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wav2vec2-base-hyperVQ-timit-fine-tuned
subatomicseer/wav2vec2-base-hyperVQ-timit-fine-tuned
wav2vec2-base-hyperVQ-timit-fine-tuned is a automatic speech recognition model from subatomicseer. Use it when you need speech turned into text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- Model type
- wav2vec2
- Access
- Public
- Created
- Jan 2, 2024
- Updated
- Jan 2, 2024
- SHA
- a49566aa
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
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
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- Created
- Jan 2, 2024
- Updated
- Jan 2, 2024