Instructions to use kosamit/whisper-tiny-deltamon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kosamit/whisper-tiny-deltamon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kosamit/whisper-tiny-deltamon")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kosamit/whisper-tiny-deltamon") model = AutoModelForSpeechSeq2Seq.from_pretrained("kosamit/whisper-tiny-deltamon", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from kosamit/whisper-tiny-deltamon: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/kosamit/whisper-tiny-deltamon/resolve/main/README.md
- Command line
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hf download hf://kosamit/whisper-tiny-deltamon/README.md
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curl -L -o README.md https://huggingface.co/kosamit/whisper-tiny-deltamon/resolve/main/README.md
1.57 kB
metadata
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tiny-deltamon
results: []
whisper-tiny-deltamon
This model is a fine-tuned version of openai/whisper-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0007
- Wer: 0.0
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: 16
- 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: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.0453 | 5.0 | 10 | 5.1551 | 100.0 |
| 2.8084 | 10.0 | 20 | 0.9061 | 13.3333 |
| 0.3357 | 15.0 | 30 | 0.0096 | 0.0 |
| 0.0031 | 20.0 | 40 | 0.0007 | 0.0 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.1.1+cu118
- Datasets 2.18.0
- Tokenizers 0.13.3