Instructions to use Taeham/wav2vec2-ksponspeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taeham/wav2vec2-ksponspeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Taeham/wav2vec2-ksponspeech")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Taeham/wav2vec2-ksponspeech") model = AutoModelForCTC.from_pretrained("Taeham/wav2vec2-ksponspeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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This model was trained by two dataset :
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- Train1 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-
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- Train2 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train2 (20100 ~ 40100th data in Ksponspeech)
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- Validation : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (20000 ~ 20100th data in Ksponspeech)
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- Third party test : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (60000 ~ 20100th data in Ksponspeech)
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This model was trained by two dataset :
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- Train1 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train (1 ~ 20000th data in Ksponspeech)
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- Train2 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train2 (20100 ~ 40100th data in Ksponspeech)
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- Validation : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (20000 ~ 20100th data in Ksponspeech)
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- Third party test : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (60000 ~ 20100th data in Ksponspeech)
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