# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3")
model = AutoModelForCTC.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3", device_map="auto")Quick Links
wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3
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: 0.0558
- Per: 0.0156
- Wer: 0.0184
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: 0.0005
- train_batch_size: 8
- eval_batch_size: 6
- 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: 250
- num_epochs: 30.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Per | Wer |
|---|---|---|---|---|---|
| 2.4598 | 1.0 | 546 | 1.8270 | 0.9903 | 0.9822 |
| 1.8808 | 2.0 | 1093 | 1.8033 | 0.9898 | 0.9813 |
| 1.8584 | 3.0 | 1640 | 1.7560 | 0.9950 | 0.9906 |
| 1.8313 | 4.0 | 2187 | 1.6726 | 0.9881 | 0.9817 |
| 1.764 | 5.0 | 2733 | 1.4356 | 0.9688 | 0.9680 |
| 1.5369 | 6.0 | 3280 | 0.6934 | 0.5617 | 0.5871 |
| 1.228 | 7.0 | 3827 | 0.3561 | 0.2158 | 0.2483 |
| 1.0419 | 8.0 | 4374 | 0.1982 | 0.0925 | 0.1170 |
| 0.9134 | 9.0 | 4920 | 0.1557 | 0.0692 | 0.0806 |
| 0.813 | 10.0 | 5467 | 0.1070 | 0.0507 | 0.0589 |
| 0.7471 | 11.0 | 6014 | 0.0969 | 0.0389 | 0.0453 |
| 0.693 | 12.0 | 6561 | 0.0840 | 0.0311 | 0.0375 |
| 0.6506 | 13.0 | 7107 | 0.0745 | 0.0285 | 0.0339 |
| 0.6159 | 14.0 | 7654 | 0.0765 | 0.0264 | 0.0321 |
| 0.5839 | 15.0 | 8201 | 0.0652 | 0.0253 | 0.0307 |
| 0.5578 | 16.0 | 8748 | 0.0672 | 0.0236 | 0.0281 |
| 0.5259 | 17.0 | 9294 | 0.0664 | 0.0231 | 0.0270 |
| 0.5011 | 18.0 | 9841 | 0.0656 | 0.0215 | 0.0254 |
| 0.4755 | 19.0 | 10388 | 0.0674 | 0.0206 | 0.0239 |
| 0.4471 | 20.0 | 10935 | 0.0649 | 0.0203 | 0.0242 |
| 0.4276 | 21.0 | 11481 | 0.0636 | 0.0202 | 0.0229 |
| 0.4065 | 22.0 | 12028 | 0.0589 | 0.0183 | 0.0214 |
| 0.3889 | 23.0 | 12575 | 0.0554 | 0.0179 | 0.0210 |
| 0.3695 | 24.0 | 13122 | 0.0572 | 0.0171 | 0.0202 |
| 0.3536 | 25.0 | 13668 | 0.0551 | 0.0167 | 0.0189 |
| 0.3385 | 26.0 | 14215 | 0.0604 | 0.0169 | 0.0197 |
| 0.3276 | 27.0 | 14762 | 0.0581 | 0.0157 | 0.0183 |
| 0.3174 | 28.0 | 15309 | 0.0564 | 0.0157 | 0.0181 |
| 0.3093 | 29.0 | 15855 | 0.0564 | 0.0153 | 0.0180 |
| 0.3017 | 29.96 | 16380 | 0.0558 | 0.0156 | 0.0184 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
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Model tree for nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3
Base model
facebook/wav2vec2-xls-r-300m
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD-experiment3")