Instructions to use Dhanang12/whisper-tiny-id-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhanang12/whisper-tiny-id-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dhanang12/whisper-tiny-id-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Dhanang12/whisper-tiny-id-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("Dhanang12/whisper-tiny-id-v3") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-id-v3
This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.7430
- eval_wer: 47.6221
- eval_runtime: 606.3896
- eval_samples_per_second: 6.018
- eval_steps_per_second: 0.754
- epoch: 7.9136
- step: 2500
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Dhanang12/whisper-tiny-id-v3
Base model
openai/whisper-tiny