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Sort results to make it more convenient to compare decoding results (#522)

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* Sort result to make it more convenient to compare decoding results

* Add cut_id to recognition results

* add cut_id to results for all recipes

* Fix torch.jit.script

* Fix comments

* Minor fixes

* Fix torch.jit.tracing for Pytorch version before v1.9.0
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Wei Kang 2022-08-12 07:12:50 +08:00 committed by GitHub
parent 5149788cb2
commit 5c17255eec
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60 changed files with 379 additions and 126 deletions
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8
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py
View File

@ -367,6 +367,7 @@ def decode_dataset(
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
texts = [list(str(text).replace(" ", "")) for text in texts]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -379,8 +380,8 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
this_batch.append((ref_text, hyp_words))
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)
@ -405,6 +406,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -528,6 +530,8 @@ def main():
from lhotse import CutSet
from lhotse.dataset.webdataset import export_to_webdataset
# we need cut ids to display recognition results.
args.return_cuts = True
aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)
dev = "dev"

8
egs/aishell/ASR/conformer_ctc/decode.py
View File

@ -374,6 +374,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -389,9 +390,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
@ -419,6 +420,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
logging.info(f"The transcripts are stored in {recog_path}")
@ -537,6 +539,8 @@ def main():
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)
test_cuts = aishell.test_cuts()
test_dl = aishell.test_dataloaders(test_cuts)

8
egs/aishell/ASR/conformer_mmi/decode.py
View File

@ -386,6 +386,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -401,9 +402,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
@ -431,6 +432,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
logging.info(f"The transcripts are stored in {recog_path}")
@ -556,6 +558,8 @@ def main():
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)
test_cuts = aishell.test_cuts()
test_dl = aishell.test_dataloaders(test_cuts)

8
egs/aishell/ASR/pruned_transducer_stateless3/decode.py
View File

@ -377,6 +377,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -389,9 +390,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -416,6 +417,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -606,6 +608,8 @@ def main():
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
asr_datamodule = AsrDataModule(args)
aishell = AIShell(manifest_dir=args.manifest_dir)
test_cuts = aishell.test_cuts()

8
egs/aishell/ASR/tdnn_lstm_ctc/decode.py
View File

@ -241,6 +241,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -253,9 +254,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
@ -278,6 +279,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -365,6 +367,8 @@ def main():
model.to(device)
model.eval()
# we need cut ids to display recognition results.
args.return_cuts = True
aishell = AishellAsrDataModule(args)
test_cuts = aishell.test_cuts()
test_dl = aishell.test_dataloaders(test_cuts)

10
egs/aishell/ASR/transducer_stateless/decode.py
View File

@ -38,8 +38,8 @@ from icefall.utils import (
AttributeDict,
setup_logger,
store_transcripts,
write_error_stats,
str2bool,
write_error_stats,
)
@ -296,6 +296,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -307,9 +308,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -334,6 +335,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
# The following prints out WERs, per-word error statistics and aligned
@ -438,6 +440,8 @@ def main():
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)
test_cuts = aishell.test_cuts()
test_dl = aishell.test_dataloaders(test_cuts)

8
egs/aishell/ASR/transducer_stateless_modified-2/decode.py
View File

@ -341,6 +341,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -353,9 +354,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -380,6 +381,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -496,6 +498,8 @@ def main():
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
asr_datamodule = AsrDataModule(args)
aishell = AIShell(manifest_dir=args.manifest_dir)
test_cuts = aishell.test_cuts()

8
egs/aishell/ASR/transducer_stateless_modified/decode.py
View File

@ -345,6 +345,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -357,9 +358,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -384,6 +385,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -498,6 +500,8 @@ def main():
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)
test_cuts = aishell.test_cuts()
test_dl = aishell.test_dataloaders(test_cuts)

8
egs/aishell2/ASR/pruned_transducer_stateless5/decode.py
View File

@ -514,6 +514,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -527,8 +528,8 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
this_batch.append((ref_text, hyp_words))
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)
@ -553,6 +554,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -756,6 +758,8 @@ def main():
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
aishell2 = AiShell2AsrDataModule(args)
valid_cuts = aishell2.valid_cuts()

8
egs/aishell4/ASR/pruned_transducer_stateless5/decode.py
View File

@ -378,6 +378,7 @@ def decode_dataset(
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
texts = [list(str(text).replace(" ", "")) for text in texts]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -390,8 +391,8 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
this_batch.append((ref_text, hyp_words))
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)
@ -416,6 +417,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -607,6 +609,8 @@ def main():
c.supervisions[0].text = text_normalize(text)
return c
# we need cut ids to display recognition results.
args.return_cuts = True
aishell4 = Aishell4AsrDataModule(args)
test_cuts = aishell4.test_cuts()
test_cuts = test_cuts.map(text_normalize_for_cut)

