Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Indonesian
whisper
whisper-event
Generated from Trainer
Instructions to use TheRains/output-small-id-yt-batch6-gradient24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheRains/output-small-id-yt-batch6-gradient24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TheRains/output-small-id-yt-batch6-gradient24")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("TheRains/output-small-id-yt-batch6-gradient24") model = AutoModelForSpeechSeq2Seq.from_pretrained("TheRains/output-small-id-yt-batch6-gradient24") - Notebooks
- Google Colab
- Kaggle
Whisper small ID - Augmented
This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3282
- Wer: 25.8872
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: 1e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4684 | 0.77 | 250 | 0.4644 | 46.6638 |
| 0.3389 | 1.55 | 500 | 0.3913 | 41.6009 |
| 0.2009 | 2.32 | 750 | 0.3432 | 27.4754 |
| 0.1216 | 3.09 | 1000 | 0.3282 | 25.8872 |
Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
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