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Wav2vec2 Xls R 300m Okinoerabu Lr 3e 4

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.2382 Cer: 0.0746 Wer: 0.3453

Sourcehuggingface.co/larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4/tree/9510da288345c42e85e015e768172ce6443b6350
TypeOtherModèle de basefacebook/wav2vec2-xls-r-300mVersionRevision 9510da2Éditeur@larrycmuCClasse

Source du modèle

Type
Other
Taille du fichier
1,18 GB
Modèle de base
facebook/wav2vec2-xls-r-300m
Version
vRevision 9510da2
Mis à jour
4 oct. 2026
Éditeur
@larrycmu
Téléchargements
20
Paramètres
300M
Tâches
Vidéo en vidéo
Source
Hugging Face

Description de la source

wav2vec2-xls-r-300m-okinoerabu-lr-3e-4

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2382
  • Cer: 0.0746
  • Wer: 0.3453

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • 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

Training results

Training LossEpochStepValidation LossCerWer
131.59630.740710046.40790.99991.0
71.00361.481520023.69390.99991.0
41.53792.222230014.31870.99991.0
22.46542.96304007.11780.99991.0
10.98293.70375004.30450.99991.0
8.67484.44446004.18530.99991.0
8.40735.18527004.10680.99991.0
8.26995.92598004.07330.99991.0
8.12016.66679004.03410.99991.0
8.06087.407410004.02570.99991.0
8.06078.148111004.02500.99991.0
8.06838.888912004.01830.99991.0
8.02769.629613004.00600.99991.0
8.067410.370414004.00260.99991.0
7.994411.111115003.98100.99991.0
7.842211.851916003.89680.99991.0
7.807512.592617004.04290.99991.0
7.772313.333318003.94070.99991.0
7.742314.074119003.86310.99991.0
7.624614.814820003.77440.99991.0
7.085215.555621003.02020.92321.0
4.692616.296322001.46900.37700.9971
2.730617.037023000.95290.24110.9055
1.931317.777824000.67340.15140.6376
1.497618.518525000.53980.13030.5314
1.264019.259326000.45330.12100.5166
1.047120.027000.37330.10920.4789
0.894020.740728000.34310.10370.4603
0.789221.481529000.31140.10180.4458
0.751122.222230000.28960.09990.4518
0.697822.963031000.27010.09610.4435
0.651223.703732000.25180.09040.4127
0.635324.444433000.23880.09000.4167
0.587725.185234000.23580.08790.4081
0.558125.925935000.22680.08650.4055
0.559726.666736000.23150.08710.4047
0.514527.407437000.21720.08430.3853
0.504528.148138000.21850.08590.4030
0.475628.888939000.21110.08560.3958
0.485529.629640000.20290.08180.3838
0.450930.370441000.20240.08050.3710
0.440731.111142000.20040.08080.3736
0.421731.851943000.19560.08080.3761
0.412032.592644000.19140.07960.3656
0.387933.333345000.19150.07900.3616
0.395834.074146000.18870.07880.3630
0.400734.814847000.19590.07920.3673
0.398135.555648000.19000.08000.3776
0.365536.296349000.18700.07910.3707
0.378337.037050000.18410.07790.3576
0.348437.777851000.19090.07890.3639
0.347338.518552000.19020.07890.3650
0.357039.259353000.21050.08500.4018
0.312840.054000.18760.07990.3701
0.328840.740755000.18680.07860.3587
0.326641.481556000.19230.07840.3656
0.320942.222257000.20730.08240.3827
0.302942.963058000.20260.08000.3704
0.294143.703759000.20730.08120.3759
0.299944.444460000.19270.07870.3679
0.291045.185261000.20420.08130.3659
0.298645.925962000.19640.07720.3610
0.289446.666763000.20380.07990.3679
0.271447.407464000.19900.07740.3607
0.287148.148165000.20230.07770.3636
0.267348.888966000.19730.07630.3599
0.268849.629667000.19440.07740.3624
0.256150.370468000.20420.07720.3593
0.245551.111169000.19520.07750.3647
0.240351.851970000.19450.07670.3604
0.244052.592671000.19370.07640.3567
0.232153.333372000.20210.07930.3647
0.212654.074173000.20220.07650.3590
0.219454.814874000.19930.07770.3604
0.228655.555675000.19520.07620.3556
0.219256.296376000.21410.07840.3690
0.215357.037077000.20490.07590.3527
0.214857.777878000.20820.07680.3582
0.218758.518579000.20120.07770.3610
0.204259.259380000.20800.07570.3527
0.198160.081000.21310.07900.3673
0.211860.740782000.21440.07800.3604
0.201161.481583000.21560.07730.3564
0.197862.222284000.20950.07590.3545
0.185962.963085000.22940.07880.3604
0.196063.703786000.20880.07620.3533
0.189564.444487000.22430.07950.3650
0.189565.185288000.21740.07500.3527
0.185965.925989000.22500.07920.3619
0.180066.666790000.21290.07670.3522
0.185167.407491000.21390.07680.3542
0.176768.148192000.21210.07460.3479
0.180668.888993000.21520.07540.3510
0.186169.629694000.22010.07510.3522
0.180570.370495000.21340.07600.3513
0.184271.111196000.21960.07650.3539
0.168871.851997000.21920.07630.3547
0.164772.592698000.22150.07460.3467
0.180473.333399000.22160.07430.3482
0.155474.0741100000.22840.07590.3536
0.164774.8148101000.21850.07580.3485
0.165575.5556102000.21680.07570.3545
0.152276.2963103000.22230.07550.3505
0.156677.0370104000.22000.07460.3459
0.154477.7778105000.22790.07550.3493
0.158678.5185106000.22540.07540.3513
0.147179.2593107000.22290.07540.3536
0.153180.0108000.22480.07600.3516
0.153980.7407109000.23300.07600.3487
0.154381.4815110000.22510.07550.3533
0.154682.2222111000.23180.07570.3490
0.145382.9630112000.22940.07580.3539
0.149283.7037113000.23010.07590.3513
0.156984.4444114000.23240.07410.3462
0.147485.1852115000.23350.07510.3482
0.138985.9259116000.23220.07500.3507
0.145086.6667117000.23760.07440.3462
0.144887.4074118000.24130.07530.3490
0.144088.1481119000.23780.07490.3465
0.138488.8889120000.23960.07410.3465
0.148689.6296121000.23750.07490.3473
0.140490.3704122000.23490.07470.3487
0.137791.1111123000.23620.07470.3479
0.142891.8519124000.23440.07490.3476
0.144692.5926125000.23270.07460.3465
0.137893.3333126000.23370.07480.3467
0.133794.0741127000.23570.07440.3447
0.137694.8148128000.23460.07460.3470
0.131195.5556129000.23770.07490.3485
0.131896.2963130000.23700.07500.3465
0.135997.0370131000.23850.07470.3473
0.141897.7778132000.23830.07430.3445
0.136498.5185133000.23830.07470.3450
0.119699.2593134000.23800.07420.3436
0.1342100.0135000.23820.07460.3453

