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wav2vec2-2-gpt2-no-adapter-regularisation
sanchit-gandhi/wav2vec2-2-gpt2-no-adapter-regularisation
wav2vec2-2-gpt2-no-adapter-regularisation is a automatic speech recognition model from sanchit-gandhi. 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
- speech-encoder-decoder
- Access
- Public
- Created
- Mar 17, 2022
- Updated
- Mar 19, 2022
- SHA
- d38079fe
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. -->
This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set:
- Loss: 1.7494
- Wer: 1.0532
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.4828 | 2.8 | 2500 | 4.0554 | 1.7873 |
| 0.8683 | 5.61 | 5000 | 2.5401 | 1.3156 |
| 0.4394 | 8.41 | 7500 | 1.7519 | 1.1129 |
| 0.0497 | 11.21 | 10000 | 1.7102 | 1.0738 |
| 0.031 | 14.01 | 12500 | 1.7395 | 1.0512 |
| 0.0508 | 16.82 | 15000 | 1.7254 | 1.0463 |
| 0.0462 | 19.62 | 17500 | 1.7494 | 1.0532 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- speech-encoder-decoder
- Created
- Mar 17, 2022
- Updated
- Mar 19, 2022