim: 
-  delta_delta: False
-  stride_ms: 10.0
-  window_ms: 20.0
-  n_fft: None
-  max_freq: None
-  target_sample_rate: 16000
-  use_dB_normalization: True
-  target_dB: -20
-  dither: 1.0
-  keep_transcription_text: False
-  sortagrad: True
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+batch_size: 64 # one gpu
+mean_std_filepath: data/mean_std.npz
+unit_type: char
+vocab_filepath: data/vocab.txt 
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+delta_delta: False
+stride_ms: 10.0
+window_ms: 20.0
+n_fft: None
+max_freq: None
+target_sample_rate: 16000
+use_dB_normalization: True
+target_dB: -20
+dither: 1.0
+keep_transcription_text: False
+sortagrad: True
+shuffle_method: batch_shuffle
+num_workers: 2
 
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 3
-  rnn_layer_size: 1024
-  use_gru: True 
-  share_rnn_weights: False
-  blank_id: 4333
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 3
+rnn_layer_size: 1024
+use_gru: True 
+share_rnn_weights: False
+blank_id: 4333
 
-training:
-  n_epoch: 80
-  accum_grad: 1
-  lr: 2e-3
-  lr_decay: 0.83
-  weight_decay: 1e-06
-  global_grad_clip: 3.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 80
+accum_grad: 1
+lr: 2e-3
+lr_decay: 0.83
+weight_decay: 1e-06
+global_grad_clip: 3.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
+  
   
-decoding:
-  batch_size: 32
-  error_rate_type: cer 
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
-  alpha: 2.6
-  beta: 5.0
-  beam_size: 300
-  cutoff_prob: 0.99
-  cutoff_top_n: 40
-  num_proc_bsearch: 8

examples/other/1xt2x/aishell/conf/tuning/decode.yaml

+decode_batch_size: 32
+error_rate_type: cer 
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
+alpha: 2.6
+beta: 5.0
+beam_size: 300
+cutoff_prob: 0.99
+cutoff_top_n: 40
+num_proc_bsearch: 8
\ No newline at end of file

examples/other/1xt2x/aishell/local/test.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
 
 # download language model
 bash local/download_lm_ch.sh
@@ -21,6 +22,7 @@ fi
 python3 -u ${BIN_DIR}/test.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type}

examples/other/1xt2x/aishell/run.sh

@@ -5,6 +5,7 @@ source path.sh
 stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=1
 model_type=offline
 gpus=2
@@ -23,6 +24,6 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
 fi
 

examples/other/1xt2x/baidu_en8k/conf/deepspeech2.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test-clean
-  min_input_len: 0.0
-  max_input_len: .inf # second
-  min_output_len: 0.0
-  max_output_len: .inf
-  min_output_input_ratio: 0.00
-  max_output_input_ratio: .inf
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test-clean
+min_input_len: 0.0
+max_input_len: .inf # second
+min_output_len: 0.0
+max_output_len: .inf
+min_output_input_ratio: 0.00
+max_output_input_ratio: .inf
 
-collator:
-  batch_size: 64 # one gpu
-  mean_std_filepath: data/mean_std.npz
-  unit_type: char
-  vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  feat_dim: 
-  delta_delta: False
-  stride_ms: 10.0
-  window_ms: 20.0
-  n_fft: None
-  max_freq: None
-  target_sample_rate: 16000
-  use_dB_normalization: True
-  target_dB: -20
-  dither: 1.0
-  keep_transcription_text: False
-  sortagrad: True
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+batch_size: 64 # one gpu
+mean_std_filepath: data/mean_std.npz
+unit_type: char
+vocab_filepath: data/vocab.txt 
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+delta_delta: False
+stride_ms: 10.0
+window_ms: 20.0
+n_fft: None
+max_freq: None
+target_sample_rate: 16000
+use_dB_normalization: True
+target_dB: -20
+dither: 1.0
+keep_transcription_text: False
+sortagrad: True
+shuffle_method: batch_shuffle
+num_workers: 2
 
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 3
-  rnn_layer_size: 1024
-  use_gru: True
-  share_rnn_weights: False
-  blank_id: 28
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 3
+rnn_layer_size: 1024
+use_gru: True
+share_rnn_weights: False
+blank_id: 28
+
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 80
+accum_grad: 1
+lr: 2e-3
+lr_decay: 0.83
+weight_decay: 1e-06
+global_grad_clip: 3.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
 
-training:
-  n_epoch: 80
-  accum_grad: 1
-  lr: 2e-3
-  lr_decay: 0.83
-  weight_decay: 1e-06
-  global_grad_clip: 3.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-  
-decoding:
-  batch_size: 32
-  error_rate_type: wer 
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 1.4
-  beta: 0.35
-  beam_size: 500
-  cutoff_prob: 1.0
-  cutoff_top_n: 40
-  num_proc_bsearch: 8

examples/other/1xt2x/baidu_en8k/conf/tuning/decode.yaml

+decode_batch_size: 32
+error_rate_type: wer 
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+alpha: 1.4
+beta: 0.35
+beam_size: 500
+cutoff_prob: 1.0
+cutoff_top_n: 40
+num_proc_bsearch: 8
\ No newline at end of file

examples/other/1xt2x/baidu_en8k/local/test.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
 
 # download language model
 bash local/download_lm_en.sh
@@ -21,6 +22,7 @@ fi
 python3 -u ${BIN_DIR}/test.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type}

examples/other/1xt2x/baidu_en8k/run.sh

@@ -5,6 +5,7 @@ source path.sh
 stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=1
 model_type=offline
 gpus=0
@@ -23,6 +24,6 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
 fi
 

examples/other/1xt2x/librispeech/conf/deepspeech2.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test-clean
-  min_input_len: 0.0
-  max_input_len: 1000.0 # second
-  min_output_len: 0.0
-  max_output_len: .inf
-  min_output_input_ratio: 0.00
-  max_output_input_ratio: .inf
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test-clean
+min_input_len: 0.0
+max_input_len: 1000.0 # second
+min_output_len: 0.0
+max_output_len: .inf
+min_output_input_ratio: 0.00
+max_output_input_ratio: .inf
 
-collator:
-  batch_size: 64 # one gpu
-  mean_std_filepath: data/mean_std.npz
-  unit_type: char
-  vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  feat_dim: 
-  delta_delta: False
-  stride_ms: 10.0
-  window_ms: 20.0
-  n_fft: None
-  max_freq: None
-  target_sample_rate: 16000
-  use_dB_normalization: True
-  target_dB: -20
-  dither: 1.0
-  keep_transcription_text: False
-  sortagrad: True
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+batch_size: 64 # one gpu
+mean_std_filepath: data/mean_std.npz
+unit_type: char
+vocab_filepath: data/vocab.txt 
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+delta_delta: False
+stride_ms: 10.0
+window_ms: 20.0
+n_fft: None
+max_freq: None
+target_sample_rate: 16000
+use_dB_normalization: True
+target_dB: -20
+dither: 1.0
+keep_transcription_text: False
+sortagrad: True
+shuffle_method: batch_shuffle
+num_workers: 2
 
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 3
-  rnn_layer_size: 2048
-  use_gru: False
-  share_rnn_weights: True
-  blank_id: 28
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 3
+rnn_layer_size: 2048
+use_gru: False
+share_rnn_weights: True
+blank_id: 28
+
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 80
+accum_grad: 1
+lr: 2e-3
+lr_decay: 0.83
+weight_decay: 1e-06
+global_grad_clip: 3.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
 
-training:
-  n_epoch: 80
-  accum_grad: 1
-  lr: 2e-3
-  lr_decay: 0.83
-  weight_decay: 1e-06
-  global_grad_clip: 3.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-  
-decoding:
-  batch_size: 32
-  error_rate_type: wer 
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 500
-  cutoff_prob: 1.0
-  cutoff_top_n: 40
-  num_proc_bsearch: 8

examples/other/1xt2x/librispeech/conf/tuning/decode.yaml

+decode_batch_size: 32
+error_rate_type: wer 
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+alpha: 2.5
+beta: 0.3
+beam_size: 500
+cutoff_prob: 1.0
+cutoff_top_n: 40
+num_proc_bsearch: 8
\ No newline at end of file

examples/other/1xt2x/librispeech/local/test.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
 
 # download language model
 bash local/download_lm_en.sh
@@ -21,6 +22,7 @@ fi
 python3 -u ${BIN_DIR}/test.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type}

examples/other/1xt2x/librispeech/run.sh

@@ -5,6 +5,7 @@ source path.sh
 stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=1
 model_type=offline
 gpus=1
@@ -23,5 +24,5 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
 fi

examples/other/1xt2x/src_deepspeech2x/bin/test.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """Evaluation for DeepSpeech2 model."""
 from src_deepspeech2x.test_model import DeepSpeech2Tester as Tester
+from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.deepspeech2.config import get_cfg_defaults
 from paddlespeech.s2t.training.cli import default_argument_parser
@@ -44,6 +45,10 @@ if __name__ == "__main__":
     config = get_cfg_defaults(args.model_type)
     if args.config:
         config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()

examples/other/1xt2x/src_deepspeech2x/models/ds2/deepspeech2.py

@@ -233,11 +233,11 @@ class DeepSpeech2Model(nn.Layer):
         """
         model = cls(feat_size=dataloader.collate_fn.feature_size,
                     dict_size=len(dataloader.collate_fn.vocab_list),
-                    num_conv_layers=config.model.num_conv_layers,
-                    num_rnn_layers=config.model.num_rnn_layers,
-                    rnn_size=config.model.rnn_layer_size,
-                    use_gru=config.model.use_gru,
-                    share_rnn_weights=config.model.share_rnn_weights)
+                    num_conv_layers=config.num_conv_layers,
+                    num_rnn_layers=config.num_rnn_layers,
+                    rnn_size=config.rnn_layer_size,
+                    use_gru=config.use_gru,
+                    share_rnn_weights=config.share_rnn_weights)
         infos = Checkpoint().load_parameters(
             model, checkpoint_path=checkpoint_path)
         logger.info(f"checkpoint info: {infos}")
@@ -250,7 +250,7 @@ class DeepSpeech2Model(nn.Layer):
         Parameters
 
         config: yacs.config.CfgNode
-            config.model
+            config
         Returns
         -------
         DeepSpeech2Model

examples/other/1xt2x/src_deepspeech2x/test_model.py

@@ -64,7 +64,7 @@ class DeepSpeech2Trainer(Trainer):
         super().__init__(config, args)
 
     def train_batch(self, batch_index, batch_data, msg):
-        train_conf = self.config.training
+        train_conf = self.config
         start = time.time()
 
         # forward
@@ -98,7 +98,7 @@ class DeepSpeech2Trainer(Trainer):
         iteration_time = time.time() - start
 
         msg += "train time: {:>.3f}s, ".format(iteration_time)
-        msg += "batch size: {}, ".format(self.config.collator.batch_size)
+        msg += "batch size: {}, ".format(self.config.batch_size)
         msg += "accum: {}, ".format(train_conf.accum_grad)
         msg += ', '.join('{}: {:>.6f}'.format(k, v)
                          for k, v in losses_np.items())
@@ -126,7 +126,7 @@ class DeepSpeech2Trainer(Trainer):
                 total_loss += float(loss) * num_utts
                 valid_losses['val_loss'].append(float(loss))
 
-            if (i + 1) % self.config.training.log_interval == 0:
+            if (i + 1) % self.config.log_interval == 0:
                 valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
                 valid_dump['val_history_loss'] = total_loss / num_seen_utts
 
@@ -146,15 +146,15 @@ class DeepSpeech2Trainer(Trainer):
     def setup_model(self):
         config = self.config.clone()
         config.defrost()
-        config.model.feat_size = self.train_loader.collate_fn.feature_size
-        #config.model.dict_size = self.train_loader.collate_fn.vocab_size
-        config.model.dict_size = len(self.train_loader.collate_fn.vocab_list)
+        config.feat_size = self.train_loader.collate_fn.feature_size
+        #config.dict_size = self.train_loader.collate_fn.vocab_size
+        config.dict_size = len(self.train_loader.collate_fn.vocab_list)
         config.freeze()
 
         if self.args.model_type == 'offline':
-            model = DeepSpeech2Model.from_config(config.model)
+            model = DeepSpeech2Model.from_config(config)
         elif self.args.model_type == 'online':
-            model = DeepSpeech2ModelOnline.from_config(config.model)
+            model = DeepSpeech2ModelOnline.from_config(config)
         else:
             raise Exception("wrong model type")
         if self.parallel:
@@ -163,17 +163,13 @@ class DeepSpeech2Trainer(Trainer):
         logger.info(f"{model}")
         layer_tools.print_params(model, logger.info)
 
-        grad_clip = ClipGradByGlobalNormWithLog(
-            config.training.global_grad_clip)
+        grad_clip = ClipGradByGlobalNormWithLog(config.global_grad_clip)
         lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
-            learning_rate=config.training.lr,
-            gamma=config.training.lr_decay,
-            verbose=True)
+            learning_rate=config.lr, gamma=config.lr_decay, verbose=True)
         optimizer = paddle.optimizer.Adam(
             learning_rate=lr_scheduler,
             parameters=model.parameters(),
-            weight_decay=paddle.regularizer.L2Decay(
-                config.training.weight_decay),
+            weight_decay=paddle.regularizer.L2Decay(config.weight_decay),
             grad_clip=grad_clip)
 
         self.model = model
@@ -184,59 +180,59 @@ class DeepSpeech2Trainer(Trainer):
     def setup_dataloader(self):
         config = self.config.clone()
         config.defrost()
-        config.collator.keep_transcription_text = False
+        config.keep_transcription_text = False
 
-        config.data.manifest = config.data.train_manifest
+        config.manifest = config.train_manifest
         train_dataset = ManifestDataset.from_config(config)
 
-        config.data.manifest = config.data.dev_manifest
+        config.manifest = config.dev_manifest
         dev_dataset = ManifestDataset.from_config(config)
 
-        config.data.manifest = config.data.test_manifest
+        config.manifest = config.test_manifest
         test_dataset = ManifestDataset.from_config(config)
 
         if self.parallel:
             batch_sampler = SortagradDistributedBatchSampler(
                 train_dataset,
-                batch_size=config.collator.batch_size,
+                batch_size=config.batch_size,
                 num_replicas=None,
                 rank=None,
                 shuffle=True,
                 drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
+                sortagrad=config.sortagrad,
+                shuffle_method=config.shuffle_method)
         else:
             batch_sampler = SortagradBatchSampler(
                 train_dataset,
                 shuffle=True,
-                batch_size=config.collator.batch_size,
+                batch_size=config.batch_size,
                 drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
+                sortagrad=config.sortagrad,
+                shuffle_method=config.shuffle_method)
 
         collate_fn_train = SpeechCollator.from_config(config)
 
-        config.collator.augmentation_config = ""
+        config.augmentation_config = ""
         collate_fn_dev = SpeechCollator.from_config(config)
 
