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# Copyright (c) 2020, 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 dataclasses import asdict
import pytorch_lightning as pl
from nemo.collections.asr.models import EncDecCTCModel, configs
from nemo.core.config import modelPT, optimizers, schedulers
from nemo.utils.exp_manager import exp_manager
"""
python speech_to_text_structured_v2.py
"""
# fmt: off
LABELS = [
" ", "a", "b", "c", "d", "e",
"f", "g", "h", "i", "j", "k",
"l", "m", "n", "o", "p", "q",
"r", "s", "t", "u", "v", "w",
"x", "y", "z", "'",
]
optim_cfg = optimizers.NovogradParams(
lr=0.01,
betas=(0.8, 0.5),
weight_decay=0.001
)
sched_cfg = schedulers.CosineAnnealingParams(
warmup_steps=None,
warmup_ratio=None,
min_lr=0.0,
)
# fmt: on
def main():
# NeMo Model config
cfg = modelPT.NemoConfig(name='Custom QuartzNet')
# Generate default asr model config
builder = configs.EncDecCTCModelConfigBuilder(name='quartznet_15x5')
# set model global values
builder.set_labels(LABELS)
builder.set_optim(cfg=optim_cfg, sched_cfg=sched_cfg)
model_cfg = builder.build()
# set the model config to the NeMo Model
cfg.model = model_cfg
# Update values
# MODEL UPDATES
# train ds
model_cfg.train_ds.manifest_filepath = ""
# validation ds
model_cfg.validation_ds.manifest_filepath = ""
# Trainer config
cfg.trainer.devices = 1
cfg.trainer.max_epochs = 5
# Exp Manager config
cfg.exp_manager.name = cfg.name
# Note usage of asdict
trainer = pl.Trainer(**asdict(cfg.trainer))
exp_manager(trainer, asdict(cfg.exp_manager))
asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
trainer.fit(asr_model)
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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