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.
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

	# 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