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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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | # 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.
from pathlib import Path
import pytest
from nemo.collections.asr.models import ASRModel
from nemo.collections.asr.parts.utils.manifest_utils import read_manifest, write_manifest
from tests.collections.asr.decoding.utils import make_preprocessor_deterministic, preserve_decoding_cfg_and_cpu_device
CHECKPOINTS_PATH = Path("/home/TestData/asr")
@pytest.fixture(scope="session")
def an4_val_manifest_corrected(tmp_path_factory, test_data_dir):
"""
Correct an4_val manifest audio filepaths, e.g.,
"tests/data/asr/test/an4/wav/an440-mjgm-b.wav" -> test_data_dir / "test/an4/wav/an440-mjgm-b.wav"
"""
an4_val_manifest_orig_path = Path(test_data_dir) / "asr/an4_val.json"
an4_val_manifest_corrected_path = tmp_path_factory.mktemp("manifests") / "an4_val_corrected.json"
an4_val_records = read_manifest(an4_val_manifest_orig_path)
for record in an4_val_records:
record["audio_filepath"] = record["audio_filepath"].replace(
"tests/data/asr", str(an4_val_manifest_orig_path.resolve().parent)
)
write_manifest(an4_val_manifest_corrected_path, an4_val_records)
return an4_val_manifest_corrected_path
@pytest.fixture(scope="session")
def an4_train_manifest_corrected(tmp_path_factory, test_data_dir):
"""
Correct an4_train manifest audio filepaths, e.g.,
"tests/data/asr/test/an4/wav/an440-mjgm-b.wav" -> test_data_dir / "test/an4/wav/an440-mjgm-b.wav"
"""
an4_train_manifest_orig_path = Path(test_data_dir) / "asr/an4_train.json"
an4_train_manifest_corrected_path = tmp_path_factory.mktemp("manifests") / "an4_train_corrected.json"
an4_train_records = read_manifest(an4_train_manifest_orig_path)
for record in an4_train_records:
record["audio_filepath"] = record["audio_filepath"].replace(
"tests/data/asr", str(an4_train_manifest_orig_path.resolve().parent)
)
write_manifest(an4_train_manifest_corrected_path, an4_train_records)
return an4_train_manifest_corrected_path
@pytest.fixture(scope="package")
def _stt_en_conformer_transducer_small_raw():
if CHECKPOINTS_PATH.exists():
model = ASRModel.restore_from(
str(CHECKPOINTS_PATH / "stt_en_conformer_transducer_small.nemo"), map_location="cpu"
)
else:
model_name = "stt_en_conformer_transducer_small"
model = ASRModel.from_pretrained(model_name, map_location="cpu")
make_preprocessor_deterministic(model)
return model
@pytest.fixture(scope="package")
def _stt_en_fastconformer_transducer_large_raw():
if CHECKPOINTS_PATH.exists():
model = ASRModel.restore_from(
str(CHECKPOINTS_PATH / "stt_en_fastconformer_transducer_large.nemo"), map_location="cpu"
)
else:
model_name = "stt_en_fastconformer_transducer_large"
model = ASRModel.from_pretrained(model_name, map_location="cpu")
make_preprocessor_deterministic(model)
return model
@pytest.fixture(scope="package")
def _stt_en_fastconformer_tdt_large_raw():
if CHECKPOINTS_PATH.exists():
model = ASRModel.restore_from(
str(CHECKPOINTS_PATH / "stt_en_fastconformer_tdt_large.nemo"), map_location="cpu"
)
else:
model_name = "nvidia/stt_en_fastconformer_tdt_large"
model = ASRModel.from_pretrained(model_name, map_location="cpu")
make_preprocessor_deterministic(model)
return model
@pytest.fixture(scope="package")
def _canary_180m_flash_raw():
model_name = "nvidia/canary-180m-flash"
model = ASRModel.from_pretrained(model_name, map_location="cpu")
make_preprocessor_deterministic(model)
return model
@pytest.fixture
def stt_en_conformer_transducer_small(_stt_en_conformer_transducer_small_raw):
"""Function-level fixture for model. Guarantees to preserve decoding config and device"""
model = _stt_en_conformer_transducer_small_raw
with preserve_decoding_cfg_and_cpu_device(model):
yield model
@pytest.fixture
def stt_en_fastconformer_transducer_large(_stt_en_fastconformer_transducer_large_raw):
"""Function-level fixture for model. Guarantees to preserve decoding config and device"""
model = _stt_en_fastconformer_transducer_large_raw
with preserve_decoding_cfg_and_cpu_device(model):
yield model
@pytest.fixture
def stt_en_fastconformer_tdt_large(_stt_en_fastconformer_tdt_large_raw):
"""Function-level fixture for model. Guarantees to preserve decoding config and device"""
model = _stt_en_fastconformer_tdt_large_raw
with preserve_decoding_cfg_and_cpu_device(model):
yield model
@pytest.fixture
def canary_180m_flash(_canary_180m_flash_raw):
"""Function-level fixture for model. Guarantees to preserve decoding config and device"""
model = _canary_180m_flash_raw
with preserve_decoding_cfg_and_cpu_device(model):
yield model
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