Add Conv2dSubsampling1 module and test it in AphasiaBank ASR recipe

egs2/aphasiabank/asr1/README.md

@@ -9,6 +9,24 @@
 - Git hash: `39c1ec0509904f16ac36d25efc971e2a94ff781f`
     - Commit date: `Wed Dec 21 12:50:18 2022 -0500`
 
+## asr_train_asr_ebranchformer_small_wavlm_large1
+
+- [train_asr_ebranchformer_small_wavlm_large.yaml](conf/tuning/train_asr_ebranchformer_small_wavlm_large.yaml)
+- Control group data is included
+- [Hugging Face](https://huggingface.co/espnet/jiyang_tang_aphsiabank_english_asr_ebranchformer_small_wavlm_large1)
+
+### WER
+
+| dataset                             | Snt   | Wrd    | Corr | Sub  | Del | Ins | Err  | S.Err |
+|-------------------------------------|-------|--------|------|------|-----|-----|------|-------|
+| decode_asr_model_valid.acc.ave/test | 16380 | 120684 | 77.5 | 16.4 | 6.1 | 4.2 | 26.7 | 70.8  |
+
+### CER
+
+| dataset                             | Snt   | Wrd    | Corr | Sub | Del | Ins | Err  | S.Err |
+|-------------------------------------|-------|--------|------|-----|-----|-----|------|-------|
+| decode_asr_model_valid.acc.ave/test | 16380 | 530731 | 87.6 | 5.4 | 6.9 | 4.7 | 17.0 | 70.8  |
+
 ## asr_train_asr_ebranchformer_small_wavlm_large
 
 - [train_asr_ebranchformer_small_wavlm_large.yaml](conf/tuning/train_asr_ebranchformer_small_wavlm_large.yaml)

egs2/aphasiabank/asr1/conf/tuning/train_asr_ebranchformer_small_wavlm_large.yaml

@@ -34,7 +34,7 @@ encoder_conf:
   positional_dropout_rate: 0.1
   attention_dropout_rate: 0.1
   layer_drop_rate: 0.1
-  input_layer: conv2d2 # subsampling rate = 2
+  input_layer: conv2d2 # subsampling rate = 2 (WavLM) * 2 (conv2d2)
   macaron_ffn: true
   pos_enc_layer_type: rel_pos
   attention_layer_type: rel_selfattn

egs2/aphasiabank/asr1/conf/tuning/train_asr_ebranchformer_small_wavlm_large1.yaml

@@ -0,0 +1,106 @@
+# https://github.com/espnet/espnet/blob/master/egs2/librispeech/asr1/conf/tuning/train_asr_conformer7_wavlm_large.yaml
+unused_parameters: true
+freeze_param: [
+  "frontend.upstream"
+]
+frontend: s3prl
+frontend_conf:
+  frontend_conf:
+    upstream: wavlm_large  # Note: If the upstream is changed, please change the input_size in the preencoder.
+  download_dir: ./hub
+  multilayer_feature: True
+
+preencoder: linear
+preencoder_conf:
+  input_size: 1024  # Note: If the upstream is changed, please change this value accordingly.
+  output_size: 80
+
+model_conf:
+  ctc_weight: 0.3
+  lsm_weight: 0.1
+  length_normalized_loss: false
+  extract_feats_in_collect_stats: false # Note: "False" means during collect stats (stage 10), generating dummy stats files rather than extract_feats by forward frontend.
+
+
+# Based on https://github.com/espnet/espnet/blob/master/egs2/librispeech/asr1/conf/tuning/train_asr_e_branchformer.yaml
+# The encoder is smaller as we keep the size roughly the same as the conformer and transformer experiments
+encoder: e_branchformer
+encoder_conf:
+  output_size: 256
+  attention_heads: 4
+  linear_units: 1024
+  num_blocks: 12
+  dropout_rate: 0.1
+  positional_dropout_rate: 0.1
+  attention_dropout_rate: 0.1
+  layer_drop_rate: 0.1
+  input_layer: conv2d1 # subsampling rate = 2 (WavLM) * 1 (conv2d1)
+  macaron_ffn: true
+  pos_enc_layer_type: rel_pos
+  attention_layer_type: rel_selfattn
+  rel_pos_type: latest
+  cgmlp_linear_units: 3072
+  cgmlp_conv_kernel: 31
+  use_linear_after_conv: false
+  gate_activation: identity
+  positionwise_layer_type: linear
+  use_ffn: true
+  merge_conv_kernel: 31
+
+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: 0.1
+  src_attention_dropout_rate: 0.1
+  layer_drop_rate: 0.2
+
+seed: 2022
+log_interval: 200
+num_att_plot: 0
+num_workers: 4
+sort_in_batch: descending
+sort_batch: descending
+batch_type: numel
+batch_bins: 3000000
+accum_grad: 16
+grad_clip: 5
+max_epoch: 30
+patience: none
+init: none
+best_model_criterion:
+  - - valid
+    - acc
+    - max
+keep_nbest_models: 10
+
+use_amp: true
+cudnn_deterministic: false
+cudnn_benchmark: false
+
+optim: adam
+optim_conf:
+  lr: 0.001
+  weight_decay: 0.000001
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 2500
+
+specaug: specaug
+specaug_conf:
+  apply_time_warp: true
+  time_warp_window: 5
+  time_warp_mode: bicubic
+  apply_freq_mask: true
+  freq_mask_width_range:
+    - 0
+    - 27
+  num_freq_mask: 2
+  apply_time_mask: true
+  time_mask_width_ratio_range:
+    - 0.
+    - 0.05
+  num_time_mask: 5

