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93 lines
3.8 KiB
Python
93 lines
3.8 KiB
Python
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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# Training the model
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```sh
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python speech_to_text_aed.py \
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# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
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model.train_ds.tarred_audio_filepaths=<path to tar files with audio> \
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model.train_ds.manifest_filepath=<path to audio data manifest> \
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model.train_ds.batch_duration=360 \
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model.train_ds.num_buckets=30 \
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model.train_ds.bucket_duration_bins=<optional list of precomputed float bins for bucket durations, speeds up init> \
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model.validation_ds.manifest_filepath=<path to validation manifest> \
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model.test_ds.manifest_filepath=<path to test manifest> \
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model.model_defaults.asr_enc_hidden=1024 \
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model.model_defaults.lm_enc_hidden=512 \
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model.model_defaults.lm_dec_hidden=1024 \
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model.tokenizer.langs.spl_tokens.dir=<path to the directory of prompt special tokens tokenizer> \
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model.tokenizer.langs.spl_tokens.type=bpe \
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model.tokenizer.langs.en.dir=<path to the directory of en language tokenizer (add new langs the same way)> \
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model.tokenizer.langs.en.type=bpe \
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model.prompt_format="canary" \
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trainer.devices=-1 \
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trainer.accelerator="ddp" \
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trainer.max_steps=100000 \
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+trainer.limit_train_batches=20000 \
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trainer.val_check_interval=5000 \
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+trainer.use_distributed_sampler=false \
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model.optim.name="adamw" \
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model.optim.lr=0.001 \
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model.optim.betas=[0.9,0.999] \
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model.optim.weight_decay=0.0001 \
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model.optim.sched.warmup_steps=2000 \
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exp_manager.create_wandb_logger=True \
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exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
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exp_manager.wandb_logger_kwargs.project="<Name of project>"
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```
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"""
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import lightning.pytorch as pl
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from omegaconf import OmegaConf
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from nemo.collections.asr.models import EncDecMultiTaskModel
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from nemo.core.config import hydra_runner
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from nemo.utils import logging, model_utils
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from nemo.utils.exp_manager import exp_manager
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from nemo.utils.trainer_utils import resolve_trainer_cfg
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@hydra_runner(config_path="../conf/speech_multitask/", config_name="fast-conformer_aed")
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def main(cfg):
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logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
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trainer = pl.Trainer(**resolve_trainer_cfg(cfg.trainer))
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exp_manager(trainer, cfg.get("exp_manager", None))
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# Check for spl tokens to create spl_tokenizer.
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if cfg.get("spl_tokens"):
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logging.info("Detected spl_tokens config. Building tokenizer.")
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spl_cfg = cfg["spl_tokens"]
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spl_tokenizer_cls = model_utils.import_class_by_path(cfg.model.tokenizer.custom_tokenizer["_target_"])
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spl_tokenizer_cls.build_special_tokenizer(
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spl_cfg["tokens"], spl_cfg["model_dir"], force_rebuild=spl_cfg["force_rebuild"]
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)
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cfg.model.tokenizer.langs.spl_tokens.dir = spl_cfg["model_dir"]
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aed_model = EncDecMultiTaskModel(cfg=cfg.model, trainer=trainer)
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# Initialize the weights of the model from another model, if provided via config
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aed_model.maybe_init_from_pretrained_checkpoint(cfg)
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trainer.fit(aed_model)
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if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
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if aed_model.prepare_test(trainer):
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trainer.test(aed_model)
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if __name__ == '__main__':
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main()
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