8
egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py
View File

@ -367,6 +367,7 @@ def decode_dataset(
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
texts = [list(str(text).replace(" ", "")) for text in texts]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -379,8 +380,8 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
this_batch.append((ref_text, hyp_words))
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)
@ -405,6 +406,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -535,6 +537,8 @@ def main():
from lhotse import CutSet
from lhotse.dataset.webdataset import export_to_webdataset
# we need cut ids to display recognition results.
args.return_cuts = True
alimeeting = AlimeetingAsrDataModule(args)
dev = "eval"

8
egs/gigaspeech/ASR/conformer_ctc/decode.py
View File

@ -451,6 +451,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -469,9 +470,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
else:
@ -512,6 +513,7 @@ def save_results(
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = post_processing(results)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
logging.info(f"The transcripts are stored in {recog_path}")
@ -676,6 +678,8 @@ def main():
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
gigaspeech = GigaSpeechAsrDataModule(args)
dev_cuts = gigaspeech.dev_cuts()

8
egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py
View File

@ -374,6 +374,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -386,9 +387,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -414,6 +415,7 @@ def save_results(
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = post_processing(results)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -544,6 +546,8 @@ def main():
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
gigaspeech = GigaSpeechAsrDataModule(args)
dev_cuts = gigaspeech.dev_cuts()

8
egs/librispeech/ASR/conformer_ctc/decode.py
View File

@ -525,6 +525,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -544,9 +545,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
else:
@ -586,6 +587,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
logging.info(f"The transcripts are stored in {recog_path}")
@ -779,6 +781,8 @@ def main():
)
rnn_lm_model.eval()
# we need cut ids to display recognition results.
args.return_cuts = True
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

11
egs/librispeech/ASR/conformer_ctc2/decode.py
View File

@ -31,14 +31,13 @@ import torch.nn as nn
from asr_datamodule import LibriSpeechAsrDataModule
from conformer import Conformer
from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
from icefall.checkpoint import (
average_checkpoints,
average_checkpoints_with_averaged_model,
find_checkpoints,
load_checkpoint,
)
from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
from icefall.decode import (
get_lattice,
nbest_decoding,
@ -633,6 +632,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -652,9 +652,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
else:
@ -694,6 +694,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
logging.info(f"The transcripts are stored in {recog_path}")
@ -956,6 +957,8 @@ def main():
)
rnn_lm_model.eval()
# we need cut ids to display recognition results.
args.return_cuts = True
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

8
egs/librispeech/ASR/conformer_mmi/decode.py
View File

@ -449,6 +449,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -466,9 +467,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
@ -496,6 +497,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
if enable_log:
logging.info(f"The transcripts are stored in {recog_path}")
@ -661,6 +663,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
# CAUTION: `test_sets` is for displaying only.
# If you want to skip test-clean, you have to skip

8
egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py
View File

@ -403,6 +403,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -415,9 +416,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -442,6 +443,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -624,6 +626,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

6
egs/librispeech/ASR/conv_emformer_transducer_stateless/stream.py
View File

@ -29,6 +29,7 @@ class Stream(object):
def __init__(
self,
params: AttributeDict,
cut_id: str,
decoding_graph: Optional[k2.Fsa] = None,
device: torch.device = torch.device("cpu"),
LOG_EPS: float = math.log(1e-10),
@ -44,6 +45,7 @@ class Stream(object):
The device to run this stream.
"""
self.LOG_EPS = LOG_EPS
self.cut_id = cut_id
# Containing attention caches and convolution caches
self.states: Optional[
@ -138,6 +140,10 @@ class Stream(object):
"""Return True if all feature frames are processed."""
return self._done
@property
def id(self) -> str:
return self.cut_id
def decoding_result(self) -> List[int]:
"""Obtain current decoding result."""
if self.decoding_method == "greedy_search":

5
egs/librispeech/ASR/conv_emformer_transducer_stateless/streaming_decode.py
View File

