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Daniel Povey 2021-09-19 22:12:17 +08:00
parent 3bad661f6f
commit 39b6879d72
2 changed files with 12 additions and 12 deletions
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3
egs/librispeech/ASR/conformer_ctc_bn_2d/conformer.py
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@ -572,10 +572,7 @@ class BidirectionalConformer(nn.Module):
tokens_padded = pad_sequence(token_ids_tensors, batch_first=True,
padding_value=padding_id).to(positive_embed_shifted.device)
print("tokens_padded = ", tokens_padded)
tokens_key_padding_mask = decoder_padding_mask(tokens_padded, ignore_id=padding_id)
print("tokens_key_padding_mask=", tokens_key_padding_mask)
# Let S be the length of the longest sentence (padded)
token_embedding = self.token_embed(tokens_padded) * self.token_embed_scale # (N, S) -> (N, S, C)

21
egs/librispeech/ASR/conformer_ctc_bn_2d/train.py
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@ -15,6 +15,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#--master-port 12344 --world-size 3 --max-duration=200 --bucketing-sampler=True --start-epoch=5
import argparse
import collections
@ -22,7 +24,7 @@ import logging
from pathlib import Path
import random # temp..
from shutil import copyfile
from typing import Optional, Tuple
from typing import Optional, Tuple, List
import k2
import torch
@ -174,6 +176,7 @@ def get_params() -> AttributeDict:
"attention_dim": 512,
"nhead": 8,
"num_trunk_encoder_layers": 12,
"num_ctc_encoder_layers": 2,
"num_decoder_layers": 6,
"num_reverse_encoder_layers": 4,
"num_reverse_decoder_layers": 4,
@ -285,7 +288,7 @@ class LossRecord(collections.defaultdict):
# makes undefined items default to int() which is zero.
super(LossRecord, self).__init__(int)
def __add__(self, other: LossRecord) -> LossRecord:
def __add__(self, other: 'LossRecord') -> 'LossRecord':
ans = LossRecord()
for k, v in self.items():
ans[k] = v
@ -293,7 +296,7 @@ class LossRecord(collections.defaultdict):
ans[k] = ans[k] + v
return ans
def __mul__(self, alpha: float) -> LossRecord:
def __mul__(self, alpha: float) -> 'LossRecord':
ans = LossRecord()
for k, v in self.items():
ans[k] = v * alpha
@ -303,13 +306,13 @@ class LossRecord(collections.defaultdict):
def __str__(self) -> str:
ans = ''
for k, v in self.norm_items():
norm_value = '%.2g' % v
norm_value = '%.4g' % v
ans += (str(k) + '=' + str(norm_value) + ', ')
frames = str(self['frames'])
ans += 'over ' + frames + ' frames.'
return ans
def norm_items(self) -> List[Tuple[string, float]]
def norm_items(self) -> List[Tuple[str, float]]:
"""
Returns a list of pairs, like:
[('ctc_loss', 0.1), ('att_loss', 0.07)]
@ -320,7 +323,7 @@ class LossRecord(collections.defaultdict):
if k != 'frames':
norm_value = float(v) / num_frames
ans.append((k, norm_value))
return ans
def reduce(self, device):
"""
@ -353,7 +356,7 @@ def compute_loss(
batch: dict,
graph_compiler: BpeCtcTrainingGraphCompiler,
is_training: bool,
) -> Tuple[Tensor, LossRecord]
) -> Tuple[Tensor, LossRecord]:
"""
Compute loss function (including CTC, attention, and reverse-attention terms).
@ -562,7 +565,7 @@ def train_one_epoch(
"""
model.train()
tot_loss = LossInfo()
tot_loss = LossRecord()
for batch_idx, batch in enumerate(train_dl):
params.batch_idx_train += 1
@ -679,7 +682,7 @@ def run(rank, world_size, args):
num_self_predictor_layers=params.num_self_predictor_layers,
subsampling_factor=params.subsampling_factor,
is_bpe=params.is_bpe,
discretization_tot_classes=params.discretization_tot_clases,
discretization_tot_classes=params.discretization_tot_classes,
discretization_num_groups=params.discretization_num_groups,
)