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#
# 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
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