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wav2vec2-base-timit-mseva-tokenizer – AI Model by saribalgar | AlphaNeural AI
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saribalgar
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wav2vec2-base-timit-mseva-tokenizer
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transformers
pytorch
wav2vec2
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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wav2vec2-base-timit-mseva-tokenizer
This model is a fine-tuned version of
facebook/wav2vec2-large
on the None dataset.
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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 300
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.24.0
Pytorch 2.0.0+cu118
Datasets 1.18.3
Tokenizers 0.13.3
