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Merge branch 'cr-ctc-aishell' into cr-ctc
This commit is contained in:
commit
906e833361
1
egs/aishell/ASR/zipformer/attention_decoder.py
Symbolic link
1
egs/aishell/ASR/zipformer/attention_decoder.py
Symbolic link
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|
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../../../librispeech/ASR/zipformer/attention_decoder.py
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egs/aishell/ASR/zipformer/ctc_decode.py
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egs/aishell/ASR/zipformer/ctc_decode.py
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#!/usr/bin/env python3
|
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#
|
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# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
|
||||
# Liyong Guo,
|
||||
# Quandong Wang,
|
||||
# Zengwei Yao)
|
||||
#
|
||||
# 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.
|
||||
"""
|
||||
Usage:
|
||||
|
||||
(1) ctc-greedy-search
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--decoding-method ctc-greedy-search
|
||||
|
||||
(2) ctc-decoding
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--decoding-method ctc-decoding
|
||||
|
||||
(3) 1best
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--decoding-method 1best
|
||||
|
||||
(4) nbest
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--decoding-method nbest
|
||||
|
||||
(5) nbest-rescoring
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--nbest-scale 1.0 \
|
||||
--lm-dir data/lm \
|
||||
--decoding-method nbest-rescoring
|
||||
|
||||
(6) whole-lattice-rescoring
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--nbest-scale 1.0 \
|
||||
--lm-dir data/lm \
|
||||
--decoding-method whole-lattice-rescoring
|
||||
|
||||
(7) attention-decoder-rescoring-no-ngram
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--use-attention-decoder 1 \
|
||||
--max-duration 100 \
|
||||
--decoding-method attention-decoder-rescoring-no-ngram
|
||||
|
||||
(8) attention-decoder-rescoring-with-ngram
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--use-attention-decoder 1 \
|
||||
--max-duration 100 \
|
||||
--hlg-scale 0.6 \
|
||||
--nbest-scale 1.0 \
|
||||
--lm-dir data/lm \
|
||||
--decoding-method attention-decoder-rescoring-with-ngram
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import k2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import AishellAsrDataModule
|
||||
from lhotse import set_caching_enabled
|
||||
from lhotse.cut import Cut
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.decode import (
|
||||
ctc_greedy_search,
|
||||
get_lattice,
|
||||
one_best_decoding,
|
||||
rescore_with_attention_decoder_no_ngram,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
get_texts,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
LOG_EPS = math.log(1e-10)
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=30,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
You can specify --avg to use more checkpoints for model averaging.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--iter",
|
||||
type=int,
|
||||
default=0,
|
||||
help="""If positive, --epoch is ignored and it
|
||||
will use the checkpoint exp_dir/checkpoint-iter.pt.
|
||||
You can specify --avg to use more checkpoints for model averaging.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--avg",
|
||||
type=int,
|
||||
default=15,
|
||||
help="Number of checkpoints to average. Automatically select "
|
||||
"consecutive checkpoints before the checkpoint specified by "
|
||||
"'--epoch' and '--iter'",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-averaged-model",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Whether to load averaged model. Currently it only supports "
|
||||
"using --epoch. If True, it would decode with the averaged model "
|
||||
"over the epoch range from `epoch-avg` (excluded) to `epoch`."
|
||||
"Actually only the models with epoch number of `epoch-avg` and "
|
||||
"`epoch` are loaded for averaging. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="zipformer/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default="data/lang_char",
|
||||
help="The lang dir containing word table and LG graph",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="ctc-decoding",
|
||||
help="""Decoding method.
|
||||
Supported values are:
|
||||
- (1) ctc-greedy-search. Use CTC greedy search. It uses a sentence piece
|
||||
model, i.e., lang_dir/bpe.model, to convert word pieces to words.
|
||||
It needs neither a lexicon nor an n-gram LM.
|
||||
- (2) ctc-decoding. Use CTC decoding. It uses a sentence piece
|
||||
model, i.e., lang_dir/bpe.model, to convert word pieces to words.
|
||||
It needs neither a lexicon nor an n-gram LM.
|
||||
- (3) attention-decoder-rescoring-no-ngram. Extract n paths from the decoding
|
||||
lattice, rescore them with the attention decoder.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=100,
|
||||
help="""Number of paths for n-best based decoding method.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
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:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
A smaller value results in more unique paths.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--skip-scoring",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""Skip scoring, but still save the ASR output (for eval sets)."""
