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subatomicseer/
wav2vec2-base-hyperVQ-timit-fine-tuned

Name: wav2vec2-base-hyperVQ-timit-fine-tuned
Availability: InStock

transformers

safetensors

wav2vec2

automatic-speech-recognition

timit_asr

generated_from_trainer

timit_asr

model-index

endpoints_compatible

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wav2vec2-base-hyperVQ-timit-fine-tuned

This model is a fine-tuned version of wav2vec2-pretrained-base-hyperVQ on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

Loss: 3.3628
Wer: 0.9993

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: 32
eval_batch_size: 32
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: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.2725	10.0	1450	3.4699	1.0006
3.1682	20.0	2900	3.3628	0.9993

Framework versions

Transformers 4.36.2
Pytorch 2.3.0.dev20231229+cu118
Datasets 2.16.0
Tokenizers 0.15.0

subatomicseer/wav2vec2-base-hyperVQ-timit-fine-tuned

wav2vec2-base-hyperVQ-timit-fine-tuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

subatomicseer/
wav2vec2-base-hyperVQ-timit-fine-tuned