chore: import upstream snapshot with attribution

examples/asr/speech_classification/speech_to_frame_label.py

@@ -0,0 +1,71 @@
+# Copyright (c) 2023, 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.
+
+"""
+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