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def get_decoding_params() -> AttributeDict:
|
||||
"""Parameters for decoding."""
|
||||
params = AttributeDict(
|
||||
{
|
||||
"frame_shift_ms": 10,
|
||||
"search_beam": 20,
|
||||
"output_beam": 8,
|
||||
"min_active_states": 30,
|
||||
"max_active_states": 10000,
|
||||
"use_double_scores": True,
|
||||
}
|
||||
)
|
||||
return params
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
lexicon: Lexicon,
|
||||
batch: dict,
|
||||
H: Optional[k2.Fsa],
|
||||
) -> Dict[str, List[List[str]]]:
|
||||
"""Decode one batch and return the result in a dict. The dict has the
|
||||
following format:
|
||||
- key: It indicates the setting used for decoding. For example,
|
||||
if no rescoring is used, the key is the string `no_rescore`.
|
||||
If LM rescoring is used, the key is the string `lm_scale_xxx`,
|
||||
where `xxx` is the value of `lm_scale`. An example key is
|
||||
`lm_scale_0.7`
|
||||
- value: It contains the decoding result. `len(value)` equals to
|
||||
batch size. `value[i]` is the decoding result for the i-th
|
||||
utterance in the given batch.
|
||||
|
||||
Args:
|
||||
params:
|
||||
It's the return value of :func:`get_params`.
|
||||
|
||||
- params.decoding_method is "1best", it uses 1best decoding without LM rescoring.
|
||||
- params.decoding_method is "nbest", it uses nbest decoding without LM rescoring.
|
||||
- params.decoding_method is "nbest-rescoring", it uses nbest LM rescoring.
|
||||
- params.decoding_method is "whole-lattice-rescoring", it uses whole lattice LM
|
||||
rescoring.
|
||||
|
||||
model:
|
||||
The neural model.
|
||||
HLG:
|
||||
The decoding graph. Used only when params.decoding_method is NOT ctc-decoding.
|
||||
H:
|
||||
The ctc topo. Used only when params.decoding_method is ctc-decoding.
|
||||
bpe_model:
|
||||
The BPE model. Used only when params.decoding_method is ctc-decoding.
|
||||
batch:
|
||||
It is the return value from iterating
|
||||
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
|
||||
for the format of the `batch`.
|
||||
word_table:
|
||||
The word symbol table.
|
||||
G:
|
||||
An LM. It is not None when params.decoding_method is "nbest-rescoring"
|
||||
or "whole-lattice-rescoring". In general, the G in HLG
|
||||
is a 3-gram LM, while this G is a 4-gram LM.
|
||||
Returns:
|
||||
Return the decoding result. See above description for the format of
|
||||
the returned dict. Note: If it decodes to nothing, then return None.
|
||||
"""
|
||||
# TODO
|
||||
device = next(model.parameters()).device
|
||||
feature = batch["inputs"]
|
||||
assert feature.ndim == 3
|
||||
feature = feature.to(device)
|
||||
# at entry, feature is (N, T, C)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
feature_lens = supervisions["num_frames"].to(device)
|
||||
|
||||
if params.causal:
|
||||
# this seems to cause insertions at the end of the utterance if used with zipformer.
|
||||
pad_len = 30
|
||||
feature_lens += pad_len
|
||||
feature = torch.nn.functional.pad(
|
||||
feature,
|
||||
pad=(0, 0, 0, pad_len),
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
|
||||
ctc_output = model.ctc_output(encoder_out) # (N, T, C)
|
||||
|
||||
batch_size = encoder_out.size(0)
|
||||
|
||||
if params.decoding_method == "ctc-greedy-search":
|
||||
hyp_tokens = ctc_greedy_search(ctc_output, encoder_out_lens)
|
||||
hyps = []
|
||||
for i in range(batch_size):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
key = "ctc-greedy-search"
|
||||
return {key: hyps}
|
||||
|
||||
supervision_segments = torch.stack(
|
||||
(
|
||||
supervisions["sequence_idx"],
|
||||
torch.div(
|
||||
supervisions["start_frame"],
|
||||
params.subsampling_factor,
|
||||
rounding_mode="floor",
|
||||
),
|
||||
torch.div(
|
||||
supervisions["num_frames"],
|
||||
params.subsampling_factor,
|
||||
rounding_mode="floor",
|
||||
),
|
||||
),
|
||||
1,
|
||||
).to(torch.int32)
|
||||
|
||||
assert H is not None
|
||||
decoding_graph = H
|
||||
lattice = get_lattice(
|
||||
nnet_output=ctc_output,
|
||||
decoding_graph=decoding_graph,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
if params.decoding_method == "ctc-decoding":
