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a7c2243 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# 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 pytest
import torch
from nemo.collections.asr.inference.model_wrappers.ctc_inference_wrapper import CTCInferenceWrapper
from nemo.collections.asr.inference.utils.bpe_decoder import BPEDecoder
from nemo.collections.asr.inference.utils.text_segment import TextSegment, Word
from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecodingConfig
@pytest.fixture(scope="module")
def bpe_decoder():
asr_model = CTCInferenceWrapper(
model_name="stt_en_conformer_ctc_small",
decoding_cfg=CTCDecodingConfig(),
device="cuda" if torch.cuda.is_available() else "cpu",
)
return BPEDecoder(
vocabulary=asr_model.get_vocabulary(),
tokenizer=asr_model.tokenizer,
confidence_aggregator=min,
asr_supported_puncts=asr_model.supported_punctuation(),
word_boundary_tolerance=0.0, # Set to 0.0 for easy testing
token_duration_in_secs=asr_model.get_model_stride(in_secs=True),
)
class TestBPEDecoder:
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.parametrize(
"text",
[
"the quick brown fox jumps over the lazy dog",
"lorem ipsum dolor sit amet",
"this a test sentence",
],
)
def test_group_tokens_into_words(self, bpe_decoder, text):
ground_truth_words = text.split()
tokens = bpe_decoder.tokenizer.text_to_ids(text)
n_tokens = len(tokens)
timestamps = [float(i) for i in range(n_tokens)]
confidences = [0.1] * n_tokens
words, need_merge = bpe_decoder.group_tokens_into_words(tokens, timestamps, confidences)
assert len(words) == len(ground_truth_words)
prev_word_end = -1
for word, ground_truth_word in zip(words, ground_truth_words):
assert isinstance(word, Word)
assert word.text == ground_truth_word
assert word.conf == 0.1
assert word.end > word.start and word.start >= prev_word_end
prev_word_end = word.end
assert need_merge == False
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.parametrize(
"text",
[
"the quick brown fox jumps over the lazy dog",
"lorem ipsum dolor sit amet",
"this a test sentence",
],
)
def test_group_tokens_into_segment(self, bpe_decoder, text):
tokens = bpe_decoder.tokenizer.text_to_ids(text)
n_tokens = len(tokens)
timestamps = [float(i) for i in range(n_tokens)]
confidences = [0.1] * n_tokens
segment, need_merge = bpe_decoder.group_tokens_into_segment(tokens, timestamps, confidences)
assert isinstance(segment, TextSegment)
assert need_merge == False
assert segment.text == text
assert segment.start == 0.0
assert segment.end == (n_tokens - 1) * bpe_decoder.token_duration_in_secs
assert segment.conf == 0.1
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