fix the bidirect rnn, add deepspeech2.yaml for aishell, tiny, librispeech

deepspeech/models/ds2_online/deepspeech2.py

@@ -56,12 +56,17 @@ class CRNNEncoder(nn.Layer):
         self.rnn = nn.LayerList()
         self.layernorm_list = nn.LayerList()
         self.fc_layers_list = nn.LayerList()
-        layernorm_size = rnn_size
+        if rnn_direction == 'bidirect' or rnn_direction == 'bidirectional':
+            layernorm_size = 2 * rnn_size
+        elif rnn_direction == 'forward':
+            layernorm_size = rnn_size
+        else:
+            raise Exception("Wrong rnn direction")
         for i in range(0, num_rnn_layers):
             if i == 0:
                 rnn_input_size = i_size
             else:
-                rnn_input_size = rnn_size
+                rnn_input_size = layernorm_size
             if use_gru == True:
                 self.rnn.append(
                     nn.GRU(
@@ -78,7 +83,7 @@ class CRNNEncoder(nn.Layer):
                         direction=rnn_direction))
             self.layernorm_list.append(nn.LayerNorm(layernorm_size))
 
-        fc_input_size = rnn_size
+        fc_input_size = layernorm_size
         for i in range(self.num_fc_layers):
             self.fc_layers_list.append(
                 nn.Linear(fc_input_size, fc_layers_size_list[i]))
@@ -385,8 +390,8 @@ class DeepSpeech2InferModelOnline(DeepSpeech2ModelOnline):
             self,
             input_spec=[
                 paddle.static.InputSpec(
-                    shape=[None, None, self.encoder.feat_size
-                           ],  #[B, chunk_size, feat_dim]
+                    shape=[None, None,
+                           self.encoder.feat_size],  #[B, chunk_size, feat_dim]
                     dtype='float32'),  # audio, [B,T,D]
                 paddle.static.InputSpec(shape=[None],
                                         dtype='int64'),  # audio_length, [B]

examples/aishell/s0/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
+
+collator:
+  batch_size: 32 # one gpu
+  mean_std_filepath: data/mean_std.json
+  unit_type: char
+  vocab_filepath: data/vocab.txt 
+  augmentation_config: conf/augmentation.json
+  random_seed: 0
+  spm_model_prefix: 
+  specgram_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: 4
+  rnn_layer_size: 1024
+  rnn_direction: bidirect
+  num_fc_layers: 2
+  fc_layers_size_list: 512, 256
+  use_gru: True 
+
+training:
+  n_epoch: 50
+  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: 64
+  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/librispeech/s0/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: 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: 20
+  mean_std_filepath: data/mean_std.json
+  unit_type: char
+  vocab_filepath: data/vocab.txt 
+  augmentation_config: conf/augmentation.json
+  random_seed: 0
+  spm_model_prefix: 
+  specgram_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
+
+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 
+
+training:
+  n_epoch: 50
+  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

examples/tiny/s0/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: 27.0
+  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/vocab.txt
+  augmentation_config: conf/augmentation.json
+  random_seed: 0
+  spm_model_prefix: 
+  specgram_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 
+
+training:
+  n_epoch: 10
+  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