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
# Note: `best_path.aux_labels` contains token IDs, not word IDs
|
||||
# since we are using H, not HLG here.
|
||||
#
|
||||
# token_ids is a lit-of-list of IDs
|
||||
hyp_tokens = get_texts(best_path)
|
||||
hyps = []
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
key = "ctc-decoding"
|
||||
return {key: hyps} # note: returns words
|
||||
|
||||
if params.decoding_method == "attention-decoder-rescoring-no-ngram":
|
||||
best_path_dict = rescore_with_attention_decoder_no_ngram(
|
||||
lattice=lattice,
|
||||
num_paths=params.num_paths,
|
||||
attention_decoder=model.attention_decoder,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
ans = dict()
|
||||
for a_scale_str, best_path in best_path_dict.items():
|
||||
# token_ids is a lit-of-list of IDs
|
||||
hyps = []
|
||||
hyp_tokens = get_texts(best_path)
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
ans[a_scale_str] = hyps
|
||||
return ans
|
||||
else:
|
||||
assert False, f"Unsupported decoding method: {params.decoding_method}"
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
lexicon: Lexicon,
|
||||
H: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
dl:
|
||||
PyTorch's dataloader containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
HLG:
|
||||
The decoding graph. Used only when params.decoding_method is NOT ctc-decoding.
|
||||
H:
|
||||
The ctc topo. Used only when params.decoding_method is ctc-decoding.
|
||||
bpe_model:
|
||||
The BPE model. Used only when params.decoding_method is ctc-decoding.
|
||||
word_table:
|
||||
It is the word symbol table.
|
||||
G:
|
||||
An LM. It is not None when params.decoding_method is "nbest-rescoring"
|
||||
or "whole-lattice-rescoring". In general, the G in HLG
|
||||
is a 3-gram LM, while this G is a 4-gram LM.
|
||||
Returns:
|
||||
Return a dict, whose key may be "no-rescore" if no LM rescoring
|
||||
is used, or it may be "lm_scale_0.7" if LM rescoring is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
num_cuts = 0
|
||||
|
||||
try:
|
||||
num_batches = len(dl)
|
||||
except TypeError:
|
||||
num_batches = "?"
|
||||
|
||||
results = defaultdict(list)
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
texts = batch["supervisions"]["text"]
|
||||
texts = [list("".join(text.split())) for text in texts]
|
||||
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
|
||||
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
batch=batch,
|
||||
lexicon=lexicon,
|
||||
H=H,
|
||||
)
|
||||
|
||||
for name, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
assert len(hyps) == len(texts)
|
||||
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
|
||||
this_batch.append((cut_id, ref_text, hyp_words))
|
||||
|
||||
results[name].extend(this_batch)
|
||||
|
||||
num_cuts += len(texts)
|
||||
|
||||
if batch_idx % 100 == 0:
|
||||
batch_str = f"{batch_idx}/{num_batches}"
|
||||
|
||||
logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
|
||||
return results
|
||||
|
||||
|
||||
def save_asr_output(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
|
||||
):
|
||||
"""
|
||||
Save text produced by ASR.
|
||||
"""
|
||||
for key, results in results_dict.items():
|
||||
|
||||
recogs_filename = (
|
||||
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recogs_filename, texts=results, char_level=True)
|
||||
|
||||
logging.info(f"The transcripts are stored in {recogs_filename}")
|
||||
|
||||
|
||||
def save_wer_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
|
||||
):
|
||||
if params.decoding_method == "attention-decoder-rescoring-no-ngram":