@ -74,7 +74,6 @@ from pathlib import Path
from typing import Dict, List, Optional, Tuple
import k2
from lhotse import CutSet
import numpy as np
import sentencepiece as spm
import torch
@ -83,6 +82,7 @@ from asr_datamodule import LibriSpeechAsrDataModule
from beam_search import Hypothesis, HypothesisList, get_hyps_shape
from emformer import LOG_EPSILON, stack_states, unstack_states
from kaldifeat import Fbank, FbankOptions
from lhotse import CutSet
from stream import Stream
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
@ -678,6 +678,7 @@ def decode_dataset(
# Each utterance has a Stream.
stream = Stream(
params=params,
cut_id=cut.id,
decoding_graph=decoding_graph,
device=device,
LOG_EPS=LOG_EPSILON,
@ -711,6 +712,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
streams[i].id,
streams[i].ground_truth.split(),
sp.decode(streams[i].decoding_result()).split(),
)
@ -731,6 +733,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
streams[i].id,
streams[i].ground_truth.split(),
sp.decode(streams[i].decoding_result()).split(),
)

8
egs/librispeech/ASR/conv_emformer_transducer_stateless2/decode.py
View File

@ -403,6 +403,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -415,9 +416,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -442,6 +443,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -624,6 +626,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

5
egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming_decode.py
View File

@ -74,7 +74,6 @@ from pathlib import Path
from typing import Dict, List, Optional, Tuple
import k2
from lhotse import CutSet
import numpy as np
import sentencepiece as spm
import torch
@ -83,6 +82,7 @@ from asr_datamodule import LibriSpeechAsrDataModule
from beam_search import Hypothesis, HypothesisList, get_hyps_shape
from emformer import LOG_EPSILON, stack_states, unstack_states
from kaldifeat import Fbank, FbankOptions
from lhotse import CutSet
from stream import Stream
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
@ -678,6 +678,7 @@ def decode_dataset(
# Each utterance has a Stream.
stream = Stream(
params=params,
cut_id=cut.id,
decoding_graph=decoding_graph,
device=device,
LOG_EPS=LOG_EPSILON,
@ -711,6 +712,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
streams[i].id,
streams[i].ground_truth.split(),
sp.decode(streams[i].decoding_result()).split(),
)
@ -731,6 +733,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
streams[i].id,
streams[i].ground_truth.split(),
sp.decode(streams[i].decoding_result()).split(),
)

8
egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py
View File

@ -391,6 +391,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -403,9 +404,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -430,6 +431,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -612,6 +614,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

10
egs/librispeech/ASR/pruned_transducer_stateless/decode.py
View File

@ -551,6 +551,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -564,9 +565,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -591,6 +592,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -631,6 +633,8 @@ def main():
LibriSpeechAsrDataModule.add_arguments(parser)
args = parser.parse_args()
args.exp_dir = Path(args.exp_dir)
# we need cut ids to display recognition results.
args.return_cuts = True
params = get_params()
params.update(vars(args))
@ -754,6 +758,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

6
egs/librispeech/ASR/pruned_transducer_stateless/decode_stream.py
View File

@ -28,6 +28,7 @@ class DecodeStream(object):
def __init__(
self,
params: AttributeDict,
cut_id: str,
initial_states: List[torch.Tensor],
decoding_graph: Optional[k2.Fsa] = None,
device: torch.device = torch.device("cpu"),
@ -48,6 +49,7 @@ class DecodeStream(object):
assert device == decoding_graph.device
self.params = params
self.cut_id = cut_id
self.LOG_EPS = math.log(1e-10)
self.states = initial_states
@ -102,6 +104,10 @@ class DecodeStream(object):
"""Return True if all the features are processed."""
return self._done
@property
def id(self) -> str:
return self.cut_id
def set_features(
self,
features: torch.Tensor,

3
egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py
View File

@ -356,6 +356,7 @@ def decode_dataset(
# each utterance has a DecodeStream.
decode_stream = DecodeStream(
params=params,
cut_id=cut.id,
initial_states=initial_states,
decoding_graph=decoding_graph,
device=device,
@ -385,6 +386,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)
@ -402,6 +404,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)

