File size: 3,653 Bytes
7934b29 |
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 |
# Copyright (c) 2022, 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.
# NOTE: the file name does not contain "test" on purpose to avoid executing
# these tests outside of the CI machines environment, where test data is
# stored
import pytest
from examples.asr.transcribe_speech import TranscriptionConfig
from omegaconf import OmegaConf
from nemo.collections.asr.parts.utils.transcribe_utils import prepare_audio_data, setup_model
TEST_DATA_PATH = "/home/TestData/an4_dataset/an4_val.json"
PRETRAINED_MODEL_NAME = "stt_en_conformer_transducer_small"
def get_rnnt_alignments(strategy: str):
cfg = OmegaConf.structured(TranscriptionConfig(pretrained_name=PRETRAINED_MODEL_NAME))
cfg.rnnt_decoding.confidence_cfg.preserve_frame_confidence = True
cfg.rnnt_decoding.preserve_alignments = True
cfg.rnnt_decoding.strategy = strategy
cfg.dataset_manifest = TEST_DATA_PATH
filepaths = prepare_audio_data(cfg)[0][:10] # selecting 10 files only
model = setup_model(cfg, map_location="cuda")[0]
model.change_decoding_strategy(cfg.rnnt_decoding)
transcriptions = model.transcribe(
paths2audio_files=filepaths,
batch_size=cfg.batch_size,
num_workers=cfg.num_workers,
return_hypotheses=True,
channel_selector=cfg.channel_selector,
)[0]
for transcription in transcriptions:
for align_elem, frame_confidence in zip(transcription.alignments, transcription.frame_confidence):
assert len(align_elem) == len(frame_confidence) # frame confidences have to match alignments
assert len(align_elem) > 0 # no empty alignments
for idx, pred in enumerate(align_elem):
if idx < len(align_elem) - 1:
assert pred[1].item() != model.decoder.blank_idx # all except last have to be non-blank
else:
assert pred[1].item() == model.decoder.blank_idx # last one has to be blank
return transcriptions
@pytest.fixture(autouse=True)
def cleanup_local_folder():
"""Overriding global fixture to make sure it's not applied for this test.
Otherwise, there will be errors in the CI in github.
"""
return
# TODO: add the same tests for multi-blank RNNT decoding
def test_rnnt_alignments():
# using greedy as baseline and comparing all other configurations to it
ref_transcriptions = get_rnnt_alignments("greedy")
transcriptions = get_rnnt_alignments("greedy_batch")
# comparing that label sequence in alignments is exactly the same
# we can't compare logits as well, because they are expected to be
# slightly different in batched and single-sample mode
assert len(ref_transcriptions) == len(transcriptions)
for ref_transcription, transcription in zip(ref_transcriptions, transcriptions):
for ref_align_elem, align_elem in zip(ref_transcription.alignments, transcription.alignments):
assert len(ref_align_elem) == len(align_elem)
for ref_pred, pred in zip(ref_align_elem, align_elem):
assert ref_pred[1].item() == pred[1].item()
|