|
||||
# Set it to False since there are too many logs.
|
||||
enable_log = False
|
||||
else:
|
||||
enable_log = True
|
||||
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
with open(errs_filename, "w", encoding="utf8") as fd:
|
||||
wer = write_error_stats(
|
||||
fd,
|
||||
f"{test_set_name}-{key}",
|
||||
results,
|
||||
enable_log=enable_log,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info(f"Wrote detailed error stats to {errs_filename}")
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
|
||||
wer_filename = params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
|
||||
with open(wer_filename, "w", encoding="utf8") as fd:
|
||||
print("settings\tWER", file=fd)
|
||||
for key, val in test_set_wers:
|
||||
print(f"{key}\t{val}", file=fd)
|
||||
|
||||
s = f"\nFor {test_set_name}, WER of different settings are:\n"
|
||||
note = f"\tbest for {test_set_name}"
|
||||
for key, val in test_set_wers:
|
||||
s += f"{key}\t{val}{note}\n"
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
AishellAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
args.lang_dir = Path(args.lang_dir)
|
||||
|
||||
params = get_params()
|
||||
# add decoding params
|
||||
params.update(get_decoding_params())
|
||||
params.update(vars(args))
|
||||
|
||||
# enable AudioCache
|
||||
set_caching_enabled(True) # lhotse
|
||||
|
||||
assert params.decoding_method in (
|
||||
"ctc-greedy-search",
|
||||
"ctc-decoding",
|
||||
"attention-decoder-rescoring-no-ngram",
|
||||
)
|
||||
params.res_dir = params.exp_dir / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}_avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}_avg-{params.avg}"
|
||||
|
||||
if params.causal:
|
||||
assert (
|
||||
"," not in params.chunk_size
|
||||
), "chunk_size should be one value in decoding."
|
||||
assert (
|
||||
"," not in params.left_context_frames
|
||||
), "left_context_frames should be one value in decoding."
|
||||
params.suffix += f"_chunk-{params.chunk_size}"
|
||||
params.suffix += f"_left-context-{params.left_context_frames}"
|
||||
|
||||
if params.use_averaged_model:
|
||||
params.suffix += "_use-averaged-model"
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
||||
logging.info("Decoding started")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"Device: {device}")
|
||||
logging.info(params)
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
max_token_id = max(lexicon.tokens)
|
||||
num_classes = max_token_id + 1 # +1 for the blank
|
||||
|
||||
params.vocab_size = num_classes
|
||||
# <blk> and <unk> are defined in local/train_bpe_model.py
|
||||
params.blank_id = 0
|
||||
params.eos_id = 1
|
||||
params.sos_id = 1
|
||||
|
||||
if params.decoding_method != "ctc-greedy-search":
|
||||
H = k2.ctc_topo(
|
||||
max_token=max_token_id,
|
||||
modified=True,
|
||||
device=device,
|
||||
)
|
||||
else:
|
||||
H = None
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_model(params)
|
||||
|
||||
if not params.use_averaged_model:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
elif params.avg == 1:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
else:
|
||||
start = params.epoch - params.avg + 1
|
||||
filenames = []
|
||||
for i in range(start, params.epoch + 1):
|
||||
if i >= 1:
|
||||
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
else:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg + 1
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg + 1:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
filename_start = filenames[-1]
|
||||
filename_end = filenames[0]
|
||||
logging.info(
|
||||
"Calculating the averaged model over iteration checkpoints"
|
||||
f" from {filename_start} (excluded) to {filename_end}"
|
||||
)
|
||||
model.to(device)
|
||||
model.load_state_dict(
|
||||
average_checkpoints_with_averaged_model(
|
||||
filename_start=filename_start,
|
||||
filename_end=filename_end,
|
||||
device=device,
|
||||
)
|
||||
)
|
||||
else:
|
||||
assert params.avg > 0, params.avg
|
||||
start = params.epoch - params.avg
|
||||
assert start >= 1, start
|
||||
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
|
||||
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
|
||||
logging.info(
|
||||
f"Calculating the averaged model over epoch range from "
|
||||
f"{start} (excluded) to {params.epoch}"
|
||||
)
|
||||
model.to(device)
|
||||
model.load_state_dict(
|
||||
average_checkpoints_with_averaged_model(
|
||||
filename_start=filename_start,
|
||||
filename_end=filename_end,
|
||||
device=device,
|
||||
)
|
||||
)
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
# we need cut ids to display recognition results.
|
||||
args.return_cuts = True
|
||||
aishell = AishellAsrDataModule(args)
|
||||
|
||||
def remove_short_utt(c: Cut):
|
||||
T = ((c.num_frames - 7) // 2 + 1) // 2
|
||||
if T <= 0:
|
||||
logging.warning(
|
||||
f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}."
