Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
Generated from Trainer
Instructions to use phucdyale/ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phucdyale/ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="phucdyale/ko")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("phucdyale/ko") model = AutoModelForSpeechSeq2Seq.from_pretrained("phucdyale/ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from phucdyale/ko: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/phucdyale/ko/resolve/main/README.md
- Command line
-
hf download hf://phucdyale/ko/README.md
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curl -L -o README.md https://huggingface.co/phucdyale/ko/resolve/main/README.md
1.21 kB
metadata
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: Whisper Small - Trial ko
results: []
Whisper Small - Trial ko
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13.0 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-05
- train_batch_size: 16
- 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: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1
- Datasets 2.15.0
- Tokenizers 0.15.0