File size: 2,500 Bytes
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# Copyright 2026 Xiaomi Corp. (authors: Han Zhu)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# 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.
"""Training CLI for OmniVoice.
Launches distributed training via HuggingFace Accelerate.
Supports pre-training on Emilia data and finetuning on custom data.
Usage:
accelerate launch --gpu_ids 0,1,2,3 --num_processes 4 \\
-m omnivoice.cli.train \\
--train_config train_config.json \\
--data_config data_config.json \\
--output_dir output/
See examples/run_emilia.sh and examples/run_finetune.sh for full pipelines.
"""
import argparse
from omnivoice.training.builder import build_dataloaders, build_model_and_tokenizer
from omnivoice.training.config import TrainingConfig
from omnivoice.training.trainer import OmniTrainer
def main():
parser = argparse.ArgumentParser(description="OmniVoice Training Entry Point")
parser.add_argument(
"--train_config", type=str, required=True, help="Path to config JSON"
)
parser.add_argument(
"--output_dir", type=str, required=True, help="Where to save checkpoints"
)
parser.add_argument(
"--data_config", type=str, required=True, help="Path to data config JSON"
)
args = parser.parse_args()
# 1. Load Configuration
config = TrainingConfig.from_json(args.train_config)
config.output_dir = args.output_dir
config.data_config = args.data_config
# 2. Build Components
model, tokenizer = build_model_and_tokenizer(config)
train_loader, eval_loader = build_dataloaders(config, tokenizer)
# 3. Initialize Trainer and Start
trainer = OmniTrainer(
model=model,
config=config,
train_dataloader=train_loader,
eval_dataloader=eval_loader,
tokenizer=tokenizer,
)
trainer.train()
if __name__ == "__main__":
main()
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