Instructions to use casual/whisper_tiny_til2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use casual/whisper_tiny_til2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="casual/whisper_tiny_til2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("casual/whisper_tiny_til2") model = AutoModelForSpeechSeq2Seq.from_pretrained("casual/whisper_tiny_til2") - Notebooks
- Google Colab
- Kaggle
whisper_tiny_til2
This model is a fine-tuned version of casual/whisper_tiny_24til on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_wer: 0.0
- eval_runtime: 780.534
- eval_samples_per_second: 4.484
- eval_steps_per_second: 0.561
- epoch: 6.2785
- step: 2750
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: 8
- 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: 10
- training_steps: 4000
Framework versions
- Transformers 4.40.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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