Instructions to use khaoulaoub/wav2vec2-large-xls-r-darija-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khaoulaoub/wav2vec2-large-xls-r-darija-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="khaoulaoub/wav2vec2-large-xls-r-darija-colab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("khaoulaoub/wav2vec2-large-xls-r-darija-colab") model = AutoModelForCTC.from_pretrained("khaoulaoub/wav2vec2-large-xls-r-darija-colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-large-xls-r-darija-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4380
- Wer: 0.3190
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.024 | 5.18 | 400 | 0.4523 | 0.5311 |
| 0.3011 | 10.36 | 800 | 0.3492 | 0.3792 |
| 0.1199 | 15.53 | 1200 | 0.3939 | 0.3499 |
| 0.0661 | 20.71 | 1600 | 0.4154 | 0.3309 |
| 0.0471 | 25.89 | 2000 | 0.4380 | 0.3190 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Downloads last month
- 2
Model tree for khaoulaoub/wav2vec2-large-xls-r-darija-colab
Base model
facebook/wav2vec2-xls-r-300m