Minor fixes to the RNN-T Conformer model (#152)

* Disable weight decay.

* Remove input feature batchnorm..

* Replace BatchNorm in the Conformer model with LayerNorm.

* Use tanh in the joint network.

* Remove sos ID.

* Reduce the number of decoder layers from 4 to 2.

* Minor fixes.

* Fix typos.

.github/workflows/run-pretrained-transducer-stateless.yml

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-name: run-pre-trained-tranducer-stateless
+name: run-pre-trained-trandsucer-stateless
 
 on:
   push:

.github/workflows/run-pretrained-transducer.yml

@@ -0,0 +1,109 @@
+# Copyright      2021  Fangjun Kuang (csukuangfj@gmail.com)
+
+# See ../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: run-pre-trained-transducer
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+jobs:
+  run_pre_trained_transducer:
+    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-18.04]
+        python-version: [3.7, 3.8, 3.9]
+        torch: ["1.10.0"]
+        torchaudio: ["0.10.0"]
+        k2-version: ["1.9.dev20211101"]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Python dependencies
+        run: |
+          python3 -m pip install --upgrade pip pytest
+          # numpy 1.20.x does not support python 3.6
+          pip install numpy==1.19
+          pip install torch==${{ matrix.torch }}+cpu torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
+          pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
+
+          python3 -m pip install git+https://github.com/lhotse-speech/lhotse
+          python3 -m pip install kaldifeat
+          # We are in ./icefall and there is a file: requirements.txt in it
+          pip install -r requirements.txt
+
+      - name: Install graphviz
+        shell: bash
+        run: |
+          python3 -m pip install -qq graphviz
+          sudo apt-get -qq install graphviz
+
+      - name: Download pre-trained model
+        shell: bash
+        run: |
+          sudo apt-get -qq install git-lfs tree sox
+          cd egs/librispeech/ASR
+          mkdir tmp
+          cd tmp
+          git lfs install
+          git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23
+
+          cd ..
+          tree tmp
+          soxi tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/*.wav
+          ls -lh tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/*.wav
+
+      - name: Run greedy search decoding
+        shell: bash
+        run: |
+          export PYTHONPATH=$PWD:PYTHONPATH
+          cd egs/librispeech/ASR
+          ./transducer/pretrained.py \
+            --method greedy_search \
+            --checkpoint ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/exp/pretrained.pt \
+            --bpe-model ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/data/lang_bpe_500/bpe.model \
+            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1089-134686-0001.wav \
+            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0001.wav \
+            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0002.wav
+
+      - name: Run beam search decoding
+        shell: bash
+        run: |
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          cd egs/librispeech/ASR
+          ./transducer/pretrained.py \
+            --method beam_search \
+            --beam-size 4 \
+            --checkpoint ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/exp/pretrained.pt \
+            --bpe-model ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/data/lang_bpe_500/bpe.model \
+            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1089-134686-0001.wav \
+            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0001.wav \
+            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0002.wav

README.md

@@ -71,7 +71,7 @@ The best WER with greedy search is:
 
 |     | test-clean | test-other |
 |-----|------------|------------|
-| WER | 3.16       | 7.71       |
+| WER | 3.07       | 7.51       |
 
 We provide a Colab notebook to run a pre-trained RNN-T conformer model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1_u6yK9jDkPwG_NLrZMN2XK7Aeq4suMO2?usp=sharing)
 

egs/librispeech/ASR/RESULTS.md

@@ -2,7 +2,10 @@
 
 ### LibriSpeech BPE training results (Transducer)
 
-#### 2021-12-22
+#### Conformer encoder + embedding decoder
+
+Using commit `fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32`.
+
 Conformer encoder + non-current decoder. The decoder
 contains only an embedding layer and a Conv1d (with kernel size 2).
 