|
||||
)
|
||||
return T > 0
|
||||
|
||||
dev_cuts = aishell.valid_cuts()
|
||||
dev_cuts = dev_cuts.filter(remove_short_utt)
|
||||
dev_dl = aishell.valid_dataloaders(dev_cuts)
|
||||
|
||||
test_cuts = aishell.test_cuts()
|
||||
test_cuts = test_cuts.filter(remove_short_utt)
|
||||
test_dl = aishell.test_dataloaders(test_cuts)
|
||||
|
||||
test_sets = ["dev", "test"]
|
||||
test_dls = [dev_dl, test_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dls):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
H=H,
|
||||
lexicon=lexicon,
|
||||
)
|
||||
|
||||
save_asr_output(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
if not params.skip_scoring:
|
||||
save_wer_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
1
egs/aishell/ASR/zipformer/label_smoothing.py
Symbolic link
1
egs/aishell/ASR/zipformer/label_smoothing.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/zipformer/label_smoothing.py
|
1
egs/aishell/ASR/zipformer/spec_augment.py
Symbolic link
1
egs/aishell/ASR/zipformer/spec_augment.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/zipformer/spec_augment.py
|
@ -61,6 +61,7 @@ import torch
|
||||
import torch.multiprocessing as mp
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import AishellAsrDataModule
|
||||
from attention_decoder import AttentionDecoderModel
|
||||
from decoder import Decoder
|
||||
from joiner import Joiner
|
||||
from lhotse.cut import Cut
|
||||
@ -96,6 +97,7 @@ from icefall.utils import (
|
||||
setup_logger,
|
||||
str2bool,
|
||||
)
|
||||
from spec_augment import SpecAugment
|
||||
|
||||
LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
|
||||
|
||||
@ -216,6 +218,41 @@ def add_model_arguments(parser: argparse.ArgumentParser):
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--attention-decoder-dim",
|
||||
type=int,
|
||||
default=512,
|
||||
help="""Dimension used in the attention decoder""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--attention-decoder-num-layers",
|
||||
type=int,
|
||||
default=6,
|
||||
help="""Number of transformer layers used in attention decoder""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--attention-decoder-attention-dim",
|
||||
type=int,
|
||||
default=512,
|
||||
help="""Attention dimension used in attention decoder""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--attention-decoder-num-heads",
|
||||
type=int,
|
||||
default=8,
|
||||
help="""Number of attention heads used in attention decoder""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--attention-decoder-feedforward-dim",
|
||||
type=int,
|
||||
default=2048,
|
||||
help="""Feedforward dimension used in attention decoder""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--causal",
|
||||
type=str2bool,
|
||||
@ -239,6 +276,34 @@ def add_model_arguments(parser: argparse.ArgumentParser):
|
||||
chunk left-context frames will be chosen randomly from this list; else not relevant.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-transducer",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="If True, use Transducer head.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-ctc",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="If True, use CTC head.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-attention-decoder",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="If True, use attention-decoder head.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-cr-ctc",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="If True, use consistency-regularized CTC.",
|
||||
)
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
@ -379,6 +444,41 @@ def get_parser():
|
||||
with this parameter before adding to the final loss.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ctc-loss-scale",
|
||||
type=float,
|
||||
default=0.2,
|
||||
help="Scale for CTC loss.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--cr-loss-scale",
|
||||
type=float,
|
||||
default=0.15,
|
||||
help="Scale for consistency-regularization loss.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--time-mask-ratio",
|
||||
type=float,
|
||||
default=2.0,
|
||||
help="When using cr-ctc, we increase the time-masking ratio.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--cr-loss-masked-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="The value used to scale up the cr_loss at masked positions",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--attention-decoder-loss-scale",
|
||||
type=float,
|
||||
default=0.8,
|
||||
help="Scale for attention-decoder loss.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
@ -507,6 +607,9 @@ def get_params() -> AttributeDict:
|
||||
# parameters for zipformer
|
||||
"feature_dim": 80,
|
||||
"subsampling_factor": 4, # not passed in, this is fixed.
