Hugging Face's logo

infinitejoy
/
wav2vec2-large-xls-r-300m-breton

Automatic Speech Recognition Transformers PyTorch
Breton wav2vec2 generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_7_0 robust-speech-event Eval Results Inference Endpoints
Model card Files Community
wav2vec2-large-xls-r-300m-breton / README.md
anton-l's picture
anton-l HF staff
Upload README.md
4b89b33
preview code
|
raw history blame contribute delete
No virus
2.79 kB
metadata
language:
  - br
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - Breton
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: br
        metrics:
          - name: Test WER
            type: wer
            value: 107.955
          - name: Test CER
            type: cer
            value: 379.33

wav2vec2-large-xls-r-300m-breton

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

  • Loss: 0.6102
  • Wer: 0.4455

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: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.9205 3.33 500 2.8659 1.0
1.6403 6.67 1000 0.9440 0.7593
1.3483 10.0 1500 0.7580 0.6215
1.2255 13.33 2000 0.6851 0.5722
1.1139 16.67 2500 0.6409 0.5220
1.0688 20.0 3000 0.6245 0.5055
0.99 23.33 3500 0.6142 0.4874
0.9345 26.67 4000 0.5946 0.4829
0.9058 30.0 4500 0.6229 0.4704
0.8683 33.33 5000 0.6153 0.4666
0.8367 36.67 5500 0.5952 0.4542
0.8162 40.0 6000 0.6030 0.4541
0.8042 43.33 6500 0.5972 0.4485
0.7836 46.67 7000 0.6070 0.4497
0.7556 50.0 7500 0.6102 0.4455

Framework versions

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