Add Parakeet Hybrid RNNT CTC BPE Model with target language support

examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe_prompt.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.
+
+"""
+# Preparing the Tokenizer for the dataset
+Use the `process_asr_text_tokenizer.py` script under <NEMO_ROOT>/scripts/tokenizers/ in order to prepare the tokenizer.
+
+#Manifest file example:
+{"audio_filepath":"/data/fleurs/any2any/14403911078888113072.wav","duration":12.12,"id":1510,"text":"The U . N . Also hopes to finalize a fund to help countries affected by global warming to cope with the impacts .","lang":"en-US","source_lang":"ar-AR","target_lang":"en-US","pnc":"true","taskname":"translate"}
+{"audio_filepath":"/data/fleurs/any2any/7340766134437057689.wav","duration":9.12,"id":1511,"text":"Soon , officers equipped with riot gear entered the yard and cornered the inmates with tear gas .","lang":"en-US","source_lang":"ar-AR","target_lang":"en-US","pnc":"true","taskname":"translate"}
+
+
+```sh
+python <NEMO_ROOT>/scripts/tokenizers/process_asr_text_tokenizer.py \
+        --manifest=<path to train manifest files, seperated by commas>
+        OR
+        --data_file=<path to text data, seperated by commas> \
+        --data_root="<output directory>" \
+        --vocab_size=<number of tokens in vocabulary> \
+        --tokenizer=<"spe" or "wpe"> \
+        --no_lower_case \
+        --spe_type=<"unigram", "bpe", "char" or "word"> \
+        --spe_character_coverage=1.0 \
+        --log
+```
+
+# Training the model
+```sh
+python speech_to_text_hybrid_rnnt_ctc_bpe_prompt.py \
+    # (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
+    model.train_ds.manifest_filepath=<path to train manifest> \
+    model.validation_ds.manifest_filepath=<path to val/test manifest> \
+    model.tokenizer.dir=<path to directory of tokenizer (not full path to the vocab file!)> \
+    model.tokenizer.type=<either bpe or wpe> \
+    model.aux_ctc.ctc_loss_weight=0.1 \
+    trainer.devices=-1 \
+    trainer.max_epochs=100 \
+    model.optim.name="adamw" \
+    model.optim.lr=0.001 \
+    model.optim.betas=[0.9,0.999] \
+    model.optim.weight_decay=0.0001 \
+    model.optim.sched.warmup_steps=2000
+    exp_manager.create_wandb_logger=True \
+    exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
+    exp_manager.wandb_logger_kwargs.project="<Name of project>"
+```
+
+# Fine-tune a model
+
+For documentation on fine-tuning this model, please visit -
+https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations
+
+"""
+
+import lightning.pytorch as pl
+from omegaconf import OmegaConf
+
+from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModelWithPrompt
+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
+
+
+@hydra_runner(
+    config_path="../conf/fastconformer/hybrid_transducer_ctc/",
+    config_name="fastconformer_hybrid_transducer_ctc_bpe_prompt.yaml",
+)
+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 = EncDecHybridRNNTCTCBPEModelWithPrompt(cfg=cfg.model, trainer=trainer)
+
+    # Initialize the weights of the model from another model, if provided via config
+    asr_model.maybe_init_from_pretrained_checkpoint(cfg)
+
+    trainer.fit(asr_model)
+
+    if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
+        if asr_model.prepare_test(trainer):
+            trainer.test(asr_model)
+
+
+if __name__ == '__main__':
+    main()  # noqa pylint: disable=no-value-for-parameter