---
language:
- lus
license: apache-2.0
pipeline_tag: automatic-speech-recognition
base_model: facebook/omniASR-CTC-300M
tags:
- generated_from_trainer
datasets:
- andrewbawitlung/MiZonal-v3.0
metrics:
- wer
- cer
model-index:
- name: omni-asr-ctc-300m-v2-mizonal3-E4-lus-v2026.06
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MiZonal v3.0
type: andrewbawitlung/MiZonal-v3.0
config: default
split: test
metrics:
- name: Wer
type: wer
value: 18.0524
- name: Cer
type: cer
value: 3.2913
- name: Real Time Factor
type: rtf
value: 0.0065
---
> **Disclaimer / Notice:** Details for these are in Peer Review and publications of the paper will be made available soon for more details.
# omni-asr-ctc-300m-v2-mizonal3-E4-lus-v2026.06
This model is a fine-tuned version of [facebook/omniASR-CTC-300M](https://huggingface.co/facebook/omniASR-CTC-300M) on the **MiZonal v3.0** dataset.
It achieves the following results on the evaluation set:
- Wer: 18.0524
- Cer: 3.2913
- Real Time Factor: 0.0065
## Quick Inference
```python
# Google Colab Ready Quick Inference
# 1. Install dependencies:
!pip install huggingface_hub omnilingual-asr torchaudio
# 2. Download the model checkpoint directly from Hugging Face:
import torch
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
repo_id="andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E4-lus-v2026.06",
filename="sdp_00.pt"
)
# 3. Load the base model pipeline and inject fine-tuned weights:
from omnilingual_asr.models.inference.pipeline import ASRInferencePipeline
print("Loading base model pipeline...")
pipeline = ASRInferencePipeline(model_card="omniASR_CTC_300M_v2")
print("Loading fine-tuned checkpoint weights...")
checkpoint = torch.load(model_path, map_location="cpu")
state_dict = checkpoint['model'] if 'model' in checkpoint else checkpoint
pipeline.model.load_state_dict(state_dict, strict=False)
# 4. Run transcription (make sure audio files are present in the specified paths):
audio_files = ["/content/segment_003.wav", "/content/segment_108.wav"]
lang = ["lus_Latn", "lus_Latn"]
transcriptions = pipeline.transcribe(audio_files, lang=lang, batch_size=2)
print("Transcription 0:", transcriptions[0])
print("Transcription 1:", transcriptions[1])
```
## Model descripti...
