# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("monideep2255/pseudolabeling-step2-F04")
model = AutoModelForCTC.from_pretrained("monideep2255/pseudolabeling-step2-F04")Quick Links
pseudolabeling-step2-F04
This model is a fine-tuned version of yip-i/wav2vec2-pretrain-demo on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.2502
- Wer: 1.0
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: 16
- 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 74.4163 | 3.36 | 500 | 3.6878 | 1.0 |
| 3.3612 | 6.71 | 1000 | 3.5619 | 1.0 |
| 3.3127 | 10.07 | 1500 | 3.5773 | 1.0 |
| 3.2104 | 13.42 | 2000 | 3.5299 | 1.0 |
| 3.2067 | 16.78 | 2500 | 3.5704 | 0.9922 |
| 3.1511 | 20.13 | 3000 | 4.3842 | 1.0 |
| 3.0825 | 23.49 | 3500 | 4.2644 | 1.0 |
| 3.0959 | 26.85 | 4000 | 5.2502 | 1.0 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- 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="monideep2255/pseudolabeling-step2-F04")