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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

Quellehuggingface.co/larrycmu/wav2vec2-xls-r-300m-okinoerabu-lr-3e-4/tree/9510da288345c42e85e015e768172ce6443b6350
ArtOtherBasismodellfacebook/wav2vec2-xls-r-300mVersionRevision 9510da2Verleger@larrycmuCBewertung

Modellquelle

Art
Other
Dateigröße
1,18 GB
Basismodell
facebook/wav2vec2-xls-r-300m
Version
vRevision 9510da2
Aktualisiert
4. Okt. 2026
Verleger
@larrycmu
Downloads
20
Parameter
300M
Aufgaben
Video zu Video
Quelle
Hugging Face

Quellenbeschreibung

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

Quellen

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

Verifiziert 4. Okt.

Modellartefakte

1 Artefakt

model.safetensors

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

Herunterladen

Quellenauszüge

3 Auszüge
Readme11. Julihuggingface.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. Okt.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. Julihuggingface.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