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multilingual-w2v-bert-2.0
kesbeast23/multilingual-w2v-bert-2.0
multilingual-w2v-bert-2.0 is a automatic speech recognition model from kesbeast23. 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
- Nov 27, 2025
- Updated
- Dec 25, 2025
- SHA
- 4a7cdfbe
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. -->
multilingual-w2v-bert-2.0
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1569
- Wer: 0.1822
- Cer: 0.0391
- Bertscore Precision: 0.9494
- Bertscore Recall: 0.9490
- Bertscore F1: 0.9492
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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Use adamw_torch_fused with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Bertscore Precision | Bertscore Recall | Bertscore F1 |
|---|---|---|---|---|---|---|---|---|
| 0.5819 | 0.9833 | 500 | 0.4666 | 0.4358 | 0.1111 | 0.8899 | 0.8918 | 0.8908 |
| 0.3311 | 1.9656 | 1000 | 0.3473 | 0.3259 | 0.0870 | 0.9206 | 0.9182 | 0.9194 |
| 0.2909 | 2.9479 | 1500 | 0.3059 | 0.2708 | 0.0721 | 0.9344 | 0.9338 | 0.9340 |
| 0.2001 | 3.9302 | 2000 | 0.2830 | 0.2520 | 0.0683 | 0.9388 | 0.9384 | 0.9386 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Nov 27, 2025
- Updated
- Dec 25, 2025