@@ -60,8 +63,8 @@ avg=10
 ```
 
 
-#### 2021-12-17
+#### Conformer encoder + LSTM decoder
-Using commit `cb04c8a7509425ab45fae888b0ca71bbbd23f0de`.
+Using commit `TODO`.
 
 Conformer encoder + LSTM decoder.
 
@@ -69,9 +72,9 @@ The best WER is
 
 |     | test-clean | test-other |
 |-----|------------|------------|
-| WER | 3.16       | 7.71       |
+| WER | 3.07       | 7.51       |
 
-using `--epoch 26 --avg 12` with **greedy search**.
+using `--epoch 34 --avg 11` with **greedy search**.
 
 The training command to reproduce the above WER is:
 
@@ -80,19 +83,19 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 ./transducer/train.py \
   --world-size 4 \
-  --num-epochs 30 \
+  --num-epochs 35 \
   --start-epoch 0 \
   --exp-dir transducer/exp-lr-2.5-full \
   --full-libri 1 \
-  --max-duration 250 \
+  --max-duration 180 \
   --lr-factor 2.5
 ```
 
 The decoding command is:
 
 ```
-epoch=26
+epoch=34
-avg=12
+avg=11
 
 ./transducer/decode.py \
   --epoch $epoch \
@@ -102,7 +105,7 @@ avg=12
   --max-duration 100
 ```
 
-You can find the tensorboard log at: <https://tensorboard.dev/experiment/PYIbeD6zRJez1ViXaRqqeg/>
+You can find the tensorboard log at: <https://tensorboard.dev/experiment/D7NQc3xqTpyVmWi5FnWjrA>
 
 
 ### LibriSpeech BPE training results (Conformer-CTC)

egs/librispeech/ASR/transducer/beam_search.py

@@ -111,7 +111,6 @@ def beam_search(
     # support only batch_size == 1 for now
     assert encoder_out.size(0) == 1, encoder_out.size(0)
     blank_id = model.decoder.blank_id
-    sos_id = model.decoder.sos_id
     device = model.device
 
     sos = torch.tensor([blank_id], device=device).reshape(1, 1)
@@ -192,7 +191,7 @@ def beam_search(
 
             # Second, choose other labels
             for i, v in enumerate(log_prob.tolist()):
-                if i in (blank_id, sos_id):
+                if i == blank_id:
                     continue
                 new_ys = y_star.ys + [i]
                 new_log_prob = y_star.log_prob + v

egs/librispeech/ASR/transducer/conformer.py

@@ -56,7 +56,6 @@ class Conformer(Transformer):
         cnn_module_kernel: int = 31,
         normalize_before: bool = True,
         vgg_frontend: bool = False,
-        use_feat_batchnorm: bool = False,
     ) -> None:
         super(Conformer, self).__init__(
             num_features=num_features,
@@ -69,7 +68,6 @@ class Conformer(Transformer):
             dropout=dropout,
             normalize_before=normalize_before,
             vgg_frontend=vgg_frontend,
-            use_feat_batchnorm=use_feat_batchnorm,
         )
 
         self.encoder_pos = RelPositionalEncoding(d_model, dropout)
@@ -107,11 +105,6 @@ class Conformer(Transformer):
             - logit_lens, a tensor of shape (batch_size,) containing the number
               of frames in `logits` before padding.
         """
-        if self.use_feat_batchnorm:
-            x = x.permute(0, 2, 1)  # (N, T, C) -> (N, C, T)
-            x = self.feat_batchnorm(x)
-            x = x.permute(0, 2, 1)  # (N, C, T) -> (N, T, C)
-
         x = self.encoder_embed(x)
         x, pos_emb = self.encoder_pos(x)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
@@ -873,7 +866,7 @@ class ConvolutionModule(nn.Module):
             groups=channels,
             bias=bias,
         )
-        self.norm = nn.BatchNorm1d(channels)
+        self.norm = nn.LayerNorm(channels)
         self.pointwise_conv2 = nn.Conv1d(
             channels,
             channels,
@@ -903,7 +896,12 @@ class ConvolutionModule(nn.Module):
 
