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
with a CTC head.
Input requirements
- Audio must be 16 kHz, mono.
- Resample any other sample rate to 16 kHz before running inference.
Usage
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.