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metadata
language:
  - hsb
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - hsb
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-hsb-v1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: hsb
        metrics:
          - name: Test WER
            type: wer
            value: 0.4393
          - name: Test CER
            type: cer
            value: 0.1036
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: hsb
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-hsb-v1

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

  • Loss: 0.5684
  • Wer: 0.4402

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v1 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs

  1. To evaluate on speech-recognition-community-v2/dev_data

Upper Sorbian language isn't available in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.00045
  • 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: 500
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
8.972 3.23 100 3.7498 1.0
3.3401 6.45 200 3.2320 1.0
3.2046 9.68 300 3.1741 0.9806
2.4031 12.9 400 1.0579 0.8996
1.0427 16.13 500 0.7989 0.7557
0.741 19.35 600 0.6405 0.6299
0.5699 22.58 700 0.6129 0.5928
0.4607 25.81 800 0.6548 0.5695
0.3827 29.03 900 0.6268 0.5190
0.3282 32.26 1000 0.5919 0.5016
0.2764 35.48 1100 0.5953 0.4805
0.2335 38.71 1200 0.5717 0.4728
0.2106 41.94 1300 0.5674 0.4569
0.1859 45.16 1400 0.5685 0.4502
0.1592 48.39 1500 0.5684 0.4402

Framework versions

  • Transformers 4.16.1
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.2
  • Tokenizers 0.11.0