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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
KindOtherBase modelfacebook/wav2vec2-xls-r-300mVersionRevision 9510da2Publisher@larrycmuCgrade

Model source

Kind
Other
File size
1.18 GB
Base model
facebook/wav2vec2-xls-r-300m
Version
vRevision 9510da2
Updated
Oct 4, 2026
Publisher
@larrycmu
Downloads
20
Parameters
300M
Tasks
Video to video
Source
Hugging Face

Source description

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 FacePrimaryVersion 9510da288345c42e85e015e768172ce6443b6350
huggingface.co/larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4/tree/9510da288345c42e85e015e768172ce6443b6350

Verified Oct 4

Model artifacts

1 artifact

model.safetensors

safetensors · 1.18 GB · SHA-256 c1dac920146a…afad · Hugging Face

Download

Source excerpts

3 excerpts
ReadmeJul 11huggingface.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...

NoteOct 4huggingface.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

NoteJul 17huggingface.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