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wav2vec2-large-xls-r-300m-nepali

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Nepali
wav2vec2
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wav2vec2-large-xls-r-300m-nepali / README.md
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metadata
library_name: transformers
language:
  - ne
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - kiranpantha/OpenSLR54-Balanced-Nepali
metrics:
  - wer
model-index:
  - name: XLSR-300M-Nepali
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: OpenSLR54
          type: kiranpantha/OpenSLR54-Balanced-Nepali
          config: default
          split: test
          args: 'config: ne, split: train,test'
        metrics:
          - name: Wer
            type: wer
            value: 0.5244204160175937

XLSR-300M-Nepali

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OpenSLR54 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2681
  • Wer: 0.5244

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.2642 0.0722 300 2.9627 1.0
2.1949 0.1444 600 1.5526 1.0160
1.4595 0.2166 900 1.1674 0.9810
1.2128 0.2888 1200 0.9901 0.9668
0.976 0.3610 1500 0.6942 0.7696
0.8267 0.4332 1800 0.6314 0.7552
0.7542 0.5054 2100 0.5522 0.7156
0.7228 0.5776 2400 0.5210 0.6960
0.6707 0.6498 2700 0.4744 0.6581
0.6368 0.7220 3000 0.4529 0.6535
0.5944 0.7942 3300 0.4229 0.6264
0.5651 0.8664 3600 0.4061 0.6161
0.5469 0.9386 3900 0.3788 0.6103
0.5308 1.0108 4200 0.3668 0.5957
0.4684 1.0830 4500 0.3509 0.5920
0.4382 1.1552 4800 0.3398 0.5920
0.4424 1.2274 5100 0.3260 0.5767
0.4159 1.2996 5400 0.3189 0.5690
0.419 1.3718 5700 0.3067 0.5581
0.4114 1.4440 6000 0.3019 0.5568
0.3903 1.5162 6300 0.2982 0.5549
0.3915 1.5884 6600 0.2887 0.5493
0.3789 1.6606 6900 0.2813 0.5398
0.3725 1.7329 7200 0.2763 0.5339
0.3706 1.8051 7500 0.2704 0.5285
0.3624 1.8773 7800 0.2706 0.5264
0.357 1.9495 8100 0.2681 0.5244

Framework versions

  • Transformers 4.45.0.dev0
  • Pytorch 2.4.1+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1