egs2/aphasiabank/asr1/run.sh

@@ -10,7 +10,7 @@ valid_set="val"
 test_sets="test"
 include_control=true
 
-asr_config=conf/train_asr.yaml
+asr_config=conf/tuning/train_asr_ebranchformer_small_wavlm_large.yaml
 
 feats_normalize=global_mvn
 if [[ ${asr_config} == *"hubert"* ]] || [[ ${asr_config} == *"wavlm"* ]]; then

espnet/nets/pytorch_backend/transformer/subsampling.py

@@ -30,6 +30,8 @@ def __init__(self, message, actual_size, limit):
 
 def check_short_utt(ins, size):
     """Check if the utterance is too short for subsampling."""
+    if isinstance(ins, Conv2dSubsampling1) and size < 5:
+        return True, 5
     if isinstance(ins, Conv2dSubsampling2) and size < 7:
         return True, 7
     if isinstance(ins, Conv2dSubsampling) and size < 7:
@@ -100,6 +102,63 @@ def __getitem__(self, key):
         return self.out[key]
 
 
+class Conv2dSubsampling1(torch.nn.Module):
+    """Convolutional 2D subsampling (to the same length).
+
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        dropout_rate (float): Dropout rate.
+        pos_enc (torch.nn.Module): Custom position encoding layer.
+
+    """
+
+    def __init__(self, idim, odim, dropout_rate, pos_enc=None):
+        """Construct an Conv2dSubsampling1 object."""
+        super(Conv2dSubsampling1, self).__init__()
+        self.conv = torch.nn.Sequential(
+            torch.nn.Conv2d(1, odim, 3, 1),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(odim, odim, 3, 1),
+            torch.nn.ReLU(),
+        )
+        self.out = torch.nn.Sequential(
+            torch.nn.Linear(odim * (idim - 4), odim),
+            pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
+        )
+
+    def forward(self, x, x_mask):
+        """Subsample x with a ratio of 1.
+
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, idim).
+            x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+        Returns:
+            torch.Tensor: Subsampled tensor (#batch, time, odim).
+            torch.Tensor: Subsampled mask (#batch, 1, time).
+
+        """
+        x = x.unsqueeze(1)  # (b, c, t, f)
+        x = self.conv(x)
+        b, c, t, f = x.size()
+        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+        if x_mask is None:
+            return x, None
+        return x, x_mask[:, :, :-4]
+
+    def __getitem__(self, key):
+        """Get item.
+
+        When reset_parameters() is called, if use_scaled_pos_enc is used,
+            return the positioning encoding.
+
+        """
+        if key != -1:
+            raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
+        return self.out[key]
+
+
 class Conv2dSubsampling2(torch.nn.Module):
     """Convolutional 2D subsampling (to 1/2 length).
 

espnet2/asr/encoder/branchformer_encoder.py

@@ -37,6 +37,7 @@
 from espnet.nets.pytorch_backend.transformer.repeat import repeat
 from espnet.nets.pytorch_backend.transformer.subsampling import (
     Conv2dSubsampling,
+    Conv2dSubsampling1,
     Conv2dSubsampling2,
     Conv2dSubsampling6,
     Conv2dSubsampling8,
@@ -366,6 +367,13 @@ def __init__(
                 dropout_rate,
                 pos_enc_class(output_size, positional_dropout_rate),
             )
+        elif input_layer == "conv2d1":
+            self.embed = Conv2dSubsampling1(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate),
+            )
         elif input_layer == "conv2d2":
             self.embed = Conv2dSubsampling2(
                 input_size,
@@ -521,6 +529,7 @@ def forward(
 
         if (
             isinstance(self.embed, Conv2dSubsampling)
+            or isinstance(self.embed, Conv2dSubsampling1)
             or isinstance(self.embed, Conv2dSubsampling2)
             or isinstance(self.embed, Conv2dSubsampling6)
             or isinstance(self.embed, Conv2dSubsampling8)

espnet2/asr/encoder/conformer_encoder.py

@@ -36,6 +36,7 @@
 from espnet.nets.pytorch_backend.transformer.repeat import repeat
 from espnet.nets.pytorch_backend.transformer.subsampling import (
     Conv2dSubsampling,
+    Conv2dSubsampling1,
     Conv2dSubsampling2,
     Conv2dSubsampling6,
     Conv2dSubsampling8,
@@ -155,6 +156,13 @@ def __init__(
                 dropout_rate,
                 pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
             )
+        elif input_layer == "conv2d1":
+            self.embed = Conv2dSubsampling1(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
         elif input_layer == "conv2d2":
             self.embed = Conv2dSubsampling2(
                 input_size,
@@ -313,6 +321,7 @@ def forward(
 