|
||||
# parameters for attention-decoder
|
||||
"ignore_id": -1,
|
||||
"label_smoothing": 0.1,
|
||||
"warm_step": 2000,
|
||||
"env_info": get_env_info(),
|
||||
}
|
||||
@ -579,24 +682,79 @@ def get_joiner_model(params: AttributeDict) -> nn.Module:
|
||||
return joiner
|
||||
|
||||
|
||||
def get_attention_decoder_model(params: AttributeDict) -> nn.Module:
|
||||
decoder = AttentionDecoderModel(
|
||||
vocab_size=params.vocab_size,
|
||||
decoder_dim=params.attention_decoder_dim,
|
||||
num_decoder_layers=params.attention_decoder_num_layers,
|
||||
attention_dim=params.attention_decoder_attention_dim,
|
||||
num_heads=params.attention_decoder_num_heads,
|
||||
feedforward_dim=params.attention_decoder_feedforward_dim,
|
||||
memory_dim=max(_to_int_tuple(params.encoder_dim)),
|
||||
sos_id=params.sos_id,
|
||||
eos_id=params.eos_id,
|
||||
ignore_id=params.ignore_id,
|
||||
label_smoothing=params.label_smoothing,
|
||||
)
|
||||
return decoder
|
||||
|
||||
|
||||
def get_model(params: AttributeDict) -> nn.Module:
|
||||
assert params.use_transducer or params.use_ctc, (
|
||||
f"At least one of them should be True, "
|
||||
f"but got params.use_transducer={params.use_transducer}, "
|
||||
f"params.use_ctc={params.use_ctc}"
|
||||
)
|
||||
|
||||
encoder_embed = get_encoder_embed(params)
|
||||
encoder = get_encoder_model(params)
|
||||
decoder = get_decoder_model(params)
|
||||
joiner = get_joiner_model(params)
|
||||
|
||||
if params.use_transducer:
|
||||
decoder = get_decoder_model(params)
|
||||
joiner = get_joiner_model(params)
|
||||
else:
|
||||
decoder = None
|
||||
joiner = None
|
||||
|
||||
if params.use_attention_decoder:
|
||||
attention_decoder = get_attention_decoder_model(params)
|
||||
else:
|
||||
attention_decoder = None
|
||||
|
||||
model = AsrModel(
|
||||
encoder_embed=encoder_embed,
|
||||
encoder=encoder,
|
||||
decoder=decoder,
|
||||
joiner=joiner,
|
||||
encoder_dim=int(max(params.encoder_dim.split(","))),
|
||||
attention_decoder=attention_decoder,
|
||||
encoder_dim=max(_to_int_tuple(params.encoder_dim)),
|
||||
decoder_dim=params.decoder_dim,
|
||||
vocab_size=params.vocab_size,
|
||||
use_transducer=params.use_transducer,
|
||||
use_ctc=params.use_ctc,
|
||||
use_attention_decoder=params.use_attention_decoder,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def get_spec_augment(params: AttributeDict) -> SpecAugment:
|
||||
num_frame_masks = int(10 * params.time_mask_ratio)
|
||||
max_frames_mask_fraction = 0.15 * params.time_mask_ratio
|
||||
logging.info(
|
||||
f"num_frame_masks: {num_frame_masks}, "
|
||||
f"max_frames_mask_fraction: {max_frames_mask_fraction}"
|
||||
)
|
||||
spec_augment = SpecAugment(
|
||||
time_warp_factor=0, # Do time warping in model.py
|
||||
num_frame_masks=num_frame_masks, # default: 10
|
||||
features_mask_size=27,
|
||||
num_feature_masks=2,
|
||||
frames_mask_size=100,
|
||||
max_frames_mask_fraction=max_frames_mask_fraction, # default: 0.15
|
||||
)
|
||||
return spec_augment
|
||||
|
||||
|
||||
def load_checkpoint_if_available(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
@ -722,6 +880,7 @@ def compute_loss(
|
||||
graph_compiler: CharCtcTrainingGraphCompiler,
|
||||
batch: dict,
|
||||
is_training: bool,
|
||||
spec_augment: Optional[SpecAugment] = None,
|
||||
) -> Tuple[Tensor, MetricsTracker]:
|
||||
"""
|
||||
Compute CTC loss given the model and its inputs.
|
||||
@ -738,8 +897,8 @@ def compute_loss(
|
||||
True for training. False for validation. When it is True, this
|
||||
function enables autograd during computation; when it is False, it
|
||||
disables autograd.