22
egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py
View File

@ -32,7 +32,7 @@ from scaling import (
)
from torch import Tensor, nn
from icefall.utils import make_pad_mask, subsequent_chunk_mask
from icefall.utils import is_jit_tracing, make_pad_mask, subsequent_chunk_mask
class Conformer(EncoderInterface):
@ -155,7 +155,7 @@ class Conformer(EncoderInterface):
# Note: rounding_mode in torch.div() is available only in torch >= 1.8.0
lengths = (((x_lens - 1) >> 1) - 1) >> 1
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert x.size(0) == lengths.max().item()
src_key_padding_mask = make_pad_mask(lengths)
@ -788,7 +788,7 @@ class RelPositionalEncoding(torch.nn.Module):
) -> None:
"""Construct an PositionalEncoding object."""
super(RelPositionalEncoding, self).__init__()
if torch.jit.is_tracing():
if is_jit_tracing():
# 10k frames correspond to ~100k ms, e.g., 100 seconds, i.e.,
# It assumes that the maximum input won't have more than
# 10k frames.
@ -1015,12 +1015,12 @@ class RelPositionMultiheadAttention(nn.Module):
(batch_size, num_heads, time1, n) = x.shape
time2 = time1 + left_context
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert (
n == left_context + 2 * time1 - 1
), f"{n} == {left_context} + 2 * {time1} - 1"
if torch.jit.is_tracing():
if is_jit_tracing():
rows = torch.arange(start=time1 - 1, end=-1, step=-1)
cols = torch.arange(time2)
rows = rows.repeat(batch_size * num_heads).unsqueeze(-1)
@ -1111,12 +1111,12 @@ class RelPositionMultiheadAttention(nn.Module):
"""
tgt_len, bsz, embed_dim = query.size()
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert embed_dim == embed_dim_to_check
assert key.size(0) == value.size(0) and key.size(1) == value.size(1)
head_dim = embed_dim // num_heads
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert (
head_dim * num_heads == embed_dim
), "embed_dim must be divisible by num_heads"
@ -1232,7 +1232,7 @@ class RelPositionMultiheadAttention(nn.Module):
src_len = k.size(0)
if key_padding_mask is not None and not torch.jit.is_tracing():
if key_padding_mask is not None and not is_jit_tracing():
assert key_padding_mask.size(0) == bsz, "{} == {}".format(
key_padding_mask.size(0), bsz
)
@ -1243,7 +1243,7 @@ class RelPositionMultiheadAttention(nn.Module):
q = q.transpose(0, 1) # (batch, time1, head, d_k)
pos_emb_bsz = pos_emb.size(0)
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert pos_emb_bsz in (1, bsz) # actually it is 1
p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim)
@ -1280,7 +1280,7 @@ class RelPositionMultiheadAttention(nn.Module):
bsz * num_heads, tgt_len, -1
)
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert list(attn_output_weights.size()) == [
bsz * num_heads,
tgt_len,
@ -1345,7 +1345,7 @@ class RelPositionMultiheadAttention(nn.Module):
attn_output = torch.bmm(attn_output_weights, v)
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert list(attn_output.size()) == [
bsz * num_heads,
tgt_len,

8
egs/librispeech/ASR/pruned_transducer_stateless2/decode.py
View File

@ -574,6 +574,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -587,9 +588,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -614,6 +615,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -777,6 +779,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

11
egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py
View File

@ -14,13 +14,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from scaling import ScaledConv1d, ScaledEmbedding
from icefall.utils import is_jit_tracing
class Decoder(nn.Module):
"""This class modifies the stateless decoder from the following paper:
@ -80,7 +80,10 @@ class Decoder(nn.Module):
self.conv = nn.Identity()
def forward(
self, y: torch.Tensor, need_pad: Union[bool, torch.Tensor] = True
self,
y: torch.Tensor,
need_pad: bool = True # Annotation should be Union[bool, torch.Tensor]
# but, torch.jit.script does not support Union.
) -> torch.Tensor:
"""
Args:
@ -108,7 +111,7 @@ class Decoder(nn.Module):
else:
# During inference time, there is no need to do extra padding
# as we only need one output
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert embedding_out.size(-1) == self.context_size
embedding_out = self.conv(embedding_out)
embedding_out = embedding_out.permute(0, 2, 1)

4
egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py
View File

@ -18,6 +18,8 @@ import torch
import torch.nn as nn
from scaling import ScaledLinear
from icefall.utils import is_jit_tracing
class Joiner(nn.Module):
def __init__(
@ -52,7 +54,7 @@ class Joiner(nn.Module):
Returns:
Return a tensor of shape (N, T, s_range, C).
"""
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert encoder_out.ndim == decoder_out.ndim
assert encoder_out.ndim in (2, 4)
assert encoder_out.shape == decoder_out.shape