-        config.collator.keep_transcription_text = True
-        config.collator.augmentation_config = ""
+        config.keep_transcription_text = True
+        config.augmentation_config = ""
         collate_fn_test = SpeechCollator.from_config(config)
 
         self.train_loader = DataLoader(
             train_dataset,
             batch_sampler=batch_sampler,
             collate_fn=collate_fn_train,
-            num_workers=config.collator.num_workers)
+            num_workers=config.num_workers)
         self.valid_loader = DataLoader(
             dev_dataset,
-            batch_size=config.collator.batch_size,
+            batch_size=config.batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=collate_fn_dev)
         self.test_loader = DataLoader(
             test_dataset,
-            batch_size=config.decoding.batch_size,
+            batch_size=config.decode.decode_batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=collate_fn_test)
@@ -274,7 +270,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
     def __init__(self, config, args):
 
         self._text_featurizer = TextFeaturizer(
-            unit_type=config.collator.unit_type, vocab_filepath=None)
+            unit_type=config.unit_type, vocab=None)
         super().__init__(config, args)
 
     def ordid2token(self, texts, texts_len):
@@ -293,7 +289,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
                         texts,
                         texts_len,
                         fout=None):
-        cfg = self.config.decoding
+        cfg = self.config.decode
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
         error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
@@ -399,31 +395,3 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             self.export()
         except KeyboardInterrupt:
             exit(-1)
-
-    def setup(self):
-        """Setup the experiment.
-        """
-        paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
-
-        self.setup_output_dir()
-        self.setup_checkpointer()
-
-        self.setup_dataloader()
-        self.setup_model()
-
-        self.iteration = 0
-        self.epoch = 0
-
-    def setup_output_dir(self):
-        """Create a directory used for output.
-        """
-        # output dir
-        if self.args.output:
-            output_dir = Path(self.args.output).expanduser()
-            output_dir.mkdir(parents=True, exist_ok=True)
-        else:
-            output_dir = Path(
-                self.args.checkpoint_path).expanduser().parent.parent
-            output_dir.mkdir(parents=True, exist_ok=True)
-
-        self.output_dir = output_dir

examples/ted_en_zh/st0/conf/transformer.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train.tiny
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.05  # second
-  max_input_len: 30.0 # second
-  min_output_len: 0.0 # tokens
-  max_output_len: 400.0 # tokens
-  min_output_input_ratio: 0.01
-  max_output_input_ratio: 20.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train.tiny
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+min_input_len: 0.05  # second
+max_input_len: 30.0 # second
+min_output_len: 0.0 # tokens
+max_output_len: 400.0 # tokens
+min_output_input_ratio: 0.01
+max_output_input_ratio: 20.0
 
-collator:
-  vocab_filepath: data/lang_char/vocab.txt
-  unit_type: 'spm'
-  spm_model_prefix: data/lang_char/bpe_unigram_8000
-  mean_std_filepath: ""
-  # augmentation_config: conf/augmentation.json
-  batch_size: 10
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt
+unit_type: 'spm'
+spm_model_prefix: data/lang_char/bpe_unigram_8000
+mean_std_filepath: ""
+# augmentation_config: conf/augmentation.json
+batch_size: 10
+raw_wav: True  # use raw_wav or kaldi feature
+spectrum_type: fbank #linear, mfcc, fbank
+feat_dim: 80
+delta_delta: False
+dither: 1.0
+target_sample_rate: 16000
+max_freq: None
+n_fft: None
+stride_ms: 10.0
+window_ms: 25.0
+use_dB_normalization: True
+target_dB: -20
+random_seed: 0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 2
 
 
-# network architecture
-model:
-    cmvn_file: "data/mean_std.json"
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: transformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: true
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: "data/mean_std.json"
+cmvn_file_type: "json"
+# encoder related
+encoder: transformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        asr_weight: 0.0
-        ctc_weight: 0.0
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    asr_weight: 0.0
+    ctc_weight: 0.0
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
-
-training:
-  n_epoch: 120
-  accum_grad: 2
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 0.004
-    weight_decay: 1e-06
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 5
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-
-
-decoding:
-  batch_size: 5
-  error_rate_type: char-bleu
-  decoding_method: fullsentence  # 'fullsentence', 'simultaneous'
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 120
+accum_grad: 2
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 0.004
+  weight_decay: 1.0e-06
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 5
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/ted_en_zh/st0/conf/transformer_mtl_noam.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.05  # second
-  max_input_len: 30.0 # second
-  min_output_len: 0.0 # tokens
-  max_output_len: 400.0 # tokens
-  min_output_input_ratio: 0.01
-  max_output_input_ratio: 20.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+min_input_len: 0.05  # second
+max_input_len: 30.0 # second
+min_output_len: 0.0 # tokens
+max_output_len: 400.0 # tokens
+min_output_input_ratio: 0.01
+max_output_input_ratio: 20.0
 
-collator:
-  vocab_filepath: data/lang_char/vocab.txt
-  unit_type: 'spm'
-  spm_model_prefix: data/lang_char/bpe_unigram_8000
-  mean_std_filepath: ""
-  # augmentation_config: conf/augmentation.json
-  batch_size: 10
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt
+unit_type: 'spm'
+spm_model_prefix: data/lang_char/bpe_unigram_8000
+mean_std_filepath: ""
+# augmentation_config: conf/augmentation.json
+batch_size: 10
+raw_wav: True  # use raw_wav or kaldi feature
+spectrum_type: fbank #linear, mfcc, fbank
+feat_dim: 80
+delta_delta: False
+dither: 1.0
+target_sample_rate: 16000
+max_freq: None
+n_fft: None
+stride_ms: 10.0
+window_ms: 25.0
+use_dB_normalization: True
+target_dB: -20
+random_seed: 0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 2
 
 
-# network architecture
-model:
-    cmvn_file: "data/mean_std.json"
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: transformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: true
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: "data/mean_std.json"
+cmvn_file_type: "json"
+# encoder related
+encoder: transformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        asr_weight: 0.5
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    asr_weight: 0.5
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
 
-training:
-  n_epoch: 120
-  accum_grad: 2
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 2.5
-    weight_decay: 1e-06
-  scheduler: noam    
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 50
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 120
+accum_grad: 2
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 2.5
+  weight_decay: 1.0e-06
+scheduler: noam    
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 50
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
 
 
-decoding:
-  batch_size: 5
-  error_rate_type: char-bleu
-  decoding_method: fullsentence  # 'fullsentence', 'simultaneous'
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  word_reward: 0.7
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
-
 

examples/ted_en_zh/st0/conf/tuning/decode.yaml

+batch_size: 5
+error_rate_type: char-bleu
+decoding_method: fullsentence  # 'fullsentence', 'simultaneous'
+beam_size: 10
+word_reward: 0.7
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.
\ No newline at end of file

examples/ted_en_zh/st0/local/test.sh

 #! /usr/bin/env bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 for type in fullsentence; do
     echo "decoding ${type}"
@@ -17,10 +18,11 @@ for type in fullsentence; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${ckpt_prefix}.${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"

examples/ted_en_zh/st0/run.sh

@@ -6,6 +6,7 @@ gpus=0,1,2,3
 stage=0
 stop_stage=50
 conf_path=conf/transformer_mtl_noam.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=5
 data_path=./TED_EnZh # path to unzipped data
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
@@ -32,7 +33,7 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then

examples/ted_en_zh/st1/conf/transformer.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train.tiny
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 5.0  # frame
-  max_input_len: 3000.0 # frame
-  min_output_len: 0.0 # tokens
-  max_output_len: 400.0 # tokens
-  min_output_input_ratio: 0.01
-  max_output_input_ratio: 20.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train.tiny
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+min_input_len: 5.0  # frame
+max_input_len: 3000.0 # frame
+min_output_len: 0.0 # tokens
+max_output_len: 400.0 # tokens
+min_output_input_ratio: 0.01
+max_output_input_ratio: 20.0
 
-collator:
-  vocab_filepath: data/lang_char/vocab.txt
-  unit_type: 'spm'
-  spm_model_prefix: data/lang_char/bpe_unigram_8000
-  mean_std_filepath: ""
-  # augmentation_config: conf/augmentation.json
-  batch_size: 10
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 83
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt
+unit_type: 'spm'
+spm_model_prefix: data/lang_char/bpe_unigram_8000
+mean_std_filepath: ""
+# augmentation_config: conf/augmentation.json
+batch_size: 10
+raw_wav: True  # use raw_wav or kaldi feature
+spectrum_type: fbank #linear, mfcc, fbank
+feat_dim: 83
+delta_delta: False
+dither: 1.0
+target_sample_rate: 16000
+max_freq: None
+n_fft: None
+stride_ms: 10.0
+window_ms: 25.0
+use_dB_normalization: True
+target_dB: -20
+random_seed: 0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 2
 
 
-# network architecture
-model:
-    cmvn_file: None
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: transformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: true
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: None
+cmvn_file_type: "json"
+# encoder related
+encoder: transformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        asr_weight: 0.0
-        ctc_weight: 0.0
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    asr_weight: 0.0
+    ctc_weight: 0.0
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
 
-training:
-  n_epoch: 20
-  accum_grad: 2
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 0.004
-    weight_decay: 1e-06
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 5
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-
-
-decoding:
-  batch_size: 5
-  error_rate_type: char-bleu
-  decoding_method: fullsentence  # 'fullsentence', 'simultaneous'
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  word_reward: 0.7
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 20
+accum_grad: 2
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 0.004
+  weight_decay: 1.0e-06
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 5
+checkpoint:
+  kbest_n: 50
+  latest_n: 5

examples/ted_en_zh/st1/conf/transformer_mtl_noam.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 5.0  # frame
-  max_input_len: 3000.0 # frame
-  min_output_len: 0.0 # tokens
-  max_output_len: 400.0 # tokens
-  min_output_input_ratio: 0.01
-  max_output_input_ratio: 20.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
+min_input_len: 5.0  # frame
+max_input_len: 3000.0 # frame
+min_output_len: 0.0 # tokens
+max_output_len: 400.0 # tokens
+min_output_input_ratio: 0.01
+max_output_input_ratio: 20.0
 
-collator:
-  vocab_filepath: data/lang_char/ted_en_zh_bpe8000.txt
-  unit_type: 'spm'
-  spm_model_prefix: data/lang_char/ted_en_zh_bpe8000
-  mean_std_filepath: ""
-  # augmentation_config: conf/augmentation.json
-  batch_size: 10
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 83
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/ted_en_zh_bpe8000.txt
+unit_type: 'spm'
+spm_model_prefix: data/lang_char/ted_en_zh_bpe8000
+mean_std_filepath: ""
+# augmentation_config: conf/augmentation.json
+batch_size: 10
+raw_wav: True  # use raw_wav or kaldi feature
+spectrum_type: fbank #linear, mfcc, fbank
+feat_dim: 83
+delta_delta: False
+dither: 1.0
+target_sample_rate: 16000
+max_freq: None
+n_fft: None
+stride_ms: 10.0
+window_ms: 25.0
+use_dB_normalization: True
+target_dB: -20
+random_seed: 0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 2
 
 
-# network architecture
-model:
-    cmvn_file: None
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: transformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: true
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: None
+cmvn_file_type: "json"
+# encoder related
+encoder: transformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        asr_weight: 0.5
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    asr_weight: 0.5
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
 
-training:
-  n_epoch: 20
-  accum_grad: 2
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 2.5
-    weight_decay: 1e-06
-  scheduler: noam    
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 5
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-
-
-decoding:
-  batch_size: 5
-  error_rate_type: char-bleu
-  decoding_method: fullsentence  # 'fullsentence', 'simultaneous'
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  word_reward: 0.7
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 20
+accum_grad: 2
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 2.5
+  weight_decay: 1.0e-06
+scheduler: noam    
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 5
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
\ No newline at end of file

examples/ted_en_zh/st1/conf/tuning/decode.yaml

+
+batch_size: 5
+error_rate_type: char-bleu
+decoding_method: fullsentence  # 'fullsentence', 'simultaneous'
+beam_size: 10
+word_reward: 0.7
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.
\ No newline at end of file

examples/ted_en_zh/st1/local/test.sh

 #! /usr/bin/env bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 for type in fullsentence; do
     echo "decoding ${type}"
@@ -17,10 +18,11 @@ for type in fullsentence; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${ckpt_prefix}.${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"

examples/ted_en_zh/st1/run.sh

@@ -7,6 +7,7 @@ gpus=0,1,2,3
 stage=1
 stop_stage=4
 conf_path=conf/transformer_mtl_noam.yaml
+decode_conf_path=conf/tuning/decode.yaml
 ckpt_path= # paddle.98 # (finetune from FAT-ST pretrained model)
 avg_num=5
 data_path=./TED_EnZh # path to unzipped data
@@ -38,5 +39,5 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_pat} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
\ No newline at end of file

examples/timit/asr1/conf/transformer.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.0  # second
-  max_input_len: 10.0 # second
-  min_output_len: 0.0 # tokens
-  max_output_len: 150.0 # tokens
-  min_output_input_ratio: 0.005
-  max_output_input_ratio: 1000.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
 
-collator:
-  vocab_filepath: data/lang_char/vocab.txt
-  unit_type: "word"
-  mean_std_filepath: ""
-  augmentation_config: conf/preprocess.yaml
-  batch_size: 64
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt
+spm_model_prefix: ''
+unit_type: "word"
+mean_std_filepath: ""
+preprocess_config: conf/preprocess.yaml
+feat_dim: 80
+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: 64
+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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
 
 
-# network architecture
-model:
-    cmvn_file: 
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: transformer
-    encoder_conf:
-        output_size: 128    # dimension of attention
-        attention_heads: 4
-        linear_units: 1024  # the number of units of position-wise feed forward
-        num_blocks: 6      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: true
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: 
+cmvn_file_type: "json"
+# encoder related
+encoder: transformer
+encoder_conf:
+    output_size: 128    # dimension of attention
+    attention_heads: 4
+    linear_units: 1024  # the number of units of position-wise feed forward
+    num_blocks: 6      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 1024
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 1024
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.5
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.5
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
 
-training:
-  n_epoch: 50
-  accum_grad: 1
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 0.004
-    weight_decay: 1e-06
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 1200
-    lr_decay: 1.0
-  log_interval: 10
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-
-
-decoding:
-  batch_size: 64
-  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
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
-
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 50
+accum_grad: 1
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 0.004
+  weight_decay: 1.0e-6
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 1200
+  lr_decay: 1.0
+log_interval: 10
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
 

examples/timit/asr1/conf/tuning/decode.yaml

+decode_batch_size: 64
+error_rate_type: wer
+decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+beam_size: 10
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.

examples/timit/asr1/local/align.sh

 #!/bin/bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 batch_size=1
 output_dir=${ckpt_prefix}
@@ -20,9 +21,10 @@ mkdir -p ${output_dir}
 python3 -u ${BIN_DIR}/alignment.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${output_dir}/${type}.align \
 --checkpoint_path ${ckpt_prefix} \
---opts decoding.batch_size ${batch_size}
+--opts decode.decode_batch_size ${batch_size}
 
 if [ $? -ne 0 ]; then
     echo "Failed in ctc alignment!"

