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Xlsr Luganda Waxal

This model is a fine-tuned version of sulaimank/wav2vec2-xlsr-swahili-400hr on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.1404 Wer: 0.2144 Cer: 0.0446 Zindi: 0.8705

KindOtherBase modelsulaimank/wav2vec2-xlsr-swahili-400hrVersionRevision 0cd0152Cgrade

Model source

Kind
Other
File size
1.18 GB
Base model
sulaimank/wav2vec2-xlsr-swahili-400hr
Version
vRevision 0cd0152
Updated
Sep 27, 2026
Downloads
13
Source
Hugging Face

Source description

xlsr-luganda-waxal

This model is a fine-tuned version of sulaimank/wav2vec2-xlsr-swahili-400hr on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1404
  • Wer: 0.2144
  • Cer: 0.0446
  • Zindi: 0.8705

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100.0

Training results

Training LossEpochStepValidation LossWerCerZindi
4.75860.11165001.75331.00.79360.1032
1.15270.223310000.49040.73000.15410.5580
1.00810.334915000.42320.64160.13470.6119
0.88070.446620000.39160.59540.12500.6398
0.86720.558225000.36240.55550.11600.6643
0.80810.669930000.34480.54030.11040.6746
0.77360.781535000.33180.52130.10620.6862
0.75320.893240000.32070.50100.10340.6978
0.72971.004745000.31560.48120.10010.7093
0.69811.116350000.30630.47090.09830.7154
0.67851.228055000.29240.44810.09250.7297
0.64161.339660000.28590.44270.09200.7327
0.62931.451365000.28060.42970.08910.7406
0.62731.562970000.27140.41660.08640.7485
0.60361.674675000.26700.41240.08480.7514
0.60411.786280000.25900.39970.08250.7589
0.59801.897885000.26110.40300.08350.7567
0.56232.009490000.25730.38790.08110.7655
0.56172.121095000.25160.38590.08050.7668
0.56632.2327100000.24640.37000.07840.7758
0.54332.3443105000.24620.36710.07820.7774
0.52662.4560110000.23810.36460.07580.7798
0.52492.5676115000.23440.35400.07400.7860
0.52592.6792120000.23480.35080.07400.7876
0.51672.7909125000.23090.34810.07380.7891
0.50502.9025130000.22950.34460.07230.7915
0.48663.0141135000.22690.33750.07120.7957
0.47133.1257140000.22580.33830.07090.7954
0.47473.2374145000.22180.33170.06980.7992
0.47253.3490150000.22050.33050.06940.8001
0.49513.4606155000.21860.32400.06870.8036
0.45293.5723160000.21400.32080.06740.8059
0.46683.6839165000.21310.32040.06790.8059
0.46483.7956170000.20800.31200.06570.8112
0.46363.9072175000.20730.31090.06530.8119
0.44204.0188180000.20850.31100.06600.8115
0.44884.1304185000.20410.30870.06460.8134
0.42104.2420190000.20500.30590.06430.8149
0.43374.3537195000.20310.30420.06420.8158
0.45624.4653200000.20160.30200.06400.8170
0.43834.5770205000.19900.29740.06310.8198
0.43104.6886210000.19970.30150.06350.8175
0.43204.8003215000.19540.29650.06190.8208
0.41974.9119220000.19540.29200.06190.8230
0.41465.0234225000.19240.28930.06060.8251
0.40955.1351230000.19170.29090.06080.8242
0.39895.2467235000.19220.28580.06060.8268
0.40335.3584240000.19290.28720.06120.8258
0.39205.4700245000.19070.28720.06040.8262
0.39345.5817250000.18840.27950.05940.8305
0.38815.6933255000.18560.27950.05890.8308
0.38355.8050260000.18380.27540.05790.8333
0.38275.9166265000.18460.27620.05870.8326
0.37746.0281270000.18230.27460.05770.8338
0.37756.1398275000.18010.27350.05740.8345
0.37626.2514280000.18160.27270.05790.8347
0.37556.3631285000.17960.27030.05690.8364
0.36766.4747290000.18050.26910.05720.8368
0.38306.5864295000.17810.26580.05670.8387
0.37146.6980300000.17750.26820.05660.8376
0.36486.8096305000.17660.26530.05590.8394
0.36656.9213310000.17360.26210.05540.8413
0.36677.0328315000.17580.26340.05620.8402
0.36347.1445320000.17780.26310.05680.8400
0.35197.2561325000.17310.25740.05470.8440
0.36437.3678330000.17090.26230.05490.8414
0.34517.4794335000.17160.25710.05470.8441
0.34977.5910340000.17100.25460.05450.8454
0.35637.7027345000.17190.25670.05520.8440
0.33937.8143350000.16960.25510.05440.8453
0.34947.9260355000.16640.25550.05380.8454
0.34358.0375360000.16720.250.05320.8484
0.34158.1492365000.16620.25030.05330.8482
0.32978.2608370000.16550.25070.05260.8484
0.33168.3724375000.16580.25290.05320.8469
0.32918.4841380000.16590.24680.05250.8504
0.33328.5957385000.16430.24780.05230.8500
0.34808.7074390000.16220.24510.05200.8514
0.32658.8190395000.16230.24640.05210.8508
0.33238.9307400000.16250.24830.05220.8498
0.33119.0422405000.16170.24840.05200.8498
0.33429.1538410000.16250.24120.05130.8538
0.32489.2655415000.16130.24540.05210.8513
0.30819.3771420000.16210.24200.05130.8534
0.31039.4888425000.16030.23950.05050.8550
0.30889.6004430000.15900.24050.05100.8542
0.31009.7121435000.15900.24040.05040.8546
0.30359.8237440000.16090.24330.05130.8527
0.32309.9354445000.15970.23970.05090.8547
0.307010.0469450000.15920.24070.05060.8544
0.309810.1585455000.15780.24100.05090.8541
0.293210.2702460000.15710.24010.05000.8549
0.296110.3818465000.15620.23660.05000.8567
0.299810.4935470000.15510.23470.04960.8579
0.308810.6051475000.15490.23750.05020.8562
0.301410.7168480000.15490.23610.05000.8569
0.299610.8284485000.15490.23300.04930.8589
0.307110.9400490000.15490.23310.04950.8587
0.297611.0516495000.15500.23310.04940.8587
0.296511.1632500000.15460.23190.04950.8593
0.294311.2749505000.15330.23370.04870.8588
0.286211.3865510000.15270.22600.04780.8631
0.300411.4982515000.15290.22810.04860.8616
0.283111.6098520000.15180.23130.04870.8600
0.285711.7214525000.14940.22350.04770.8644
0.275111.8331530000.14950.22410.04750.8642
0.279911.9447535000.15030.22630.04780.8629
0.284312.0563540000.15000.22750.04750.8625
0.267912.1679545000.15020.23050.04820.8606
0.286112.2796550000.15000.22810.04790.8620
0.288212.3912555000.14920.22740.04780.8624
0.278412.5028560000.14950.22520.04790.8634
0.277012.6145565000.14830.22330.04730.8647
0.273312.7261570000.14940.22750.04780.8623
0.279612.8378575000.14800.22690.04790.8626
0.277912.9494580000.14730.22560.04720.8636
0.261213.0610585000.14880.22470.04770.8638
0.269013.1726590000.14870.22510.04760.8636
0.279913.2842595000.14790.22380.04710.8645
0.260713.3959600000.14810.22410.04710.8644
0.274913.5075605000.14860.22460.04750.8639
0.253813.6192610000.14730.22140.04670.8659
0.283213.7308615000.14580.22000.04680.8666
0.264013.8425620000.14470.21910.04600.8674
0.275413.9541625000.14670.22020.04650.8666
0.263614.0656630000.14580.22060.04680.8663
0.250914.1773635000.14600.22330.04690.8649
0.257714.2889640000.14410.21670.04610.8686
0.262414.4006645000.14470.21840.04590.8678
0.254214.5122...

