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wav2vec2-large-xls-r-300m-ab-CV7

Automatic Speech Recognition Transformers PyTorch
Abkhaz 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:
  - ab
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - ab
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wav2vec2-large-xls-r-300m-ab-CV7
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: ab
        metrics:
          - name: Test WER
            type: wer
            value: 0.5291160452450775
          - name: Test CER
            type: cer
            value: 0.10630270750110964
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: ab
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

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

  • Loss: 0.5620
  • Wer: 0.5651

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-ab-CV7 --dataset mozilla-foundation/common_voice_7_0 --config ab --split test --log_outputs

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

NA

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7.5e-05
  • 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: 2000
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
9.6445 13.64 300 4.3963 1.0
3.6459 27.27 600 3.2267 1.0
3.0978 40.91 900 3.0927 1.0
2.8357 54.55 1200 2.1462 1.0029
1.2723 68.18 1500 0.6747 0.6996
0.6528 81.82 1800 0.5928 0.6422
0.4905 95.45 2100 0.5587 0.5681

Framework versions

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