examples/timit/asr1/local/test.sh

@@ -7,8 +7,8 @@ stop_stage=50
 
 . ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -17,7 +17,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 chunk_mode=false
 if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
@@ -43,10 +44,11 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
         python3 -u ${BIN_DIR}/test.py \
         --ngpu ${ngpu} \
         --config ${config_path} \
+        --decode_cfg ${decode_config_path} \
         --result_file ${ckpt_prefix}.${type}.rsl \
         --checkpoint_path ${ckpt_prefix} \
-        --opts decoding.decoding_method ${type} \
-        --opts decoding.batch_size ${batch_size}
+        --opts decode.decoding_method ${type} \
+        --opts decode.decode_batch_size ${batch_size}
     
         if [ $? -ne 0 ]; then
             echo "Failed in evaluation!"
@@ -63,10 +65,11 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
         python3 -u ${BIN_DIR}/test.py \
         --ngpu ${ngpu}  \
         --config ${config_path} \
+        --decode_cfg ${decode_config_path} \
         --result_file ${ckpt_prefix}.${type}.rsl \
         --checkpoint_path ${ckpt_prefix} \
-        --opts decoding.decoding_method ${type} \
-        --opts decoding.batch_size ${batch_size}
+        --opts decode.decoding_method ${type} \
+        --opts decode.decode_batch_size ${batch_size}
     
         if [ $? -ne 0 ]; then
             echo "Failed in evaluation!"
@@ -82,10 +85,11 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
         python3 -u ${BIN_DIR}/test.py \
         --ngpu ${ngpu}  \
         --config ${config_path} \
+        --decode_cfg ${decode_config_path} \
         --result_file ${ckpt_prefix}.${type}.rsl \
         --checkpoint_path ${ckpt_prefix} \
-        --opts decoding.decoding_method ${type} \
-        --opts decoding.batch_size ${batch_size}
+        --opts decode.decoding_method ${type} \
+        --opts decode.decode_batch_size ${batch_size}
     
         if [ $? -ne 0 ]; then
             echo "Failed in evaluation!"

examples/timit/asr1/run.sh

@@ -7,6 +7,7 @@ gpus=0,1,2,3
 stage=0
 stop_stage=50
 conf_path=conf/transformer.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=10
 TIMIT_path=/path/to/TIMIT
 
@@ -34,15 +35,15 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # ctc alignment of test data
-    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
-# if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
-#     # export ckpt avg_n
-#     CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
-# fi
+if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then
+     # export ckpt avg_n
+     CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+fi

examples/tiny/asr0/conf/deepspeech2.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.tiny
-  dev_manifest: data/manifest.tiny
-  test_manifest: data/manifest.tiny 
-  min_input_len: 0.0
-  max_input_len: 30.0
-  min_output_len: 0.0
-  max_output_len: 400.0
-  min_output_input_ratio: 0.05
-  max_output_input_ratio: 10.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.tiny
+dev_manifest: data/manifest.tiny
+test_manifest: data/manifest.tiny 
+min_input_len: 0.0
+max_input_len: 30.0
+min_output_len: 0.0
+max_output_len: 400.0
+min_output_input_ratio: 0.05
+max_output_input_ratio: 10.0
 
 
-collator:
-  mean_std_filepath: data/mean_std.json
-  unit_type: char
-  vocab_filepath: data/lang_char/vocab.txt
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  feat_dim: 
-  delta_delta: False
-  stride_ms: 10.0
-  window_ms: 20.0
-  n_fft: None
-  max_freq: None
-  target_sample_rate: 16000
-  use_dB_normalization: True
-  target_dB: -20
-  dither: 1.0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
-  batch_size: 4
+###########################################
+#              Dataloader                 #
+###########################################
+mean_std_filepath: data/mean_std.json
+unit_type: char
+vocab_filepath: data/lang_char/vocab.txt
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+delta_delta: False
+stride_ms: 10.0
+window_ms: 20.0
+n_fft: None
+max_freq: None
+target_sample_rate: 16000
+use_dB_normalization: True
+target_dB: -20
+dither: 1.0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 2
+batch_size: 4
   
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 3
-  rnn_layer_size: 2048
-  use_gru: False 
-  share_rnn_weights: True 
-  blank_id: 0
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 3
+rnn_layer_size: 2048
+use_gru: False 
+share_rnn_weights: True 
+blank_id: 0
   
 
-training:
-  n_epoch: 5
-  accum_grad: 1
-  lr: 1e-5 
-  lr_decay: 0.8 
-  weight_decay: 1e-06
-  global_grad_clip: 5.0
-  log_interval: 1
-  checkpoint:
-    kbest_n: 3
-    latest_n: 2
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 5
+accum_grad: 1
+lr: 1e-5 
+lr_decay: 0.8 
+weight_decay: 1e-06
+global_grad_clip: 5.0
+log_interval: 1
+checkpoint:
+  kbest_n: 3
+  latest_n: 2
 
 
-decoding:
-  batch_size: 128
-  error_rate_type: wer
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 500
-  cutoff_prob: 1.0
-  cutoff_top_n: 40
-  num_proc_bsearch: 8

examples/tiny/asr0/conf/deepspeech2_online.yaml

 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.tiny
-  dev_manifest: data/manifest.tiny
-  test_manifest: data/manifest.tiny 
-  min_input_len: 0.0
-  max_input_len: 30.0
-  min_output_len: 0.0
-  max_output_len: 400.0
-  min_output_input_ratio: 0.05
-  max_output_input_ratio: 10.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.tiny
+dev_manifest: data/manifest.tiny
+test_manifest: data/manifest.tiny 
+min_input_len: 0.0
+max_input_len: 30.0
+min_output_len: 0.0
+max_output_len: 400.0
+min_output_input_ratio: 0.05
+max_output_input_ratio: 10.0
 
 
-collator:
-  mean_std_filepath: data/mean_std.json
-  unit_type: char
-  vocab_filepath: data/lang_char/vocab.txt
-  augmentation_config: conf/augmentation.json
-  random_seed: 0
-  spm_model_prefix: 
-  spectrum_type: linear
-  feat_dim: 
-  delta_delta: False
-  stride_ms: 10.0
-  window_ms: 20.0
-  n_fft: None
-  max_freq: None
-  target_sample_rate: 16000
-  use_dB_normalization: True
-  target_dB: -20
-  dither: 1.0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
-  batch_size: 4
+###########################################
+#              Dataloader                 #
+###########################################
+mean_std_filepath: data/mean_std.json
+unit_type: char
+vocab_filepath: data/lang_char/vocab.txt
+augmentation_config: conf/augmentation.json
+random_seed: 0
+spm_model_prefix: 
+spectrum_type: linear
+feat_dim: 
+delta_delta: False
+stride_ms: 10.0
+window_ms: 20.0
+n_fft: None
+max_freq: None
+target_sample_rate: 16000
+use_dB_normalization: True
+target_dB: -20
+dither: 1.0
+keep_transcription_text: False
+sortagrad: True 
+shuffle_method: batch_shuffle
+num_workers: 0
+batch_size: 4
   
-model:
-  num_conv_layers: 2
-  num_rnn_layers: 4
-  rnn_layer_size: 2048
-  rnn_direction: forward
-  num_fc_layers: 2
-  fc_layers_size_list: 512, 256
-  use_gru: True 
-  blank_id: 0
+############################################
+#           Network Architecture           #
+############################################
+num_conv_layers: 2
+num_rnn_layers: 4
+rnn_layer_size: 2048
+rnn_direction: forward
+num_fc_layers: 2
+fc_layers_size_list: 512, 256
+use_gru: True 
+blank_id: 0
   
 
-training:
-  n_epoch: 5
-  accum_grad: 1
-  lr: 1e-5 
-  lr_decay: 1.0 
-  weight_decay: 1e-06
-  global_grad_clip: 5.0
-  log_interval: 1
-  checkpoint:
-    kbest_n: 3
-    latest_n: 2
+###########################################
+#                Training                 #
+###########################################
+n_epoch: 5
+accum_grad: 1
+lr: 1e-5 
+lr_decay: 1.0 
+weight_decay: 1e-06
+global_grad_clip: 5.0
+log_interval: 1
+checkpoint:
+  kbest_n: 3
+  latest_n: 2
 
-
-decoding:
-  batch_size: 128
-  error_rate_type: wer
-  decoding_method: ctc_beam_search
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 500
-  cutoff_prob: 1.0
-  cutoff_top_n: 40
-  num_proc_bsearch: 8

examples/tiny/asr0/conf/tuning/chunk_decode.yaml

+decode_batch_size: 128
+error_rate_type: wer
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+alpha: 2.5
+beta: 0.3
+beam_size: 500
+cutoff_prob: 1.0
+cutoff_top_n: 40
+num_proc_bsearch: 8

examples/tiny/asr0/conf/tuning/decode.yaml

+decode_batch_size: 128
+error_rate_type: wer
+decoding_method: ctc_beam_search
+lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+alpha: 2.5
+beta: 0.3
+beam_size: 500
+cutoff_prob: 1.0
+cutoff_top_n: 40
+num_proc_bsearch: 8

examples/tiny/asr0/local/test.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix model_type"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-model_type=$3
+decode_config_path=$2
+ckpt_prefix=$3
+model_type=$4
 
 # download language model
 bash local/download_lm_en.sh
@@ -21,6 +22,7 @@ fi
 python3 -u ${BIN_DIR}/test.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${ckpt_prefix}.rsl \
 --checkpoint_path ${ckpt_prefix} \
 --model_type ${model_type}

examples/tiny/asr0/run.sh

@@ -6,6 +6,7 @@ gpus=0
 stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=1
 model_type=offline
 
@@ -32,7 +33,7 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${model_type} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then

examples/tiny/asr1/conf/chunk_confermer.yaml

-# https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.tiny
-  dev_manifest: data/manifest.tiny
-  test_manifest: data/manifest.tiny
-  min_input_len: 0.5  # second
-  max_input_len: 30.0 # second
-  min_output_len: 0.0 # tokens
-  max_output_len: 400.0 # tokens
-  min_output_input_ratio: 0.05
-  max_output_input_ratio: 10.0
-  
-collator:
-  mean_std_filepath: ""
-  vocab_filepath: data/lang_char/vocab.txt 
-  unit_type: 'spm'
-  spm_model_prefix: 'data/lang_char/bpe_unigram_200'
-  augmentation_config: conf/preprocess.yaml
-  batch_size: 4
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
-
-
-# network architecture
-model:
-    cmvn_file: "data/mean_std.json"
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: conformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: True
-        use_cnn_module: True
-        cnn_module_kernel: 15
-        activation_type: 'swish'
-        pos_enc_layer_type: 'rel_pos'
-        selfattention_layer_type: 'rel_selfattn'
-        causal: True
-        use_dynamic_chunk: True
-        cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
-        use_dynamic_left_chunk: false
-
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: "data/mean_std.json"
+cmvn_file_type: "json"
+# encoder related
+encoder: conformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: True
+    use_cnn_module: True
+    cnn_module_kernel: 15
+    activation_type: 'swish'
+    pos_enc_layer_type: 'rel_pos'
+    selfattention_layer_type: 'rel_selfattn'
+    causal: True
+    use_dynamic_chunk: True
+    cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
+    use_dynamic_left_chunk: false
 
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
-training:
-  n_epoch: 5
-  accum_grad: 1
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 0.001
-    weight_decay: 1e-06
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 1
-  checkpoint:
-    kbest_n: 10
-    latest_n: 1
 
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.tiny
+dev_manifest: data/manifest.tiny
+test_manifest: data/manifest.tiny
 
-decoding:
-  batch_size: 64
-  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
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
-
+  
+###########################################
+#              Dataloader                 #
+###########################################
+mean_std_filepath: ""
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: 'spm'
+spm_model_prefix: 'data/lang_char/bpe_unigram_200'
+preprocess_config: conf/preprocess.yaml
+feat_dim: 80
+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: 4
+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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+augmentation_config: conf/preprocess.yaml 
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
+  
 
+###########################################
+#                 Training                #
+###########################################
+n_epoch: 5
+accum_grad: 1
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 0.001
+  weight_decay: 1.0e-06
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 1
+checkpoint:
+  kbest_n: 10
+  latest_n: 1

examples/tiny/asr1/conf/chunk_transformer.yaml

-# https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.tiny
-  dev_manifest: data/manifest.tiny
-  test_manifest: data/manifest.tiny
-  min_input_len: 0.5  # second
-  max_input_len: 20.0 # second
-  min_output_len: 0.0 # tokens
-  max_output_len: 400.0 # tokens
-  min_output_input_ratio: 0.05
-  max_output_input_ratio: 10.0
-  
-collator:
-  mean_std_filepath: ""
-  vocab_filepath: data/lang_char/vocab.txt 
-  unit_type: 'spm'
-  spm_model_prefix: 'data/lang_char/bpe_unigram_200'
-  augmentation_config: conf/preprocess.yaml
-  batch_size: 4
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
-
-
-# network architecture
-model:
-    cmvn_file: "data/mean_std.json"
-    cmvn_file_type: "json"
-    # encoder related
-    encoder: transformer
-    encoder_conf:
-        output_size: 256    # dimension of attention
-        attention_heads: 4
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: true
-        use_dynamic_chunk: true
-        use_dynamic_left_chunk: false
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: "data/mean_std.json"
+cmvn_file_type: "json"
+# encoder related
+encoder: transformer
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
+    use_dynamic_chunk: true
+    use_dynamic_left_chunk: false
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 4
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
 
-training:
-  n_epoch: 5
-  accum_grad: 1
-  global_grad_clip: 5.0
-  optim: adam
-  optim_conf:
-    lr: 0.002
-    weight_decay: 1e-06
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 25000
-    lr_decay: 1.0
-  log_interval: 1
-  checkpoint:
-    kbest_n: 10
-    latest_n: 1
-
+# https://yaml.org/type/float.html
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.tiny
+dev_manifest: data/manifest.tiny
+test_manifest: data/manifest.tiny
+  
+###########################################
+#              Dataloader                 #
+###########################################
+mean_std_filepath: ""
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: 'spm'
+spm_model_prefix: 'data/lang_char/bpe_unigram_200'
+preprocess_config: conf/preprocess.yaml
+feat_dim: 80
+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: 4
+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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
 
-decoding:
-  batch_size: 64
-  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
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
 
+###########################################
+#                 Training                #
+###########################################
+n_epoch: 5
+accum_grad: 1
+global_grad_clip: 5.0
+optim: adam
+optim_conf:
+  lr: 0.002
+  weight_decay: 1.0e-06
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 25000
+  lr_decay: 1.0
+log_interval: 1
+checkpoint:
+  kbest_n: 10
+  latest_n: 1
 

examples/tiny/asr1/conf/conformer.yaml

 # https://yaml.org/type/float.html
-###########################################
-#                   Data                  #
-###########################################
-train_manifest: data/manifest.tiny
-dev_manifest: data/manifest.tiny
-test_manifest: data/manifest.tiny
-min_input_len: 0.5  # second
-max_input_len: 20.0 # second
-min_output_len: 0.0 # tokens
-max_output_len: 400.0 # tokens
-min_output_input_ratio: 0.05
-max_output_input_ratio: 10.0
-  
-
-###########################################
-#              Dataloader                 #
-###########################################
-mean_std_filepath: ""
-vocab_filepath: data/lang_char/vocab.txt 
-unit_type: 'spm'
-spm_model_prefix: 'data/lang_char/bpe_unigram_200'
-augmentation_config: conf/preprocess.yaml
-batch_size: 4
-raw_wav: True  # use raw_wav or kaldi feature
-spectrum_type: fbank #linear, mfcc, fbank
-feat_dim: 80
-delta_delta: False
-dither: 1.0
-target_sample_rate: 16000
-max_freq: None
-n_fft: None
-stride_ms: 10.0
-window_ms: 25.0
-use_dB_normalization: True
-target_dB: -20
-random_seed: 0
-keep_transcription_text: False
-sortagrad: True 
-shuffle_method: batch_shuffle
-num_workers: 2
-
-
 ############################################
 #           Network Architecture           #
 ############################################
@@ -83,7 +41,41 @@ model_conf:
 