Sources

1 source
Hugging FacePrimaryVersion 0cd0152e6a4cd0b1ace9432ec41b20ea819318ce
huggingface.co/VinyVan/xlsr-luganda-waxal/tree/0cd0152e6a4cd0b1ace9432ec41b20ea819318ce

Verified Aug 30

Model artifacts

1 artifact

model.safetensors

safetensors · 1.18 GB · SHA-256 d1929a4c25fc…7075 · Hugging Face

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Source excerpts

2 excerpts

--- library_name: transformers license: apache-2.0 base_model: sulaimank/wav2vec2-xlsr-swahili-400hr tags: - generated_from_trainer metrics: - wer model-index: - name: xlsr-luganda-waxal results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlsr-luganda-waxal This model is a fine-tuned version of [sulaimank/wav2vec2-xlsr-swahili-400hr](https://huggingface.co/sulaimank/wav2vec2-xlsr-swahili-400hr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1404 - Wer: 0.2144 - Cer: 0.0446 - Zindi: 0.8705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Zindi | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:| | 4.7586 | 0.1116 | 500 | 1.7533 | 1.0 | 0.7936 | 0.1032 | | 1.1527 | 0.2233 | 1000 | 0.4904 | 0.7300 | 0.1541 | 0.5580 | | 1.0081 | 0.3349 | 1500 | 0.4232 | 0.6416 | 0.1347 | 0.6119 | | 0.8807 | 0.4466 | 2000 | 0.3916 | 0.5954 | 0.1250 | 0.6398 | | 0.8672 | 0.5582 | 2500 | 0.3624 | 0.5555 | 0.1160 | 0.6643 | | 0.8081 | 0.6699 | 3000 | 0.3448 | 0.5403 | 0.1104 | 0.6746 | | 0.7736 | 0.7815 | 3500 | 0.3318 | 0.5213 | 0.1062 | 0.6862 | | 0.7532 | 0.8932 | 4000 | 0.3207 | 0.5010 | 0.1034 | 0.6978 | | 0.7297 | 1.0047 | 4500 | 0.3156 | 0.4812 | 0.1001 | 0.7093 | | 0.6981 | 1.1163 | 5000 | 0.3063 | 0.4709 | 0.0983 | 0.7154 | | 0.6785 | 1.2280 | 5500 | 0.2924 | 0.4481 | 0.0925 | 0.7297 | | 0.6416 | 1.3396 | 6000 | 0.2859 | 0.4427 | 0.0920 | 0.7327 | | 0.6293 | 1.4513 | 6500 | 0.2806 | 0.4297 | 0.0891 | 0.7406 | | 0.6273 | 1.5629 | 7000 | 0.2714 | 0.4166 | 0.0864 | 0.7485 | | 0.6036 |...

Source context: 13 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo VinyVan/xlsr-luganda-waxal