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adding robust speech event tag
f5c5dca
metadata
language:
  - bas
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-basaa
    results: []

wav2vec2-large-xls-r-300m-basaa

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

  • Loss: 0.5975
  • Wer: 0.4981

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: 200.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.9287 15.62 500 2.8774 1.0
1.1182 31.25 1000 0.6248 0.7131
0.8329 46.88 1500 0.5573 0.5792
0.7109 62.5 2000 0.5420 0.5683
0.6295 78.12 2500 0.5166 0.5395
0.5715 93.75 3000 0.5487 0.5629
0.5016 109.38 3500 0.5370 0.5471
0.4661 125.0 4000 0.5621 0.5395
0.423 140.62 4500 0.5658 0.5248
0.3793 156.25 5000 0.5921 0.4981
0.3651 171.88 5500 0.5987 0.4888
0.3351 187.5 6000 0.6017 0.4948

Framework versions

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