Automatic Speech Recognition
Transformers
PyTorch
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use jiwon65/checkpoint-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jiwon65/checkpoint-5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jiwon65/checkpoint-5000")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jiwon65/checkpoint-5000") model = AutoModelForSpeechSeq2Seq.from_pretrained("jiwon65/checkpoint-5000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jiwon65/checkpoint-5000: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/jiwon65/checkpoint-5000/resolve/main/README.md
- Command line
-
hf download hf://jiwon65/checkpoint-5000/README.md
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curl -L -o README.md https://huggingface.co/jiwon65/checkpoint-5000/resolve/main/README.md
1.27 kB
| language: | |
| - ko | |
| license: apache-2.0 | |
| base_model: openai/whisper-tiny | |
| tags: | |
| - hf-asr-leaderboard | |
| - generated_from_trainer | |
| datasets: | |
| - haseong8012/child-50k | |
| model-index: | |
| - name: whisper-small_child-50k2/checkpoint-5000 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # whisper-small_child-50k2/checkpoint-5000 | |
| This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the child-50k 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: 1.25e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - training_steps: 10000 | |
| ### Framework versions | |
| - Transformers 4.34.0 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.14.1 | |