mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-26 18:24:18 +00:00
91 lines
2.5 KiB
Python
Executable File
91 lines
2.5 KiB
Python
Executable File
#!/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
|
|
|
|
import k2
|
|
import torch
|
|
from lhotse import load_manifest
|
|
from lhotse.dataset import K2SpeechRecognitionDataset, SingleCutSampler
|
|
from torch.nn.utils.rnn import pad_sequence
|
|
from torch.utils.data import DataLoader
|
|
|
|
from icefall.ali import (
|
|
convert_alignments_to_tensor,
|
|
load_alignments,
|
|
lookup_alignments,
|
|
)
|
|
from icefall.decode import get_lattice, one_best_decoding
|
|
from icefall.lexicon import Lexicon
|
|
from icefall.utils import get_texts
|
|
|
|
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/token_ali/cuts_test-clean.json.gz"
|
|
|
|
|
|
def data_exists():
|
|
return cuts_json.exists() and lang_dir.exists()
|
|
|
|
|
|
def get_dataloader():
|
|
cuts = load_manifest(cuts_json)
|
|
cuts = cuts.with_features_path_prefix(egs_dir)
|
|
sampler = SingleCutSampler(
|
|
cuts,
|
|
max_duration=40,
|
|
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
|
|
device = torch.device("cpu")
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda", 0)
|
|
dl = get_dataloader()
|
|
for batch in dl:
|
|
supervisions = batch["supervisions"]
|
|
cuts = supervisions["cut"]
|
|
print(cuts)
|
|
break
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test()
|