Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words. Besides being hard to read, the ASR output could be an input to named entity recognition, machine translation or text-to-speech models. If the input text has punctuation and words are capitalized correctly, this could potentially boost the performance of such models.
For each word in the input text, the model:
predicts a punctuation mark that should follow the word (if any). The model supports commas, periods and question marks. predicts if the word should be capitalized or not.
The Punctuation and Capitalization model consists of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) followed by two token classification heads. One classification head is responsible for the punctuation task, the other one handles the capitalization task. Both token level classification heads take the BERT encoded representation of the [CLS] token as input. Such architecture allows this model to solve two tasks at once with only a single pass through the BERT. Finally, all the parameters are fine-tuned on this joint task.
The punctuation model currently supports only commas, periods, hyphens and question marks.
The model was trained with BERT base multilingual cased checkpoint on a subset of data from the following sources:
Each word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation: (1) marking the whole entity as a single label (2) perform evaluation on the sub token level
During training, the first approach was applied, and the predictions for the first token of the input were used to label the whole word. Each task is evaluated separately. Due to the high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).
This model was evaluated on an internal dataset, and it reached the F1 score of 88%.
This pre-trained model needs to be used with NVIDIA Hardware and Software. This model can be used with Train Adapt Optimize (TAO) Toolkit with model load key: tlt_encode (Please make sure to use this as the key for all TAO commands that require a model load key).
For more details on how to prepare your data for this model and how to fine-tune the model with TAO, see TAO user guide for Punctuation and Capitalization model.
More information about the TAO Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/tao-toolkit. Read the TAO getting Started guide and release notes. More information about the experiment spec files can be found in the TAO User Guide. More information about deploying the model with Riva Services see this. If you have any questions or feedback, please refer to the discussions on TAO Toolkit Developer Forums
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