# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Sjdan/m11")
model = AutoModelForCTC.from_pretrained("Sjdan/m11", device_map="auto")Quick Links
m11
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: 0.4602
- Wer: 1.7979
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 9.7789 | 1.32 | 500 | 3.1221 | 1.0 |
| 2.7753 | 2.64 | 1000 | 1.9766 | 2.5697 |
| 1.1122 | 3.96 | 1500 | 0.5972 | 2.0359 |
| 0.2963 | 5.28 | 2000 | 0.5035 | 1.6932 |
| 0.1178 | 6.6 | 2500 | 0.4602 | 1.7979 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/m11")