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language:
- kmr
license: mit
library_name: transformers
tags:
- automatic-speech-recognition
- wav2vec2-bert
- kurdish
- badini
- kurmanji
- low-resource
base_model: facebook/w2v-bert-2.0
pipeline_tag: automatic-speech-recognition
---
# Badini Kurdish ASR (Wav2Vec2-BERT)
An automatic speech recognition (ASR) model for **Badini Kurdish** (a variety of
Kurmanji, written in Arabic script), fine-tuned from
[`facebook/w2v-bert-2.0`](https://huggingface.co/facebook/w2v-bert-2.0)
with a CTC head.
## Input requirements
- Audio must be **16 kHz, mono**.
- Resample any other sample rate to 16 kHz before running inference.
## Usage
```python
import torch, soundfile as sf
from transformers import Wav2Vec2BertForCTC, Wav2Vec2BertProcessor
repo_id = "computeram/badini-w2v-bert"
processor = Wav2Vec2BertProcessor.from_pretrained(repo_id)
model = Wav2Vec2BertForCTC.from_pretrained(repo_id).eval()
speech, sr = sf.read("audio.wav") # 16 kHz mono
inputs = processor(speech, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
pred_ids = torch.argmax(logits, dim=-1)
print(processor.batch_decode(pred_ids)[0])
```
## Important: text normalization
This repository contains **only the acoustic model**. The raw output is plain CTC
decoding. A separate normalization pipeline — punctuation restoration,
number-to-word expansion (with Badini-specific remapping), character
normalization, and word-variant correction — is applied in the downstream
application and is **not** included here. Output straight from this model will
not match the fully normalized transcripts.
## Intended use
Speech transcription for Badini Kurdish. Given the WER, best used with
downstream normalization/post-processing rather than as raw final text.
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