Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use DragonLine/train03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DragonLine/train03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DragonLine/train03")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("DragonLine/train03") model = AutoModelForSpeechSeq2Seq.from_pretrained("DragonLine/train03", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from DragonLine/train03: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/DragonLine/train03/resolve/main/README.md
- Command line
-
hf download hf://DragonLine/train03/README.md
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curl -L -o README.md https://huggingface.co/DragonLine/train03/resolve/main/README.md
1.84 kB
| language: | |
| - ko | |
| license: apache-2.0 | |
| base_model: openai/whisper-base | |
| tags: | |
| - hf-asr-leaderboard | |
| - generated_from_trainer | |
| datasets: | |
| - DragonLine/ksponspeech_03 | |
| model-index: | |
| - name: train03 | |
| 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. --> | |
| # train03 | |
| This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the ksponspeech_03 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4521 | |
| - Cer: 14.7492 | |
| ## 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: 64 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 1 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Cer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.7999 | 0.13 | 100 | 0.6914 | 20.0296 | | |
| | 0.5632 | 0.26 | 200 | 0.5602 | 16.9970 | | |
| | 0.5309 | 0.39 | 300 | 0.5195 | 15.8134 | | |
| | 0.4961 | 0.52 | 400 | 0.4940 | 15.6669 | | |
| | 0.4865 | 0.65 | 500 | 0.4764 | 15.1768 | | |
| | 0.4688 | 0.77 | 600 | 0.4607 | 14.9871 | | |
| | 0.4357 | 0.9 | 700 | 0.4521 | 14.7492 | | |
| ### Framework versions | |
| - Transformers 4.36.0.dev0 | |
| - Pytorch 1.13.1+cu117 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |