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wav2vec2-large-xls-r-300m-bg-d2

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Bulgarian
wav2vec2
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results
Inference Endpoints
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metadata
language:
  - bg
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - bg
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-bg-d2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: bg
        metrics:
          - name: Test WER
            type: wer
            value: []
          - name: Test CER
            type: cer
            value: []

wav2vec2-large-xls-r-300m-bg-d2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3421
  • Wer: 0.2860

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: 0.00025
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 700
  • num_epochs: 35
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
6.8791 1.74 200 3.1902 1.0
3.0441 3.48 400 2.8098 0.9864
1.1499 5.22 600 0.4668 0.5014
0.4968 6.96 800 0.4162 0.4472
0.3553 8.7 1000 0.3580 0.3777
0.3027 10.43 1200 0.3422 0.3506
0.2562 12.17 1400 0.3556 0.3639
0.2272 13.91 1600 0.3621 0.3583
0.2125 15.65 1800 0.3436 0.3358
0.1904 17.39 2000 0.3650 0.3545
0.1695 19.13 2200 0.3366 0.3241
0.1532 20.87 2400 0.3550 0.3311
0.1453 22.61 2600 0.3582 0.3131
0.1359 24.35 2800 0.3524 0.3084
0.1233 26.09 3000 0.3503 0.2973
0.1114 27.83 3200 0.3434 0.2946
0.1051 29.57 3400 0.3474 0.2956
0.0965 31.3 3600 0.3426 0.2907
0.0923 33.04 3800 0.3478 0.2894
0.0894 34.78 4000 0.3421 0.2860

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0