xls-r-300m-cv_8-fr / README.md
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metadata
language:
  - fr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
model-index:
  - name: ''
    results: []

Model description

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.

Training and evaluation data

It achieves the following results on the evaluation set (Step 17000):

  • Wer: 0.2172

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7.5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 5.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.9114 0.29 1000 inf 0.9997
1.2436 0.57 2000 inf 0.4310
1.0552 0.86 3000 inf 0.3144
1.0044 1.15 4000 inf 0.2814
0.9718 1.43 5000 inf 0.2658
0.9502 1.72 6000 inf 0.2566
0.9418 2.01 7000 inf 0.2476
0.9215 2.29 8000 inf 0.2420
0.9236 2.58 9000 inf 0.2388
0.9014 2.87 10000 inf 0.2354
0.8814 3.15 11000 inf 0.2312
0.8809 3.44 12000 inf 0.2285
0.8717 3.73 13000 inf 0.2263
0.8787 4.01 14000 inf 0.2218
0.8567 4.3 15000 inf 0.2193
0.8488 4.59 16000 inf 0.2187
0.8359 4.87 17000 inf 0.2172

Got some issue with validation loss calculation.

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3.dev0
  • Tokenizers 0.11.0