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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

TypeOtherModèle de basesulaimank/wav2vec2-xlsr-swahili-400hrVersionRevision 3912684

Source du modèle

Type
Other
Taille du fichier
1,18 GB
Modèle de base
sulaimank/wav2vec2-xlsr-swahili-400hr
Version
vRevision 3912684
Mis à jour
28 sept. 2026
Téléchargements
10
Source
Hugging Face

Description de la source

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 FacePrincipalVersion 39126845a3806ee7fc21f63a7f14a72fcd87211f
huggingface.co/sulaimank/xlsr-luganda-waxal/tree/39126845a3806ee7fc21f63a7f14a72fcd87211f

Vérifié 22 sept.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 1,18 GB · SHA-256 d1929a4c25fc…7075 · Hugging Face

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Extraits de sources

2 extraits

--- 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: 10 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo sulaimank/xlsr-luganda-waxal