Instructions to use anniev18/whisper-tiny-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anniev18/whisper-tiny-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="anniev18/whisper-tiny-finetune")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("anniev18/whisper-tiny-finetune") model = AutoModelForSpeechSeq2Seq.from_pretrained("anniev18/whisper-tiny-finetune") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-finetune
This model is a fine-tuned version of openai/whisper-tiny.en on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.5619
- eval_wer: 20.3008
- eval_runtime: 53.1964
- eval_samples_per_second: 9.399
- eval_steps_per_second: 1.184
- epoch: 20.8333
- step: 750
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: 128
- 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: 500
- training_steps: 1000
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
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Model tree for anniev18/whisper-tiny-finetune
Base model
openai/whisper-tiny.en