Instructions to use zhdoka/bori-asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhdoka/bori-asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zhdoka/bori-asr")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("zhdoka/bori-asr") model = AutoModelForMultimodalLM.from_pretrained("zhdoka/bori-asr") - Notebooks
- Google Colab
- Kaggle
Böri — Kazakh ASR (bori-asr)
Whisper large-v3-turbo fine-tuned (LoRA, merged) on Kazakh Speech Corpus 2.
Metrics (500-sample eval, greedy)
- CER: 3.84%
- WER: 17.5%
Usage
import torch, librosa, numpy as np
from transformers import WhisperForConditionalGeneration, WhisperProcessor
m = WhisperForConditionalGeneration.from_pretrained('zhdoka/bori-asr').eval()
p = WhisperProcessor.from_pretrained('zhdoka/bori-asr')
a,_ = librosa.load('audio.wav', sr=16000, mono=True); a = a/ (np.abs(a).max() or 1); a = a[:30*16000]
feat = p(a, sampling_rate=16000, return_tensors='pt').input_features
ids = m.generate(feat, language='kazakh', task='transcribe', num_beams=1, max_new_tokens=225)
print(p.batch_decode(ids, skip_special_tokens=True)[0].lower().strip())
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Model tree for zhdoka/bori-asr
Base model
openai/whisper-large-v3 Finetuned
openai/whisper-large-v3-turbo