dayyanj/dj-ai-asr-grammar-corrector-small

DJ-AI ASR Grammar Corrector (T5-Small)

A lightweight grammar correction model fine-tuned from t5-small, specifically designed to correct common errors in automatic speech recognition (ASR) outputs — including homophones, verb tense issues, contractions, duplicated words, and more. Optimized for fast inference in (near) real-time ASR pipelines.

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Model Details

• Base model: t5-small
• Fine-tuned on: 90 million synthetic (noisy → clean) sentence pairs
• Training objective: Correct ASR-style transcription errors into clean, grammatical English
• Token count: ~60 million tokens per epoch
• Framework: Hugging Face Transformers + PyTorch

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Benchmark Results

Model	Type	Precision	Latency (s/sample)	VRAM (MB)	BLEU	ROUGE-L	Accuracy (%)¹	Token Accuracy (%)²	Size (MB)
dj-ai-asr-grammar-corrector-t5-base	HF	fp32	0.1151	24.98	78.92	90.31	44.62	90.39	5956.76
dj-ai-asr-grammar-corrector-t5-small	HF	fp32	0.0648	6.27	76.47	89.54	39.59	88.76	1620.15
dj-ai-asr-grammar-corrector-t5-small-streaming	HF	fp32	0.0634	14.77	76.25	89.61	39.9	88.54	1620.65

1. Accuracy is a measure of how well the model performs across the full sentence. That is, a prediction is only counted as "correct" if the entire corrected sentence exactly matches the reference sentence. So if the model corrects 1 out of 2 errors, but the final output does not exactly match the expected sentence, it's counted as a fail.
2. Token Accuracy is a measure of how well the model performs at the token level. $$\text{Token Accuracy (\%)}=\left(\frac{\text{Number of Matched Tokens}}{\text{Total Reference Tokens}}\right)\times 100\backslash text\{Token\; Accuracy\; (\backslash \%)\}\; =\; \backslash left(\; \backslash frac\{\backslash text\{Number\; of\; Matched\; Tokens\}\}\{\backslash text\{Total\; Reference\; Tokens\}\}\; \backslash right)\; \backslash times\; 100$$

Intended Use

Use Case	✅ Supported	🚫 Not Recommended
Post-ASR correction	✅ Yes
Real-time ASR pipelines	✅ Yes
Batch transcript cleanup	✅ Yes
Grammar education tools	✅ Yes
Formal document editing	🚫	Model may be too informal
Multilingual input	🚫	English-only fine-tuning

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Corrects Common ASR Errors:

• Homophone mistakes (their → they're)
• Subject-verb disagreement (he go → he goes)
• Verb tense corruption (i seen → i saw)
• Missing auxiliaries (you going → are you going)
• Contraction normalization (she is not → she isn't)
• Repeated words (i i want → i want)
• Misused articles/prepositions/pronouns

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Example

DEMO: https://huggingface.co/spaces/dayyanj/dj-ai-asr-grammar-corrector-demo

Input (noisy ASR):

Git Repository: https://github.com/dayyanj/DJ-AI-ASR-GRAMMAR-CORRECTOR