8
egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py
View File

@ -23,6 +23,8 @@ import torch
import torch.nn as nn
from torch import Tensor
from icefall.utils import is_jit_tracing
def _ntuple(n):
def parse(x):
@ -152,7 +154,7 @@ class BasicNorm(torch.nn.Module):
self.register_buffer("eps", torch.tensor(eps).log().detach())
def forward(self, x: Tensor) -> Tensor:
if not torch.jit.is_tracing():
if not is_jit_tracing():
assert x.shape[self.channel_dim] == self.num_channels
scales = (
torch.mean(x ** 2, dim=self.channel_dim, keepdim=True)
@ -424,7 +426,7 @@ class ActivationBalancer(torch.nn.Module):
self.max_abs = max_abs
def forward(self, x: Tensor) -> Tensor:
if torch.jit.is_scripting() or torch.jit.is_tracing():
if torch.jit.is_scripting() or is_jit_tracing():
return x
else:
return ActivationBalancerFunction.apply(
@ -473,7 +475,7 @@ class DoubleSwish(torch.nn.Module):
"""Return double-swish activation function which is an approximation to Swish(Swish(x)),
that we approximate closely with x * sigmoid(x-1).
"""
if torch.jit.is_scripting() or torch.jit.is_tracing():
if torch.jit.is_scripting() or is_jit_tracing():
return x * torch.sigmoid(x - 1.0)
else:
return DoubleSwishFunction.apply(x)

3
egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py
View File

@ -358,6 +358,7 @@ def decode_dataset(
# each utterance has a DecodeStream.
decode_stream = DecodeStream(
params=params,
cut_id=cut.id,
initial_states=initial_states,
decoding_graph=decoding_graph,
device=device,
@ -388,6 +389,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)
@ -405,6 +407,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)

7
egs/librispeech/ASR/pruned_transducer_stateless3/decode-giga.py
View File

@ -422,6 +422,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -434,9 +435,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -610,6 +611,8 @@ def main():
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
asr_datamodule = AsrDataModule(args)
gigaspeech = GigaSpeech(manifest_dir=args.manifest_dir)

8
egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
View File

@ -745,6 +745,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -760,9 +761,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -787,6 +788,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -1067,6 +1069,8 @@ def main():
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
asr_datamodule = AsrDataModule(args)
librispeech = LibriSpeech(manifest_dir=args.manifest_dir)

26
egs/librispeech/ASR/pruned_transducer_stateless3/scaling_converter.py
View File

@ -25,6 +25,7 @@ life by converting them to their non-scaled version during inference.
import copy
import re
from typing import List
import torch
import torch.nn as nn
@ -54,7 +55,10 @@ def scaled_linear_to_linear(scaled_linear: ScaledLinear) -> nn.Linear:
in_features=scaled_linear.in_features,
out_features=scaled_linear.out_features,
bias=True, # otherwise, it throws errors when converting to PNNX format.
device=weight.device,
# device=weight.device, # Pytorch version before v1.9.0 does not has
# this argument. Comment out for now, we will
# see if it will raise error for versions
# after v1.9.0
)
linear.weight.data.copy_(weight)
@ -164,6 +168,24 @@ def scaled_embedding_to_embedding(
return embedding
# Copied from https://pytorch.org/docs/1.9.0/_modules/torch/nn/modules/module.html#Module.get_submodule
# get_submodule was added to nn.Module at v1.9.0
def get_submodule(model, target):
if target == "":
return model
atoms: List[str] = target.split(".")
mod: torch.nn.Module = model
for item in atoms:
if not hasattr(mod, item):
raise AttributeError(
mod._get_name() + " has no " "attribute `" + item + "`"
)
mod = getattr(mod, item)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("`" + item + "` is not " "an nn.Module")
return mod
def convert_scaled_to_non_scaled(model: nn.Module, inplace: bool = False):
"""Convert `ScaledLinear`, `ScaledConv1d`, and `ScaledConv2d`
in the given modle to their unscaled version `nn.Linear`, `nn.Conv1d`,
@ -200,7 +222,7 @@ def convert_scaled_to_non_scaled(model: nn.Module, inplace: bool = False):
for k, v in d.items():
if "." in k:
parent, child = k.rsplit(".", maxsplit=1)
setattr(model.get_submodule(parent), child, v)
setattr(get_submodule(model, parent), child, v)
else:
setattr(model, k, v)

3
egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py
View File

@ -359,6 +359,7 @@ def decode_dataset(
# each utterance has a DecodeStream.
decode_stream = DecodeStream(
params=params,
cut_id=cut.id,
initial_states=initial_states,
decoding_graph=decoding_graph,
device=device,
@ -389,6 +390,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)
@ -406,6 +408,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)

8
egs/librispeech/ASR/pruned_transducer_stateless4/decode.py
View File

@ -578,6 +578,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -591,9 +592,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -618,6 +619,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -831,6 +833,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

3
egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py
View File

@ -371,6 +371,7 @@ def decode_dataset(
# each utterance has a DecodeStream.
decode_stream = DecodeStream(
params=params,
cut_id=cut.id,
initial_states=initial_states,
decoding_graph=decoding_graph,
device=device,
@ -401,6 +402,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)
@ -418,6 +420,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)

