Automatic Speech Recognition
Transformers
TensorBoard
Divehi
wav2vec2-bert
Generated from Trainer
Eval Results (legacy)
Instructions to use alakxender/w2v-bert-2.0-dhivehi-syn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alakxender/w2v-bert-2.0-dhivehi-syn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alakxender/w2v-bert-2.0-dhivehi-syn")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("alakxender/w2v-bert-2.0-dhivehi-syn") model = AutoModelForCTC.from_pretrained("alakxender/w2v-bert-2.0-dhivehi-syn") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - dv | |
| tags: | |
| - generated_from_trainer | |
| base_model: alakxender/w2v-bert-2.0-dhivehi-cv | |
| datasets: | |
| - mozilla-foundation/common_voice_17_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: w2v Bert 2.0 Dv - alakxender | |
| results: | |
| - task: | |
| type: automatic-speech-recognition | |
| name: Automatic Speech Recognition | |
| dataset: | |
| name: Common Voice 17.0 | |
| type: mozilla-foundation/common_voice_17_0 | |
| config: dv | |
| split: test | |
| args: 'config: dv, split: test' | |
| metrics: | |
| - type: wer | |
| value: 0.45908364040881594 | |
| name: Wer | |
| <!-- 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 Dv - alakxender | |
| This model is a fine-tuned version of [alakxender/w2v-bert-2.0-dhivehi-cv](https://huggingface.co/alakxender/w2v-bert-2.0-dhivehi-cv) on the Common Voice 17.0 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3580 | |
| - Wer: 0.4591 | |
| ## 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: 32 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 64 | |
| - 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 | | |
| |:-------------:|:------:|:----:|:---------------:|:------:| | |
| | 1.9272 | 3.8961 | 300 | 0.3712 | 0.5096 | | |
| | 0.1846 | 7.7922 | 600 | 0.3580 | 0.4591 | | |
| ### Framework versions | |
| - Transformers 4.41.0.dev0 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 | |