Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use gingercake01/STT0529_1000audio_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gingercake01/STT0529_1000audio_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gingercake01/STT0529_1000audio_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("gingercake01/STT0529_1000audio_base") model = AutoModelForSpeechSeq2Seq.from_pretrained("gingercake01/STT0529_1000audio_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from gingercake01/STT0529_1000audio_base: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/gingercake01/STT0529_1000audio_base/resolve/main/README.md
- Command line
-
hf download hf://gingercake01/STT0529_1000audio_base/README.md
-
curl -L -o README.md https://huggingface.co/gingercake01/STT0529_1000audio_base/resolve/main/README.md
1.2 kB
metadata
language:
- ko
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- gingercake01/0529_1000audio_base
model-index:
- name: baseWhisper_finetune
results: []
baseWhisper_finetune
This model is a fine-tuned version of openai/whisper-base on the 1000freetalksample 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: 8
- 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
- num_epochs: 3
Training results
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1