8
egs/librispeech/ASR/pruned_transducer_stateless5/decode.py
View File

@ -564,6 +564,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -577,9 +578,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -604,6 +605,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -817,6 +819,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

3
egs/librispeech/ASR/pruned_transducer_stateless5/streaming_decode.py
View File

@ -371,6 +371,7 @@ def decode_dataset(
# each utterance has a DecodeStream.
decode_stream = DecodeStream(
params=params,
cut_id=cut.id,
initial_states=initial_states,
decoding_graph=decoding_graph,
device=device,
@ -401,6 +402,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)
@ -418,6 +420,7 @@ def decode_dataset(
for i in sorted(finished_streams, reverse=True):
decode_results.append(
(
decode_streams[i].id,
decode_streams[i].ground_truth.split(),
sp.decode(decode_streams[i].decoding_result()).split(),
)

8
egs/librispeech/ASR/pruned_transducer_stateless6/decode.py
View File

@ -387,6 +387,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -399,9 +400,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -426,6 +427,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -608,6 +610,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

8
egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
View File

@ -311,6 +311,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -324,9 +325,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
@ -349,6 +350,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -473,6 +475,8 @@ def main():
model.to(device)
model.eval()
# we need cut ids to display recognition results.
args.return_cuts = True
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

8
egs/librispeech/ASR/transducer/decode.py
View File

@ -295,6 +295,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -306,9 +307,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -333,6 +334,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -424,6 +426,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

8
egs/librispeech/ASR/transducer_lstm/decode.py
View File

@ -292,6 +292,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -303,9 +304,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -330,6 +331,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -422,6 +424,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

8
egs/librispeech/ASR/transducer_stateless/decode.py
View File

@ -350,6 +350,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -362,9 +363,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -389,6 +390,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -500,6 +502,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

8
egs/librispeech/ASR/transducer_stateless2/decode.py
View File

@ -350,6 +350,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -362,9 +363,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -389,6 +390,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -500,6 +502,8 @@ def main():
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
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()

8
egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py
View File

@ -351,6 +351,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -363,9 +364,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -390,6 +391,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -503,6 +505,8 @@ def main():
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
asr_datamodule = AsrDataModule(args)
librispeech = LibriSpeech(manifest_dir=args.manifest_dir)

8
egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py
View File

@ -365,6 +365,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -377,9 +378,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -405,6 +406,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -561,6 +563,8 @@ def main():
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
spgispeech = SPGISpeechAsrDataModule(args)
dev_cuts = spgispeech.dev_cuts()

16
egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py
View File

@ -453,6 +453,7 @@ def decode_dataset(
zh_char = "[\u4e00-\u9fa5]+" # Chinese chars
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
zh_texts = []
en_texts = []
for i in range(len(texts)):
@ -487,14 +488,14 @@ def decode_dataset(
# print(hyps_texts)
hyps, zh_hyps, en_hyps = hyps_texts
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
this_batch.append((ref_text, hyp_words))
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
this_batch.append((cut_id, ref_text, hyp_words))
for hyp_words, ref_text in zip(zh_hyps, zh_texts):
this_batch_zh.append((ref_text, hyp_words))
for cut_id, hyp_words, ref_text in zip(cut_ids, zh_hyps, zh_texts):
this_batch_zh.append((cut_id, ref_text, hyp_words))
for hyp_words, ref_text in zip(en_hyps, en_texts):
this_batch_en.append((ref_text, hyp_words))
for cut_id, hyp_words, ref_text in zip(cut_ids, en_hyps, en_texts):
this_batch_en.append((cut_id, ref_text, hyp_words))
results[name].extend(this_batch)
zh_results[name + "_zh"].extend(this_batch_zh)
@ -521,6 +522,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -710,6 +712,8 @@ def main():
c.supervisions[0].text = text_normalize(text)
return c
# we need cut ids to display recognition results.
args.return_cuts = True
tal_csasr = TAL_CSASRAsrDataModule(args)
dev_cuts = tal_csasr.valid_cuts()

8
egs/tedlium3/ASR/pruned_transducer_stateless/decode.py
View File

@ -350,6 +350,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -362,9 +363,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -389,6 +390,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -498,6 +500,8 @@ def main():
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
tedlium = TedLiumAsrDataModule(args)
dev_cuts = tedlium.dev_cuts()
test_cuts = tedlium.test_cuts()

