Instructions to use moinbach7/asr_en_text_normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moinbach7/asr_en_text_normalization with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="moinbach7/asr_en_text_normalization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("moinbach7/asr_en_text_normalization")
model = AutoModelForSeq2SeqLM.from_pretrained("moinbach7/asr_en_text_normalization")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moinbach7/asr_en_text_normalization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moinbach7/asr_en_text_normalization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moinbach7/asr_en_text_normalization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/moinbach7/asr_en_text_normalization

SGLang

How to use moinbach7/asr_en_text_normalization with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "moinbach7/asr_en_text_normalization" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moinbach7/asr_en_text_normalization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "moinbach7/asr_en_text_normalization" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moinbach7/asr_en_text_normalization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use moinbach7/asr_en_text_normalization with Docker Model Runner:
```
docker model run hf.co/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.

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

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)

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Safetensors

Model size

0.1B params

Tensor type

F32

ASR Inverse Text Normalization

Model Overview

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

Quickstart

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