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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.
import k2
import pytest
import torch
from icefall.env import get_env_info
from icefall.utils import (
AttributeDict,
add_eos,
add_sos,
encode_supervisions,
get_texts,
make_pad_mask,
)
@pytest.fixture
def sup():
sequence_idx = torch.tensor([0, 1, 2])
start_frame = torch.tensor([1, 3, 9])
num_frames = torch.tensor([20, 30, 10])
text = ["one", "two", "three"]
return {
"sequence_idx": sequence_idx,
"start_frame": start_frame,
"num_frames": num_frames,
"text": text,
}
def test_encode_supervisions(sup):
supervision_segments, texts = encode_supervisions(sup, subsampling_factor=4)
assert torch.all(
torch.eq(
supervision_segments,
torch.tensor([[1, 0, 30 // 4], [0, 0, 20 // 4], [2, 9 // 4, 10 // 4]]),
)
)
assert texts == ["two", "one", "three"]
def test_get_texts_ragged():
fsa1 = k2.Fsa.from_str(
"""
0 1 1 10
1 2 2 20
2 3 3 30
3 4 -1 0
4
"""
)
fsa1.aux_labels = k2.RaggedTensor("[ [1 3 0 2] [] [4 0 1] [-1]]")
fsa2 = k2.Fsa.from_str(
"""
0 1 1 1
1 2 2 2
2 3 -1 0
3
"""
)
fsa2.aux_labels = k2.RaggedTensor("[[3 0 5 0 8] [0 9 7 0] [-1]]")
fsas = k2.Fsa.from_fsas([fsa1, fsa2])
texts = get_texts(fsas)
assert texts == [[1, 3, 2, 4, 1], [3, 5, 8, 9, 7]]
def test_get_texts_regular():
fsa1 = k2.Fsa.from_str(
"""
0 1 1 3 10
1 2 2 0 20
2 3 3 2 30
3 4 -1 -1 0
4
""",
num_aux_labels=1,
)
fsa2 = k2.Fsa.from_str(
"""
0 1 1 10 1
1 2 2 5 2
2 3 -1 -1 0
3
""",
num_aux_labels=1,
)
fsas = k2.Fsa.from_fsas([fsa1, fsa2])
texts = get_texts(fsas)
assert texts == [[3, 2], [10, 5]]
def test_attribute_dict():
s = AttributeDict({"a": 10, "b": 20})
assert s.a == 10
assert s["b"] == 20
s.c = 100
assert s["c"] == 100
assert hasattr(s, "a")
assert hasattr(s, "b")
assert getattr(s, "a") == 10
del s.a
assert hasattr(s, "a") is False
setattr(s, "c", 100)
s.c = 100
try:
del s.a
except AttributeError as ex:
print(f"Caught exception: {ex}")
def test_get_env_info():
s = get_env_info()
print(s)
def test_makd_pad_mask():
lengths = torch.tensor([1, 3, 2])
mask = make_pad_mask(lengths)
expected = torch.tensor(
[
[False, True, True],
[False, False, False],
[False, False, True],
]
)
assert torch.all(torch.eq(mask, expected))
assert (~expected).sum() == lengths.sum()
def test_add_sos():
sos_id = 100
ragged = k2.RaggedTensor([[1, 2], [3], [0]])
sos_ragged = add_sos(ragged, sos_id)
expected = k2.RaggedTensor([[sos_id, 1, 2], [sos_id, 3], [sos_id, 0]])
assert str(sos_ragged) == str(expected)
def test_add_eos():
eos_id = 30
ragged = k2.RaggedTensor([[1, 2], [3], [], [5, 8, 9]])
ragged_eos = add_eos(ragged, eos_id)
expected = k2.RaggedTensor(
[[1, 2, eos_id], [3, eos_id], [eos_id], [5, 8, 9, eos_id]]
)
assert str(ragged_eos) == str(expected)
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