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Remove batchnorm, weight decay, and SOS from transducer conformer encoder (#155)

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* Remove batchnorm, weight decay, and SOS.

* Make --context-size configurable.

* Update results.
This commit is contained in:
Fangjun Kuang 2021-12-27 16:01:10 +08:00 committed by GitHub
parent 8187d6236c
commit 14c93add50
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GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 70 additions and 79 deletions
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26
.github/workflows/run-pretrained-transducer-stateless.yml vendored
View File

@ -74,11 +74,11 @@ jobs:
mkdir tmp
cd tmp
git lfs install
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27
cd ..
tree tmp
soxi tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/*.wav
ls -lh tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/*.wav
soxi tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/*.wav
ls -lh tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/*.wav
- name: Run greedy search decoding
shell: bash
@ -87,11 +87,11 @@ jobs:
cd egs/librispeech/ASR
./transducer_stateless/pretrained.py \
--method greedy_search \
--checkpoint ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/1221-135766-0002.wav
--checkpoint ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0002.wav
- name: Run beam search decoding
shell: bash
@ -101,8 +101,8 @@ jobs:
./transducer_stateless/pretrained.py \
--method beam_search \
--beam-size 4 \
--checkpoint ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/test_wavs/1221-135766-0002.wav
--checkpoint ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0002.wav

2
README.md
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@ -84,7 +84,7 @@ The best WER using beam search with beam size 4 is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.92 | 7.37 |
| WER | 2.83 | 7.19 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.

