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#
# 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.
"""
The script trains a model that peforms classification on each frame of the input audio.
The default config (i.e., marblenet_3x2x64_20ms.yaml) outputs 20ms frames.
## Training
```sh
python speech_to_frame_label.py \
--config-path=<path to dir of configs e.g. "../conf/marblenet">
--config-name=<name of config without .yaml e.g. "marblenet_3x2x64_20ms"> \
model.train_ds.manifest_filepath="<path to train manifest>" \
model.train_ds.augmentor.noise.manifest_path="<path to noise manifest>" \
model.validation_ds.manifest_filepath=["<path to val manifest>","<path to test manifest>"] \
trainer.devices=2 \
trainer.accelerator="gpu" \
strategy="ddp" \
trainer.max_epochs=200
```
The input manifest must be a manifest json file, where each line is a Python dictionary. The fields ["audio_filepath", "offset", "duration", "label"] are required. An example of a manifest file is:
```
{"audio_filepath": "/path/to/audio_file1", "offset": 0, "duration": 10000, "label": "0 1 0 0 1"}
{"audio_filepath": "/path/to/audio_file2", "offset": 0, "duration": 10000, "label": "0 0 0 1 1 1 1 0 0"}
```
For example, if you have a 1s audio file, you'll need to have 50 frame labels in the manifest entry like "0 0 0 0 1 1 0 1 .... 0 1".
However, shorter label strings are also supported for smaller file sizes. For example, you can prepare the `label` in 40ms frame, and the model will properly repeat the label for each 20ms frame.
"""
import lightning.pytorch as pl
from omegaconf import OmegaConf
from nemo.collections.asr.models.classification_models import EncDecFrameClassificationModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="../conf/marblenet", config_name="marblenet_3x2x64_20ms")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = EncDecFrameClassificationModel(cfg=cfg.model, trainer=trainer)
# Initialize the weights of the model from another model, if provided via config
model.maybe_init_from_pretrained_checkpoint(cfg)
trainer.fit(model)
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
if model.prepare_test(trainer):
trainer.test(model)
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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