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wav2vec2-bloom-speech-fra

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
French
French
wav2vec2
sil-ai/bloom-speech
Generated from Trainer
Eval Results
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wav2vec2-bloom-speech-fra

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Model description

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - FRA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9346
  • Wer: 0.3051
  • Cer: 0.0978

Users should refer to the original model for tutorials on using a trained model for inference.

Intended uses & limitations

Users of this model must abide by the SIL RAIL-M License.

This model is created as a proof of concept and no guarantees are made regarding the performance of the model is specific situations.

Training and evaluation data

Training, Validation, and Test datasets were generated from the same corpus, ensuring that no duplicate files were used.

Training procedure

Standard finetuning of XLS-R was used based on the examples in the Hugging Face Transformers Github

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • 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: 250
  • num_epochs: 1000.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
No log 41.62 250 1.0210 0.7260 0.2412
2.4099 83.31 500 0.8338 0.4774 0.1514
2.4099 124.92 750 0.8023 0.4138 0.1205
0.0364 166.62 1000 0.7624 0.4068 0.1261
0.0364 208.31 1250 0.8166 0.3644 0.1149
0.0226 249.92 1500 0.8175 0.3686 0.1202
0.0226 291.62 1750 0.9210 0.3715 0.1170
0.0143 333.31 2000 0.8273 0.3729 0.1175
0.0143 374.92 2250 0.8743 0.3418 0.1109
0.0093 416.62 2500 0.8424 0.3432 0.1165
0.0093 458.31 2750 0.8365 0.3333 0.1053
0.0064 499.92 3000 0.9097 0.3432 0.1111
0.0064 541.62 3250 0.9744 0.3404 0.1098
0.0041 583.31 3500 0.9257 0.3489 0.1079
0.0041 624.92 3750 0.9047 0.3220 0.1031
0.0026 666.62 4000 0.8852 0.3263 0.1026
0.0026 708.31 4250 0.9000 0.3249 0.0986
0.0016 749.92 4500 0.9389 0.3376 0.1005
0.0016 791.62 4750 0.9329 0.3079 0.0970
0.0017 833.31 5000 0.9322 0.3164 0.0991
0.0017 874.92 5250 0.9351 0.3079 0.0991
0.0008 916.62 5500 0.9346 0.3051 0.0978
0.0008 958.31 5750 0.9391 0.3093 0.0991
0.0007 999.92 6000 0.9435 0.3065 0.0978

Framework versions

  • Transformers 4.21.0.dev0
  • Pytorch 1.9.0+cu111
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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Evaluation results

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