|
||||
warmup: a floating point value which increases throughout training;
|
||||
values >= 1.0 are fully warmed up and have all modules present.
|
||||
spec_augment:
|
||||
The SpecAugment instance used only when use_cr_ctc is True.
|
||||
"""
|
||||
device = model.device if isinstance(model, DDP) else next(model.parameters()).device
|
||||
feature = batch["inputs"]
|
||||
@ -757,32 +916,62 @@ def compute_loss(
|
||||
y = graph_compiler.texts_to_ids(texts)
|
||||
y = k2.RaggedTensor(y).to(device)
|
||||
|
||||
use_cr_ctc = params.use_cr_ctc
|
||||
use_spec_aug = use_cr_ctc and is_training
|
||||
if use_spec_aug:
|
||||
supervision_intervals = batch["supervisions"]
|
||||
supervision_segments = torch.stack(
|
||||
[
|
||||
supervision_intervals["sequence_idx"],
|
||||
supervision_intervals["start_frame"],
|
||||
supervision_intervals["num_frames"],
|
||||
],
|
||||
dim=1,
|
||||
) # shape: (S, 3)
|
||||
else:
|
||||
supervision_segments = None
|
||||
|
||||
with torch.set_grad_enabled(is_training):
|
||||
losses = model(
|
||||
simple_loss, pruned_loss, ctc_loss, attention_decoder_loss, cr_loss = model(
|
||||
x=feature,
|
||||
x_lens=feature_lens,
|
||||
y=y,
|
||||
prune_range=params.prune_range,
|
||||
am_scale=params.am_scale,
|
||||
lm_scale=params.lm_scale,
|
||||
)
|
||||
simple_loss, pruned_loss = losses[:2]
|
||||
|
||||
s = params.simple_loss_scale
|
||||
# take down the scale on the simple loss from 1.0 at the start
|
||||
# to params.simple_loss scale by warm_step.
|
||||
simple_loss_scale = (
|
||||
s
|
||||
if batch_idx_train >= warm_step
|
||||
else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
|
||||
)
|
||||
pruned_loss_scale = (
|
||||
1.0
|
||||
if batch_idx_train >= warm_step
|
||||
else 0.1 + 0.9 * (batch_idx_train / warm_step)
|
||||
use_cr_ctc=use_cr_ctc,
|
||||
use_spec_aug=use_spec_aug,
|
||||
spec_augment=spec_augment,
|
||||
supervision_segments=supervision_segments,
|
||||
time_warp_factor=params.spec_aug_time_warp_factor,
|
||||
cr_loss_masked_scale=params.cr_loss_masked_scale,
|
||||
)
|
||||
|
||||
loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
|
||||
loss = 0.0
|
||||
|
||||
if params.use_transducer:
|
||||
s = params.simple_loss_scale
|
||||
# take down the scale on the simple loss from 1.0 at the start
|
||||
# to params.simple_loss scale by warm_step.
|
||||
simple_loss_scale = (
|
||||
s
|
||||
if batch_idx_train >= warm_step
|
||||
else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
|
||||
)
|
||||
pruned_loss_scale = (
|
||||
1.0
|
||||
if batch_idx_train >= warm_step
|
||||
else 0.1 + 0.9 * (batch_idx_train / warm_step)
|
||||
)
|
||||
loss += simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
|
||||
|
||||
if params.use_ctc:
|
||||
loss += params.ctc_loss_scale * ctc_loss
|
||||
if use_cr_ctc:
|
||||
loss += params.cr_loss_scale * cr_loss
|
||||
|
||||
if params.use_attention_decoder:
