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
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
NOTICE clarification for indic-transcribe-core revision e083ab7
#7 opened 4 days ago
by
Deepak1106
Word Level Timestamps and GGUF
π 1
#6 opened 11 days ago
by
th3w33knd
Time Stamps Feature in Transcribe-Core
π 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
π 2
#3 opened about 2 months ago
by
trysem