 
 ###########################################
-#                 training                #
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.tiny
+dev_manifest: data/manifest.tiny
+test_manifest: data/manifest.tiny
+  
+
+###########################################
+#              Dataloader                 #
+###########################################
+mean_std_filepath: ""
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: 'spm'
+spm_model_prefix: 'data/lang_char/bpe_unigram_200'
+preprocess_config: conf/preprocess.yaml
+feat_dim: 80
+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: 4
+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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
+
+
+###########################################
+#                 Training                #
 ###########################################
 n_epoch: 5
 accum_grad: 4
@@ -91,7 +83,7 @@ global_grad_clip: 5.0
 optim: adam
 optim_conf:
   lr: 0.002
-  weight_decay: 1e-06
+  weight_decay: 1.0e-06
 scheduler: warmuplr     
 scheduler_conf:
   warmup_steps: 25000

examples/tiny/asr1/conf/transformer.yaml

 # https://yaml.org/type/float.html
-###########################################
-#                   Data                  #
-###########################################
-train_manifest: data/manifest.tiny
-dev_manifest: data/manifest.tiny
-test_manifest: data/manifest.tiny
-min_input_len: 0.5  # second
-max_input_len: 20.0 # second
-min_output_len: 0.0 # tokens
-max_output_len: 400.0 # tokens
-min_output_input_ratio: 0.05
-max_output_input_ratio: 10.0
-  
-###########################################
-#              Dataloader                 #
-###########################################
-mean_std_filepath: data/mean_std.json
-vocab_filepath: data/lang_char/vocab.txt 
-unit_type: 'spm'
-spm_model_prefix: 'data/lang_char/bpe_unigram_200'
-augmentation_config: conf/preprocess.yaml
-batch_size: 4
-raw_wav: True  # use raw_wav or kaldi feature
-spectrum_type: fbank #linear, mfcc, fbank
-feat_dim: 80
-delta_delta: False
-dither: 1.0
-target_sample_rate: 16000
-max_freq: None
-n_fft: None
-stride_ms: 10.0
-window_ms: 25.0
-use_dB_normalization: True
-target_dB: -20
-random_seed: 0
-keep_transcription_text: False
-sortagrad: True 
-shuffle_method: batch_shuffle
-num_workers: 2
-
 ############################################
 #           Network Architecture           #
 ############################################
@@ -74,9 +34,41 @@ model_conf:
     lsm_weight: 0.1     # label smoothing option
     length_normalized_loss: false
 
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.tiny
+dev_manifest: data/manifest.tiny
+test_manifest: data/manifest.tiny
+  
+###########################################
+#              Dataloader                 #
+###########################################
+mean_std_filepath: data/mean_std.json
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: 'spm'
+spm_model_prefix: 'data/lang_char/bpe_unigram_200'
+preprocess_config: conf/preprocess.yaml
+feat_dim: 80
+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: 4
+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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
+
 
 ###########################################
-#                 training                #
+#                 Training                #
 ###########################################
 n_epoch: 5
 accum_grad: 1
@@ -84,7 +76,7 @@ global_grad_clip: 5.0
 optim: adam
 optim_conf:
   lr: 0.002
-  weight_decay: 1e-06
+  weight_decay: 1.0e-06
 scheduler: warmuplr     
 scheduler_conf:
   warmup_steps: 25000

examples/tiny/asr1/conf/tuning/chunk_decode.yaml

+decode_batch_size: 8 #64
+error_rate_type: wer
+decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+beam_size: 10
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.
\ No newline at end of file

examples/tiny/asr1/conf/tuning/decode.yaml

+decode_batch_size: 8 #64
+error_rate_type: wer
+decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+beam_size: 10
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.

examples/tiny/asr1/local/align.sh

 #!/bin/bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 batch_size=1
 output_dir=${ckpt_prefix}
@@ -20,9 +21,10 @@ mkdir -p ${output_dir}
 python3 -u ${BIN_DIR}/alignment.py \
 --ngpu ${ngpu} \
 --config ${config_path} \
+--decode_cfg ${decode_config_path} \
 --result_file ${output_dir}/${type}.align \
 --checkpoint_path ${ckpt_prefix} \
---opts decoding.batch_size ${batch_size}
+--opts decode.decode_batch_size ${batch_size}
 
 if [ $? -ne 0 ]; then
     echo "Failed in ctc alignment!"

examples/tiny/asr1/local/test.sh

 #!/bin/bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 chunk_mode=false
 if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
@@ -33,10 +34,11 @@ for type in attention ctc_greedy_search; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${ckpt_prefix}.${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"
@@ -50,10 +52,11 @@ for type in ctc_prefix_beam_search attention_rescoring; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${ckpt_prefix}.${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"

examples/tiny/asr1/run.sh

@@ -6,6 +6,7 @@ gpus=0
 stage=0
 stop_stage=50
 conf_path=conf/transformer.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=1
 
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
@@ -31,12 +32,12 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # ctc alignment of test data
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/align.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then

examples/wenetspeech/asr1/conf/conformer.yaml

-# network architecture
-model:
-    # encoder related
-    encoder: conformer
-    encoder_conf:
-        output_size: 512    # dimension of attention
-        attention_heads: 8
-        linear_units: 2048  # the number of units of position-wise feed forward
-        num_blocks: 12      # the number of encoder blocks
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        attention_dropout_rate: 0.0
-        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-        normalize_before: True
-        use_cnn_module: True
-        cnn_module_kernel: 15
-        cnn_module_norm: layer_norm
-        activation_type: swish
-        pos_enc_layer_type: rel_pos
-        selfattention_layer_type: rel_selfattn
+############################################
+#           Network Architecture           #
+############################################
+cmvn_file: 
+cmvn_file_type: "json"
+# encoder related
+encoder: conformer
+encoder_conf:
+    output_size: 512    # dimension of attention
+    attention_heads: 8
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: True
+    use_cnn_module: True
+    cnn_module_kernel: 15
+    cnn_module_norm: layer_norm
+    activation_type: swish
+    pos_enc_layer_type: rel_pos
+    selfattention_layer_type: rel_selfattn
 
-    # decoder related
-    decoder: transformer
-    decoder_conf:
-        attention_heads: 8
-        linear_units: 2048
-        num_blocks: 6
-        dropout_rate: 0.1
-        positional_dropout_rate: 0.1
-        self_attention_dropout_rate: 0.0
-        src_attention_dropout_rate: 0.0
+# decoder related
+decoder: transformer
+decoder_conf:
+    attention_heads: 8
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
 
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-  min_input_len: 0.1 # second
-  max_input_len: 12.0 # second
-  min_output_len: 1.0
-  max_output_len: 400.0
-  min_output_input_ratio: 0.05
-  max_output_input_ratio: 10.0
+###########################################
+#                   Data                  #
+###########################################
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
 
-collator:
-  vocab_filepath: data/lang_char/vocab.txt 
-  unit_type: 'char'
-  spm_model_prefix: ''
-  augmentation_config: conf/preprocess.yaml
-  batch_size: 64
-  raw_wav: True  # use raw_wav or kaldi feature
-  spectrum_type: fbank #linear, mfcc, fbank
-  feat_dim: 80
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
-  stride_ms: 10.0
-  window_ms: 25.0
-  use_dB_normalization: True 
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 2
+###########################################
+#              Dataloader                 #
+###########################################
+vocab_filepath: data/lang_char/vocab.txt 
+unit_type: 'char'
+preprocess_config: conf/preprocess.yaml
+spm_model_prefix: ''
+feat_dim: 80
+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: 64
+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
+batch_count: auto
+batch_bins: 0 
+batch_frames_in: 0
+batch_frames_out: 0
+batch_frames_inout: 0
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
 
 
-training:
-  n_epoch: 240 
-  accum_grad: 16
-  global_grad_clip: 5.0
-  log_interval: 100
-  checkpoint:
-    kbest_n: 50
-    latest_n: 5
-  optim: adam
-  optim_conf:
-    lr: 0.001
-    weight_decay: 1e-6
-  scheduler: warmuplr     
-  scheduler_conf:
-    warmup_steps: 5000
-    lr_decay: 1.0
-
-
-decoding:
-  batch_size: 128
-  error_rate_type: cer 
-  decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
-  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-  alpha: 2.5
-  beta: 0.3
-  beam_size: 10
-  cutoff_prob: 1.0
-  cutoff_top_n: 0
-  num_proc_bsearch: 8
-  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-      # <0: for decoding, use full chunk.
-      # >0: for decoding, use fixed chunk size as set.
-      # 0: used for training, it's prohibited here. 
-  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+###########################################
+#                 Training                #
+###########################################
+n_epoch: 240 
+accum_grad: 16
+global_grad_clip: 5.0
+log_interval: 100
+checkpoint:
+  kbest_n: 50
+  latest_n: 5
+optim: adam
+optim_conf:
+  lr: 0.001
+  weight_decay: 1.0e-6
+scheduler: warmuplr     
+scheduler_conf:
+  warmup_steps: 5000
+  lr_decay: 1.0
\ No newline at end of file

examples/wenetspeech/asr1/conf/tuning/decode.yaml

+decode_batch_size: 128
+error_rate_type: cer 
+decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+beam_size: 10
+ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+    # <0: for decoding, use full chunk.
+    # >0: for decoding, use fixed chunk size as set.
+    # 0: used for training, it's prohibited here. 
+num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+simulate_streaming: False  # simulate streaming inference. Defaults to False.
\ No newline at end of file

examples/wenetspeech/asr1/local/test.sh

 #!/bin/bash
 
-if [ $# != 2 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix"
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix"
     exit -1
 fi
 
@@ -9,7 +9,8 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
+decode_config_path=$2
+ckpt_prefix=$3
 
 chunk_mode=false
 if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
@@ -36,10 +37,11 @@ for type in attention ctc_greedy_search; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"
@@ -55,10 +57,11 @@ for type in ctc_prefix_beam_search attention_rescoring; do
     python3 -u ${BIN_DIR}/test.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size}
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size}
 
     if [ $? -ne 0 ]; then
         echo "Failed in evaluation!"

examples/wenetspeech/asr1/local/test_wav.sh

 #!/bin/bash
 
-if [ $# != 3 ];then
-    echo "usage: ${0} config_path ckpt_path_prefix audio_file"
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
     exit -1
 fi
 
@@ -9,8 +9,9 @@ ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
 echo "using $ngpu gpus..."
 
 config_path=$1
-ckpt_prefix=$2
-audio_file=$3
+decode_config_path=$2
+ckpt_prefix=$3
+audio_file=$4
 
 mkdir -p data
 wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
@@ -43,10 +44,11 @@ for type in  attention_rescoring; do
     python3 -u ${BIN_DIR}/test_wav.py \
     --ngpu ${ngpu} \
     --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
     --result_file ${output_dir}/${type}.rsl \
     --checkpoint_path ${ckpt_prefix} \
-    --opts decoding.decoding_method ${type} \
-    --opts decoding.batch_size ${batch_size} \
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size} \
     --audio_file ${audio_file}
 
     if [ $? -ne 0 ]; then

examples/wenetspeech/asr1/run.sh

@@ -7,7 +7,7 @@ gpus=0,1,2,3,4,5,6,7
 stage=0
 stop_stage=100
 conf_path=conf/conformer.yaml
-
+decode_conf_path=conf/tuning/decode.yaml
 average_checkpoint=true
 avg_num=10
 
@@ -36,12 +36,12 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # ctc alignment of test data
-    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
@@ -51,5 +51,5 @@ fi
 
 if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
 fi

paddlespeech/s2t/exps/deepspeech2/bin/deploy/runtime.py

@@ -80,13 +80,13 @@ def inference(config, args):
 def start_server(config, args):
     """Start the ASR server"""
     config.defrost()
-    config.data.manifest = config.data.test_manifest
+    config.manifest = config.test_manifest
     dataset = ManifestDataset.from_config(config)
 
-    config.collator.augmentation_config = ""
-    config.collator.keep_transcription_text = True
-    config.collator.batch_size = 1
-    config.collator.num_workers = 0
+    config.augmentation_config = ""
+    config.keep_transcription_text = True
+    config.batch_size = 1
+    config.num_workers = 0
     collate_fn = SpeechCollator.from_config(config)
     test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
 
@@ -105,14 +105,14 @@ def start_server(config, args):
             paddle.to_tensor(audio),
             paddle.to_tensor(audio_len),
             vocab_list=test_loader.collate_fn.vocab_list,
-            decoding_method=config.decoding.decoding_method,
-            lang_model_path=config.decoding.lang_model_path,
-            beam_alpha=config.decoding.alpha,
-            beam_beta=config.decoding.beta,
-            beam_size=config.decoding.beam_size,
-            cutoff_prob=config.decoding.cutoff_prob,
-            cutoff_top_n=config.decoding.cutoff_top_n,
-            num_processes=config.decoding.num_proc_bsearch)
+            decoding_method=config.decode.decoding_method,
+            lang_model_path=config.decode.lang_model_path,
+            beam_alpha=config.decode.alpha,
+            beam_beta=config.decode.beta,
+            beam_size=config.decode.beam_size,
+            cutoff_prob=config.decode.cutoff_prob,
+            cutoff_top_n=config.decode.cutoff_top_n,
+            num_processes=config.decode.num_proc_bsearch)
         return result_transcript[0]
 
     # warming up with utterrances sampled from Librispeech
@@ -179,12 +179,16 @@ if __name__ == "__main__":
     config = get_cfg_defaults()
     if args.config:
         config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()
     print(config)
 
-    args.warmup_manifest = config.data.test_manifest
+    args.warmup_manifest = config.test_manifest
     print_arguments(args, globals())
 
     if args.dump_config:

paddlespeech/s2t/exps/deepspeech2/bin/deploy/server.py

@@ -33,13 +33,13 @@ from paddlespeech.s2t.utils.utility import print_arguments
 def start_server(config, args):
     """Start the ASR server"""
     config.defrost()
-    config.data.manifest = config.data.test_manifest
+    config.manifest = config.test_manifest
     dataset = ManifestDataset.from_config(config)
 
-    config.collator.augmentation_config = ""
-    config.collator.keep_transcription_text = True
-    config.collator.batch_size = 1
-    config.collator.num_workers = 0
+    config.augmentation_config = ""
+    config.keep_transcription_text = True
+    config.batch_size = 1
+    config.num_workers = 0
     collate_fn = SpeechCollator.from_config(config)
     test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
 
@@ -62,14 +62,14 @@ def start_server(config, args):
             paddle.to_tensor(audio),
             paddle.to_tensor(audio_len),
             vocab_list=test_loader.collate_fn.vocab_list,
-            decoding_method=config.decoding.decoding_method,
-            lang_model_path=config.decoding.lang_model_path,
-            beam_alpha=config.decoding.alpha,
-            beam_beta=config.decoding.beta,
-            beam_size=config.decoding.beam_size,
-            cutoff_prob=config.decoding.cutoff_prob,
-            cutoff_top_n=config.decoding.cutoff_top_n,
-            num_processes=config.decoding.num_proc_bsearch)
+            decoding_method=config.decode.decoding_method,
+            lang_model_path=config.decode.lang_model_path,
+            beam_alpha=config.decode.alpha,
+            beam_beta=config.decode.beta,
+            beam_size=config.decode.beam_size,
+            cutoff_prob=config.decode.cutoff_prob,
+            cutoff_top_n=config.decode.cutoff_top_n,
+            num_processes=config.decode.num_proc_bsearch)
         return result_transcript[0]
 
