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---
license: mit
tags:
- lip-reading
- visual-speech-recognition
- vsr
- silent-speech
- auto-avsr
library_name: pytorch
pipeline_tag: video-classification
---
# Silent Lip Reader β€” VSR weights
The visual-speech-recognition (lip-reading) model weights used by the
[**Silent Lip Reader** Space](https://huggingface.co/spaces/aaahmet/silent-lip-reader).
Re-hosted here so that the open-source Space is **self-contained** and does not break
if upstream repos move.
- **Architecture:** Auto-AVSR β€” ResNet-3D + Conformer encoder, Transformer decoder,
joint CTC/attention. Input: 88Γ—88 grayscale mouth crops @ 25fps. Output: text via a
5000-unit SentencePiece (`unigram5000`) vocabulary. **Video-only** (no audio path).
- **Files:** `pytorch_model.pt` (state dict), `unigram5000.model`, `unigram5000_units.txt`.
## Credits / provenance (please read)
This checkpoint is **not** trained by the re-host. Honest attribution:
- **Model architecture & training:** **Auto-AVSR** (Pingchuan Ma et al., *"Auto-AVSR:
Audio-Visual Speech Recognition with Automatic Labels"*). All model credit to the
original authors.
- **Checkpoint source:** mirrored from `AD1TEYA/lip-reading-model` on the Hub.
- **Re-host + the surrounding system, demo, visual-VAD pipeline, evaluation and research:**
**[Ahmet Dedeler](https://ahmetdedeler.com)** ([πŸ€— aaahmet](https://huggingface.co/aaahmet)).
## Intended use
Research and demos of silent visual speech recognition. The weights were trained on
LRS3-derived data; treat as **research use**. Best on clear, frontal, well-articulated
English. ~25–30% WER on clean speech, higher on casual speech (lip reading is inherently
ambiguous β€” many phonemes look identical on the lips).
## Usage
Used by the Silent Lip Reader Space β€” record a (silent) video, it crops your mouth,
chunks utterances by lip motion, and decodes text. See the
[Space](https://huggingface.co/spaces/aaahmet/silent-lip-reader) for the full pipeline
and research log.
---
Built / curated by **Ahmet Dedeler** β€” https://ahmetdedeler.com. A credit/link back is
appreciated if you use this. License MIT (follows the upstream Space).