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wav2vec2-2-bart-base – AI Model by patrickvonplaten | AlphaNeural AI
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wav2vec2-2-bart-base
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transformers
pytorch
tensorboard
speech-encoder-decoder
automatic-speech-recognition
librispeech_asr
generated_from_trainer
asr_seq2esq
endpoints_compatible
us
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Model card
Files and Versions
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To rerun this experiment, please clone this directory and run:
python create_model.py
followed by
./run_librispeech.sh
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