     # warming up with utterrances sampled from Librispeech
@@ -114,12 +114,16 @@ if __name__ == "__main__":
     config = get_cfg_defaults()
     if args.config:
         config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()
     print(config)
 
-    args.warmup_manifest = config.data.test_manifest
+    args.warmup_manifest = config.test_manifest
     print_arguments(args, globals())
 
     if args.dump_config:

paddlespeech/s2t/exps/deepspeech2/bin/test.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Evaluation for DeepSpeech2 model."""
+from yacs.config import CfgNode
+
 from paddlespeech.s2t.exps.deepspeech2.config import get_cfg_defaults
 from paddlespeech.s2t.exps.deepspeech2.model import DeepSpeech2Tester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
@@ -44,6 +46,10 @@ if __name__ == "__main__":
     config = get_cfg_defaults(args.model_type)
     if args.config:
         config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()

paddlespeech/s2t/exps/deepspeech2/bin/test_export.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Evaluation for DeepSpeech2 model."""
+from yacs.config import CfgNode
+
 from paddlespeech.s2t.exps.deepspeech2.config import get_cfg_defaults
 from paddlespeech.s2t.exps.deepspeech2.model import DeepSpeech2ExportTester as ExportTester
 from paddlespeech.s2t.training.cli import default_argument_parser
@@ -49,6 +51,10 @@ if __name__ == "__main__":
     config = get_cfg_defaults(args.model_type)
     if args.config:
         config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()

paddlespeech/s2t/exps/deepspeech2/bin/test_wav.py

@@ -18,6 +18,7 @@ from pathlib import Path
 
 import paddle
 import soundfile
+from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.deepspeech2.config import get_cfg_defaults
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
@@ -41,7 +42,7 @@ class DeepSpeech2Tester_hub():
         self.audio_file = args.audio_file
         self.collate_fn_test = SpeechCollator.from_config(config)
         self._text_featurizer = TextFeaturizer(
-            unit_type=config.collator.unit_type, vocab=None)
+            unit_type=config.unit_type, vocab=None)
 
     def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
         result_transcripts = self.model.decode(
@@ -74,7 +75,7 @@ class DeepSpeech2Tester_hub():
         audio = paddle.unsqueeze(audio, axis=0)
         vocab_list = collate_fn_test.vocab_list
         result_transcripts = self.compute_result_transcripts(
-            audio, audio_len, vocab_list, cfg.decoding)
+            audio, audio_len, vocab_list, cfg.decode)
         logger.info("result_transcripts: " + result_transcripts[0])
 
     def run_test(self):
@@ -110,13 +111,13 @@ class DeepSpeech2Tester_hub():
     def setup_model(self):
         config = self.config.clone()
         with UpdateConfig(config):
-            config.model.input_dim = self.collate_fn_test.feature_size
-            config.model.output_dim = self.collate_fn_test.vocab_size
+            config.input_dim = self.collate_fn_test.feature_size
+            config.output_dim = self.collate_fn_test.vocab_size
 
         if self.args.model_type == 'offline':
-            model = DeepSpeech2Model.from_config(config.model)
+            model = DeepSpeech2Model.from_config(config)
         elif self.args.model_type == 'online':
-            model = DeepSpeech2ModelOnline.from_config(config.model)
+            model = DeepSpeech2ModelOnline.from_config(config)
         else:
             raise Exception("wrong model type")
 
@@ -134,8 +135,8 @@ class DeepSpeech2Tester_hub():
         self.checkpoint_dir = checkpoint_dir
 
         self.checkpoint = Checkpoint(
-            kbest_n=self.config.training.checkpoint.kbest_n,
-            latest_n=self.config.training.checkpoint.latest_n)
+            kbest_n=self.config.checkpoint.kbest_n,
+            latest_n=self.config.checkpoint.latest_n)
 
     def resume(self):
         """Resume from the checkpoint at checkpoints in the output
@@ -190,6 +191,10 @@ if __name__ == "__main__":
     config = get_cfg_defaults(args.model_type)
     if args.config:
         config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()

paddlespeech/s2t/exps/deepspeech2/config.py

@@ -23,17 +23,6 @@ from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
 
 def get_cfg_defaults(model_type='offline'):
     _C = CfgNode()
-    _C.data = ManifestDataset.params()
-    _C.collator = SpeechCollator.params()
-    _C.training = DeepSpeech2Trainer.params()
-    _C.decoding = DeepSpeech2Tester.params()
-    if model_type == 'offline':
-        _C.model = DeepSpeech2Model.params()
-    else:
-        _C.model = DeepSpeech2ModelOnline.params()
-    """Get a yacs CfgNode object with default values for my_project."""
-    # Return a clone so that the defaults will not be altered
-    # This is for the "local variable" use pattern
     config = _C.clone()
     config.set_new_allowed(True)
     return config

paddlespeech/s2t/exps/deepspeech2/model.py

@@ -69,8 +69,8 @@ class DeepSpeech2Trainer(Trainer):
         super().__init__(config, args)
 
     def train_batch(self, batch_index, batch_data, msg):
-        batch_size = self.config.collator.batch_size
-        accum_grad = self.config.training.accum_grad
+        batch_size = self.config.batch_size
+        accum_grad = self.config.accum_grad
 
         start = time.time()
 
@@ -133,7 +133,7 @@ class DeepSpeech2Trainer(Trainer):
                 total_loss += float(loss) * num_utts
                 valid_losses['val_loss'].append(float(loss))
 
-            if (i + 1) % self.config.training.log_interval == 0:
+            if (i + 1) % self.config.log_interval == 0:
                 valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
                 valid_dump['val_history_loss'] = total_loss / num_seen_utts
 
@@ -154,16 +154,16 @@ class DeepSpeech2Trainer(Trainer):
         config = self.config.clone()
         with UpdateConfig(config):
             if self.train:
-                config.model.input_dim = self.train_loader.collate_fn.feature_size
-                config.model.output_dim = self.train_loader.collate_fn.vocab_size
+                config.input_dim = self.train_loader.collate_fn.feature_size
+                config.output_dim = self.train_loader.collate_fn.vocab_size
             else:
-                config.model.input_dim = self.test_loader.collate_fn.feature_size
-                config.model.output_dim = self.test_loader.collate_fn.vocab_size
+                config.input_dim = self.test_loader.collate_fn.feature_size
+                config.output_dim = self.test_loader.collate_fn.vocab_size
 
         if self.args.model_type == 'offline':
-            model = DeepSpeech2Model.from_config(config.model)
+            model = DeepSpeech2Model.from_config(config)
         elif self.args.model_type == 'online':
-            model = DeepSpeech2ModelOnline.from_config(config.model)
+            model = DeepSpeech2ModelOnline.from_config(config)
         else:
             raise Exception("wrong model type")
         if self.parallel:
@@ -177,17 +177,13 @@ class DeepSpeech2Trainer(Trainer):
         if not self.train:
             return
 
-        grad_clip = ClipGradByGlobalNormWithLog(
-            config.training.global_grad_clip)
+        grad_clip = ClipGradByGlobalNormWithLog(config.global_grad_clip)
         lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
-            learning_rate=config.training.lr,
-            gamma=config.training.lr_decay,
-            verbose=True)
+            learning_rate=config.lr, gamma=config.lr_decay, verbose=True)
         optimizer = paddle.optimizer.Adam(
             learning_rate=lr_scheduler,
             parameters=model.parameters(),
-            weight_decay=paddle.regularizer.L2Decay(
-                config.training.weight_decay),
+            weight_decay=paddle.regularizer.L2Decay(config.weight_decay),
             grad_clip=grad_clip)
         self.optimizer = optimizer
         self.lr_scheduler = lr_scheduler
@@ -198,66 +194,67 @@ class DeepSpeech2Trainer(Trainer):
         config.defrost()
         if self.train:
             # train
-            config.data.manifest = config.data.train_manifest
+            config.manifest = config.train_manifest
             train_dataset = ManifestDataset.from_config(config)
             if self.parallel:
                 batch_sampler = SortagradDistributedBatchSampler(
                     train_dataset,
-                    batch_size=config.collator.batch_size,
+                    batch_size=config.batch_size,
                     num_replicas=None,
                     rank=None,
                     shuffle=True,
                     drop_last=True,
-                    sortagrad=config.collator.sortagrad,
-                    shuffle_method=config.collator.shuffle_method)
+                    sortagrad=config.sortagrad,
+                    shuffle_method=config.shuffle_method)
             else:
                 batch_sampler = SortagradBatchSampler(
                     train_dataset,
                     shuffle=True,
-                    batch_size=config.collator.batch_size,
+                    batch_size=config.batch_size,
                     drop_last=True,
-                    sortagrad=config.collator.sortagrad,
-                    shuffle_method=config.collator.shuffle_method)
+                    sortagrad=config.sortagrad,
+                    shuffle_method=config.shuffle_method)
 
-            config.collator.keep_transcription_text = False
+            config.keep_transcription_text = False
             collate_fn_train = SpeechCollator.from_config(config)
             self.train_loader = DataLoader(
                 train_dataset,
                 batch_sampler=batch_sampler,
                 collate_fn=collate_fn_train,
-                num_workers=config.collator.num_workers)
+                num_workers=config.num_workers)
 
             # dev
-            config.data.manifest = config.data.dev_manifest
+            config.manifest = config.dev_manifest
             dev_dataset = ManifestDataset.from_config(config)
 
-            config.collator.augmentation_config = ""
-            config.collator.keep_transcription_text = False
+            config.augmentation_config = ""
+            config.keep_transcription_text = False
             collate_fn_dev = SpeechCollator.from_config(config)
             self.valid_loader = DataLoader(
                 dev_dataset,
-                batch_size=int(config.collator.batch_size),
+                batch_size=int(config.batch_size),
                 shuffle=False,
                 drop_last=False,
                 collate_fn=collate_fn_dev,
-                num_workers=config.collator.num_workers)
+                num_workers=config.num_workers)
             logger.info("Setup train/valid  Dataloader!")
         else:
             # test
-            config.data.manifest = config.data.test_manifest
+            config.manifest = config.test_manifest
             test_dataset = ManifestDataset.from_config(config)
 
-            config.collator.augmentation_config = ""
-            config.collator.keep_transcription_text = True
+            config.augmentation_config = ""
+            config.keep_transcription_text = True
             collate_fn_test = SpeechCollator.from_config(config)
-
+            decode_batch_size = config.get('decode', dict()).get(
+                'decode_batch_size', 1)
             self.test_loader = DataLoader(
                 test_dataset,
-                batch_size=config.decoding.batch_size,
+                batch_size=decode_batch_size,
                 shuffle=False,
                 drop_last=False,
                 collate_fn=collate_fn_test,
-                num_workers=config.collator.num_workers)
+                num_workers=config.num_workers)
             logger.info("Setup test  Dataloader!")
 
 
@@ -286,7 +283,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
     def __init__(self, config, args):
         super().__init__(config, args)
         self._text_featurizer = TextFeaturizer(
-            unit_type=config.collator.unit_type, vocab=None)
+            unit_type=config.unit_type, vocab=None)
 
     def ordid2token(self, texts, texts_len):
         """ ord() id to chr() chr """
@@ -304,17 +301,17 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
                         texts,
                         texts_len,
                         fout=None):
-        cfg = self.config.decoding
+        decode_cfg = self.config.decode
         errors_sum, len_refs, num_ins = 0.0, 0, 0
-        errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
-        error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
+        errors_func = error_rate.char_errors if decode_cfg.error_rate_type == 'cer' else error_rate.word_errors
+        error_rate_func = error_rate.cer if decode_cfg.error_rate_type == 'cer' else error_rate.wer
 
         vocab_list = self.test_loader.collate_fn.vocab_list
 
         target_transcripts = self.ordid2token(texts, texts_len)
 
-        result_transcripts = self.compute_result_transcripts(audio, audio_len,
-                                                             vocab_list, cfg)
+        result_transcripts = self.compute_result_transcripts(
+            audio, audio_len, vocab_list, decode_cfg)
 
         for utt, target, result in zip(utts, target_transcripts,
                                        result_transcripts):
@@ -327,29 +324,31 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             logger.info(f"Utt: {utt}")
             logger.info(f"Ref: {target}")
             logger.info(f"Hyp: {result}")
-            logger.info("Current error rate [%s] = %f" %
-                        (cfg.error_rate_type, error_rate_func(target, result)))
+            logger.info(
+                "Current error rate [%s] = %f" %
+                (decode_cfg.error_rate_type, error_rate_func(target, result)))
 
         return dict(
             errors_sum=errors_sum,
             len_refs=len_refs,
             num_ins=num_ins,
             error_rate=errors_sum / len_refs,
-            error_rate_type=cfg.error_rate_type)
+            error_rate_type=decode_cfg.error_rate_type)
 
-    def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
+    def compute_result_transcripts(self, audio, audio_len, vocab_list,
+                                   decode_cfg):
         result_transcripts = self.model.decode(
             audio,
             audio_len,
             vocab_list,
-            decoding_method=cfg.decoding_method,
-            lang_model_path=cfg.lang_model_path,
-            beam_alpha=cfg.alpha,
-            beam_beta=cfg.beta,
-            beam_size=cfg.beam_size,
-            cutoff_prob=cfg.cutoff_prob,
-            cutoff_top_n=cfg.cutoff_top_n,
-            num_processes=cfg.num_proc_bsearch)
+            decoding_method=decode_cfg.decoding_method,
+            lang_model_path=decode_cfg.lang_model_path,
+            beam_alpha=decode_cfg.alpha,
+            beam_beta=decode_cfg.beta,
+            beam_size=decode_cfg.beam_size,
+            cutoff_prob=decode_cfg.cutoff_prob,
+            cutoff_top_n=decode_cfg.cutoff_top_n,
+            num_processes=decode_cfg.num_proc_bsearch)
 
         return result_transcripts
 
@@ -358,7 +357,6 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
     def test(self):
         logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
         self.model.eval()
-        cfg = self.config
         error_rate_type = None
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         with jsonlines.open(self.args.result_file, 'w') as fout:
@@ -412,11 +410,10 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
         if self.args.enable_auto_log is True:
             from paddlespeech.s2t.utils.log import Autolog
             self.autolog = Autolog(
-                batch_size=self.config.decoding.batch_size,
+                batch_size=self.config.decode.decode_batch_size,
                 model_name="deepspeech2",
                 model_precision="fp32").getlog()
         self.model.eval()
-        cfg = self.config
         error_rate_type = None
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         with jsonlines.open(self.args.result_file, 'w') as fout:
@@ -441,7 +438,8 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
         if self.args.enable_auto_log is True:
             self.autolog.report()
 
-    def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
+    def compute_result_transcripts(self, audio, audio_len, vocab_list,
+                                   decode_cfg):
         if self.args.model_type == "online":
             output_probs, output_lens = self.static_forward_online(audio,
                                                                    audio_len)
@@ -454,13 +452,15 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
         self.predictor.clear_intermediate_tensor()
         self.predictor.try_shrink_memory()
 
