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finetune_wav2vec2_base_thirty
ImanN1/finetune_wav2vec2_base_thirty
finetune_wav2vec2_base_thirty is a automatic speech recognition model from ImanN1. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- May 8, 2024
- Updated
- May 8, 2024
- SHA
- 15d6e1da
Base models
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. -->
finetune_wav2vec2_base_thirty
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3014
- Wer: 68.3822
- Cer: 31.2142
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 4500
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.4046 | 2.8090 | 1000 | 1.3290 | 68.5064 | 31.3589 |
| 0.7428 | 5.6180 | 2000 | 0.6691 | 34.7663 | 15.3556 |
| 0.4195 | 8.4270 | 3000 | 0.6144 | 29.5906 | 13.1851 |
| 0.2851 | 11.2360 | 4000 | 0.6656 | 27.3526 | 12.3476 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- May 8, 2024
- Updated
- May 8, 2024