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96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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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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from unittest.mock import Mock
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import pytest
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import sentencepiece as spm
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from omegaconf import OmegaConf
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from nemo.collections.asr.parts.mixins import ASRBPEMixin
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from nemo.collections.common.tokenizers.canary_tokenizer import DEFAULT_TOKENS, CanaryTokenizer
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from nemo.collections.common.tokenizers.sentencepiece_tokenizer import SentencePieceTokenizer, create_spt_model
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from nemo.core import Serialization
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@pytest.fixture(scope="session")
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def special_tokenizer_path(tmp_path_factory) -> str:
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tokens = ["asr", "ast", "en", "de", "fr", "es"]
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tmpdir = tmp_path_factory.mktemp("spl_tokens")
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CanaryTokenizer.build_special_tokenizer(tokens, tmpdir)
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return str(tmpdir)
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@pytest.fixture(scope="session")
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def lang_tokenizer_path(tmp_path_factory) -> str:
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tmpdir = tmp_path_factory.mktemp("klingon_tokens")
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text_path = tmpdir / "text.txt"
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text_path.write_text("a\nb\nc\nd\n")
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create_spt_model(text_path, vocab_size=8, sample_size=-1, do_lower_case=False, output_dir=str(tmpdir))
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return str(tmpdir)
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def test_canary_tokenizer_build_special_tokenizer(tmp_path):
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tokens = ["asr", "ast", "en", "de", "fr", "es"]
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tokenizer = CanaryTokenizer.build_special_tokenizer(tokens, tmp_path)
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expected_tokens = DEFAULT_TOKENS + [f"<|{t}|>" for t in tokens] + ["▁", "<unk>"]
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tokens = []
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for i in range(tokenizer.tokenizer.vocab_size()):
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tokens.append(tokenizer.tokenizer.IdToPiece(i))
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expected_tokens.sort(), tokens.sort()
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print(expected_tokens, tokens)
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assert expected_tokens == tokens
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def test_canary_tokenizer_init_from_cfg(special_tokenizer_path, lang_tokenizer_path):
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class DummyModel(ASRBPEMixin, Serialization):
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pass
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model = DummyModel()
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model.register_artifact = Mock(side_effect=lambda self, x: x)
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config = OmegaConf.create(
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{
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"type": "agg",
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"dir": None,
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"langs": {
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"spl_tokens": {"dir": special_tokenizer_path, "type": "bpe"},
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"en": {"dir": lang_tokenizer_path, "type": "bpe"},
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},
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"custom_tokenizer": {
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"_target_": "nemo.collections.common.tokenizers.canary_tokenizer.CanaryTokenizer",
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},
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}
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)
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model._setup_aggregate_tokenizer(config)
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tokenizer = model.tokenizer
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assert isinstance(tokenizer, CanaryTokenizer)
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assert len(tokenizer.tokenizers_dict) == 2
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assert set(tokenizer.tokenizers_dict.keys()) == {"spl_tokens", "en"}
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assert isinstance(tokenizer.tokenizers_dict["spl_tokens"], SentencePieceTokenizer)
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assert tokenizer.tokenizers_dict["spl_tokens"].vocab_size == 14
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assert isinstance(tokenizer.tokenizers_dict["en"], SentencePieceTokenizer)
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assert tokenizer.tokenizers_dict["en"].vocab_size == 6
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assert tokenizer.text_to_ids("<|startoftranscript|><|en|><|asr|><|en|><|pnc|>", lang_id="spl_tokens") == [
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4,
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9,
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7,
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9,
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5,
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]
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assert tokenizer.text_to_ids("a", lang_id="en") == [14 + 1, 14 + 2]
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