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wav2vec2-large-xls-r-300m-greek

Automatic Speech Recognition
Transformers
PyTorch
Greek
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results
Inference Endpoints
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metadata
language:
  - el
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - el
  - robust-speech-event
  - model_for_talk
datasets:
  - common_voice
model-index:
  - name: XLS-R-300M - Greek
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: el
        metrics:
          - name: Test WER
            type: wer
            value: 102.23963133640552
          - name: Test CER
            type: cer
            value: 146.28
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: el
        metrics:
          - name: Test WER
            type: wer
            value: 99.92
          - name: Test CER
            type: cer
            value: 132.38

wav2vec2-large-xls-r-300m-greek

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

  • Loss: 0.6592
  • Wer: 0.4564

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: 32
  • 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: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.0928 4.42 500 3.0804 1.0073
1.4505 8.85 1000 0.9038 0.7330
1.2207 13.27 1500 0.7375 0.6045
1.0695 17.7 2000 0.7119 0.5441
1.0104 22.12 2500 0.6069 0.5296
0.9299 26.55 3000 0.6168 0.5206
0.8588 30.97 3500 0.6382 0.5171
0.7942 35.4 4000 0.6048 0.4988
0.7808 39.82 4500 0.6730 0.5084
0.743 44.25 5000 0.6749 0.5012
0.6652 48.67 5500 0.6491 0.4735
0.6386 53.1 6000 0.6928 0.4954
0.5945 57.52 6500 0.6359 0.4798
0.5561 61.95 7000 0.6409 0.4799
0.5464 66.37 7500 0.6452 0.4691
0.5119 70.8 8000 0.6376 0.4657
0.474 75.22 8500 0.6541 0.4700
0.45 79.65 9000 0.6374 0.4571
0.4315 84.07 9500 0.6568 0.4625
0.3967 88.5 10000 0.6636 0.4605
0.3937 92.92 10500 0.6537 0.4597
0.3788 97.35 11000 0.6614 0.4589

Framework versions

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