         # 1D Depthwise Conv
         x = self.depthwise_conv(x)
-        x = self.activation(self.norm(x))
+        # x is (batch, channels, time)
+        x = x.permute(0, 2, 1)
+        x = self.norm(x)
+        x = x.permute(0, 2, 1)
+
+        x = self.activation(x)
 
         x = self.pointwise_conv2(x)  # (batch, channel, time)
 

egs/librispeech/ASR/transducer/decode.py

@@ -70,14 +70,14 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=26,
+        default=34,
         help="It specifies the checkpoint to use for decoding."
         "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
-        default=12,
+        default=11,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
         "'--epoch'. ",
@@ -129,10 +129,9 @@ def get_params() -> AttributeDict:
             "dim_feedforward": 2048,
             "num_encoder_layers": 12,
             "vgg_frontend": False,
-            "use_feat_batchnorm": True,
             # decoder params
             "decoder_embedding_dim": 1024,
-            "num_decoder_layers": 4,
+            "num_decoder_layers": 2,
             "decoder_hidden_dim": 512,
             "env_info": get_env_info(),
         }
@@ -151,7 +150,6 @@ def get_encoder_model(params: AttributeDict):
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
         vgg_frontend=params.vgg_frontend,
-        use_feat_batchnorm=params.use_feat_batchnorm,
     )
     return encoder
 
@@ -161,7 +159,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -401,7 +398,6 @@ def main():
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)

egs/librispeech/ASR/transducer/decoder.py

@@ -27,7 +27,6 @@ class Decoder(nn.Module):
         vocab_size: int,
         embedding_dim: int,
         blank_id: int,
-        sos_id: int,
         num_layers: int,
         hidden_dim: int,
         output_dim: int,
@@ -42,8 +41,6 @@ class Decoder(nn.Module):
             Dimension of the input embedding.
           blank_id:
             The ID of the blank symbol.
-          sos_id:
-            The ID of the SOS symbol.
           num_layers:
             Number of LSTM layers.
           hidden_dim:
@@ -71,7 +68,6 @@ class Decoder(nn.Module):
             dropout=rnn_dropout,
         )
         self.blank_id = blank_id
-        self.sos_id = sos_id
         self.output_linear = nn.Linear(hidden_dim, output_dim)
 
     def forward(

egs/librispeech/ASR/transducer/export.py

@@ -23,8 +23,8 @@ Usage:
 ./transducer/export.py \
   --exp-dir ./transducer/exp \
   --bpe-model data/lang_bpe_500/bpe.model \
-  --epoch 26 \
+  --epoch 34 \
-  --avg 12
+  --avg 11
 
 It will generate a file exp_dir/pretrained.pt
 
@@ -66,7 +66,7 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=26,
+        default=34,
         help="It specifies the checkpoint to use for decoding."
         "Note: Epoch counts from 0.",
     )
@@ -74,7 +74,7 @@ def get_parser():
     parser.add_argument(
         "--avg",
         type=int,
-        default=12,
+        default=11,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
         "'--epoch'. ",
@@ -119,10 +119,9 @@ def get_params() -> AttributeDict:
             "dim_feedforward": 2048,
             "num_encoder_layers": 12,
             "vgg_frontend": False,
-            "use_feat_batchnorm": True,
             # decoder params
             "decoder_embedding_dim": 1024,
-            "num_decoder_layers": 4,
+            "num_decoder_layers": 2,
             "decoder_hidden_dim": 512,
             "env_info": get_env_info(),
         }
@@ -140,7 +139,6 @@ def get_encoder_model(params: AttributeDict):
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
         vgg_frontend=params.vgg_frontend,
-        use_feat_batchnorm=params.use_feat_batchnorm,
     )
     return encoder
 
