def main(cfg):
# Generate default asr model config
asr_model_config = configs.EncDecCTCModelConfig()
# Merge hydra updates with model config
# `drop_missing_subconfig=True` is necessary here. Without it, while the data class will instantiate and be added
# to the config, it contains test_ds.sample_rate = MISSING and test_ds.labels = MISSING.
# This will raise a OmegaConf MissingMandatoryValue error when processing the dataloaders inside
# model_utils.resolve_test_dataloaders(model=self) (used for multi data loader support).
# In general, any operation that tries to use a DictConfig with MISSING in it will fail,
# other than explicit update operations to change MISSING to some actual value.
asr_model_config = update_model_config(asr_model_config,
cfg,
drop_missing_subconfigs=True)
# From here on out, its a general OmegaConf DictConfig, directly usable by our code.
trainer = pl.Trainer(**asr_model_config.trainer)
exp_manager(trainer, asr_model_config.get("exp_manager", None))
asr_model = EncDecCTCModel(cfg=asr_model_config.model, trainer=trainer)
trainer.fit(asr_model)
def test_dataclass_instantiation(self, asr_model):
model_cfg = configs.EncDecCTCModelConfig()
# Update mandatory values
vocabulary = asr_model.decoder.vocabulary
model_cfg.model.labels = vocabulary
# Update encoder
model_cfg.model.encoder.activation = 'relu'
model_cfg.model.encoder.feat_in = 64
model_cfg.model.encoder.jasper = [
nemo_asr.modules.conv_asr.JasperEncoderConfig(
filters=1024,
repeat=1,
kernel=[1],
stride=[1],
dilation=[1],
dropout=0.0,
residual=False,
se=True,
se_context_size=-1,
)
]
# Update decoder
model_cfg.model.decoder.feat_in = 1024
model_cfg.model.decoder.num_classes = 28
model_cfg.model.decoder.vocabulary = vocabulary
# Construct the model
asr_cfg = OmegaConf.create({'model': asr_model.cfg})
model_cfg_v1 = update_model_config(model_cfg, asr_cfg)
new_model = EncDecCTCModel(cfg=model_cfg_v1.model)
assert new_model.num_weights == asr_model.num_weights
# trainer and exp manager should be there
# assert 'trainer' in model_cfg_v1
# assert 'exp_manager' in model_cfg_v1
# datasets and optim/sched should not be there after ModelPT.update_model_dataclass()
assert 'train_ds' not in model_cfg_v1.model
assert 'validation_ds' not in model_cfg_v1.model
assert 'test_ds' not in model_cfg_v1.model
assert 'optim' not in model_cfg_v1.model
# Construct the model, without dropping additional keys
asr_cfg = OmegaConf.create({'model': asr_model.cfg})
model_cfg_v2 = update_model_config(model_cfg,
asr_cfg,
drop_missing_subconfigs=False)
# Assert all components are in config
# assert 'trainer' in model_cfg_v2
# assert 'exp_manager' in model_cfg_v2
assert 'train_ds' in model_cfg_v2.model
assert 'validation_ds' in model_cfg_v2.model
assert 'test_ds' in model_cfg_v2.model
assert 'optim' in model_cfg_v2.model
# Remove extra components (optim and sched can be kept without issue)
with open_dict(model_cfg_v2.model):
model_cfg_v2.model.pop('train_ds')
model_cfg_v2.model.pop('validation_ds')
model_cfg_v2.model.pop('test_ds')
new_model = EncDecCTCModel(cfg=model_cfg_v2.model)
assert new_model.num_weights == asr_model.num_weights
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import asdict
import pytorch_lightning as pl
import nemo.collections.asr as nemo_asr
from nemo.collections.asr.models import EncDecCTCModel, configs
from nemo.utils.exp_manager import exp_manager
"""
python speech_to_text_structured.py
"""
# Generate default asr model config
cfg = configs.EncDecCTCModelConfig()
# set global values
cfg.model.repeat = 5
cfg.model.separable = True
# fmt: off
LABELS = [
" ",
"a",
"b",
"c",
"d",
"e",
"f",
"g",