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

Automatic Speech Recognition Transformers PyTorch TensorBoard
Basa (Cameroon) wav2vec2 mozilla-foundation/common_voice_8_0 generated_from_trainer robust-speech-event model_for_talk hf-asr-leaderboard Eval Results Inference Endpoints
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metadata
language:
  - bas
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - bas
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-bas-v1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: bas
        metrics:
          - name: Test WER
            type: wer
            value: 0.3566497929130234
          - name: Test CER
            type: cer
            value: 0.1102657634184471
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: bas
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

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

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

  • Loss: 0.5997
  • Wer: 0.3870

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-bas-v1 --dataset mozilla-foundation/common_voice_8_0 --config bas --split test --log_outputs

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

Basaa (bas) language isn't available in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.000111
  • 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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
12.7076 5.26 200 3.6361 1.0
3.1657 10.52 400 3.0101 1.0
2.3987 15.78 600 0.9125 0.6774
1.0079 21.05 800 0.6477 0.5352
0.7392 26.31 1000 0.5432 0.4929
0.6114 31.57 1200 0.5498 0.4639
0.5222 36.83 1400 0.5220 0.4561
0.4648 42.1 1600 0.5586 0.4289
0.4103 47.36 1800 0.5337 0.4082
0.3692 52.62 2000 0.5421 0.3861
0.3403 57.88 2200 0.5549 0.4096
0.3011 63.16 2400 0.5833 0.3925
0.2932 68.42 2600 0.5674 0.3815
0.2696 73.68 2800 0.5734 0.3889
0.2496 78.94 3000 0.5968 0.3985
0.2289 84.21 3200 0.5888 0.3893
0.2091 89.47 3400 0.5849 0.3852
0.2005 94.73 3600 0.5938 0.3875
0.1876 99.99 3800 0.5997 0.3870

Framework versions

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