Framework versions

  • Transformers 5.8.1
  • Pytorch 2.11.0+cu128
  • Datasets 3.6.0
  • Tokenizers 0.22.2

Sources

1 source
Hugging FacePrincipalVersion 9510da288345c42e85e015e768172ce6443b6350
huggingface.co/larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4/tree/9510da288345c42e85e015e768172ce6443b6350

Vérifié 4 oct.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 1,18 GB · SHA-256 c1dac920146a…afad · Hugging Face

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Extraits de sources

3 extraits
Readme11 juil.huggingface.co/larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4

--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-okinoerabu-lr-3e-4 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. --> # wav2vec2-xls-r-300m-okinoerabu-lr-3e-4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2382 - Cer: 0.0746 - Wer: 0.3453 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| | 131.5963 | 0.7407 | 100 | 46.4079 | 0.9999 | 1.0 | | 71.0036 | 1.4815 | 200 | 23.6939 | 0.9999 | 1.0 | | 41.5379 | 2.2222 | 300 | 14.3187 | 0.9999 | 1.0 | | 22.4654 | 2.9630 | 400 | 7.1178 | 0.9999 | 1.0 | | 10.9829 | 3.7037 | 500 | 4.3045 | 0.9999 | 1.0 | | 8.6748 | 4.4444 | 600 | 4.1853 | 0.9999 | 1.0 | | 8.4073 | 5.1852 | 700 | 4.1068 | 0.9999 | 1.0 | | 8.2699 | 5.9259 | 800 | 4.0733 | 0.9999 | 1.0 | | 8.1201 | 6.6667 | 900 | 4.0341 | 0.9999 | 1.0 | | 8.0608 | 7.4074 | 1000 | 4.0257 | 0.9999 | 1.0 | | 8.0607 | 8.1481 | 1100 | 4.0250 | 0.9999 | 1.0 | | 8.0683 | 8.8889 | 1200 | 4.0183 | 0.9999 | 1.0 | | 8.0276 | 9.6296 | 1300 | 4.0060 | 0.9999 | 1.0 | | 8.0674 | 10.3704 | 1400 | 4.0026 | 0.9999 | 1.0 | | 7.9944 | 11.1111 | 1500 | 3.9810 | 0.9999 | 1.0 | | 7.8422 | 11.8519 | 1600 | 3.8968 | 0.9999 | 1.0 | | 7.8075 | 12.5926 | 1700 | 4.0429 | 0.9999 | 1.0...

Note4 oct.huggingface.co/larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4

Source context: 20 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4

Note17 juil.huggingface.co/larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4

Source context: 1075 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4