# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("menhior/wav2vec2-large-xls-r-300m-azeri-colab-main1")
model = AutoModelForCTC.from_pretrained("menhior/wav2vec2-large-xls-r-300m-azeri-colab-main1", device_map="auto")Quick Links
wav2vec2-large-xls-r-300m-azeri-colab-main1
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: 0.5900
- Wer: 0.4642
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: 6e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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 |
|---|---|---|---|---|
| 6.7125 | 4.4 | 400 | 3.2283 | 1.0 |
| 2.1744 | 8.79 | 800 | 0.7684 | 0.6507 |
| 0.4205 | 13.19 | 1200 | 0.5740 | 0.4830 |
| 0.2284 | 17.58 | 1600 | 0.6050 | 0.4975 |
| 0.1659 | 21.98 | 2000 | 0.5869 | 0.4722 |
| 0.1332 | 26.37 | 2400 | 0.5900 | 0.4642 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 1.18.3
- Tokenizers 0.14.1
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Model tree for menhior/wav2vec2-large-xls-r-300m-azeri-colab-main1
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="menhior/wav2vec2-large-xls-r-300m-azeri-colab-main1")