# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("ali-issa/FYP_ARABIZI")
model = AutoModelForCTC.from_pretrained("ali-issa/FYP_ARABIZI", device_map="auto")Quick Links
wav2vec2-Arabizi-gpu-colab-similar-to-german-param
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5609
- Wer: 0.4042
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: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.6416 | 2.83 | 400 | 2.8983 | 1.0 |
| 1.4951 | 5.67 | 800 | 0.6272 | 0.6097 |
| 0.6419 | 8.51 | 1200 | 0.5491 | 0.5069 |
| 0.4767 | 11.35 | 1600 | 0.5152 | 0.4553 |
| 0.3899 | 14.18 | 2000 | 0.5436 | 0.4475 |
| 0.3342 | 17.02 | 2400 | 0.5400 | 0.4431 |
| 0.2982 | 19.85 | 2800 | 0.5599 | 0.4248 |
| 0.2738 | 22.69 | 3200 | 0.5401 | 0.4103 |
| 0.2563 | 25.53 | 3600 | 0.5710 | 0.4198 |
| 0.2443 | 28.37 | 4000 | 0.5609 | 0.4042 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
- Downloads last month
- 10
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ali-issa/FYP_ARABIZI")