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Minor fixes to the decode.py

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This commit is contained in:
pkufool 2023-06-02 16:25:44 +08:00
parent 8a94476fd9
commit 57c1d762b6
6 changed files with 109 additions and 66 deletions
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74
egs/aishell/ASR/pruned_transducer_stateless7/decode.py
View File

@ -58,6 +58,7 @@ Usage:
import argparse
import logging
import os
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple
@ -76,6 +77,8 @@ from beam_search import (
)
from train import add_model_arguments, get_params, get_transducer_model
from icefall import ContextGraph, LmScorer, NgramLm
from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
from icefall.checkpoint import (
average_checkpoints,
average_checkpoints_with_averaged_model,
@ -211,6 +214,26 @@ def get_parser():
Used only when --decoding_method is greedy_search""",
)
parser.add_argument(
"--context-score",
type=float,
default=2,
help="""
The bonus score of each token for the context biasing words/phrases.
Used only when --decoding_method is modified_beam_search.
""",
)
parser.add_argument(
"--context-file",
type=str,
default="",
help="""
The path of the context biasing lists, one word/phrase each line
Used only when --decoding_method is modified_beam_search.
""",
)
add_model_arguments(parser)
return parser
@ -222,6 +245,7 @@ def decode_one_batch(
token_table: k2.SymbolTable,
batch: dict,
decoding_graph: Optional[k2.Fsa] = None,
context_graph: Optional[ContextGraph] = None,
) -> Dict[str, List[List[str]]]:
"""Decode one batch and return the result in a dict. The dict has the
following format:
@ -285,6 +309,7 @@ def decode_one_batch(
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=params.beam_size,
context_graph=context_graph,
)
else:
hyp_tokens = []
@ -324,7 +349,12 @@ def decode_one_batch(
): hyps
}
else:
return {f"beam_size_{params.beam_size}": hyps}
key = f"beam_size_{params.beam_size}"
if params.has_contexts:
key += f"-context-score-{params.context_score}"
else:
key += "-no-context-words"
return {key: hyps}
def decode_dataset(
@ -333,6 +363,7 @@ def decode_dataset(
model: nn.Module,
token_table: k2.SymbolTable,
decoding_graph: Optional[k2.Fsa] = None,
context_graph: Optional[ContextGraph] = None,
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
"""Decode dataset.
@ -377,6 +408,7 @@ def decode_dataset(
model=model,
token_table=token_table,
decoding_graph=decoding_graph,
context_graph=context_graph,
batch=batch,
)
@ -407,16 +439,17 @@ def save_results(
for key, results in results_dict.items():
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
# we compute CER for aishell dataset.
results_char = []
for res in results:
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
store_transcripts(filename=recog_path, texts=results_char)
logging.info(f"The transcripts are stored in {recog_path}")
# The following prints out WERs, per-word error statistics and aligned
# ref/hyp pairs.
errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
# we compute CER for aishell dataset.
results_char = []
for res in results:
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
with open(errs_filename, "w") as f:
wer = write_error_stats(
f, f"{test_set_name}-{key}", results_char, enable_log=True
@ -457,6 +490,12 @@ def main():
"fast_beam_search",
"modified_beam_search",
)
if os.path.exists(params.context_file):
params.has_contexts = True
else:
params.has_contexts = False
params.res_dir = params.exp_dir / params.decoding_method
if params.iter > 0:
@ -470,6 +509,10 @@ def main():
params.suffix += f"-max-states-{params.max_states}"
elif "beam_search" in params.decoding_method:
params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
if params.has_contexts:
params.suffix += f"-context-score-{params.context_score}"
else:
params.suffix += "-no-contexts-words"
else:
params.suffix += f"-context-{params.context_size}"
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@ -490,6 +533,11 @@ def main():
params.blank_id = 0
params.vocab_size = max(lexicon.tokens) + 1
graph_compiler = CharCtcTrainingGraphCompiler(
lexicon=lexicon,
device=device,
)
logging.info(params)
logging.info("About to create model")
@ -586,6 +634,19 @@ def main():
else:
decoding_graph = None
if params.decoding_method == "modified_beam_search":
if os.path.exists(params.context_file):
contexts_text = []
for line in open(params.context_file).readlines():
contexts_text.append(line.strip())
contexts = graph_compiler.texts_to_ids(contexts_text)
context_graph = ContextGraph(params.context_score)
context_graph.build(contexts)
else:
context_graph = None
else:
context_graph = None
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")
@ -608,6 +669,7 @@ def main():
model=model,
token_table=lexicon.token_table,
decoding_graph=decoding_graph,
context_graph=context_graph,
)
save_results(

1
egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
View File

@ -15,7 +15,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import warnings
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple, Union

