# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("menhior/wav2vec2-large-xls-r-300m-azeri-colab-main")
model = AutoModelForCTC.from_pretrained("menhior/wav2vec2-large-xls-r-300m-azeri-colab-main", device_map="auto")Quick Links
wav2vec2-large-xls-r-300m-azeri-colab-main
This model is a fine-tuned version of menhior/wav2vec2-large-xls-r-300m-turkish-colab-full on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5709
- Wer: 0.2893
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: 8e-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: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.6518 | 4.4 | 400 | 2.5189 | 1.0 |
| 0.638 | 8.79 | 800 | 0.4597 | 0.3461 |
| 0.1682 | 13.19 | 1200 | 0.4817 | 0.3135 |
| 0.0884 | 17.58 | 1600 | 0.4995 | 0.3111 |
| 0.0549 | 21.98 | 2000 | 0.5301 | 0.3018 |
| 0.0391 | 26.37 | 2400 | 0.5407 | 0.2949 |
| 0.0286 | 30.77 | 2800 | 0.5563 | 0.2959 |
| 0.0218 | 35.16 | 3200 | 0.5655 | 0.2893 |
| 0.0186 | 39.56 | 3600 | 0.5709 | 0.2893 |
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-main
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-main")