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Remove unnecessary code and update docs

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This commit is contained in:
pkufool 2021-09-12 15:30:41 +08:00
parent de42c0ebb5
commit 4de7f19e03
11 changed files with 70 additions and 78 deletions
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3
egs/aishell/ASR/conformer_ctc/README.md
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@ -1,3 +0,0 @@
Please visit
<https://icefall.readthedocs.io/en/latest/recipes/aishell/conformer_ctc.html>
for how to run this recipe.

28
egs/aishell/ASR/conformer_ctc/conformer.py
View File

@ -40,6 +40,7 @@ class Conformer(Transformer):
cnn_module_kernel (int): Kernel size of convolution module
normalize_before (bool): whether to use layer_norm before the first block.
vgg_frontend (bool): whether to use vgg frontend.
use_feat_batchnorm(bool): whether to use batch-normalize the input.
"""
def __init__(
@ -56,8 +57,6 @@ class Conformer(Transformer):
cnn_module_kernel: int = 31,
normalize_before: bool = True,
vgg_frontend: bool = False,
is_espnet_structure: bool = False,
mmi_loss: bool = True,
use_feat_batchnorm: bool = False,
) -> None:
super(Conformer, self).__init__(
@ -72,7 +71,6 @@ class Conformer(Transformer):
dropout=dropout,
normalize_before=normalize_before,
vgg_frontend=vgg_frontend,
mmi_loss=mmi_loss,
use_feat_batchnorm=use_feat_batchnorm,
)
@ -85,12 +83,10 @@ class Conformer(Transformer):
dropout,
cnn_module_kernel,
normalize_before,
is_espnet_structure,
)
self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers)
self.normalize_before = normalize_before
self.is_espnet_structure = is_espnet_structure
if self.normalize_before and self.is_espnet_structure:
if self.normalize_before:
self.after_norm = nn.LayerNorm(d_model)
else:
# Note: TorchScript detects that self.after_norm could be used inside forward()
@ -125,7 +121,7 @@ class Conformer(Transformer):
mask = mask.to(x.device)
x = self.encoder(x, pos_emb, src_key_padding_mask=mask) # (T, B, F)
if self.normalize_before and self.is_espnet_structure:
if self.normalize_before:
x = self.after_norm(x)
return x, mask
@ -159,11 +155,10 @@ class ConformerEncoderLayer(nn.Module):
dropout: float = 0.1,
cnn_module_kernel: int = 31,
normalize_before: bool = True,
is_espnet_structure: bool = False,
) -> None:
super(ConformerEncoderLayer, self).__init__()
self.self_attn = RelPositionMultiheadAttention(
d_model, nhead, dropout=0.0, is_espnet_structure=is_espnet_structure
d_model, nhead, dropout=0.0
)
self.feed_forward = nn.Sequential(
@ -436,7 +431,6 @@ class RelPositionMultiheadAttention(nn.Module):
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_espnet_structure: bool = False,
) -> None:
super(RelPositionMultiheadAttention, self).__init__()
self.embed_dim = embed_dim
@ -459,7 +453,6 @@ class RelPositionMultiheadAttention(nn.Module):
self._reset_parameters()
self.is_espnet_structure = is_espnet_structure
def _reset_parameters(self) -> None:
nn.init.xavier_uniform_(self.in_proj.weight)
@ -690,8 +683,6 @@ class RelPositionMultiheadAttention(nn.Module):
_b = _b[_start:]
v = nn.functional.linear(value, _w, _b)
if not self.is_espnet_structure:
q = q * scaling
if attn_mask is not None:
assert (
@ -785,14 +776,9 @@ class RelPositionMultiheadAttention(nn.Module):
) # (batch, head, time1, 2*time1-1)
matrix_bd = self.rel_shift(matrix_bd)
if not self.is_espnet_structure:
attn_output_weights = (
matrix_ac + matrix_bd
) # (batch, head, time1, time2)
else:
attn_output_weights = (
matrix_ac + matrix_bd
) * scaling # (batch, head, time1, time2)
attn_output_weights = (
matrix_ac + matrix_bd
) * scaling # (batch, head, time1, time2)
attn_output_weights = attn_output_weights.view(
bsz * num_heads, tgt_len, -1

