Puyush
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wav2vec2-base-speech-recoginition

Automatic Speech Recognition

generated_from_trainer

Inference Endpoints

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wav2vec2-base-speech-recoginition

This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset. It achieves the following results on the evaluation set:

Loss: 4.0532
Wer: 0.9985

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: 0.002
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9304	1.85	500	4.0532	0.9985

Framework versions

Transformers 4.35.0
Pytorch 2.0.0
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: 0

Safetensors

Model size

94.4M params

Tensor type

F32

·

Finetuned from

Dataset used to train Puyush/wav2vec2-base-speech-recoginition

Space using Puyush/wav2vec2-base-speech-recoginition 1

Evaluation results

Wer on superb
validation set self-reported

0.998

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