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

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
```

moinbach7 commited on Aug 22, 2025

Commit

220c6b0

verified ·

1 Parent(s): a7e2179

Update README.md

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@@ -2,11 +2,12 @@
 license: gpl-3.0
 language:
   - en
-pipeline_tag: text2text-generation
 tags:
   - code
   - asr
   - inverse text normalization
 datasets:
   - text-normalization-challenge-english-language
 ---
@@ -31,10 +32,6 @@ For this project:
 - Base model: `facebook/bart-base`
 - Training setup: Treated as a **sequence-to-sequence** problem
 - Dataset: [Text Normalization Challenge - English Language (Kaggle)](https://www.kaggle.com/competitions/text-normalization-challenge-english-language/data)
-- Columns used:
-  - Input: `"after"` (ASR-like text)
-  - Target: `"before"` (normalized text)
 ---
 ## Intended Use

 license: gpl-3.0
 language:
   - en
+pipeline_tag: text-generation
 tags:
   - code
   - asr
   - inverse text normalization
+  - transformers
 datasets:
   - text-normalization-challenge-english-language
 ---
 - Base model: `facebook/bart-base`
 - Training setup: Treated as a **sequence-to-sequence** problem
 - Dataset: [Text Normalization Challenge - English Language (Kaggle)](https://www.kaggle.com/competitions/text-normalization-challenge-english-language/data)
 ---
 ## Intended Use