Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use PhanithLIM/whisper-base-aug-20-april-lightning-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhanithLIM/whisper-base-aug-20-april-lightning-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="PhanithLIM/whisper-base-aug-20-april-lightning-v1")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("PhanithLIM/whisper-base-aug-20-april-lightning-v1") model = AutoModelForMultimodalLM.from_pretrained("PhanithLIM/whisper-base-aug-20-april-lightning-v1") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: Khmer Whisper Small PhanithLIM
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results:
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- generated_from_trainer
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metrics:
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- wer
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datasets:
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- PhanithLIM/ams-speech-dataset
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- openslr/openslr
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- google/fleurs
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- PhanithLIM/kh-wmc
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- PhanithLIM/wmc-international-news
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- PhanithLIM/rfi-news-dataset
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- PhanithLIM/aakanee-kh
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- rinabuoy/khm-asr-open
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- seanghay/khmer_grkpp_speech
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- seanghay/khmer_mpwt_speech
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- seanghay/km-speech-corpus
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model-index:
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- name: Khmer Whisper Small PhanithLIM
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results:
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