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#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# 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.
# Runt his file using one of the following two ways:
# (1) python3 ./test/test_ali.py
# (2) pytest ./test/test_ali.py
# The purpose of this file is to show that if we build a mask
# from alignments and add it to a randomly generated nnet_output,
# we can decode the correct transcript.
from pathlib import Path
from lhotse import CutSet, load_manifest
from lhotse.dataset import K2SpeechRecognitionDataset, SimpleCutSampler
from lhotse.dataset.collation import collate_custom_field
from torch.utils.data import DataLoader
ICEFALL_DIR = Path(__file__).resolve().parent.parent
egs_dir = ICEFALL_DIR / "egs/librispeech/ASR"
lang_dir = egs_dir / "data/lang_bpe_500"
cuts_json = egs_dir / "data/ali/cuts_dev-clean.json.gz"
def data_exists():
return cuts_json.exists() and lang_dir.exists()
def get_dataloader():
cuts = load_manifest(cuts_json)
print(cuts[0])
cuts = cuts.with_features_path_prefix(egs_dir)
sampler = SimpleCutSampler(
cuts,
max_duration=10,
shuffle=False,
)
dataset = K2SpeechRecognitionDataset(return_cuts=True)
dl = DataLoader(
dataset,
sampler=sampler,
batch_size=None,
num_workers=1,
persistent_workers=False,
)
return dl
def test():
if not data_exists():
return
dl = get_dataloader()
for batch in dl:
supervisions = batch["supervisions"]
cuts = supervisions["cut"]
labels_alignment, labels_alignment_length = collate_custom_field(
CutSet.from_cuts(cuts), "labels_alignment"
)
(
aux_labels_alignment,
aux_labels_alignment_length,
) = collate_custom_field(CutSet.from_cuts(cuts), "aux_labels_alignment")
print(labels_alignment)
print(aux_labels_alignment)
print(labels_alignment_length)
print(aux_labels_alignment_length)
break
if __name__ == "__main__":
test()
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