mirror of
https://github.com/k2-fsa/icefall.git
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4151cca147
* Add aishell recipe * Remove unnecessary code and update docs * adapt to k2 v1.7, add docs and results * Update conformer ctc model * Update docs, pretrained.py & results * Fix code style * Fix code style * Fix code style * Minor fix * Minor fix * Fix pretrained.py * Update pretrained model & corresponding docs * Export torch script model for Aishell * Add C++ deployment docs * Minor fixes * Fix unit test * Update Readme
129 lines
3.0 KiB
Python
129 lines
3.0 KiB
Python
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import k2
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import pytest
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import torch
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from icefall.env import get_env_info
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from icefall.utils import AttributeDict, encode_supervisions, get_texts
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@pytest.fixture
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def sup():
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sequence_idx = torch.tensor([0, 1, 2])
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start_frame = torch.tensor([1, 3, 9])
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num_frames = torch.tensor([20, 30, 10])
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text = ["one", "two", "three"]
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return {
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"sequence_idx": sequence_idx,
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"start_frame": start_frame,
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"num_frames": num_frames,
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"text": text,
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}
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def test_encode_supervisions(sup):
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supervision_segments, texts = encode_supervisions(sup, subsampling_factor=4)
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assert torch.all(
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torch.eq(
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supervision_segments,
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torch.tensor(
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[[1, 0, 30 // 4], [0, 0, 20 // 4], [2, 9 // 4, 10 // 4]]
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),
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)
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)
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assert texts == ["two", "one", "three"]
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def test_get_texts_ragged():
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fsa1 = k2.Fsa.from_str(
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"""
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0 1 1 10
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1 2 2 20
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2 3 3 30
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3 4 -1 0
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4
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"""
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)
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fsa1.aux_labels = k2.RaggedTensor("[ [1 3 0 2] [] [4 0 1] [-1]]")
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fsa2 = k2.Fsa.from_str(
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"""
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0 1 1 1
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1 2 2 2
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2 3 -1 0
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3
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"""
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)
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fsa2.aux_labels = k2.RaggedTensor("[[3 0 5 0 8] [0 9 7 0] [-1]]")
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fsas = k2.Fsa.from_fsas([fsa1, fsa2])
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texts = get_texts(fsas)
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assert texts == [[1, 3, 2, 4, 1], [3, 5, 8, 9, 7]]
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def test_get_texts_regular():
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fsa1 = k2.Fsa.from_str(
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"""
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0 1 1 3 10
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1 2 2 0 20
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2 3 3 2 30
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3 4 -1 -1 0
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4
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""",
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num_aux_labels=1,
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)
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fsa2 = k2.Fsa.from_str(
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"""
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0 1 1 10 1
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1 2 2 5 2
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2 3 -1 -1 0
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3
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""",
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num_aux_labels=1,
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)
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fsas = k2.Fsa.from_fsas([fsa1, fsa2])
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texts = get_texts(fsas)
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assert texts == [[3, 2], [10, 5]]
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def test_attribute_dict():
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s = AttributeDict({"a": 10, "b": 20})
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assert s.a == 10
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assert s["b"] == 20
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s.c = 100
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assert s["c"] == 100
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assert hasattr(s, "a")
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assert hasattr(s, "b")
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assert getattr(s, "a") == 10
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del s.a
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assert hasattr(s, "a") is False
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setattr(s, "c", 100)
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s.c = 100
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try:
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del s.a
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except AttributeError as ex:
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print(f"Caught exception: {ex}")
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def test_get_env_info():
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s = get_env_info()
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print(s)
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