---
license: apache-2.0
language: [en]
base_model: google/flan-t5-large
pipeline_tag: text2text-generation
library_name: transformers
tags:
  - speech-to-text
  - transcript-formatting
  - asr-post-processing
  - dictation
  - knowledge-distillation
---

# Flan-T5-Large Transcript Formatter (distilled, < 1 GB target)

Raw speech-to-text transcript **in** → clean, formatted transcript **out**
(punctuation, casing, filler/disfluency removal, ITN, homophones, proper nouns,
URLs/emails, and layout). The model takes the **raw transcript as its only input**
(no system prompt) and emits the formatted transcript.

## Distillation

- **Teacher:** GPT-OSS-120B → Phase-1 distilled **GPT-OSS-20B** formatter, which
  reproduces the L0–L5 + RL curriculum behaviour at ~99.5% adjusted accuracy.
- **Student:** `google/flan-t5-large` (783M, encoder–decoder).
- **Method:** off-policy / sequence-level knowledge distillation (prompt
  distillation). Trained on **(raw → formatted)** pairs using the curriculum
  **gold** targets (cleaner than propagating the teacher's residual errors).

## Data

Sampled from the layered curriculum (L0–L5 + RL), de-duplicated latest-layer-wins,
with the per-layer val splits held out and made disjoint from train:
**18,129 train / 2,713 validation** pairs, 21 formatting categories.

## Training

| | |
|---|---|
| Hardware | 1× RTX 5090 (32 GB), bf16 |
| Optimizer | Adafactor, lr 1e-4, warmup_ratio 0.03 |
| Batch | 8 × grad-accum 2 = effective 16 |
| Seq length | max 640 tokens (covers the longest pair, 552 tok; zero truncation) |
| Regularization | gradient checkpointing; early stopping (patience 3), best-checkpoint by val loss |
| Result | best **val loss 0.005784 @ step 2250 (~epoch 2.0)**; early-stopped at step 3000 |

![loss curves](loss_curves.png)

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tok = AutoTokenizer.from_pretrained("Akash-Sakala/flan-t5-large-transcript-formatter")
model = AutoModelForSeq2SeqLM.from_pretrained("Akash-Sakala/flan-t5-large-transcript-formatter")
raw = "i went to the the store yesterday it was closed so i couldnt get the milk"
ids = tok(raw, return_tensors="pt")
print(tok.decode(model.generate(**ids, max_new_tokens=640, num_beams=1)[0], skip_special_tokens=True))
```

## Limitations

A 783M student shows a capacity step-down from the 20B teacher on the hardest
**L4 layout** cases (long email/list blocks); T5 was pretrained mostly at 512
tokens. For an int8, CPU-only deployment (< 1 GB) convert with CTranslate2.

License Apache-2.0 (matches the `openai/gpt-oss` base and the curriculum dataset).