Automatic Speech Recognition
Transformers
Safetensors
Northern Kurdish
wav2vec2-bert
kurdish
badini
kurmanji
low-resource
Instructions to use computeram/badini-w2v-bert-normalized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use computeram/badini-w2v-bert-normalized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="computeram/badini-w2v-bert-normalized")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("computeram/badini-w2v-bert-normalized") model = AutoModelForCTC.from_pretrained("computeram/badini-w2v-bert-normalized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from computeram/badini-w2v-bert-normalized: direct link, hf CLI and curl.
- Browser
- Download file 2.13 kB
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https://huggingface.co/computeram/badini-w2v-bert-normalized/resolve/main/README.md
- Command line
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hf download hf://computeram/badini-w2v-bert-normalized/README.md
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curl -L -o README.md https://huggingface.co/computeram/badini-w2v-bert-normalized/resolve/main/README.md
2.13 kB
| 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 — Normalized (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. | |
| This repository includes a companion text-normalization module (`badini_normalize.py`) alongside the model. Following the usage example below applies punctuation handling, number expansion, and character normalization to the raw model output. | |
| ## Input requirements | |
| - Audio must be **16 kHz, mono**. | |
| - Resample any other sample rate to 16 kHz before inference. | |
| ## Usage | |
| ```python | |
| import torch, soundfile as sf | |
| from transformers import Wav2Vec2BertForCTC, Wav2Vec2BertProcessor | |
| from huggingface_hub import hf_hub_download | |
| import importlib.util | |
| repo_id = "computeram/badini-w2v-bert-normalized" | |
| # download and load the companion normalization module | |
| norm_path = hf_hub_download(repo_id, "badini_normalize.py") | |
| spec = importlib.util.spec_from_file_location("badini_normalize", norm_path) | |
| badini_normalize = importlib.util.module_from_spec(spec) | |
| spec.loader.exec_module(badini_normalize) | |
| 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) | |
| raw_text = processor.batch_decode(pred_ids)[0] | |
| # apply the normalization pipeline (punctuation handling, Badini number | |
| # expansion, character/word-variant unification) | |
| text = badini_normalize.normalize_transcript(raw_text) | |
| print(text) | |
| ``` | |
| ## Intended use | |
| Speech transcription for Badini Kurdish, with the recommended normalization | |
| already applied by following the usage example above. | |