How to Train FastPitch with custom labels?

Please provide the following information when requesting support.

• Hardware : NVIDIA TESLA V100
• Network Type : FastPitch
• Training spec file : FastPitch train specs (yaml)
• How to reproduce the issue ? : change notations in train.yaml into chars

Hello. I want to train Text to Speech FastPitch model using custom labels. In Training spec file, it used phonemes as default value for notations. I tried to change the value into chars, but it produce error as below :

TypeError: Error instantiating ‘nemo.collections.asr.data.audio_to_text.AudioToCharWithPriorAndPitchDataset’ : init() missing 1 required positional argument: 'labels’

How to add those argument ? I already tried to add labels argument in train.yaml file and add +labels argument in running command, neither of them work. Still give me the same error.

Thank you.

May I know if you meet the same issue with official released jupyter notebook?
TAO Toolkit Quick Start Guide — TAO Toolkit 3.21.11 documentation
Text to Speech Notebook | NVIDIA NGCGPU-optimized AI, Machine Learning, & HPC Software | NVIDIA NGC

Hello. Thank you for the answer.

The tutorial in official released jupyter notebook is working just fine. But I need to use chars type in the vocab to exactly match my sentences in the metadata file. is that possible to do that?

Thank you

Please double check the dataset.
More info can be found in NeMo/audio_to_text.py at main · NVIDIA/NeMo · GitHub and NeMo/vocabs.py at 213d6685d8adfb943ba763d1c7e1e4eb9c68fb62 · NVIDIA/NeMo · GitHub

You can debug below files directly inside tao docker.
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py
/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/data/vocabs.py

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