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bodhan-ai
/
indic-transcribe-core

Automatic Speech Recognition
Transformers
NeMo
Safetensors
indic_canary
feature-extraction
speech
audio
asr
multilingual
indic
code-switching
code-mixing
language-identification
canary
fastconformer
custom_code
Model card Files Files and versions
xet
Community
7

Instructions to use bodhan-ai/indic-transcribe-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bodhan-ai/indic-transcribe-core with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="bodhan-ai/indic-transcribe-core", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("bodhan-ai/indic-transcribe-core", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

NOTICE clarification for indic-transcribe-core revision e083ab7

#7 opened 4 days ago by
Deepak1106

Word Level Timestamps and GGUF

πŸ‘€ 1
#6 opened 11 days ago by
th3w33knd

Time Stamps Feature in Transcribe-Core

πŸ‘€ 1
#5 opened about 1 month ago by
BNarayanaReddy

Serving a bodhan-ai/indic-transcribe-core β€” native NeMo works, but is vLLM an option for this architecture?

#4 opened about 2 months ago by
TejaAprameya

Indvidual language wise checkpoint will be more usable

πŸ‘€ 2
#3 opened about 2 months ago by
trysem
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