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w2v-bert-2.0-mongolian-colab-CV16.0
xinliu/w2v-bert-2.0-mongolian-colab-CV16.0
w2v-bert-2.0-mongolian-colab-CV16.0 is a automatic speech recognition model from xinliu. 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
- May 23, 2024
- Updated
- May 24, 2024
- SHA
- eaf3fb2a
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. -->
w2v-bert-2.0-mongolian-colab-CV16.0
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5090
- Wer: 0.3273
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: 5e-05
- 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: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.8026 | 2.3715 | 300 | 0.6395 | 0.5274 |
| 0.3561 | 4.7431 | 600 | 0.5804 | 0.4247 |
| 0.1776 | 7.1146 | 900 | 0.5514 | 0.3697 |
| 0.0764 | 9.4862 | 1200 | 0.5090 | 0.3273 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- May 23, 2024
- Updated
- May 24, 2024