Datasets:

echodict
/

NeMo

# 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.streaming.decoders.greedy.greedy_ctc_decoder import CTCGreedyDecoder
from nemo.collections.asr.inference.streaming.decoders.greedy.greedy_rnnt_decoder import RNNTGreedyDecoder


class TestCTCGreedyDecoder:

    @pytest.mark.unit
    def test_ctc_greedy_decoder(self):

        vocab = ["a", "b", "c", "d"]
        decoder = CTCGreedyDecoder(vocabulary=vocab)

        assert decoder.blank_id == len(vocab)
        assert decoder.is_token_silent(len(vocab)) == True

        for i in range(len(vocab)):
            assert decoder.is_token_silent(i) == False

        for i in range(len(vocab)):
            assert decoder.is_token_start_of_word(i) == False

        assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 5) == 1
        assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 3) == 0
        assert decoder.first_non_silent_token([1, 2, 3, 4], 0, 5) == 0

        log_probs = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.05], [0.4, 0.3, 0.2, 0.1, 0.05]])
        assert decoder.get_labels(log_probs) == log_probs.argmax(dim=-1).tolist()

    @pytest.mark.unit
    def test_ctc_greedy_decoder_with_previous_token(self):
        vocab = ["a", "b", "c", "d"]
        decoder = CTCGreedyDecoder(vocabulary=vocab)

        log_probs = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.05], [0.1, 0.2, 0.3, 0.4, 0.05], [0.4, 0.3, 0.2, 0.1, 0.05]])
        last_token_id = 3
        output = decoder(log_probs, compute_confidence=False, previous=last_token_id)
        assert output["tokens"] == [0]
        assert output["timesteps"] == [2]

        output = decoder(log_probs, compute_confidence=False, previous=None)
        assert output["tokens"] == [3, 0]
        assert output["timesteps"] == [0, 2]


class TestRNNTGreedyDecoder:

    @pytest.mark.unit
    def test_rnnt_greedy_decoder(self):

        vocab = ["a", "b", "c", "d"]
        decoder = RNNTGreedyDecoder(vocab)

        blank_id = len(vocab)
        assert decoder.blank_id == blank_id
        assert decoder.is_token_silent(blank_id) == True

        for i in range(len(vocab)):
            assert decoder.is_token_silent(i) == False

        for i in range(len(vocab)):
            assert decoder.is_token_start_of_word(i) == False

        assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 5) == 1
        assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 3) == 0
        assert decoder.first_non_silent_token([1, 2, 3, 4], 0, 5) == 0

	# 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.streaming.decoders.greedy.greedy_ctc_decoder import CTCGreedyDecoder
	from nemo.collections.asr.inference.streaming.decoders.greedy.greedy_rnnt_decoder import RNNTGreedyDecoder


	class TestCTCGreedyDecoder:

	@pytest.mark.unit
	def test_ctc_greedy_decoder(self):

	vocab = ["a", "b", "c", "d"]
	decoder = CTCGreedyDecoder(vocabulary=vocab)

	assert decoder.blank_id == len(vocab)
	assert decoder.is_token_silent(len(vocab)) == True

	for i in range(len(vocab)):
	assert decoder.is_token_silent(i) == False

	for i in range(len(vocab)):
	assert decoder.is_token_start_of_word(i) == False

	assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 5) == 1
	assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 3) == 0
	assert decoder.first_non_silent_token([1, 2, 3, 4], 0, 5) == 0

	log_probs = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.05], [0.4, 0.3, 0.2, 0.1, 0.05]])
	assert decoder.get_labels(log_probs) == log_probs.argmax(dim=-1).tolist()

	@pytest.mark.unit
	def test_ctc_greedy_decoder_with_previous_token(self):
	vocab = ["a", "b", "c", "d"]
	decoder = CTCGreedyDecoder(vocabulary=vocab)

	log_probs = torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.05], [0.1, 0.2, 0.3, 0.4, 0.05], [0.4, 0.3, 0.2, 0.1, 0.05]])
	last_token_id = 3
	output = decoder(log_probs, compute_confidence=False, previous=last_token_id)
	assert output["tokens"] == [0]
	assert output["timesteps"] == [2]

	output = decoder(log_probs, compute_confidence=False, previous=None)
	assert output["tokens"] == [3, 0]
	assert output["timesteps"] == [0, 2]


	class TestRNNTGreedyDecoder:

	@pytest.mark.unit
	def test_rnnt_greedy_decoder(self):

	vocab = ["a", "b", "c", "d"]
	decoder = RNNTGreedyDecoder(vocab)

	blank_id = len(vocab)
	assert decoder.blank_id == blank_id
	assert decoder.is_token_silent(blank_id) == True

	for i in range(len(vocab)):
	assert decoder.is_token_silent(i) == False

	for i in range(len(vocab)):
	assert decoder.is_token_start_of_word(i) == False

	assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 5) == 1
	assert decoder.count_silent_tokens([0, 1, 2, 3, 4], 0, 3) == 0
	assert decoder.first_non_silent_token([1, 2, 3, 4], 0, 5) == 0