-        self.model.decoder.init_decode(cfg.alpha, cfg.beta, cfg.lang_model_path,
-                                       vocab_list, cfg.decoding_method)
+        self.model.decoder.init_decode(decode_cfg.alpha, decode_cfg.beta,
+                                       decode_cfg.lang_model_path, vocab_list,
+                                       decode_cfg.decoding_method)
 
         result_transcripts = self.model.decoder.decode_probs(
-            output_probs, output_lens, vocab_list, cfg.decoding_method,
-            cfg.lang_model_path, cfg.alpha, cfg.beta, cfg.beam_size,
-            cfg.cutoff_prob, cfg.cutoff_top_n, cfg.num_proc_bsearch)
+            output_probs, output_lens, vocab_list, decode_cfg.decoding_method,
+            decode_cfg.lang_model_path, decode_cfg.alpha, decode_cfg.beta,
+            decode_cfg.beam_size, decode_cfg.cutoff_prob,
+            decode_cfg.cutoff_top_n, decode_cfg.num_proc_bsearch)
         #replace the <space> with ' '
         result_transcripts = [
             self._text_featurizer.detokenize(sentence)
@@ -531,12 +531,10 @@ class DeepSpeech2ExportTester(DeepSpeech2Tester):
             num_chunk = int(num_chunk)
 
             chunk_state_h_box = np.zeros(
-                (self.config.model.num_rnn_layers, 1,
-                 self.config.model.rnn_layer_size),
+                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
                 dtype=x.dtype)
             chunk_state_c_box = np.zeros(
-                (self.config.model.num_rnn_layers, 1,
-                 self.config.model.rnn_layer_size),
+                (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
                 dtype=x.dtype)
 
             input_names = self.predictor.get_input_names()

paddlespeech/s2t/exps/u2/bin/alignment.py

@@ -43,9 +43,9 @@ if __name__ == "__main__":
     config = get_cfg_defaults()
     if args.config:
         config.merge_from_file(args.config)
-    if args.decode_config:
+    if args.decode_cfg:
         decode_confs = CfgNode(new_allowed=True)
-        decode_confs.merge_from_file(args.decode_config)
+        decode_confs.merge_from_file(args.decode_cfg)
         config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)

paddlespeech/s2t/exps/u2/bin/test.py

@@ -47,9 +47,9 @@ if __name__ == "__main__":
     config = get_cfg_defaults()
     if args.config:
         config.merge_from_file(args.config)
-    if args.decode_config:
+    if args.decode_cfg:
         decode_confs = CfgNode(new_allowed=True)
-        decode_confs.merge_from_file(args.decode_config)
+        decode_confs.merge_from_file(args.decode_cfg)
         config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)

paddlespeech/s2t/exps/u2/bin/test_wav.py

@@ -38,7 +38,7 @@ class U2Infer():
         self.config = config
         self.audio_file = args.audio_file
 
-        self.preprocess_conf = config.augmentation_config
+        self.preprocess_conf = config.preprocess_config
         self.preprocess_args = {"train": False}
         self.preprocessing = Transformation(self.preprocess_conf)
 
@@ -132,9 +132,9 @@ if __name__ == "__main__":
     config = get_cfg_defaults()
     if args.config:
         config.merge_from_file(args.config)
-    if args.decode_config:
+    if args.decode_cfg:
         decode_confs = CfgNode(new_allowed=True)
-        decode_confs.merge_from_file(args.decode_config)
+        decode_confs.merge_from_file(args.decode_cfg)
         config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)

paddlespeech/s2t/exps/u2/config.py

@@ -21,15 +21,15 @@ from paddlespeech.s2t.models.u2 import U2Model
 
 _C = CfgNode(new_allowed=True)
 
-ManifestDataset.params(_C)
+# ManifestDataset.params(_C)
 
-SpeechCollator.params(_C)
+# SpeechCollator.params(_C)
 
-U2Model.params(_C)
+# U2Model.params(_C)
 
-U2Trainer.params(_C)
+# U2Trainer.params(_C)
 
-_C.decode = U2Tester.params()
+# _C.decode = U2Tester.params()
 
 
 def get_cfg_defaults():

paddlespeech/s2t/exps/u2/model.py

@@ -264,7 +264,7 @@ class U2Trainer(Trainer):
                 batch_frames_in=config.batch_frames_in,
                 batch_frames_out=config.batch_frames_out,
                 batch_frames_inout=config.batch_frames_inout,
-                preprocess_conf=config.augmentation_config,
+                preprocess_conf=config.preprocess_config,
                 n_iter_processes=config.num_workers,
                 subsampling_factor=1,
                 num_encs=1)
@@ -283,18 +283,20 @@ class U2Trainer(Trainer):
                 batch_frames_in=0,
                 batch_frames_out=0,
                 batch_frames_inout=0,
-                preprocess_conf=config.augmentation_config,
+                preprocess_conf=config.preprocess_config,
                 n_iter_processes=config.num_workers,
                 subsampling_factor=1,
                 num_encs=1)
             logger.info("Setup train/valid Dataloader!")
         else:
+            decode_batch_size = config.get('decode', dict()).get(
+                'decode_batch_size', 1)
             # test dataset, return raw text
             self.test_loader = BatchDataLoader(
                 json_file=config.test_manifest,
                 train_mode=False,
                 sortagrad=False,
-                batch_size=config.decode.decode_batch_size,
+                batch_size=decode_batch_size,
                 maxlen_in=float('inf'),
                 maxlen_out=float('inf'),
                 minibatches=0,
@@ -304,7 +306,7 @@ class U2Trainer(Trainer):
                 batch_frames_in=0,
                 batch_frames_out=0,
                 batch_frames_inout=0,
-                preprocess_conf=config.augmentation_config,
+                preprocess_conf=config.preprocess_config,
                 n_iter_processes=1,
                 subsampling_factor=1,
                 num_encs=1)
@@ -313,7 +315,7 @@ class U2Trainer(Trainer):
                 json_file=config.test_manifest,
                 train_mode=False,
                 sortagrad=False,
-                batch_size=config.decode.decode_batch_size,
+                batch_size=decode_batch_size,
                 maxlen_in=float('inf'),
                 maxlen_out=float('inf'),
                 minibatches=0,
@@ -323,7 +325,7 @@ class U2Trainer(Trainer):
                 batch_frames_in=0,
                 batch_frames_out=0,
                 batch_frames_inout=0,
-                preprocess_conf=config.augmentation_config,
+                preprocess_conf=config.preprocess_config,
                 n_iter_processes=1,
                 subsampling_factor=1,
                 num_encs=1)
@@ -557,7 +559,7 @@ class U2Tester(U2Trainer):
                 "ref_len":
                 len_refs,
                 "decode_method":
-                self.config.decoding_method,
+                self.config.decode.decoding_method,
             })
             f.write(data + '\n')
 

paddlespeech/s2t/exps/u2/trainer.py

@@ -44,77 +44,77 @@ class U2Trainer(Trainer):
     def setup_dataloader(self):
         config = self.config.clone()
         config.defrost()
-        config.collator.keep_transcription_text = False
+        config.keep_transcription_text = False
 
         # train/valid dataset, return token ids
-        config.data.manifest = config.data.train_manifest
+        config.manifest = config.train_manifest
         train_dataset = ManifestDataset.from_config(config)
 
-        config.data.manifest = config.data.dev_manifest
+        config.manifest = config.dev_manifest
         dev_dataset = ManifestDataset.from_config(config)
 
         collate_fn_train = SpeechCollator.from_config(config)
 
-        config.collator.augmentation_config = ""
+        config.augmentation_config = ""
         collate_fn_dev = SpeechCollator.from_config(config)
 
         if self.parallel:
             batch_sampler = SortagradDistributedBatchSampler(
                 train_dataset,
-                batch_size=config.collator.batch_size,
+                batch_size=config.batch_size,
                 num_replicas=None,
                 rank=None,
                 shuffle=True,
                 drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
+                sortagrad=config.sortagrad,
+                shuffle_method=config.shuffle_method)
         else:
             batch_sampler = SortagradBatchSampler(
                 train_dataset,
                 shuffle=True,
-                batch_size=config.collator.batch_size,
+                batch_size=config.batch_size,
                 drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
+                sortagrad=config.sortagrad,
+                shuffle_method=config.shuffle_method)
         self.train_loader = DataLoader(
             train_dataset,
             batch_sampler=batch_sampler,
             collate_fn=collate_fn_train,
-            num_workers=config.collator.num_workers, )
+            num_workers=config.num_workers, )
         self.valid_loader = DataLoader(
             dev_dataset,
-            batch_size=config.collator.batch_size,
+            batch_size=config.batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=collate_fn_dev,
-            num_workers=config.collator.num_workers, )
+            num_workers=config.num_workers, )
 
         # test dataset, return raw text
-        config.data.manifest = config.data.test_manifest
+        config.manifest = config.test_manifest
         # filter test examples, will cause less examples, but no mismatch with training
         # and can use large batch size , save training time, so filter test egs now.
-        config.data.min_input_len = 0.0  # second
-        config.data.max_input_len = float('inf')  # second
-        config.data.min_output_len = 0.0  # tokens
-        config.data.max_output_len = float('inf')  # tokens
-        config.data.min_output_input_ratio = 0.00
-        config.data.max_output_input_ratio = float('inf')
+        config.min_input_len = 0.0  # second
+        config.max_input_len = float('inf')  # second
+        config.min_output_len = 0.0  # tokens
+        config.max_output_len = float('inf')  # tokens
+        config.min_output_input_ratio = 0.00
+        config.max_output_input_ratio = float('inf')
 
         test_dataset = ManifestDataset.from_config(config)
         # return text ord id
-        config.collator.keep_transcription_text = True
-        config.collator.augmentation_config = ""
+        config.keep_transcription_text = True
+        config.augmentation_config = ""
         self.test_loader = DataLoader(
             test_dataset,
-            batch_size=config.decoding.batch_size,
+            batch_size=config.decode.batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=SpeechCollator.from_config(config))
         # return text token id
-        config.collator.keep_transcription_text = False
+        config.keep_transcription_text = False
         self.align_loader = DataLoader(
             test_dataset,
-            batch_size=config.decoding.batch_size,
+            batch_size=config.decode.batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=SpeechCollator.from_config(config))
@@ -122,7 +122,7 @@ class U2Trainer(Trainer):
 
     def setup_model(self):
         config = self.config
-        model_conf = config.model
+        model_conf = config
         with UpdateConfig(model_conf):
             model_conf.input_dim = self.train_loader.collate_fn.feature_size
             model_conf.output_dim = self.train_loader.collate_fn.vocab_size
@@ -136,7 +136,7 @@ class U2Trainer(Trainer):
         logger.info(f"{model}")
         layer_tools.print_params(model, logger.info)
 
-        train_config = config.training
+        train_config = config
         optim_type = train_config.optim
         optim_conf = train_config.optim_conf
         scheduler_type = train_config.scheduler
@@ -156,7 +156,7 @@ class U2Trainer(Trainer):
                 config,
                 parameters,
                 lr_scheduler=None, ):
-            train_config = config.training
+            train_config = config
             optim_type = train_config.optim
             optim_conf = train_config.optim_conf
             scheduler_type = train_config.scheduler
@@ -182,7 +182,7 @@ class U2Trainer(Trainer):
 
     def setup_updater(self):
         output_dir = self.output_dir
-        config = self.config.training
+        config = self.config
 
         updater = U2Updater(
             model=self.model,

paddlespeech/s2t/exps/u2_kaldi/bin/test.py

@@ -69,6 +69,10 @@ if __name__ == "__main__":
     config = CfgNode()
     config.set_new_allowed(True)
     config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()

paddlespeech/s2t/exps/u2_kaldi/model.py

@@ -80,7 +80,7 @@ class U2Trainer(Trainer):
         super().__init__(config, args)
 
     def train_batch(self, batch_index, batch_data, msg):
-        train_conf = self.config.training
+        train_conf = self.config
         start = time.time()
 
         # forward
@@ -122,7 +122,7 @@ class U2Trainer(Trainer):
 
         if (batch_index + 1) % train_conf.log_interval == 0:
             msg += "train time: {:>.3f}s, ".format(iteration_time)
-            msg += "batch size: {}, ".format(self.config.collator.batch_size)
+            msg += "batch size: {}, ".format(self.config.batch_size)
             msg += "accum: {}, ".format(train_conf.accum_grad)
             msg += ', '.join('{}: {:>.6f}'.format(k, v)
                              for k, v in losses_np.items())
@@ -157,7 +157,7 @@ class U2Trainer(Trainer):
                 if ctc_loss:
                     valid_losses['val_ctc_loss'].append(float(ctc_loss))
 
-            if (i + 1) % self.config.training.log_interval == 0:
+            if (i + 1) % self.config.log_interval == 0:
                 valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
                 valid_dump['val_history_loss'] = total_loss / num_seen_utts
 
@@ -186,7 +186,7 @@ class U2Trainer(Trainer):
         self.before_train()
 
         logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
-        while self.epoch < self.config.training.n_epoch:
+        while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
                 try:
@@ -235,10 +235,10 @@ class U2Trainer(Trainer):
         config = self.config.clone()
         # train/valid dataset, return token ids
         self.train_loader = BatchDataLoader(
-            json_file=config.data.train_manifest,
+            json_file=config.train_manifest,
             train_mode=True,
             sortagrad=False,
-            batch_size=config.collator.batch_size,
+            batch_size=config.batch_size,
             maxlen_in=float('inf'),
             maxlen_out=float('inf'),
             minibatches=0,
@@ -248,16 +248,16 @@ class U2Trainer(Trainer):
             batch_frames_in=0,
             batch_frames_out=0,
             batch_frames_inout=0,
-            preprocess_conf=config.collator.augmentation_config,
-            n_iter_processes=config.collator.num_workers,
+            preprocess_conf=config.preprocess_config,
+            n_iter_processes=config.num_workers,
             subsampling_factor=1,
             num_encs=1)
 
         self.valid_loader = BatchDataLoader(
-            json_file=config.data.dev_manifest,
+            json_file=config.dev_manifest,
             train_mode=False,
             sortagrad=False,
-            batch_size=config.collator.batch_size,
+            batch_size=config.batch_size,
             maxlen_in=float('inf'),
             maxlen_out=float('inf'),
             minibatches=0,
@@ -268,16 +268,18 @@ class U2Trainer(Trainer):
             batch_frames_out=0,
             batch_frames_inout=0,
             preprocess_conf=None,
-            n_iter_processes=config.collator.num_workers,
+            n_iter_processes=config.num_workers,
             subsampling_factor=1,
             num_encs=1)
 
+        decode_batch_size = config.get('decode', dict()).get(
+            'decode_batch_size', 1)
         # test dataset, return raw text
         self.test_loader = BatchDataLoader(
-            json_file=config.data.test_manifest,
+            json_file=config.test_manifest,
             train_mode=False,
             sortagrad=False,
-            batch_size=config.decoding.batch_size,
+            batch_size=decode_batch_size,
             maxlen_in=float('inf'),
             maxlen_out=float('inf'),
             minibatches=0,
@@ -293,10 +295,10 @@ class U2Trainer(Trainer):
             num_encs=1)
 
         self.align_loader = BatchDataLoader(
-            json_file=config.data.test_manifest,
+            json_file=config.test_manifest,
             train_mode=False,
             sortagrad=False,
-            batch_size=config.decoding.batch_size,
+            batch_size=decode_batch_size,
             maxlen_in=float('inf'),
             maxlen_out=float('inf'),
             minibatches=0,
@@ -316,7 +318,7 @@ class U2Trainer(Trainer):
         config = self.config
 
