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wav2vec2-large-xls-r-300m-bulgarian

Automatic Speech Recognition Transformers PyTorch
Bulgarian 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:
  - bg
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - bg
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - Bulgarian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: bg
        metrics:
          - name: Test WER
            type: wer
            value: 46.68
          - name: Test CER
            type: cer
            value: 10.75
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: bg
        metrics:
          - name: Test WER
            type: wer
            value: 63.68
          - name: Test CER
            type: cer
            value: 19.88
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: bg
        metrics:
          - name: Test WER
            type: wer
            value: 64.08

wav2vec2-large-xls-r-300m-bulgarian

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

  • Loss: 0.4487
  • Wer: 0.4674

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: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.9774 6.33 500 2.9769 1.0
1.3453 12.66 1000 0.6523 0.6980
1.1658 18.99 1500 0.5636 0.6359
1.0797 25.32 2000 0.5004 0.5759
1.044 31.65 2500 0.4958 0.5569
0.9915 37.97 3000 0.4971 0.5350
0.9429 44.3 3500 0.4829 0.5229
0.9266 50.63 4000 0.4515 0.5074
0.8965 56.96 4500 0.4599 0.5039
0.878 63.29 5000 0.4735 0.4954
0.8494 69.62 5500 0.4460 0.4878
0.8343 75.95 6000 0.4510 0.4795
0.8236 82.28 6500 0.4538 0.4789
0.8069 88.61 7000 0.4526 0.4748
0.7958 94.94 7500 0.4496 0.4700

Framework versions

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