8
egs/tedlium3/ASR/transducer_stateless/decode.py
View File

@ -325,6 +325,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -336,9 +337,9 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[name].extend(this_batch)
@ -363,6 +364,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -462,6 +464,8 @@ def main():
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
tedlium = TedLiumAsrDataModule(args)
dev_cuts = tedlium.dev_cuts()
test_cuts = tedlium.test_cuts()

8
egs/timit/ASR/tdnn_ligru_ctc/decode.py
View File

@ -311,6 +311,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -324,9 +325,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
@ -349,6 +350,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -468,6 +470,8 @@ def main():
model.to(device)
model.eval()
# we need cut ids to display recognition results.
args.return_cuts = True
timit = TimitAsrDataModule(args)
test_set = "TEST"
test_dl = timit.test_dataloaders()

8
egs/timit/ASR/tdnn_lstm_ctc/decode.py
View File

@ -310,6 +310,7 @@ def decode_dataset(
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -323,9 +324,9 @@ def decode_dataset(
for lm_scale, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results[lm_scale].extend(this_batch)
@ -348,6 +349,7 @@ def save_results(
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -467,6 +469,8 @@ def main():
model.to(device)
model.eval()
# we need cut ids to display recognition results.
args.return_cuts = True
timit = TimitAsrDataModule(args)
test_set = "TEST"
test_dl = timit.test_dataloaders()

8
egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py
View File

@ -491,6 +491,7 @@ def decode_dataset(
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
texts = [list(str(text)) for text in texts]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -504,8 +505,8 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
this_batch.append((ref_text, hyp_words))
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)
@ -530,6 +531,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -678,6 +680,8 @@ def main():
from lhotse import CutSet
from lhotse.dataset.webdataset import export_to_webdataset
# we need cut ids to display recognition results.
args.return_cuts = True
wenetspeech = WenetSpeechAsrDataModule(args)
dev = "dev"

8
egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py
View File

@ -461,6 +461,7 @@ def decode_dataset(
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
texts = [list(str(text)) for text in texts]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps_dict = decode_one_batch(
params=params,
@ -473,8 +474,8 @@ def decode_dataset(
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
this_batch.append((ref_text, hyp_words))
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)
@ -499,6 +500,7 @@ def save_results(
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -682,6 +684,8 @@ def main():
from lhotse import CutSet
from lhotse.dataset.webdataset import export_to_webdataset
# we need cut ids to display recognition results.
args.return_cuts = True
wenetspeech = WenetSpeechAsrDataModule(args)
dev = "dev"

6
egs/wenetspeech/ASR/pruned_transducer_stateless5/decode_stream.py
View File

@ -28,6 +28,7 @@ class DecodeStream(object):
def __init__(
self,
params: AttributeDict,
cut_id: str,
initial_states: List[torch.Tensor],
decoding_graph: Optional[k2.Fsa] = None,
device: torch.device = torch.device("cpu"),
@ -48,6 +49,7 @@ class DecodeStream(object):
assert device == decoding_graph.device
self.params = params
self.cut_id = cut_id
self.LOG_EPS = math.log(1e-10)
self.states = initial_states
@ -102,6 +104,10 @@ class DecodeStream(object):
"""Return True if all the features are processed."""
return self._done
@property
def id(self) -> str:
return self.cut_id
def set_features(
self,
features: torch.Tensor,

3
egs/wenetspeech/ASR/pruned_transducer_stateless5/streaming_decode.py
View File

@ -396,6 +396,7 @@ def decode_dataset(
# each utterance has a DecodeStream.
decode_stream = DecodeStream(
params=params,
cut_id=cut.id,
initial_states=initial_states,
decoding_graph=decoding_graph,
device=device,
@ -423,6 +424,7 @@ def decode_dataset(
hyp = decode_streams[i].decoding_result()
decode_results.append(
(
decode_streams[i].id,
list(decode_streams[i].ground_truth),
[lexicon.token_table[idx] for idx in hyp],
)
@ -441,6 +443,7 @@ def decode_dataset(
hyp = decode_streams[i].decoding_result()
decode_results.append(
(
decode_streams[i].id,
list(decode_streams[i].ground_truth),
[lexicon.token_table[idx] for idx in hyp],
)

8
egs/yesno/ASR/tdnn/decode.py
View File

@ -178,6 +178,7 @@ def decode_dataset(
results = []
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps = decode_one_batch(
params=params,
@ -189,9 +190,9 @@ def decode_dataset(
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results.extend(this_batch)
@ -237,6 +238,7 @@ def save_results(
Return None.
"""
recog_path = exp_dir / f"recogs-{test_set_name}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -303,6 +305,8 @@ def main():
model.to(device)
model.eval()
# we need cut ids to display recognition results.
args.return_cuts = True
yes_no = YesNoAsrDataModule(args)
test_dl = yes_no.test_dataloaders()
results = decode_dataset(