@@ -150,7 +148,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -199,7 +196,6 @@ def main():
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)

egs/librispeech/ASR/transducer/joiner.py

@@ -16,7 +16,6 @@
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 
 class Joiner(nn.Module):
@@ -48,7 +47,7 @@ class Joiner(nn.Module):
         # Now decoder_out is (N, 1, U, C)
 
         logit = encoder_out + decoder_out
-        logit = F.relu(logit)
+        logit = torch.tanh(logit)
 
         output = self.output_linear(logit)
 

egs/librispeech/ASR/transducer/model.py

@@ -49,7 +49,7 @@ class Transducer(nn.Module):
           decoder:
             It is the prediction network in the paper. Its input shape
             is (N, U) and its output shape is (N, U, C). It should contain
-            two attributes: `blank_id` and `sos_id`.
+            one attribute: `blank_id`.
           joiner:
             It has two inputs with shapes: (N, T, C) and (N, U, C). Its
             output shape is (N, T, U, C). Note that its output contains
@@ -58,7 +58,6 @@ class Transducer(nn.Module):
         super().__init__()
         assert isinstance(encoder, EncoderInterface)
         assert hasattr(decoder, "blank_id")
-        assert hasattr(decoder, "sos_id")
 
         self.encoder = encoder
         self.decoder = decoder
@@ -97,8 +96,7 @@ class Transducer(nn.Module):
         y_lens = row_splits[1:] - row_splits[:-1]
 
         blank_id = self.decoder.blank_id
-        sos_id = self.decoder.sos_id
+        sos_y = add_sos(y, sos_id=blank_id)
-        sos_y = add_sos(y, sos_id=sos_id)
 
         sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
 

egs/librispeech/ASR/transducer/pretrained.py

@@ -116,10 +116,9 @@ def get_params() -> AttributeDict:
             "dim_feedforward": 2048,
             "num_encoder_layers": 12,
             "vgg_frontend": False,
-            "use_feat_batchnorm": True,
             # decoder params
             "decoder_embedding_dim": 1024,
-            "num_decoder_layers": 4,
+            "num_decoder_layers": 2,
             "decoder_hidden_dim": 512,
             "env_info": get_env_info(),
         }
@@ -137,7 +136,6 @@ def get_encoder_model(params: AttributeDict):
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
         vgg_frontend=params.vgg_frontend,
-        use_feat_batchnorm=params.use_feat_batchnorm,
     )
     return encoder
 
@@ -147,7 +145,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -213,7 +210,6 @@ def main():
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(f"{params}")

egs/librispeech/ASR/transducer/test_conformer.py

@@ -36,7 +36,6 @@ def test_conformer():
         nhead=8,
         dim_feedforward=2048,
         num_encoder_layers=12,
-        use_feat_batchnorm=True,
     )
     N = 3
     T = 100

egs/librispeech/ASR/transducer/test_decoder.py

@@ -29,7 +29,6 @@ from decoder import Decoder
 def test_decoder():
     vocab_size = 3
     blank_id = 0
-    sos_id = 2
     embedding_dim = 128
     num_layers = 2
     hidden_dim = 6
@@ -41,7 +40,6 @@ def test_decoder():
         vocab_size=vocab_size,
         embedding_dim=embedding_dim,
         blank_id=blank_id,
-        sos_id=sos_id,
         num_layers=num_layers,
         hidden_dim=hidden_dim,
         output_dim=output_dim,

egs/librispeech/ASR/transducer/test_transducer.py

@@ -39,7 +39,6 @@ def test_transducer():
     # decoder params
     vocab_size = 3
     blank_id = 0
-    sos_id = 2
     embedding_dim = 128
     num_layers = 2
 
@@ -51,14 +50,12 @@ def test_transducer():
         nhead=8,
         dim_feedforward=2048,
         num_encoder_layers=12,
-        use_feat_batchnorm=True,
     )
 
     decoder = Decoder(
         vocab_size=vocab_size,
         embedding_dim=embedding_dim,
         blank_id=blank_id,
-        sos_id=sos_id,
         num_layers=num_layers,
         hidden_dim=output_dim,
         output_dim=output_dim,

egs/librispeech/ASR/transducer/test_transformer.py

@@ -36,7 +36,6 @@ def test_transformer():
         nhead=8,
         dim_feedforward=2048,
         num_encoder_layers=12,
-        use_feat_batchnorm=True,
     )
     N = 3
     T = 100

egs/librispeech/ASR/transducer/train.py

@@ -23,7 +23,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 ./transducer/train.py \
   --world-size 4 \
-  --num-epochs 30 \
+  --num-epochs 35 \
   --start-epoch 0 \
   --exp-dir transducer/exp \
   --full-libri 1 \
@@ -92,7 +92,7 @@ def get_parser():
     parser.add_argument(
         "--num-epochs",
         type=int,
-        default=30,
+        default=35,
         help="Number of epochs to train.",
     )
 
@@ -171,15 +171,10 @@ def get_params() -> AttributeDict:
 
         - subsampling_factor:  The subsampling factor for the model.
 
