# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("keyi10/wav2vec2-model-training")
model = AutoModelForCTC.from_pretrained("keyi10/wav2vec2-model-training")Quick Links
wav2vec2-model-training
This model is a fine-tuned version of facebook/wav2vec2-base on the lj_speech dataset. It achieves the following results on the evaluation set:
- Loss: 0.1258
- Wer: 0.1446
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: 32
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.5099 | 1.52 | 500 | 1.1323 | 0.6740 |
| 0.3293 | 3.05 | 1000 | 0.1430 | 0.1851 |
| 0.1047 | 4.57 | 1500 | 0.1258 | 0.1446 |
Framework versions
- Transformers 4.17.0
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.2
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="keyi10/wav2vec2-model-training")