8
egs/yesno/ASR/transducer/decode.py
View File

@ -165,6 +165,7 @@ def decode_dataset(
results = []
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
hyps = decode_one_batch(
params=params,
@ -174,9 +175,9 @@ def decode_dataset(
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
this_batch.append((cut_id, ref_words, hyp_words))
results.extend(this_batch)
@ -222,6 +223,7 @@ def save_results(
Return None.
"""
recog_path = exp_dir / f"recogs-{test_set_name}.txt"
results = sorted(results)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
@ -291,6 +293,8 @@ def main():
model.eval()
model.device = device
# we need cut ids to display recognition results.
args.return_cuts = True
yes_no = YesNoAsrDataModule(args)
test_dl = yes_no.test_dataloaders()
results = decode_dataset(

1
icefall/__init__.py
View File

@ -49,6 +49,7 @@ from .utils import (
get_alignments,
get_executor,
get_texts,
is_jit_tracing,
l1_norm,
l2_norm,
linf_norm,

37
icefall/utils.py
View File

@ -42,6 +42,18 @@ from icefall.checkpoint import average_checkpoints
Pathlike = Union[str, Path]
# Pytorch issue: https://github.com/pytorch/pytorch/issues/47379
# Fixed: https://github.com/pytorch/pytorch/pull/49853
# The fix was included in v1.9.0
# https://github.com/pytorch/pytorch/releases/tag/v1.9.0
def is_jit_tracing():
if torch.jit.is_scripting():
return False
elif torch.jit.is_tracing():
return True
return False
@contextmanager
def get_executor():
# We'll either return a process pool or a distributed worker pool.
@ -321,7 +333,7 @@ def load_alignments(filename: str) -> Tuple[int, Dict[str, List[int]]]:
def store_transcripts(
filename: Pathlike, texts: Iterable[Tuple[str, str]]
filename: Pathlike, texts: Iterable[Tuple[str, str, str]]
) -> None:
"""Save predicted results and reference transcripts to a file.
@ -329,15 +341,15 @@ def store_transcripts(
filename:
File to save the results to.
texts:
An iterable of tuples. The first element is the reference transcript
while the second element is the predicted result.
An iterable of tuples. The first element is the cur_id, the second is
the reference transcript and the third element is the predicted result.
Returns:
Return None.
"""
with open(filename, "w") as f:
for ref, hyp in texts:
print(f"ref={ref}", file=f)
print(f"hyp={hyp}", file=f)
for cut_id, ref, hyp in texts:
print(f"{cut_id}:\tref={ref}", file=f)
print(f"{cut_id}:\thyp={hyp}", file=f)
def write_error_stats(
@ -372,8 +384,8 @@ def write_error_stats(
The reference word `SIR` is missing in the predicted
results (a deletion error).
results:
An iterable of tuples. The first element is the reference transcript
while the second element is the predicted result.
An iterable of tuples. The first element is the cur_id, the second is
the reference transcript and the third element is the predicted result.
enable_log:
If True, also print detailed WER to the console.
Otherwise, it is written only to the given file.
@ -389,7 +401,7 @@ def write_error_stats(
words: Dict[str, List[int]] = defaultdict(lambda: [0, 0, 0, 0, 0])
num_corr = 0
ERR = "*"
for ref, hyp in results:
for cut_id, ref, hyp in results:
ali = kaldialign.align(ref, hyp, ERR)
for ref_word, hyp_word in ali:
if ref_word == ERR:
@ -405,7 +417,7 @@ def write_error_stats(
else:
words[ref_word][0] += 1
num_corr += 1
ref_len = sum([len(r) for r, _ in results])
ref_len = sum([len(r) for _, r, _ in results])
sub_errs = sum(subs.values())
ins_errs = sum(ins.values())
del_errs = sum(dels.values())
@ -434,7 +446,7 @@ def write_error_stats(
print("", file=f)
print("PER-UTT DETAILS: corr or (ref->hyp) ", file=f)
for ref, hyp in results:
for cut_id, ref, hyp in results:
ali = kaldialign.align(ref, hyp, ERR)
combine_successive_errors = True
if combine_successive_errors:
@ -461,7 +473,8 @@ def write_error_stats(
]
print(
" ".join(
f"{cut_id}:\t"
+ " ".join(
(
ref_word
if ref_word == hyp_word