|
||||
loss += params.attention_decoder_loss_scale * attention_decoder_loss
|
||||
|
||||
assert loss.requires_grad == is_training
|
||||
|
||||
@ -793,8 +982,15 @@ def compute_loss(
|
||||
|
||||
# Note: We use reduction=sum while computing the loss.
|
||||
info["loss"] = loss.detach().cpu().item()
|
||||
info["simple_loss"] = simple_loss.detach().cpu().item()
|
||||
info["pruned_loss"] = pruned_loss.detach().cpu().item()
|
||||
if params.use_transducer:
|
||||
info["simple_loss"] = simple_loss.detach().cpu().item()
|
||||
info["pruned_loss"] = pruned_loss.detach().cpu().item()
|
||||
if params.use_ctc:
|
||||
info["ctc_loss"] = ctc_loss.detach().cpu().item()
|
||||
if params.use_cr_ctc:
|
||||
info["cr_loss"] = cr_loss.detach().cpu().item()
|
||||
if params.use_attention_decoder:
|
||||
info["attn_decoder_loss"] = attention_decoder_loss.detach().cpu().item()
|
||||
|
||||
return loss, info
|
||||
|
||||
@ -842,6 +1038,7 @@ def train_one_epoch(
|
||||
train_dl: torch.utils.data.DataLoader,
|
||||
valid_dl: torch.utils.data.DataLoader,
|
||||
scaler: GradScaler,
|
||||
spec_augment: Optional[SpecAugment] = None,
|
||||
model_avg: Optional[nn.Module] = None,
|
||||
tb_writer: Optional[SummaryWriter] = None,
|
||||
world_size: int = 1,
|
||||
@ -868,6 +1065,8 @@ def train_one_epoch(
|
||||
Dataloader for the validation dataset.
|
||||
scaler:
|
||||
The scaler used for mix precision training.
|
||||
spec_augment:
|
||||
The SpecAugment instance used only when use_cr_ctc is True.
|
||||
model_avg:
|
||||
The stored model averaged from the start of training.
|
||||
tb_writer:
|
||||
@ -917,6 +1116,7 @@ def train_one_epoch(
|
||||
graph_compiler=graph_compiler,
|
||||
batch=batch,
|
||||
is_training=True,
|
||||
spec_augment=spec_augment,
|
||||
)
|
||||
# summary stats
|
||||
tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
|
||||
@ -1080,8 +1280,18 @@ def run(rank, world_size, args):
|
||||
)
|
||||
|
||||
params.blank_id = lexicon.token_table["<blk>"]
|
||||
params.sos_id = params.eos_id = lexicon.token_table["<sos/eos>"]
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
|
||||
if not params.use_transducer:
|
||||
if not params.use_attention_decoder:
|
||||
params.ctc_loss_scale = 1.0
|
||||
else:
|
||||
assert params.ctc_loss_scale + params.attention_decoder_loss_scale == 1.0, (
|
||||
params.ctc_loss_scale,
|
||||
params.attention_decoder_loss_scale,
|
||||
)
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
@ -1090,6 +1300,13 @@ def run(rank, world_size, args):
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
if params.use_cr_ctc:
|
||||
assert params.use_ctc
|
||||
assert not params.enable_spec_aug # we will do spec_augment in model.py
|
||||
spec_augment = get_spec_augment(params)
|
||||
else:
|
||||
spec_augment = None
|
||||
|
||||
assert params.save_every_n >= params.average_period
|
||||
model_avg: Optional[nn.Module] = None
|
||||
if rank == 0:
|
||||
@ -1199,6 +1416,7 @@ def run(rank, world_size, args):
|
||||
optimizer=optimizer,
|
||||
graph_compiler=graph_compiler,
|
||||
params=params,
|
||||
spec_augment=spec_augment,
|
||||
)
|
||||
|
||||
scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
|
||||
@ -1226,6 +1444,7 @@ def run(rank, world_size, args):
|
||||
train_dl=train_dl,
|
||||
valid_dl=valid_dl,
|
||||
scaler=scaler,
|
||||
spec_augment=spec_augment,
|
||||
tb_writer=tb_writer,
|
||||
world_size=world_size,
|
||||
rank=rank,
|
||||
@ -1292,6 +1511,7 @@ def scan_pessimistic_batches_for_oom(
|
||||
optimizer: torch.optim.Optimizer,
|
||||
graph_compiler: CharCtcTrainingGraphCompiler,
|
||||
params: AttributeDict,
|
||||
spec_augment: Optional[SpecAugment] = None,
|
||||
):
|
||||
from lhotse.dataset import find_pessimistic_batches
|
||||
|
||||
@ -1309,6 +1529,7 @@ def scan_pessimistic_batches_for_oom(
|
||||
graph_compiler=graph_compiler,
|
||||
batch=batch,
|
||||
is_training=True,
|
||||
spec_augment=spec_augment,
|
||||
)
|
||||
loss.backward()
|
||||
optimizer.zero_grad()
|
||||
|
Loading…
x
Reference in New Issue
Block a user