To rerun this experiment, please clone this directory and run:

python create_model.py

followed by

./run_librispeech.sh

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wav2vec2-2-bart-base

This model is a fine-tuned version of facebook/wav2vec2-base and bart-base on the librispeech_asr - clean dataset.

It achieves the following results on the evaluation set:

• Loss: 0.405
• Wer: 0.0728

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: 0.0003
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 64
• total_eval_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 400
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

See Training Metrics Tab.

Framework versions

• Transformers 4.15.0.dev0
• Pytorch 1.9.0+cu111
• Datasets 1.16.2.dev0
• Tokenizers 0.10.3