tests/collections/asr/inference/test_greedy_decoder.py

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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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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