---
language:
- fa
license: apache-2.0
pipeline_tag: automatic-speech-recognition
base_model: facebook/wav2vec2-xls-r-300m
library_name: transformers
model_type: wav2vec2-ctc
tags:
- audio
- automatic-speech-recognition
- persian
- wav2vec2
- repr1
- avasanj
pretty_name: AvaSanj-v1-audio8437
---

# AvaSanj-v1

Persian **repr1** automatic speech recognizer (AvaSanj v1) fine-tuned from
`facebook/wav2vec2-xls-r-300m` on **8,437 approved Persian audio clips**.

Outputs phonemes in the shared **repr1** alphabet (`A C Q S Z …` + lowercase IPA-style phonemes) — the
same canonical alphabet used by the **Negara** G2P models, making this model a drop-in listener for
grapheme-to-phoneme adjudication.

## Provenance

- **Original model:** `facebook/wav2vec2-xls-r-300m` (Apache-2.0), fine-tuned for CTC.
- **Data:** 8,437 approved (lattice-supported) Persian clips; target labels are the
  `audio_target_phonemes_vnext` repr1 label field.
- **Role:** used as the **OOF (out-of-fold) listener** in the AvaSanj data pipeline — each fold scores only
  voices it never trained on — to arbitrate G2P disagreements at margin `≥ 0.1`
  (~96.3% precision / 95.9% recall on the corruption gate).
- **Selection:** best step **800**, best validation CER **0.0479**.

## Model

- Architecture: Wav2Vec2ForCTC, base `wav2vec2-xls-r-300m` (~300 M params).
- Vocabulary: repr1 (see `vocabulary.json`) — 30-token inventory incl. `<blank>` + `" "`.
- Full eval: `report.json`.

## Usage

```python
from transformers import Wav2Vec2ForCTC
model = Wav2Vec2ForCTC.from_pretrained("Reza2kn/AvaSanj-v1-audio8437")
```

Note: load with the local `vocabulary.json` for the repr1 tokenizer mapping.

## Files

- `config.json`, `model.safetensors` — HF wav2vec2 checkpoint (`best-model/`)
- `vocabulary.json` — repr1 vocab
- `report.json` — full eval report (baseline, corruption gate, OOF fold stats)