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wav2vec2-large-xls-r-300m-finnish

Automatic Speech Recognition
Transformers
PyTorch
Finnish
wav2vec2
Generated from Trainer
hf-asr-leaderboard
model_for_talk
mozilla-foundation/common_voice_7_0
robust-speech-event
Eval Results
Inference Endpoints
Model card Files Community
wav2vec2-large-xls-r-300m-finnish / README.md
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metadata
language:
  - fi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - fi
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - Finnish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: fi
        metrics:
          - name: Test WER
            type: wer
            value: 29.97
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-finnish

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

  • Loss: 0.2307
  • Wer: 0.2984

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: 7e-05
  • 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: 70.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.9032 4.39 500 2.8768 1.0
1.5724 8.77 1000 0.5638 0.6438
1.1818 13.16 1500 0.3338 0.4759
1.0798 17.54 2000 0.2876 0.4086
1.0296 21.93 2500 0.2694 0.4248
1.0014 26.32 3000 0.2626 0.3733
0.9616 30.7 3500 0.2391 0.3294
0.9303 35.09 4000 0.2352 0.3218
0.9248 39.47 4500 0.2351 0.3207
0.8837 43.86 5000 0.2341 0.3103
0.8887 48.25 5500 0.2311 0.3115
0.8529 52.63 6000 0.2230 0.3001
0.8404 57.02 6500 0.2279 0.3054
0.8242 61.4 7000 0.2298 0.3006
0.8288 65.79 7500 0.2333 0.2997

Framework versions

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