Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Thienpkae/working")
model = AutoModelForCTC.from_pretrained("Thienpkae/working")Quick Links
working
This model is a fine-tuned version of facebook/wav2vec2-base on the vivos dataset. It achieves the following results on the evaluation set:
- Loss: 0.7102
- Wer: 0.4126
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.3485 | 2.0 | 292 | 3.7183 | 1.0 |
| 3.4479 | 4.0 | 584 | 3.5977 | 1.0 |
| 2.948 | 6.0 | 876 | 1.7093 | 0.8420 |
| 1.2556 | 8.0 | 1168 | 1.0140 | 0.5846 |
| 0.9216 | 10.0 | 1460 | 0.8558 | 0.5142 |
| 0.7769 | 12.0 | 1752 | 0.7731 | 0.4643 |
| 0.6968 | 14.0 | 2044 | 0.7458 | 0.4394 |
| 0.6813 | 16.0 | 2336 | 0.7549 | 0.4385 |
| 0.5996 | 18.0 | 2628 | 0.7186 | 0.4128 |
| 0.572 | 20.0 | 2920 | 0.7102 | 0.4126 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Thienpkae/working
Base model
facebook/wav2vec2-baseDataset used to train Thienpkae/working
Evaluation results
- Wer on vivosself-reported0.413
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Thienpkae/working")