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CiceroASR
thiagolira/CiceroASR
CiceroASR is a automatic speech recognition model from thiagolira. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Jan 31, 2024
- Updated
- Feb 23, 2024
- SHA
- 15b8eb64
Base models
Spaces
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. -->
CiceroASR
This model is a fine-tuned version of facebook/w2v-bert-2.0 for the transcription of Classical Latin!
Example from the Aeneid: <video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/hYNFr2od1EKDlRRdzJmzR.webm"></video> Transcription: arma virumque cano (Of arms and men I sing)
Example from Genesis: <video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/9Q6DfG2h8FkABnl55DLBH.webm"></video> Transcription (little error there): creavit deus chaelum et terram (In the beggining God created the heaven and the earth)
It achieves the following results on the evaluation set of my dataset Latin Youtube:
- Loss: 0.5395
- Wer: 0.2220
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 300
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.6548 | 0.94 | 50 | 2.8634 | 0.9990 |
| 2.2055 | 1.89 | 100 | 1.0921 | 0.9727 |
| 1.667 | 2.83 | 150 | 0.7201 | 0.4615 |
| 1.3148 | 3.77 | 200 | 0.6431 | 0.3866 |
| 0.9899 | 4.72 | 250 | 0.5561 | 0.3116 |
| 0.9629 | 5.66 | 300 | 0.6027 | 0.3817 |
| 0.7557 | 6.6 | 350 | 0.7145 | 0.3145 |
| 0.9143 | 7.55 | 400 | 0.4926 | 0.2610 |
| 0.5837 | 8.49 | 450 | 0.5396 | 0.2619 |
| 0.7037 | 9.43 | 500 | 0.5076 | 0.2746 |
| 0.5986 | 10.38 | 550 | 0.5224 | 0.2415 |
| 0.5288 | 11.32 | 600 | 0.5332 | 0.2259 |
| 0.5034 | 12.26 | 650 | 0.5436 | 0.2249 |
| 0.4897 | 13.21 | 700 | 0.5171 | 0.2162 |
| 0.4738 | 14.15 | 750 | 0.5395 | 0.2220 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Jan 31, 2024
- Updated
- Feb 23, 2024