26
egs/librispeech/ASR/pruned_transducer_stateless4/decode.py
View File

@ -131,8 +131,6 @@ from pathlib import Path
from typing import Dict, List, Optional, Tuple
import k2
import kaldifst
import graphviz
import sentencepiece as spm
import torch
import torch.nn as nn
@ -576,7 +574,10 @@ def decode_one_batch(
return {key: (hyps, timestamps)}
else:
key = f"beam_size_{params.beam_size}"
if params.has_contexts:
key += f"-context-score-{params.context_score}"
else:
key += "-no-context-words"
return {key: (hyps, timestamps)}
@ -626,7 +627,7 @@ def decode_dataset(
if params.decoding_method == "greedy_search":
log_interval = 50
else:
log_interval = 1
log_interval = 20
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
@ -759,6 +760,12 @@ def main():
"fast_beam_search_nbest_oracle",
"modified_beam_search",
)
if os.path.exists(params.context_file):
params.has_contexts = True
else:
params.has_contexts = False
params.res_dir = params.exp_dir / params.decoding_method
if params.iter > 0:
@ -781,7 +788,10 @@ def main():
params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
elif "beam_search" in params.decoding_method:
params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
if params.has_contexts:
params.suffix += f"-context-score-{params.context_score}"
else:
params.suffix += "-no-context-words"
else:
params.suffix += f"-context-{params.context_size}"
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@ -938,14 +948,8 @@ def main():
test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
test_other_dl = librispeech.test_dataloaders(test_other_cuts)
test_book_cuts = librispeech.test_book_cuts()
test_book_dl = librispeech.test_dataloaders(test_book_cuts)
test_book2_cuts = librispeech.test_book2_cuts()
test_book2_dl = librispeech.test_dataloaders(test_book2_cuts)
test_sets = ["test-book", "test-book2", "test-clean", "test-other"]
test_dl = [test_book_dl, test_book2_dl, test_clean_dl, test_other_dl]
test_sets = ["test-clean", "test-other"]
test_dl = [test_clean_dl, test_other_dl]
for test_set, test_dl in zip(test_sets, test_dl):
results_dict = decode_dataset(

20
egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
View File

@ -389,7 +389,6 @@ class LibriSpeechAsrDataModule:
)
sampler = DynamicBucketingSampler(
cuts,
num_buckets=2,
max_duration=self.args.max_duration,
shuffle=False,
)
@ -468,25 +467,6 @@ class LibriSpeechAsrDataModule:
self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz"
)
@lru_cache()
def test_book_cuts(self) -> CutSet:
logging.info("About to get test-books cuts")
return load_manifest_lazy(self.args.manifest_dir / "libri_books_feats.jsonl.gz")
@lru_cache()
def test_book_test_cuts(self) -> CutSet:
logging.info("About to get test-books cuts")
return load_manifest_lazy(
self.args.manifest_dir / "libri_book_test_feats.jsonl.gz"
)
@lru_cache()
def test_book2_cuts(self) -> CutSet:
logging.info("About to get test-books2 cuts")
return load_manifest_lazy(
self.args.manifest_dir / "libri_books2_feats.jsonl.gz"
)
@lru_cache()
def test_other_cuts(self) -> CutSet:
logging.info("About to get test-other cuts")

13
egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
View File

@ -396,21 +396,14 @@ class WenetSpeechAsrDataModule:
@lru_cache()
def valid_cuts(self) -> CutSet:
logging.info("About to get dev cuts")
return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV2.jsonl.gz")
return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz")
@lru_cache()
def test_net_cuts(self) -> List[CutSet]:
logging.info("About to get TEST_NET cuts")
return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_NET2.jsonl.gz")
return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_NET.jsonl.gz")
@lru_cache()
def test_meeting_cuts(self) -> List[CutSet]:
logging.info("About to get TEST_MEETING cuts")
return load_manifest_lazy(
self.args.manifest_dir / "cuts_TEST_MEETING2.jsonl.gz"
)
@lru_cache()
def test_car_cuts(self) -> List[CutSet]:
logging.info("About to get TEST_CAR cuts")
return load_manifest_lazy(self.args.manifest_dir / "car_test_feats.jsonl.gz")
return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_MEETING.jsonl.gz")

35
egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py
View File

@ -533,9 +533,12 @@ def decode_one_batch(
): hyps
}
else:
return {
f"beam_size_{params.beam_size}_context_score_{params.context_score}": hyps
}
key = f"beam_size_{params.beam_size}"
if params.has_contexts:
key += f"-context-score-{params.context_score}"
else:
key += "-no-context-words"
return {key: hyps}
def decode_dataset(
@ -674,6 +677,12 @@ def main():
"modified_beam_search_lm_shallow_fusion",
"modified_beam_search_LODR",
)
if os.path.exists(params.context_file):
params.has_contexts = True
else:
params.has_contexts = False
params.res_dir = params.exp_dir / params.decoding_method
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
@ -683,7 +692,10 @@ def main():
params.suffix += f"-max-states-{params.max_states}"
elif "beam_search" in params.decoding_method:
params.suffix += f"-beam-{params.beam_size}"
if params.has_contexts:
params.suffix += f"-context-score-{params.context_score}"
else:
params.suffix += "-no-contexts-words"
else:
params.suffix += f"-context-{params.context_size}"
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@ -851,14 +863,10 @@ def main():
if params.decoding_method == "modified_beam_search":
if os.path.exists(params.context_file):
contexts = []
contexts_text = []
for line in open(params.context_file).readlines():
context_list = graph_compiler.texts_to_ids(line.strip())
tmp = []
for context in context_list:
for x in context:
tmp.append(x)
contexts.append(tmp)
contexts_text.append(line.strip())
contexts = graph_compiler.texts_to_ids(contexts_text)
context_graph = ContextGraph(params.context_score)
context_graph.build(contexts)
else:
@ -882,11 +890,8 @@ def main():
test_meeting_cuts = wenetspeech.test_meeting_cuts()
test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts)
test_car_cuts = wenetspeech.test_car_cuts()
test_car_dl = wenetspeech.test_dataloaders(test_car_cuts)
test_sets = ["CAR", "TEST_NET", "DEV", "TEST_MEETING"]
test_dls = [test_car_dl, test_net_dl, dev_dl, test_meeting_dl]
test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
test_dls = [dev_dl, test_net_dl, test_meeting_dl]
for test_set, test_dl in zip(test_sets, test_dls):
results_dict = decode_dataset(