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metadata
language:
  - mn
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - mn
  - robust-speech-event
  - model_for_talk
datasets:
  - common_voice
model-index:
  - name: XLS-R-300M - Mongolian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: mn
        metrics:
          - name: Test WER
            type: wer
            value: 44.709
          - name: Test CER
            type: cer
            value: 13.532
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: mn
        metrics:
          - name: Test WER
            type: wer
            value: 76.643
          - name: Test CER
            type: cer
            value: 36.997

wav2vec2-large-xls-r-300m-mongolian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - MN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6003
  • Wer: 0.4473

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: 0.0003
  • train_batch_size: 32
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.3677 15.87 2000 0.6432 0.6198
1.1379 31.75 4000 0.6196 0.5592
1.0093 47.62 6000 0.5828 0.5117
0.8888 63.49 8000 0.5754 0.4822
0.7985 79.37 10000 0.5987 0.4690
0.697 95.24 12000 0.6014 0.4471

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0