19
egs/aishell/ASR/conformer_ctc/decode.py
View File

@ -57,7 +57,7 @@ def get_parser():
parser.add_argument(
"--epoch",
type=int,
default=34,
default=49,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
)
@ -101,7 +101,7 @@ def get_parser():
parser.add_argument(
"--lattice-score-scale",
type=float,
default=1.0,
default=0.5,
help="""The scale to be applied to `lattice.scores`.
It's needed if you use any kinds of n-best based rescoring.
Used only when "method" is one of the following values:
@ -116,19 +116,19 @@ def get_parser():
def get_params() -> AttributeDict:
params = AttributeDict(
{
"exp_dir": Path("conformer_ctc/exp_char"),
"exp_dir": Path("conformer_ctc/exp"),
"lang_dir": Path("data/lang_char"),
"lm_dir": Path("data/lm"),
# parameters for conformer
"subsampling_factor": 4,
"feature_dim": 80,
"nhead": 4,
"attention_dim": 512,
"subsampling_factor": 4,
"num_encoder_layers": 12,
"num_decoder_layers": 6,
"vgg_frontend": False,
"is_espnet_structure": True,
"mmi_loss": False,
"use_feat_batchnorm": True,
# parameters for decoder
"search_beam": 20,
"output_beam": 7,
"min_active_states": 30,
@ -364,9 +364,12 @@ def save_results(
# The following prints out WERs, per-word error statistics and aligned
# ref/hyp pairs.
errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt"
results_tmp = []
for res in results:
results_tmp.append((list("".join(res[0])), list("".join(res[1]))))
with open(errs_filename, "w") as f:
wer = write_error_stats(
f, f"{test_set_name}-{key}", results, enable_log=enable_log
f, f"{test_set_name}-{key}", results_tmp, enable_log=enable_log
)
test_set_wers[key] = wer
@ -440,8 +443,6 @@ def main():
num_encoder_layers=params.num_encoder_layers,
num_decoder_layers=params.num_decoder_layers,
vgg_frontend=params.vgg_frontend,
is_espnet_structure=params.is_espnet_structure,
mmi_loss=params.mmi_loss,
use_feat_batchnorm=params.use_feat_batchnorm,
)

65
egs/aishell/ASR/conformer_ctc/train.py
View File

@ -1,5 +1,6 @@
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang
# Wei Kang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
@ -77,7 +78,7 @@ def get_parser():
parser.add_argument(
"--num-epochs",
type=int,
default=35,
default=50,
help="Number of epochs to train.",
)
@ -111,19 +112,6 @@ def get_params() -> AttributeDict:
- lang_dir: It contains language related input files such as
"lexicon.txt"
- lr: It specifies the initial learning rate
- feature_dim: The model input dim. It has to match the one used
in computing features.
- weight_decay: The weight_decay for the optimizer.
- subsampling_factor: The subsampling factor for the model.
- best_train_loss: Best training loss so far. It is used to select
the model that has the lowest training loss. It is
updated during the training.
- best_valid_loss: Best validation loss so far. It is used to select
the model that has the lowest validation loss. It is
updated during the training.
@ -138,23 +126,45 @@ def get_params() -> AttributeDict:
- log_interval: Print training loss if batch_idx % log_interval` is 0
- valid_interval: Run validation if batch_idx % valid_interval is 0
- reset_interval: Reset statistics if batch_idx % reset_interval is 0
- valid_interval: Run validation if batch_idx % valid_interval is 0
- beam_size: It is used in k2.ctc_loss
- reduction: It is used in k2.ctc_loss
- use_double_scores: It is used in k2.ctc_loss
- att_rate: The proportion of label smoothing loss, final loss will be
(1 - att_rate) * ctc_loss + att_rate * label_smoothing_loss
- subsampling_factor: The subsampling factor for the model.
- feature_dim: The model input dim. It has to match the one used
in computing features.
- attention_dim: Attention dimension.
- nhead: Number of heads in multi-head attention.
Must satisfy attention_dim // nhead == 0.
- num_encoder_layers: Number of attention encoder layers.
- num_decoder_layers: Number of attention decoder layers.
- use_feat_batchnorm: Whether to do normalization in the input layer.
- weight_decay: The weight_decay for the optimizer.
- lr_factor: The lr_factor for the optimizer.
- warm_step: The warm_step for the optimizer.
"""
params = AttributeDict(
{
"exp_dir": Path("conformer_ctc/exp_char"),
"exp_dir": Path("conformer_ctc/exp"),
"lang_dir": Path("data/lang_char"),
"feature_dim": 80,
"weight_decay": 1e-6,
"subsampling_factor": 4,
"best_train_loss": float("inf"),
"best_valid_loss": float("inf"),
"best_train_epoch": -1,
@ -163,18 +173,21 @@ def get_params() -> AttributeDict:
"log_interval": 10,
"reset_interval": 200,
"valid_interval": 3000,
# parameters for k2.ctc_loss
"beam_size": 10,
"reduction": "sum",
"use_double_scores": True,
"accum_grad": 1,
"att_rate": 0.7,
# parameters for conformer
"subsampling_factor": 4,
"feature_dim": 80,
"attention_dim": 512,
"nhead": 4,
"num_decoder_layers": 6,
"num_encoder_layers": 12,
"is_espnet_structure": True,
"mmi_loss": False,
"num_decoder_layers": 6,
"use_feat_batchnorm": True,
# parameters for Noam
"weight_decay": 1e-5,
"lr_factor": 5.0,
"warm_step": 36000,
}
@ -648,8 +661,6 @@ def run(rank, world_size, args):
num_encoder_layers=params.num_encoder_layers,
num_decoder_layers=params.num_decoder_layers,
vgg_frontend=False,
is_espnet_structure=params.is_espnet_structure,
mmi_loss=params.mmi_loss,
use_feat_batchnorm=params.use_feat_batchnorm,
)