         # model
-        model_conf = config.model
+        model_conf = config
         with UpdateConfig(model_conf):
             model_conf.input_dim = self.train_loader.feat_dim
             model_conf.output_dim = self.train_loader.vocab_size
@@ -392,9 +394,9 @@ class U2Tester(U2Trainer):
     def __init__(self, config, args):
         super().__init__(config, args)
         self.text_feature = TextFeaturizer(
-            unit_type=self.config.collator.unit_type,
-            vocab=self.config.collator.vocab_filepath,
-            spm_model_prefix=self.config.collator.spm_model_prefix)
+            unit_type=self.config.unit_type,
+            vocab=self.config.vocab_filepath,
+            spm_model_prefix=self.config.spm_model_prefix)
         self.vocab_list = self.text_feature.vocab_list
 
     def id2token(self, texts, texts_len, text_feature):
@@ -413,10 +415,10 @@ class U2Tester(U2Trainer):
                         texts,
                         texts_len,
                         fout=None):
-        cfg = self.config.decoding
+        decode_cfg = self.config.decode
         errors_sum, len_refs, num_ins = 0.0, 0, 0
-        errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
-        error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
+        errors_func = error_rate.char_errors if decode_cfg.error_rate_type == 'cer' else error_rate.word_errors
+        error_rate_func = error_rate.cer if decode_cfg.error_rate_type == 'cer' else error_rate.wer
 
         start_time = time.time()
         target_transcripts = self.id2token(texts, texts_len, self.text_feature)
@@ -424,12 +426,12 @@ class U2Tester(U2Trainer):
             audio,
             audio_len,
             text_feature=self.text_feature,
-            decoding_method=cfg.decoding_method,
-            beam_size=cfg.beam_size,
-            ctc_weight=cfg.ctc_weight,
-            decoding_chunk_size=cfg.decoding_chunk_size,
-            num_decoding_left_chunks=cfg.num_decoding_left_chunks,
-            simulate_streaming=cfg.simulate_streaming)
+            decoding_method=decode_cfg.decoding_method,
+            beam_size=decode_cfg.beam_size,
+            ctc_weight=decode_cfg.ctc_weight,
+            decoding_chunk_size=decode_cfg.decoding_chunk_size,
+            num_decoding_left_chunks=decode_cfg.num_decoding_left_chunks,
+            simulate_streaming=decode_cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
         for i, (utt, target, result, rec_tids) in enumerate(
@@ -449,15 +451,16 @@ class U2Tester(U2Trainer):
             logger.info(f"Utt: {utt}")
             logger.info(f"Ref: {target}")
             logger.info(f"Hyp: {result}")
-            logger.info("One example error rate [%s] = %f" %
-                        (cfg.error_rate_type, error_rate_func(target, result)))
+            logger.info(
+                "One example error rate [%s] = %f" %
+                (decode_cfg.error_rate_type, error_rate_func(target, result)))
 
         return dict(
             errors_sum=errors_sum,
             len_refs=len_refs,
             num_ins=num_ins,  # num examples
             error_rate=errors_sum / len_refs,
-            error_rate_type=cfg.error_rate_type,
+            error_rate_type=decode_cfg.error_rate_type,
             num_frames=audio_len.sum().numpy().item(),
             decode_time=decode_time)
 
@@ -468,7 +471,7 @@ class U2Tester(U2Trainer):
         self.model.eval()
         logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
 
-        stride_ms = self.config.collator.stride_ms
+        stride_ms = self.config.stride_ms
         error_rate_type = None
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         num_frames = 0.0
@@ -519,15 +522,15 @@ class U2Tester(U2Trainer):
                 "ref_len":
                 len_refs,
                 "decode_method":
-                self.config.decoding.decoding_method,
+                self.config.decode.decoding_method,
             })
             f.write(data + '\n')
 
     @paddle.no_grad()
     def align(self):
         ctc_utils.ctc_align(self.config, self.model, self.align_loader,
-                            self.config.decoding.batch_size,
-                            self.config.collator.stride_ms, self.vocab_list,
+                            self.config.decode.decode_batch_size,
+                            self.config.stride_ms, self.vocab_list,
                             self.args.result_file)
 
     def load_inferspec(self):
@@ -539,7 +542,7 @@ class U2Tester(U2Trainer):
         """
         from paddlespeech.s2t.models.u2 import U2InferModel
         infer_model = U2InferModel.from_pretrained(self.test_loader,
-                                                   self.config.model.clone(),
+                                                   self.config.clone(),
                                                    self.args.checkpoint_path)
         feat_dim = self.test_loader.feat_dim
         input_spec = [

paddlespeech/s2t/exps/u2_st/bin/test.py

@@ -14,12 +14,14 @@
 """Evaluation for U2 model."""
 import cProfile
 
+from yacs.config import CfgNode
+
 from paddlespeech.s2t.exps.u2_st.config import get_cfg_defaults
 from paddlespeech.s2t.exps.u2_st.model import U2STTester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils.utility import print_arguments
 
-# TODO(hui zhang): dynamic load 
+# TODO(hui zhang): dynamic load
 
 
 def main_sp(config, args):
@@ -35,7 +37,7 @@ def main(config, args):
 
 if __name__ == "__main__":
     parser = default_argument_parser()
-    # save asr result to 
+    # save asr result to
     parser.add_argument(
         "--result_file", type=str, help="path of save the asr result")
     args = parser.parse_args()
@@ -45,6 +47,10 @@ if __name__ == "__main__":
     config = get_cfg_defaults()
     if args.config:
         config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_conf = CfgNode(new_allowed=True)
+        decode_conf.merge_from_file(args.decode_cfg)
+        config.decode = decode_conf
     if args.opts:
         config.merge_from_list(args.opts)
     config.freeze()

paddlespeech/s2t/exps/u2_st/config.py

@@ -21,15 +21,15 @@ from paddlespeech.s2t.models.u2_st import U2STModel
 
 _C = CfgNode()
 
-_C.data = ManifestDataset.params()
+# _C.data = ManifestDataset.params()
 
-_C.collator = SpeechCollator.params()
+# _C.collator = SpeechCollator.params()
 
-_C.model = U2STModel.params()
+# _C.model = U2STModel.params()
 
-_C.training = U2STTrainer.params()
+# _C.training = U2STTrainer.params()
 
-_C.decoding = U2STTester.params()
+# _C.decoding = U2STTester.params()
 
 
 def get_cfg_defaults():

paddlespeech/s2t/exps/u2_st/model.py

@@ -78,7 +78,7 @@ class U2STTrainer(Trainer):
         super().__init__(config, args)
 
     def train_batch(self, batch_index, batch_data, msg):
-        train_conf = self.config.training
+        train_conf = self.config
         start = time.time()
         # forward
         utt, audio, audio_len, text, text_len = batch_data
@@ -127,7 +127,7 @@ class U2STTrainer(Trainer):
 
         if (batch_index + 1) % train_conf.log_interval == 0:
             msg += "train time: {:>.3f}s, ".format(iteration_time)
-            msg += "batch size: {}, ".format(self.config.collator.batch_size)
+            msg += "batch size: {}, ".format(self.config.batch_size)
             msg += "accum: {}, ".format(train_conf.accum_grad)
             msg += ', '.join('{}: {:>.6f}'.format(k, v)
                              for k, v in losses_np.items())
@@ -168,7 +168,7 @@ class U2STTrainer(Trainer):
                 if ctc_loss:
                     valid_losses['val_ctc_loss'].append(float(ctc_loss))
 
-            if (i + 1) % self.config.training.log_interval == 0:
+            if (i + 1) % self.config.log_interval == 0:
                 valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
                 valid_dump['val_history_st_loss'] = total_loss / num_seen_utts
 
@@ -197,7 +197,7 @@ class U2STTrainer(Trainer):
         self.before_train()
 
         logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
-        while self.epoch < self.config.training.n_epoch:
+        while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
                 try:
@@ -245,91 +245,93 @@ class U2STTrainer(Trainer):
     def setup_dataloader(self):
         config = self.config.clone()
         config.defrost()
-        config.collator.keep_transcription_text = False
+        config.keep_transcription_text = False
 
         # train/valid dataset, return token ids
-        config.data.manifest = config.data.train_manifest
+        config.manifest = config.train_manifest
         train_dataset = ManifestDataset.from_config(config)
 
-        config.data.manifest = config.data.dev_manifest
+        config.manifest = config.dev_manifest
         dev_dataset = ManifestDataset.from_config(config)
 
-        if config.model.model_conf.asr_weight > 0.:
+        if config.model_conf.asr_weight > 0.:
             Collator = TripletSpeechCollator
             TestCollator = SpeechCollator
         else:
             TestCollator = Collator = SpeechCollator
 
         collate_fn_train = Collator.from_config(config)
-        config.collator.augmentation_config = ""
+        config.augmentation_config = ""
         collate_fn_dev = Collator.from_config(config)
 
         if self.parallel:
             batch_sampler = SortagradDistributedBatchSampler(
                 train_dataset,
-                batch_size=config.collator.batch_size,
+                batch_size=config.batch_size,
                 num_replicas=None,
                 rank=None,
                 shuffle=True,
                 drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
+                sortagrad=config.sortagrad,
+                shuffle_method=config.shuffle_method)
         else:
             batch_sampler = SortagradBatchSampler(
                 train_dataset,
                 shuffle=True,
-                batch_size=config.collator.batch_size,
+                batch_size=config.batch_size,
                 drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
+                sortagrad=config.sortagrad,
+                shuffle_method=config.shuffle_method)
         self.train_loader = DataLoader(
             train_dataset,
             batch_sampler=batch_sampler,
             collate_fn=collate_fn_train,
-            num_workers=config.collator.num_workers, )
+            num_workers=config.num_workers, )
         self.valid_loader = DataLoader(
             dev_dataset,
-            batch_size=config.collator.batch_size,
+            batch_size=config.batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=collate_fn_dev,
-            num_workers=config.collator.num_workers, )
+            num_workers=config.num_workers, )
 
         # test dataset, return raw text
-        config.data.manifest = config.data.test_manifest
+        config.manifest = config.test_manifest
         # filter test examples, will cause less examples, but no mismatch with training
         # and can use large batch size , save training time, so filter test egs now.
-        # config.data.min_input_len = 0.0  # second
-        # config.data.max_input_len = float('inf')  # second
-        # config.data.min_output_len = 0.0  # tokens
-        # config.data.max_output_len = float('inf')  # tokens
-        # config.data.min_output_input_ratio = 0.00
-        # config.data.max_output_input_ratio = float('inf')
+        # config.min_input_len = 0.0  # second
+        # config.max_input_len = float('inf')  # second
+        # config.min_output_len = 0.0  # tokens
+        # config.max_output_len = float('inf')  # tokens
+        # config.min_output_input_ratio = 0.00
+        # config.max_output_input_ratio = float('inf')
         test_dataset = ManifestDataset.from_config(config)
         # return text ord id
-        config.collator.keep_transcription_text = True
-        config.collator.augmentation_config = ""
+        config.keep_transcription_text = True
+        config.augmentation_config = ""
+        decode_batch_size = config.get('decode', dict()).get(
+            'decode_batch_size', 1)
         self.test_loader = DataLoader(
             test_dataset,
-            batch_size=config.decoding.batch_size,
+            batch_size=decode_batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=TestCollator.from_config(config),
-            num_workers=config.collator.num_workers, )
+            num_workers=config.num_workers, )
         # return text token id
-        config.collator.keep_transcription_text = False
+        config.keep_transcription_text = False
         self.align_loader = DataLoader(
             test_dataset,
-            batch_size=config.decoding.batch_size,
+            batch_size=decode_batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=TestCollator.from_config(config),
-            num_workers=config.collator.num_workers, )
+            num_workers=config.num_workers, )
         logger.info("Setup train/valid/test/align Dataloader!")
 
     def setup_model(self):
         config = self.config
-        model_conf = config.model
+        model_conf = config
         with UpdateConfig(model_conf):
             model_conf.input_dim = self.train_loader.collate_fn.feature_size
             model_conf.output_dim = self.train_loader.collate_fn.vocab_size
@@ -342,7 +344,7 @@ class U2STTrainer(Trainer):
         logger.info(f"{model}")
         layer_tools.print_params(model, logger.info)
 
-        train_config = config.training
+        train_config = config
         optim_type = train_config.optim
         optim_conf = train_config.optim_conf
         scheduler_type = train_config.scheduler
@@ -428,7 +430,7 @@ class U2STTester(U2STTrainer):
 
     def translate(self, audio, audio_len):
         """"E2E translation from extracted audio feature"""
-        cfg = self.config.decoding
+        decode_cfg = self.config.decode
         text_feature = self.test_loader.collate_fn.text_feature
         self.model.eval()
 
@@ -436,12 +438,12 @@ class U2STTester(U2STTrainer):
             audio,
             audio_len,
             text_feature=text_feature,
-            decoding_method=cfg.decoding_method,
-            beam_size=cfg.beam_size,
-            word_reward=cfg.word_reward,
-            decoding_chunk_size=cfg.decoding_chunk_size,
-            num_decoding_left_chunks=cfg.num_decoding_left_chunks,
-            simulate_streaming=cfg.simulate_streaming)
+            decoding_method=decode_cfg.decoding_method,
+            beam_size=decode_cfg.beam_size,
+            word_reward=decode_cfg.word_reward,
+            decoding_chunk_size=decode_cfg.decoding_chunk_size,
+            num_decoding_left_chunks=decode_cfg.num_decoding_left_chunks,
+            simulate_streaming=decode_cfg.simulate_streaming)
         return hyps
 
     def compute_translation_metrics(self,
@@ -452,7 +454,7 @@ class U2STTester(U2STTrainer):
                                     texts_len,
                                     bleu_func,
                                     fout=None):
-        cfg = self.config.decoding
+        decode_cfg = self.config.decode
         len_refs, num_ins = 0, 0
 
         start_time = time.time()
@@ -467,12 +469,12 @@ class U2STTester(U2STTrainer):
             audio,
             audio_len,
             text_feature=text_feature,
-            decoding_method=cfg.decoding_method,
-            beam_size=cfg.beam_size,
-            word_reward=cfg.word_reward,
-            decoding_chunk_size=cfg.decoding_chunk_size,
-            num_decoding_left_chunks=cfg.num_decoding_left_chunks,
-            simulate_streaming=cfg.simulate_streaming)
+            decoding_method=decode_cfg.decoding_method,
+            beam_size=decode_cfg.beam_size,
+            word_reward=decode_cfg.word_reward,
+            decoding_chunk_size=decode_cfg.decoding_chunk_size,
+            num_decoding_left_chunks=decode_cfg.num_decoding_left_chunks,
+            simulate_streaming=decode_cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
         for utt, target, result in zip(utts, refs, hyps):
@@ -502,8 +504,8 @@ class U2STTester(U2STTrainer):
         self.model.eval()
         logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
 
-        cfg = self.config.decoding
-        bleu_func = bleu_score.char_bleu if cfg.error_rate_type == 'char-bleu' else bleu_score.bleu
+        decode_cfg = self.config.decode
+        bleu_func = bleu_score.char_bleu if decode_cfg.error_rate_type == 'char-bleu' else bleu_score.bleu
 
         stride_ms = self.test_loader.collate_fn.stride_ms
         hyps, refs = [], []
@@ -549,15 +551,15 @@ class U2STTester(U2STTrainer):
                 "num_examples":
                 num_ins,
                 "decode_method":
-                self.config.decoding.decoding_method,
+                self.config.decode.decoding_method,
             })
             f.write(data + '\n')
 