18
egs/librispeech/ASR/RESULTS.md
View File

@ -4,7 +4,7 @@
#### Conformer encoder + embedding decoder
Using commit `fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32`.
Using commit `TODO`.
Conformer encoder + non-current decoder. The decoder
contains only an embedding layer and a Conv1d (with kernel size 2).
@ -13,12 +13,8 @@ The WERs are
| | test-clean | test-other | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.99 | 7.52 | --epoch 20, --avg 10, --max-duration 100 |
| beam search (beam size 2) | 2.95 | 7.43 | |
| beam search (beam size 3) | 2.94 | 7.37 | |
| beam search (beam size 4) | 2.92 | 7.37 | |
| beam search (beam size 5) | 2.93 | 7.38 | |
| beam search (beam size 8) | 2.92 | 7.38 | |
| greedy search | 2.85 | 7.30 | --epoch 29, --avg 13, --max-duration 100 |
| beam search (beam size 4) | 2.83 | 7.19 | |
The training command for reproducing is given below:
@ -36,12 +32,12 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/PsJ3LgkEQfOmzedAlYfVeg/#scalars&_smoothingWeight=0>
<https://tensorboard.dev/experiment/Mjx7MeTgR3Oyr1yBCwjozw/>
The decoding command is:
```
epoch=20
avg=10
epoch=29
avg=13
## greedy search
./transducer_stateless/decode.py \
@ -64,7 +60,7 @@ avg=10
#### Conformer encoder + LSTM decoder
Using commit `TODO`.
Using commit `8187d6236c2926500da5ee854f758e621df803cc`.
Conformer encoder + LSTM decoder.

2
egs/librispeech/ASR/transducer/decode.py
View File

@ -396,7 +396,7 @@ def main():
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <sos/eos> are defined in local/train_bpe_model.py
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()

2
egs/librispeech/ASR/transducer/export.py
View File

@ -194,7 +194,7 @@ def main():
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <sos/eos> are defined in local/train_bpe_model.py
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()

2
egs/librispeech/ASR/transducer/pretrained.py
View File

@ -208,7 +208,7 @@ def main():
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <sos/eos> are defined in local/train_bpe_model.py
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()

2
egs/librispeech/ASR/transducer/train.py
View File

@ -564,7 +564,7 @@ def run(rank, world_size, args):
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <sos/eos> are defined in local/train_bpe_model.py
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()

16
egs/librispeech/ASR/transducer_stateless/conformer.py
View File

@ -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)

16
egs/librispeech/ASR/transducer_stateless/decode.py
View File

@ -70,14 +70,14 @@ def get_parser():
parser.add_argument(
"--epoch",
type=int,
default=20,
default=29,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
)
parser.add_argument(
"--avg",
type=int,
default=10,
default=13,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
@ -114,6 +114,13 @@ def get_parser():
help="Used only when --decoding-method is beam_search",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; "
"2 means tri-gram",
)
parser.add_argument(
"--max-sym-per-frame",
type=int,
@ -136,9 +143,6 @@ def get_params() -> AttributeDict:
"dim_feedforward": 2048,
"num_encoder_layers": 12,
"vgg_frontend": False,
"use_feat_batchnorm": True,
# parameters for decoder
"context_size": 2, # tri-gram
"env_info": get_env_info(),
}
)
@ -156,7 +160,6 @@ 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
@ -393,6 +396,7 @@ def main():
if params.decoding_method == "beam_search":
params.suffix += f"-beam-{params.beam_size}"
else:
params.suffix += f"-context-{params.context_size}"
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")

5
egs/librispeech/ASR/transducer_stateless/decoder.py
View File

@ -20,13 +20,14 @@ import torch.nn.functional as F
class Decoder(nn.Module):
"""This class implements the stateless decoder from the following paper:
"""This class modifies the stateless decoder from the following paper:
RNN-transducer with stateless prediction network
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419
It removes the recurrent connection from the decoder, i.e., the prediction
network.
network. Different from the above paper, it adds an extra Conv1d
right after the embedding layer.
TODO: Implement https://arxiv.org/pdf/2109.07513.pdf
"""

12
egs/librispeech/ASR/transducer_stateless/export.py
View File

@ -104,6 +104,14 @@ def get_parser():
""",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; "
"2 means tri-gram",
)
return parser
@ -119,9 +127,6 @@ def get_params() -> AttributeDict:
"dim_feedforward": 2048,
"num_encoder_layers": 12,
"vgg_frontend": False,
"use_feat_batchnorm": True,
# parameters for decoder
"context_size": 2, # tri-gram
"env_info": get_env_info(),
}
)
@ -138,7 +143,6 @@ 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

3
egs/librispeech/ASR/transducer_stateless/joiner.py
View File

@ -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)

11
egs/librispeech/ASR/transducer_stateless/pretrained.py
View File

@ -110,6 +110,13 @@ def get_parser():
help="Used only when --method is beam_search",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; "
"2 means tri-gram",
)
parser.add_argument(
"--max-sym-per-frame",
type=int,
@ -135,9 +142,6 @@ def get_params() -> AttributeDict:
"dim_feedforward": 2048,
"num_encoder_layers": 12,
"vgg_frontend": False,
"use_feat_batchnorm": True,
# parameters for decoder
"context_size": 2, # tri-gram
"env_info": get_env_info(),
}
)
@ -154,7 +158,6 @@ 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

21
egs/librispeech/ASR/transducer_stateless/train.py
View File

@ -130,6 +130,14 @@ def get_parser():
help="The lr_factor for Noam optimizer",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; "
"2 means tri-gram",
)
return parser
@ -171,15 +179,10 @@ 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(
@ -201,11 +204,7 @@ def get_params() -> AttributeDict:
"dim_feedforward": 2048,
"num_encoder_layers": 12,
"vgg_frontend": False,
"use_feat_batchnorm": True,
# parameters for decoder
"context_size": 2, # tri-gram
# parameters for Noam
"weight_decay": 1e-6,
"warm_step": 80000, # For the 100h subset, use 8k
"env_info": get_env_info(),
}
@ -225,7 +224,6 @@ 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
@ -568,7 +566,7 @@ def run(rank, world_size, args):
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <sos/eos> are defined in local/train_bpe_model.py
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
@ -593,7 +591,6 @@ 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:

11
egs/librispeech/ASR/transducer_stateless/transformer.py
View File

@ -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)