moinbach7/asr_en_text_normalization

ASR Inverse Text Normalization

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.

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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.

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Quickstart

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)