Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Thai
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use fruk19/C_ASR_MID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fruk19/C_ASR_MID with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fruk19/C_ASR_MID")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("fruk19/C_ASR_MID") model = AutoModelForSpeechSeq2Seq.from_pretrained("fruk19/C_ASR_MID", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f16187bd3b117533f365f815d3973de19f31ec3d7d929e2484c7a5340d889585
- Size of remote file:
- 4.8 kB
- SHA256:
- d3b55eaaea62ca431f0b472f9403e80d0dcb6b40b53e3cd8474d45ebcf969a98
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.