-        - use_feat_batchnorm: Whether to do batch normalization for the
-                              input features.
-
         - attention_dim: Hidden dim for multi-head attention model.
 
         - num_decoder_layers: Number of decoder layer of transformer decoder.
 
-        - weight_decay:  The weight_decay for the optimizer.
-
         - warm_step: The warm_step for Noam optimizer.
     """
     params = AttributeDict(
@@ -201,13 +196,11 @@ def get_params() -> AttributeDict:
             "dim_feedforward": 2048,
             "num_encoder_layers": 12,
             "vgg_frontend": False,
-            "use_feat_batchnorm": True,
             # decoder params
             "decoder_embedding_dim": 1024,
-            "num_decoder_layers": 4,
+            "num_decoder_layers": 2,
             "decoder_hidden_dim": 512,
             # parameters for Noam
-            "weight_decay": 1e-6,
             "warm_step": 80000,  # For the 100h subset, use 8k
             "env_info": get_env_info(),
         }
@@ -227,7 +220,6 @@ def get_encoder_model(params: AttributeDict):
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
         vgg_frontend=params.vgg_frontend,
-        use_feat_batchnorm=params.use_feat_batchnorm,
     )
     return encoder
 
@@ -237,7 +229,6 @@ def get_decoder_model(params: AttributeDict):
         vocab_size=params.vocab_size,
         embedding_dim=params.decoder_embedding_dim,
         blank_id=params.blank_id,
-        sos_id=params.sos_id,
         num_layers=params.num_decoder_layers,
         hidden_dim=params.decoder_hidden_dim,
         output_dim=params.encoder_out_dim,
@@ -575,7 +566,6 @@ def run(rank, world_size, args):
 
     # <blk> and <sos/eos> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.sos_id = sp.piece_to_id("<sos/eos>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)
@@ -599,7 +589,6 @@ def run(rank, world_size, args):
         model_size=params.attention_dim,
         factor=params.lr_factor,
         warm_step=params.warm_step,
-        weight_decay=params.weight_decay,
     )
 
     if checkpoints and "optimizer" in checkpoints:

egs/librispeech/ASR/transducer/transformer.py

@@ -39,7 +39,6 @@ class Transformer(EncoderInterface):
         dropout: float = 0.1,
         normalize_before: bool = True,
         vgg_frontend: bool = False,
-        use_feat_batchnorm: bool = False,
     ) -> None:
         """
         Args:
@@ -65,13 +64,8 @@ class Transformer(EncoderInterface):
             If True, use pre-layer norm; False to use post-layer norm.
           vgg_frontend:
             True to use vgg style frontend for subsampling.
-          use_feat_batchnorm:
-            True to use batchnorm for the input layer.
         """
         super().__init__()
-        self.use_feat_batchnorm = use_feat_batchnorm
-        if use_feat_batchnorm:
-            self.feat_batchnorm = nn.BatchNorm1d(num_features)
 
         self.num_features = num_features
         self.output_dim = output_dim
@@ -131,11 +125,6 @@ class Transformer(EncoderInterface):
             - logit_lens, a tensor of shape (batch_size,) containing the number
               of frames in `logits` before padding.
         """
-        if self.use_feat_batchnorm:
-            x = x.permute(0, 2, 1)  # (N, T, C) -> (N, C, T)
-            x = self.feat_batchnorm(x)
-            x = x.permute(0, 2, 1)  # (N, C, T) -> (N, T, C)
-
         x = self.encoder_embed(x)
         x = self.encoder_pos(x)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)