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wav2vec2-2-bart-large-cnn – AI Model by sanchit-gandhi | AlphaNeural AI
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sanchit-gandhi
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wav2vec2-2-bart-large-cnn
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transformers
pytorch
tensorboard
speech-encoder-decoder
automatic-speech-recognition
generated_from_trainer
librispeech_asr
endpoints_compatible
us
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This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set:
Loss: 0.3524
Wer: 0.1042
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: 2
gradient_accumulation_steps: 16
total_train_batch_size: 256
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10.0
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
2.7605
4.5
500
2.6299
1.4451
0.1177
9.01
1000
0.3524
0.1042
Framework versions
Transformers 4.17.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.3
Tokenizers 0.11.0
