add the whole of aishell asr1

examples/aishell/asr1/conf/chunk_conformer.yaml

-# 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
-        cnn_module_kernel: 15
-        use_cnn_module: True
-        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
+############################################
+#           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
+    cnn_module_kernel: 15
+    use_cnn_module: True
+    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
 
-    # 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
+###########################################
+#                   Data                  #
+###########################################
 
-    # hybrid CTC/attention
-    model_conf:
-        ctc_weight: 0.3
-        lsm_weight: 0.1     # label smoothing option
-        length_normalized_loss: false
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
 
 
-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'
+augmentation_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
 
-collator:
-  vocab_filepath: data/lang_char/vocab.txt 
-  unit_type: 'char'
-  augmentation_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
-
-
-training:
-  n_epoch: 240 
-  accum_grad: 2
-  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:
-  beam_size: 10
-  batch_size: 128
-  error_rate_type: cer 
-  decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
-  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: 2
+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
 

examples/aishell/asr1/conf/conformer.yaml

 ############################################
 #           Network Architecture           #
 ############################################
-#model:
 cmvn_file: 
 cmvn_file_type: "json"
 # encoder related
@@ -42,7 +41,6 @@ model_conf:
 ###########################################
 #                   Data                  #
 ###########################################
-#data:
 train_manifest: data/manifest.train
 dev_manifest: data/manifest.dev
 test_manifest: data/manifest.test
@@ -50,11 +48,9 @@ test_manifest: data/manifest.test
 ###########################################
 #              Dataloader                 #
 ###########################################
-#collator:
 vocab_filepath: data/lang_char/vocab.txt 
 unit_type: 'char'
 augmentation_config: conf/preprocess.yaml
-spm_model_prefix: ''
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
@@ -75,7 +71,6 @@ num_encs: 1
 ###########################################
 #                 training                #
 ###########################################
-#training:
 n_epoch: 240 
 accum_grad: 2
 global_grad_clip: 5.0

examples/aishell/asr1/conf/transformer.yaml

-# 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
-
-    # 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
+############################################
+#           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
 
+# hybrid CTC/attention
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
 
+###########################################
+#                   Data                  #
+###########################################
 # https://yaml.org/type/float.html
-data:
-  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.test
-
-
-collator:
-  unit_type: 'char'
-  vocab_filepath: data/lang_char/vocab.txt 
-  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
-  augmentation_config: conf/preprocess.yaml 
-  num_workers: 0
-  subsampling_factor: 1
-  num_encs: 1
-
-
+train_manifest: data/manifest.train
+dev_manifest: data/manifest.dev
+test_manifest: data/manifest.test
 
-training:
-  n_epoch: 240 
-  accum_grad: 2
-  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
 
+###########################################
+#              Dataloader                 #
+###########################################
+unit_type: 'char'
+vocab_filepath: data/lang_char/vocab.txt 
+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
+augmentation_config: conf/preprocess.yaml 
+num_workers: 0
+subsampling_factor: 1
+num_encs: 1
 
-decoding:
-  beam_size: 10
-  batch_size: 128
-  error_rate_type: cer 
-  decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
-  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
+###########################################
+#                 training                #
+###########################################
+n_epoch: 240 
+accum_grad: 2
+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

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

+beam_size: 10
+decode_batch_size: 128
+error_rate_type: cer 
+decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+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/aishell/asr1/conf/decode.yaml → examples/aishell/asr1/conf/tuning/decode.yaml

-#decoding:
 beam_size: 10
 decode_batch_size: 128
 error_rate_type: cer 

examples/aishell/asr1/run.sh

@@ -6,7 +6,7 @@ gpus=0,1,2,3
 stage=0
 stop_stage=50
 conf_path=conf/conformer.yaml
-decode_conf_path=conf/decode.yaml
+decode_conf_path=conf/tuning/decode.yaml
 avg_num=20
 audio_file=data/demo_01_03.wav