Automatic Speech Recognition
Transformers
Safetensors
indic_canary
feature-extraction
speech
audio
asr
multilingual
indic
code-switching
code-mixing
language-identification
canary
fastconformer
custom_code
Instructions to use spark-ux/indic-transcribe-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spark-ux/indic-transcribe-core with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="spark-ux/indic-transcribe-core", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("spark-ux/indic-transcribe-core", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c368aab2c7b08e6118b1259020c9243d0fa7e5adc5e152c20cccc794c19fae6e
- Size of remote file:
- 342 kB
- SHA256:
- caac4b6023fe90422bd2658f89e18b9eda53b78bb54be85a212ebba480dc29bd
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