This repository provides a fine-tuned BART model for the task of ASR Inverse Text Normalization (ITN).
The goal is to transform raw, unnormalized ASR transcripts into properly formatted text.

Model Overview

BART (Bidirectional and Auto-Regressive Transformers) is a transformer-based model introduced by Facebook AI Research.
It is designed for both text understanding and generation tasks.

Architecture: Encoder–Decoder Transformer with self-attention.
Pretraining objective: Reconstruct original text from corrupted/noisy versions.
Applications: Summarization, machine translation, question answering, and text normalization.

For this project:

Base model: facebook/bart-base
Training setup: Treated as a sequence-to-sequence problem
Dataset: Text Normalization Challenge - English Language (Kaggle)

Intended Use

The model can be applied directly to normalize ASR outputs in speech-to-text pipelines.

Quickstart

python

from transformers import pipeline

# Load pipeline
generator = pipeline(model="pavanBuduguppa/asr_inverse_text_normalization")

# Run inference
result = generator("my c v v for my card is five six seven and it expires on november twenty three")
print(result)

moinbach7/asr_en_text_normalization

ASR Inverse Text Normalization

Model Overview

Intended Use

Quickstart

moinbach7/
asr_en_text_normalization