Add ASR-EOU models

examples/asr/asr_eou/speech_to_text_eou_eval.py

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+# Copyright (c) 2025, 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.
+
+"""
+Example usage:
+
+```bash
+TEST_MANIFEST="[/path/to/your/test_manifest.json,/path/to/your/test_manifest2.json,...]"
+TEST_NAME="[test_name1,test_name2,...]"
+TEST_BATCH=32
+NUM_WORKERS=8
+
+PRETRAINED_NEMO=/path/to/EOU/model.nemo
+CONFIG_NAME=fastconformer_transducer_bpe_streaming
+
+python speech_to_text_eou_eval.py \
+    --config-name $CONFIG_NAME \
+    ++init_from_nemo_model=$PRETRAINED_NEMO \
+    ~model.train_ds \
+    ~model.validation_ds \
+    ++model.test_ds.defer_setup=true \
+    ++model.test_ds.sample_rate=16000 \
+    ++model.test_ds.manifest_filepath=$TEST_MANIFEST \
+    ++model.test_ds.name=$TEST_NAME \
+    ++model.test_ds.batch_size=$TEST_BATCH \
+    ++model.test_ds.num_workers=$NUM_WORKERS \
+    ++model.test_ds.drop_last=false \
+    ++model.test_ds.force_finite=true \
+    ++model.test_ds.shuffle=false \
+    ++model.test_ds.pin_memory=true \
+    exp_manager.create_wandb_logger=false
+```
+
+"""
+
+
+import lightning.pytorch as pl
+import torch
+
+torch.set_float32_matmul_precision("highest")
+from omegaconf import DictConfig, OmegaConf, open_dict
+
+from nemo.collections.asr.models import ASRModel
+from nemo.core.classes import typecheck
+from nemo.core.config import hydra_runner
+from nemo.utils import logging
+from nemo.utils.exp_manager import exp_manager
+from nemo.utils.trainer_utils import resolve_trainer_cfg
+
+typecheck.set_typecheck_enabled(False)
+
+
+def load_model(cfg: DictConfig, trainer: pl.Trainer) -> ASRModel:
+    if "init_from_nemo_model" in cfg:
+        logging.info(f"Loading model from local file: {cfg.init_from_nemo_model}")
+        model = ASRModel.restore_from(cfg.init_from_nemo_model, trainer=trainer)
+    elif "init_from_pretrained_model" in cfg:
+        logging.info(f"Loading model from remote: {cfg.init_from_pretrained_model}")
+        model = ASRModel.from_pretrained(cfg.init_from_pretrained_model, trainer=trainer)
+    else:
+        raise ValueError(
+            "Please provide either 'init_from_nemo_model' or 'init_from_pretrained_model' in the config file."
+        )
+    if cfg.get("init_from_ptl_ckpt", None):
+        logging.info(f"Loading weights from checkpoint: {cfg.init_from_ptl_ckpt}")
+        state_dict = torch.load(cfg.init_from_ptl_ckpt, map_location='cpu', weights_only=False)['state_dict']
+        model.load_state_dict(state_dict, strict=True)
+    return model
+
+
+@hydra_runner(config_path="../conf/asr_eou", config_name="fastconformer_transducer_bpe_streaming")
+def main(cfg):
+    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
+
+    trainer = pl.Trainer(**resolve_trainer_cfg(cfg.trainer))
+    exp_manager(trainer, cfg.get("exp_manager", None))
+
+    asr_model = load_model(cfg, trainer)
+    asr_model = asr_model.eval()  # Set the model to evaluation mode
+    if hasattr(asr_model, 'wer'):
+        asr_model.wer.log_prediction = False
+
+    with open_dict(asr_model.cfg):
+        if "save_pred_to_file" in cfg:
+            asr_model.cfg.save_pred_to_file = cfg.save_pred_to_file
+        if "calclate_eou_metrics" in cfg:
+            asr_model.cfg.calclate_eou_metrics = cfg.calclate_eou_metrics
+    if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
+        with open_dict(cfg.model.test_ds):
+            cfg.model.test_ds.pad_eou_label_secs = asr_model.cfg.get('pad_eou_label_secs', 0.0)
+        asr_model.setup_multiple_test_data(test_data_config=cfg.model.test_ds)
+        trainer.test(asr_model)
+    else:
+        raise ValueError(
+            "No test dataset provided. Please provide a test dataset in the config file under model.test_ds."
+        )
+    logging.info("Test completed.")
+
+
+if __name__ == '__main__':
+    main()  # noqa pylint: disable=no-value-for-parameter