def sample_data():
path = get_default_cache_folder()
download_url(
"https://github.com/scart97/lapsbm-backup/archive/refs/tags/lapsbm-ci.tar.gz",
download_folder=path,
resume=True,
)
extract_archive(path / "lapsbm-backup-lapsbm-ci.tar.gz", path)
return path / "lapsbm-backup-lapsbm-ci"
def download_checkpoint(name: NemoCheckpoint, checkpoint_folder: str = None) -> Path:
"""Download quartznet checkpoint by identifier.
Args:
name: Model identifier. Check checkpoint_archives.keys()
checkpoint_folder: Folder where the checkpoint will be saved to.
Returns:
Path to the saved checkpoint file.
"""
if checkpoint_folder is None:
checkpoint_folder = get_default_cache_folder()
url = name.value
filename = url.split("/")[-1]
checkpoint_path = Path(checkpoint_folder) / filename
if not checkpoint_path.exists():
wget.download(url, out=str(checkpoint_path))
return checkpoint_path
def test_expected_prediction_from_pretrained_model():
# Loading the sample file
try:
folder = get_default_cache_folder()
download_url(
"https://github.com/fastaudio/10_Speakers_Sample/raw/76f365de2f4d282ec44450d68f5b88de37b8b7ad/train/f0001_us_f0001_00001.wav",
download_folder=str(folder),
filename="f0001_us_f0001_00001.wav",
resume=True,
)
# Preparing data and model
module = QuartznetModule.load_from_nemo(
checkpoint_name=NemoCheckpoint.QuartzNet5x5LS_En)
audio, sr = torchaudio.load(folder / "f0001_us_f0001_00001.wav")
assert sr == 16000
output = module.predict(audio)
expected = "the world needs opportunities for new leaders and new ideas"
assert output[0].strip() == expected
except HTTPError:
return
def test_get_default_cache_folder():
path = get_default_cache_folder()
assert path.exists()