     @paddle.no_grad()
     def align(self):
         ctc_utils.ctc_align(self.config, self.model, self.align_loader,
-                            self.config.decoding.batch_size,
-                            self.config.collator.stride_ms, self.vocab_list,
+                            self.config.decode.decode_batch_size,
+                            self.config.stride_ms, self.vocab_list,
                             self.args.result_file)
 
     def load_inferspec(self):
@@ -569,7 +571,7 @@ class U2STTester(U2STTrainer):
         """
         from paddlespeech.s2t.models.u2 import U2InferModel
         infer_model = U2InferModel.from_pretrained(self.test_loader,
-                                                   self.config.model.clone(),
+                                                   self.config.clone(),
                                                    self.args.checkpoint_path)
         feat_dim = self.test_loader.collate_fn.feature_size
         input_spec = [

paddlespeech/s2t/io/collator.py

@@ -256,45 +256,43 @@ class SpeechCollator(SpeechCollatorBase):
         Returns:
             SpeechCollator: collator object.
         """
-        assert 'augmentation_config' in config.collator
-        assert 'keep_transcription_text' in config.collator
-        assert 'mean_std_filepath' in config.collator
-        assert 'vocab_filepath' in config.collator
-        assert 'spectrum_type' in config.collator
-        assert 'n_fft' in config.collator
-        assert config.collator
-
-        if isinstance(config.collator.augmentation_config, (str, bytes)):
-            if config.collator.augmentation_config:
+        assert 'augmentation_config' in config
+        assert 'keep_transcription_text' in config
+        assert 'mean_std_filepath' in config
+        assert 'vocab_filepath' in config
+        assert 'spectrum_type' in config
+        assert 'n_fft' in config
+        assert config
+
+        if isinstance(config.augmentation_config, (str, bytes)):
+            if config.augmentation_config:
                 aug_file = io.open(
-                    config.collator.augmentation_config,
-                    mode='r',
-                    encoding='utf8')
+                    config.augmentation_config, mode='r', encoding='utf8')
             else:
                 aug_file = io.StringIO(initial_value='{}', newline='')
         else:
-            aug_file = config.collator.augmentation_config
+            aug_file = config.augmentation_config
             assert isinstance(aug_file, io.StringIO)
 
         speech_collator = cls(
             aug_file=aug_file,
             random_seed=0,
-            mean_std_filepath=config.collator.mean_std_filepath,
-            unit_type=config.collator.unit_type,
-            vocab_filepath=config.collator.vocab_filepath,
-            spm_model_prefix=config.collator.spm_model_prefix,
-            spectrum_type=config.collator.spectrum_type,
-            feat_dim=config.collator.feat_dim,
-            delta_delta=config.collator.delta_delta,
-            stride_ms=config.collator.stride_ms,
-            window_ms=config.collator.window_ms,
-            n_fft=config.collator.n_fft,
-            max_freq=config.collator.max_freq,
-            target_sample_rate=config.collator.target_sample_rate,
-            use_dB_normalization=config.collator.use_dB_normalization,
-            target_dB=config.collator.target_dB,
-            dither=config.collator.dither,
-            keep_transcription_text=config.collator.keep_transcription_text)
+            mean_std_filepath=config.mean_std_filepath,
+            unit_type=config.unit_type,
+            vocab_filepath=config.vocab_filepath,
+            spm_model_prefix=config.spm_model_prefix,
+            spectrum_type=config.spectrum_type,
+            feat_dim=config.feat_dim,
+            delta_delta=config.delta_delta,
+            stride_ms=config.stride_ms,
+            window_ms=config.window_ms,
+            n_fft=config.n_fft,
+            max_freq=config.max_freq,
+            target_sample_rate=config.target_sample_rate,
+            use_dB_normalization=config.use_dB_normalization,
+            target_dB=config.target_dB,
+            dither=config.dither,
+            keep_transcription_text=config.keep_transcription_text)
         return speech_collator
 
 

paddlespeech/s2t/io/dataset.py

@@ -54,17 +54,17 @@ class ManifestDataset(Dataset):
         Returns:
             ManifestDataset: dataet object.
         """
-        assert 'manifest' in config.data
-        assert config.data.manifest
+        assert 'manifest' in config
+        assert config.manifest
 
         dataset = cls(
-            manifest_path=config.data.manifest,
-            max_input_len=config.data.max_input_len,
-            min_input_len=config.data.min_input_len,
-            max_output_len=config.data.max_output_len,
-            min_output_len=config.data.min_output_len,
-            max_output_input_ratio=config.data.max_output_input_ratio,
-            min_output_input_ratio=config.data.min_output_input_ratio, )
+            manifest_path=config.manifest,
+            max_input_len=config.max_input_len,
+            min_input_len=config.min_input_len,
+            max_output_len=config.max_output_len,
+            min_output_len=config.min_output_len,
+            max_output_input_ratio=config.max_output_input_ratio,
+            min_output_input_ratio=config.min_output_input_ratio, )
         return dataset
 
     def __init__(self,

paddlespeech/s2t/models/ds2/deepspeech2.py

@@ -221,12 +221,12 @@ class DeepSpeech2Model(nn.Layer):
         model = cls(
             feat_size=dataloader.collate_fn.feature_size,
             dict_size=dataloader.collate_fn.vocab_size,
-            num_conv_layers=config.model.num_conv_layers,
-            num_rnn_layers=config.model.num_rnn_layers,
-            rnn_size=config.model.rnn_layer_size,
-            use_gru=config.model.use_gru,
-            share_rnn_weights=config.model.share_rnn_weights,
-            blank_id=config.model.blank_id,
+            num_conv_layers=config.num_conv_layers,
+            num_rnn_layers=config.num_rnn_layers,
+            rnn_size=config.rnn_layer_size,
+            use_gru=config.use_gru,
+            share_rnn_weights=config.share_rnn_weights,
+            blank_id=config.blank_id,
             ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
         infos = Checkpoint().load_parameters(
             model, checkpoint_path=checkpoint_path)
@@ -240,7 +240,7 @@ class DeepSpeech2Model(nn.Layer):
         Parameters
 
         config: yacs.config.CfgNode
-            config.model
+            config
         Returns
         -------
         DeepSpeech2Model

paddlespeech/s2t/models/ds2_online/deepspeech2.py

@@ -353,14 +353,14 @@ class DeepSpeech2ModelOnline(nn.Layer):
         model = cls(
             feat_size=dataloader.collate_fn.feature_size,
             dict_size=dataloader.collate_fn.vocab_size,
-            num_conv_layers=config.model.num_conv_layers,
-            num_rnn_layers=config.model.num_rnn_layers,
-            rnn_size=config.model.rnn_layer_size,
-            rnn_direction=config.model.rnn_direction,
-            num_fc_layers=config.model.num_fc_layers,
-            fc_layers_size_list=config.model.fc_layers_size_list,
-            use_gru=config.model.use_gru,
-            blank_id=config.model.blank_id,
+            num_conv_layers=config.num_conv_layers,
+            num_rnn_layers=config.num_rnn_layers,
+            rnn_size=config.rnn_layer_size,
+            rnn_direction=config.rnn_direction,
+            num_fc_layers=config.num_fc_layers,
+            fc_layers_size_list=config.fc_layers_size_list,
+            use_gru=config.use_gru,
+            blank_id=config.blank_id,
             ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
         infos = Checkpoint().load_parameters(
             model, checkpoint_path=checkpoint_path)
@@ -374,7 +374,7 @@ class DeepSpeech2ModelOnline(nn.Layer):
         Parameters
 
         config: yacs.config.CfgNode
-            config.model
+            config
         Returns
         -------
         DeepSpeech2ModelOnline

paddlespeech/s2t/training/cli.py

@@ -101,7 +101,7 @@ def default_argument_parser(parser=None):
         title='Test Options', description=None)
 
     test_group.add_argument(
-        "--decode_config",
+        "--decode_cfg",
         metavar="DECODE_CONFIG_FILE",
         help="decode config file.")
 

tests/benchmark/conformer/run.sh

@@ -22,6 +22,7 @@ sed -i "s/  accum_grad: 2/  accum_grad: 1/g" conf/benchmark/conformer.yaml
 fp_item_list=(fp32)
 bs_item=(16)
 config_path=conf/benchmark/conformer.yaml
+decode_config_path=conf/tuning/decode.yaml
 seed=0
 output=exp/conformer
 profiler_options=None
@@ -34,13 +35,13 @@ for fp_item in ${fp_item_list[@]}; do
         echo "index is speed, 8gpus, run_mode is multi_process, begin, conformer"
         run_mode=mp
         ngpu=8
-        CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
+        CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${decode_config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
         sleep 60
         log_name=speech_${model_item}_bs${bs_item}_${fp_item}   # 如:clas_MobileNetv1_mp_bs32_fp32_8
         echo "index is speed, 1gpus, begin, ${log_name}"
         run_mode=sp
         ngpu=1
-        CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_1gpus 2>&1   #  (5min)
+        CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${decode_config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${bs_item} ${fp_item} ${model_item} | tee ${log_path}/${log_name}_speed_1gpus 2>&1   #  (5min)
         sleep 60
     done
 done

tests/benchmark/conformer/run_benchmark.sh

@@ -5,13 +5,14 @@ function _set_params(){
 
     run_mode=${1:-"sp"}          # 单卡sp|多卡mp
     config_path=${2:-"conf/conformer.yaml"}
-    output=${3:-"exp/conformer"}
-    seed=${4:-"0"}
-    ngpu=${5:-"1"}
-    profiler_options=${6:-"None"}
-    batch_size=${7:-"32"}
-    fp_item=${8:-"fp32"}
-    model_item=${9:-"conformer"}
+    decode_config_path=${3:-"conf/tuning/decode.yaml"}
+    output=${4:-"exp/conformer"}
+    seed=${5:-"0"}
+    ngpu=${6:-"1"}
+    profiler_options=${7:-"None"}
+    batch_size=${8:-"32"}
+    fp_item=${9:-"fp32"}
+    model_item=${10:-"conformer"}
     benchmark_max_step=0
     run_log_path=${TRAIN_LOG_DIR:-$(pwd)}  # TRAIN_LOG_DIR 后续QA设置该参数
 # 添加日志解析需要的参数
@@ -35,6 +36,7 @@ function _train(){
     echo "Train on ${num_gpu_devices} GPUs"
     echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
     train_cmd="--config=${config_path} \
+           --decode_cfg=${decode_config_path} \
            --output=${output} \
            --seed=${seed} \
            --ngpu=${ngpu} \
@@ -68,7 +70,7 @@ function _train(){
 }
 
 source ${BENCHMARK_ROOT}/scripts/run_model.sh   # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
-_set_params $@
-# _train       # 如果只想产出训练log,不解析,可取消注释
+#_set_params $@
+#_train       # 如果只想产出训练log,不解析,可取消注释
 _run     # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开
 

tests/chains/ds2/ds2_params_lite_train_infer.txt

@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval: ../../../paddlespeech/s2t/exps/deepspeech2/bin/test.py --ngpu 1 --config conf/deepspeech2.yaml --checkpoint_path exp/deepspeech_tiny/checkpoints/9 --result_file tests/9.rsl  --model_type offline
+eval: ../../../paddlespeech/s2t/exps/deepspeech2/bin/test.py --ngpu 1 --config conf/deepspeech2.yaml --decode_cfg conf/tuning/decode.yaml --checkpoint_path exp/deepspeech_tiny/checkpoints/4 --result_file tests/4.rsl  --model_type offline
 null:null
 ##
 ===========================infer_params===========================
 null:null
 null:null
-norm_export: ../../../paddlespeech/s2t/exps/deepspeech2/bin/export.py --ngpu 1 --config conf/deepspeech2.yaml --model_type offline --checkpoint_path exp/deepspeech_tiny/checkpoints/9 --export_path exp/deepspeech_tiny/checkpoints/9.jit
+norm_export: ../../../paddlespeech/s2t/exps/deepspeech2/bin/export.py --ngpu 1 --config conf/deepspeech2.yaml --model_type offline --checkpoint_path exp/deepspeech_tiny/checkpoints/4 --export_path exp/deepspeech_tiny/checkpoints/4.jit
 quant_export:null
 fpgm_export:null
 distill_export:null

tests/chains/ds2/ds2_params_whole_train_infer.txt

@@ -21,7 +21,7 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval: ../../../paddlespeech/s2t/exps/deepspeech2/bin/test.py --ngpu 1 --config conf/deepspeech2.yaml --result_file tests/49.rsl --checkpoint_path exp/deepspeech_whole/checkpoints/49 --model_type offline
+eval: ../../../paddlespeech/s2t/exps/deepspeech2/bin/test.py --ngpu 1 --config conf/deepspeech2.yaml --decode_cfg conf/tuning/decode.yaml --result_file tests/49.rsl --checkpoint_path exp/deepspeech_whole/checkpoints/49 --model_type offline
 null:null
 ##
 ===========================infer_params===========================

tests/chains/ds2/lite_train_infer.sh

 bash prepare.sh ds2_params_lite_train_infer.txt lite_train_infer
-cd ../../examples/tiny/s0
+cd ../../../examples/tiny/asr0
 source path.sh
-bash ../../../tests/chains/test.sh ../../../tests/chains/ds2_params_lite_train_infer.txt lite_train_infer
+bash ../../../tests/chains/ds2/test.sh ../../../tests/chains/ds2/ds2_params_lite_train_infer.txt lite_train_infer
 cd ../../../tests/chains

tests/chains/ds2/prepare.sh

@@ -34,7 +34,7 @@ MODE=$2
 if [ ${MODE} = "lite_train_infer" ];then
     # pretrain lite train data
     curPath=$(readlink -f "$(dirname "$0")")
-    cd ${curPath}/../../examples/tiny/s0
+    cd ${curPath}/../../../examples/tiny/asr0
     source path.sh
     # download audio data
     bash ./local/data.sh || exit -1
@@ -47,7 +47,7 @@ if [ ${MODE} = "lite_train_infer" ];then
 
 elif [ ${MODE} = "whole_train_infer" ];then
     curPath=$(readlink -f "$(dirname "$0")")
-    cd ${curPath}/../../examples/aishell/s0
+    cd ${curPath}/../../../examples/aishell/asr0
     source path.sh
     # download audio data
     bash ./local/data.sh || exit -1
@@ -59,7 +59,7 @@ elif [ ${MODE} = "whole_train_infer" ];then
     cd ${curPath}
 elif [ ${MODE} = "whole_infer" ];then
     curPath=$(readlink -f "$(dirname "$0")")
-    cd ${curPath}/../../examples/aishell/s0
+    cd ${curPath}/../../../examples/aishell/asr0
     source path.sh
     # download audio data
     bash ./local/data.sh || exit -1
@@ -71,7 +71,7 @@ elif [ ${MODE} = "whole_infer" ];then
     cd ${curPath}
 else
     curPath=$(readlink -f "$(dirname "$0")")
-    cd ${curPath}/../../examples/aishell/s0
+    cd ${curPath}/../../../examples/aishell/asr0
     source path.sh
     # download audio data
     bash ./local/data.sh || exit -1

tests/chains/ds2/test.sh

@@ -324,6 +324,7 @@ else
                     gsu=${gpu//,/ }
                     nump=`echo $gsu | wc -w`
                     cmd="${python} ${run_train} --ngpu=$nump"
+                    export CUDA_VISIBLE_DEVICES=${gpu}
                 else     # train with multi-machine
                     cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
                 fi