         if (
             isinstance(self.embed, Conv2dSubsampling)
+            or isinstance(self.embed, Conv2dSubsampling1)
             or isinstance(self.embed, Conv2dSubsampling2)
             or isinstance(self.embed, Conv2dSubsampling6)
             or isinstance(self.embed, Conv2dSubsampling8)

espnet2/asr/encoder/e_branchformer_encoder.py

@@ -37,6 +37,7 @@
 from espnet.nets.pytorch_backend.transformer.repeat import repeat
 from espnet.nets.pytorch_backend.transformer.subsampling import (
     Conv2dSubsampling,
+    Conv2dSubsampling1,
     Conv2dSubsampling2,
     Conv2dSubsampling6,
     Conv2dSubsampling8,
@@ -252,6 +253,13 @@ def __init__(
                 dropout_rate,
                 pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
             )
+        elif input_layer == "conv2d1":
+            self.embed = Conv2dSubsampling1(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
         elif input_layer == "conv2d2":
             self.embed = Conv2dSubsampling2(
                 input_size,
@@ -395,6 +403,7 @@ def forward(
 
         if (
             isinstance(self.embed, Conv2dSubsampling)
+            or isinstance(self.embed, Conv2dSubsampling1)
             or isinstance(self.embed, Conv2dSubsampling2)
             or isinstance(self.embed, Conv2dSubsampling6)
             or isinstance(self.embed, Conv2dSubsampling8)

espnet2/asr/encoder/longformer_encoder.py

@@ -25,6 +25,7 @@
 from espnet.nets.pytorch_backend.transformer.repeat import repeat
 from espnet.nets.pytorch_backend.transformer.subsampling import (
     Conv2dSubsampling,
+    Conv2dSubsampling1,
     Conv2dSubsampling2,
     Conv2dSubsampling6,
     Conv2dSubsampling8,
@@ -158,6 +159,13 @@ def __init__(
                 dropout_rate,
                 pos_enc_class(output_size, positional_dropout_rate),
             )
+        elif input_layer == "conv2d1":
+            self.embed = Conv2dSubsampling1(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate),
+            )
         elif input_layer == "conv2d2":
             self.embed = Conv2dSubsampling2(
                 input_size,
@@ -304,6 +312,7 @@ def forward(
         masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
         if (
             isinstance(self.embed, Conv2dSubsampling)
+            or isinstance(self.embed, Conv2dSubsampling1)
             or isinstance(self.embed, Conv2dSubsampling2)
             or isinstance(self.embed, Conv2dSubsampling6)
             or isinstance(self.embed, Conv2dSubsampling8)

espnet2/asr/encoder/transformer_encoder.py

@@ -25,6 +25,7 @@
 from espnet.nets.pytorch_backend.transformer.repeat import repeat
 from espnet.nets.pytorch_backend.transformer.subsampling import (
     Conv2dSubsampling,
+    Conv2dSubsampling1,
     Conv2dSubsampling2,
     Conv2dSubsampling6,
     Conv2dSubsampling8,
@@ -92,6 +93,8 @@ def __init__(
             )
         elif input_layer == "conv2d":
             self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
+        elif input_layer == "conv2d1":
+            self.embed = Conv2dSubsampling1(input_size, output_size, dropout_rate)
         elif input_layer == "conv2d2":
             self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
         elif input_layer == "conv2d6":
@@ -183,6 +186,7 @@ def forward(
             xs_pad = xs_pad
         elif (
             isinstance(self.embed, Conv2dSubsampling)
+            or isinstance(self.embed, Conv2dSubsampling1)
             or isinstance(self.embed, Conv2dSubsampling2)
             or isinstance(self.embed, Conv2dSubsampling6)
             or isinstance(self.embed, Conv2dSubsampling8)

espnet2/asr/encoder/transformer_encoder_multispkr.py

@@ -23,6 +23,7 @@
 from espnet.nets.pytorch_backend.transformer.repeat import repeat
 from espnet.nets.pytorch_backend.transformer.subsampling import (
     Conv2dSubsampling,
+    Conv2dSubsampling1,
     Conv2dSubsampling2,
     Conv2dSubsampling6,
     Conv2dSubsampling8,
@@ -92,6 +93,8 @@ def __init__(
             )
         elif input_layer == "conv2d":
             self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
+        elif input_layer == "conv2d1":
+            self.embed = Conv2dSubsampling1(input_size, output_size, dropout_rate)
         elif input_layer == "conv2d2":
             self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
         elif input_layer == "conv2d6":
@@ -193,6 +196,7 @@ def forward(
 
         if (
             isinstance(self.embed, Conv2dSubsampling)
+            or isinstance(self.embed, Conv2dSubsampling1)
             or isinstance(self.embed, Conv2dSubsampling2)
             or isinstance(self.embed, Conv2dSubsampling6)
             or isinstance(self.embed, Conv2dSubsampling8)