def test_deepspeech_train():
# do not call home to wandb
os.environ['WANDB_MODE'] = 'dryrun'
# hyperparams
p = model.HParams(graphemes=datasets.YESNO_GRAPHEMES)
tp = train.HParams(max_epochs=1, batch_size=8)
# build
m = model.DeepSpeech(p)
trainset, testset = datasets.splits(datasets.YesNo(p), p)
# train
t = train.Trainer(m, trainset, testset, tp)
t.train()
def load(run_path):
"Load config and model from wandb"
p, ptrain = utils.load_wandb_cfg(run_path)
p, ptrain = model.HParams(**p), train.HParams(**ptrain)
return utils.load_chkpt(model.DeepSpeech(p), run_path), ptrain
def test_hparams_nsteps_batch_too_large():
trainset_size = 80
tp = train.HParams(batch_size=80, max_epochs=10)
assert tp.n_steps(trainset_size) == (80 / 80) * 10
def test_hparams_nsteps():
trainset_size = 80
tp = train.HParams(batch_size=2, max_epochs=10)
assert tp.n_steps(trainset_size) == (80 / 2) * 10
def test_hparams_nsteps_last_batch_small():
trainset_size = 48
tp = train.HParams(batch_size=40, max_epochs=4)
assert tp.n_steps(trainset_size) == 8
def test_onecycle():
cfg = train.HParams(batch_size=1, max_epochs=1)
m = model.DeepSpeech(model.HParams())
optimizer = torch.optim.SGD(m.parameters(), lr=cfg.learning_rate)
schedule = utils.onecycle(optimizer, 9, cfg)
assert schedule.total_steps == 9
def test_lrfinder():
m = model.DeepSpeech(model.HParams())
optimizer = torch.optim.SGD(m.parameters(), lr=1e-8)
p = train.HParams(batch_size=1, max_epochs=1)
schedule = utils.lrfinder(optimizer, 9, p)
assert torch.isclose(torch.tensor(schedule.gamma), torch.tensor(10.))