13
egs/aishell/ASR/conformer_ctc/transformer.py
View File

@ -41,7 +41,6 @@ class Transformer(nn.Module):
dropout: float = 0.1,
normalize_before: bool = True,
vgg_frontend: bool = False,
mmi_loss: bool = True,
use_feat_batchnorm: bool = False,
) -> None:
"""
@ -70,7 +69,6 @@ class Transformer(nn.Module):
If True, use pre-layer norm; False to use post-layer norm.
vgg_frontend:
True to use vgg style frontend for subsampling.
mmi_loss:
use_feat_batchnorm:
True to use batchnorm for the input layer.
"""
@ -122,14 +120,9 @@ class Transformer(nn.Module):
)
if num_decoder_layers > 0:
if mmi_loss:
self.decoder_num_class = (
self.num_classes + 1
) # +1 for the sos/eos symbol
else:
self.decoder_num_class = (
self.num_classes
) # bpe model already has sos/eos symbol
self.decoder_num_class = (
self.num_classes
) # bpe model already has sos/eos symbol
self.decoder_embed = nn.Embedding(
num_embeddings=self.decoder_num_class, embedding_dim=d_model

2
egs/aishell/ASR/local/prepare_char.py
View File

@ -124,7 +124,7 @@ def lexicon_to_fst_no_sil(
def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool:
"""Return if all the tokens are in token symbol table.
"""Check if all the given tokens are in token symbol table.
Args:
token_sym_table:

5
egs/aishell/ASR/prepare.sh
View File

@ -3,7 +3,7 @@
set -eou pipefail
nj=15
stage=6
stage=-1
stop_stage=10
# We assume dl_dir (download dir) contains the following
@ -11,7 +11,7 @@ stop_stage=10
# by this script automatically.
#
# - $dl_dir/aishell
# You can data_aishell, resource_aishell inside it.
# You can find data_aishell, resource_aishell inside it.
# You can download them from https://www.openslr.org/33
#
# - $dl_dir/lm
@ -27,6 +27,7 @@ stop_stage=10
# - music
# - noise
# - speech
dl_dir=$PWD/download
. shared/parse_options.sh || exit 1

4
egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py
View File

@ -73,14 +73,14 @@ class AishellAsrDataModule(DataModule):
group.add_argument(
"--max-duration",
type=int,
default=500.0,
default=200.0,
help="Maximum pooled recordings duration (seconds) in a "
"single batch. You can reduce it if it causes CUDA OOM.",
)
group.add_argument(
"--bucketing-sampler",
type=str2bool,
default=False,
default=True,
help="When enabled, the batches will come from buckets of "
"similar duration (saves padding frames).",
)

7
egs/aishell/ASR/tdnn_lstm_ctc/decode.py
View File

@ -95,7 +95,7 @@ def get_params() -> AttributeDict:
# Possible values for method:
# - 1best
# - nbest
"method": "nbest",
"method": "1best",
# num_paths is used when method is "nbest"
"num_paths": 30,
}
@ -274,8 +274,11 @@ def save_results(
# The following prints out WERs, per-word error statistics and aligned
# ref/hyp pairs.
errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt"
results_tmp = []
for res in results:
results_tmp.append((list("".join(res[0])), list("".join(res[1]))))
with open(errs_filename, "w") as f:
wer = write_error_stats(f, f"{test_set_name}-{key}", results)
wer = write_error_stats(f, f"{test_set_name}-{key}", results_tmp)
test_set_wers[key] = wer
logging.info("Wrote detailed error stats to {}".format(errs_filename))

1
icefall/decode.py
View File

@ -883,3 +883,4 @@ def rescore_with_attention_decoder(
key = f"ngram_lm_scale_{n_scale}_attention_scale_{a_scale}"
ans[key] = best_path_fsa
return ans

1
icefall/utils.py
View File

@ -99,7 +99,6 @@ def setup_logger(
"""
now = datetime.now()
date_time = now.strftime("%Y-%m-%d-%H-%M-%S")
if dist.is_available() and dist.is_initialized():
world_size = dist.get_world_size()
rank = dist.get_rank()