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-E5-lus-v2026.06

This model is a fine-tuned version of facebook/omniASR-CTC-300M on the MiZonal v3.0 dataset.

It achieves the following results on the evaluation set:

• Wer: 15.6572
• Cer: 2.7682
• Real Time Factor: 0.0060

Quick Inference

# 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-E5-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 description

Experiment Configurations

This repository is part of a series of experiments. The different configurations are:

• E1 (Baseline): Standard training configuration.
• E2 (Noise): Training with background noise augmentation.
• E3 (Speed): Training with speed perturbation augmentation.
• E4 (SpecAug): Training with SpecAugment (time and frequency masking).
• E5 (Combined): Training with a combination of all augmentations.

All Models in this Family

Training hyperparameters

Training hyperparameters not available in bin format.

Training results