Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use norjas1/TrainEsperanto2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use norjas1/TrainEsperanto2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="norjas1/TrainEsperanto2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("norjas1/TrainEsperanto2") model = AutoModelForCTC.from_pretrained("norjas1/TrainEsperanto2") - Notebooks
- Google Colab
- Kaggle
TrainEsperanto2
This model is a fine-tuned version of norjas1/TrainEsperanto on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1393 | 6.1728 | 500 | nan | 1.0 |
| 0.0 | 12.3457 | 1000 | nan | 1.0 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.20.1
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Model tree for norjas1/TrainEsperanto2
Base model
cpierse/wav2vec2-large-xlsr-53-esperanto Finetuned
norjas1/TrainEsperantoEvaluation results
- Wer on audiofolderself-reported1.000