# 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

Experiment	Hugging Face Repository
E1 (Baseline)	andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E1-lus-v2026.06
E2 (Noise)	andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E2-lus-v2026.06
E3 (Speed)	andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E3-lus-v2026.06
E4 (SpecAug)	andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E4-lus-v2026.06
E5 (Combined)	andrewbawitlung/omni-asr-ctc-300m-v2-mizonal3-E5-lus-v2026.06

Training hyperparameters

Training hyperparameters not available in bin format.

Training results

step	epoch	train_loss	eval_loss	eval_wer	eval_cer	grad_norm
100	2.0000	109.7945				369.5147
200	4.0000	77.9571				136.2045
300	6.0000	66.0815				158.5168
400	8.0000	59.6202				104.5082
500	10.0000	55.9902				107.9677
600	12.0000	52.4551				106.8869
700	13.0000	50.7370				124.5138
800	15.0000	49.1970				95.8475
900	17.0000	45.4391				80.7042
1000	19.0000	42.8760				78.0457
1100	21.0000	41.4012	36.2955	33.5418	13.3126	93.1231
1200	23.0000	40.1431				72.7741
1300	25.0000	39.0496				91.1769
1400	26.0000	38.7845				68.6127
1500	28.0000	37.8364				74.0615
1600	30.0000	36.4003				75.8741
1700	32.0000	35.1302				61.2816
1800	34.0000	34.5149				71.4384
1900	36.0000	33.7934				64.8579
2000	38.0000	33.6305				70.0117
2100	39.0000	34.9433				63.6476
2200	41.0000	32.7293	34.9903	29.6814	12.2271	70.9184
2300	43.0000	32.0397				67.0038
2400	45.0000	31.1626				59.9783
2500	47.0000	31.1168				61.1752
2600	49.0000	31.1257				74.4588
2700	50.0000	30.3371				56.0547
2800	52.0000	32.3433				80.0529
2900	54.0000	30.0935				56.1438
3000	56.0000	29.6080				60.8715
3100	58.0000	29.2215				73.8782
3200	60.0000	29.2573				62.4088
3300	62.0000	29.2438	35.7938	28.9582	11.2497	61.0301
3400	63.0000	28.9241				61.3714
3500	65.0000	29.4754				61.4402
3600	67.0000	28.2900				56.5523
3700	69.0000	27.2281				54.8388
3800	71.0000	27.6014				55.5035
3900	73.0000	27.4812				75.8993
4000	75.0000	27.3107				64.8298
4100	76.0000	28.2458				59.6157
4200	78.0000	27.7007				56.1580
4300	80.0000	26.8254				59.5118
4400	82.0000	26.2679	39.1191	28.4793	11.8244	47.1478
4500	84.0000	26.3219				55.5331
4600	86.0000	26.7943				58.9898
4700	88.0000	26.3254				55.5184
4800	89.0000	27.3416				56.8123
4900	91.0000	26.2978				52.2135
5000	93.0000	25.5715				52.2345
5100	95.0000	25.3241				56.0211
5200	97.0000	25.2917				61.0902
5300	99.0000	25.9439				72.8719
5400	100.0000	25.5041				64.9717
5500	102.0000	26.2498	36.8365	27.6583	10.7282	54.7318
5600	104.0000	25.2009				56.9957
5700	106.0000	24.6477				64.8322
5800	108.0000	24.5322				61.3378
5900	110.0000	24.8438				65.0565
6000	112.0000	24.7003				62.3438
6100	113.0000	25.4064				69.4132
6200	115.0000	25.4864				66.2506
6300	117.0000	24.5183				63.7476
6400	119.0000	24.3968				59.3504
6500	121.0000	24.2427				53.2391
6600	123.0000	24.1187	38.1019	26.9156	10.8949	60.7356
6700	125.0000	23.9335				58.0522
6800	126.0000	25.3275				59.6575
6900	128.0000	24.6366				63.7991
7000	130.0000	24.1842				58.9771
7100	132.0000	23.5456				67.6624
7200	134.0000	23.9943				66.7438
7300	136.0000	23.7824				61.4209
7400	138.0000	24.1489				71.5598
7500	139.0000	24.8412				57.2182
7600	141.0000	24.3281				67.8653
7700	143.0000	23.9259	37.8958	26.2314	10.5401	68.4402
7800	145.0000	23.5687				69.3179
7900	147.0000	23.7210				62.9377
8000	149.0000	23.9842				70.7760
8100	150.0000	23.9772				66.2701
8200	152.0000	24.9833				65.0699
8300	154.0000	24.3973				65.3704
8400	156.0000	23.5754				64.9838
8500	158.0000	23.5062				58.6743
8600	160.0000	23.7330				76.8839
8700	162.0000	23.9548				77.5334
8800	163.0000	24.2769	37.9807	26.9253	10.6430	67.9240