Instructions to use tolgaaktas/distilled_whisper-small_teacher_whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tolgaaktas/distilled_whisper-small_teacher_whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="tolgaaktas/distilled_whisper-small_teacher_whisper-large-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("tolgaaktas/distilled_whisper-small_teacher_whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("tolgaaktas/distilled_whisper-small_teacher_whisper-large-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilled_whisper-small_teacher_whisper-large-v3
This model is a fine-tuned version of openai/whisper-small on an unknown dataset.
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-08
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch 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: 20
- training_steps: 300
Framework versions
- Transformers 4.48.3
- Pytorch 2.2.1+cu121
- Datasets 3.3.0
- Tokenizers 0.21.0
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Model tree for tolgaaktas/distilled_whisper-small_teacher_whisper-large-v3
Base model
openai/whisper-small