Instructions to use fathyy/wav2vec2-large-xlsr-53-third-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fathyy/wav2vec2-large-xlsr-53-third-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fathyy/wav2vec2-large-xlsr-53-third-train")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("fathyy/wav2vec2-large-xlsr-53-third-train") model = AutoModelForCTC.from_pretrained("fathyy/wav2vec2-large-xlsr-53-third-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-large-xlsr-53-third-train
This model is a fine-tuned version of fathyy/wav2vec2-large-xlsr-53-second-train on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9881
- Wer: 0.9367
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: 0.001
- train_batch_size: 6
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- 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: 1200
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.3482 | 2.9940 | 500 | 2.2909 | 0.9669 |
| 2.3547 | 5.9880 | 1000 | 2.4888 | 0.9759 |
| 2.3457 | 8.9820 | 1500 | 2.3550 | 0.9880 |
| 2.1551 | 11.9760 | 2000 | 2.2335 | 0.9458 |
| 1.9369 | 14.9701 | 2500 | 2.1364 | 0.9458 |
| 1.6783 | 17.9641 | 3000 | 1.9881 | 0.9367 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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