Automatic Speech Recognition
Transformers
Safetensors
indic_canary
feature-extraction
speech
audio
asr
multilingual
indic
code-switching
code-mixing
language-identification
canary
fastconformer
quantized
int8
bitsandbytes
custom_code
8-bit precision
Instructions to use ManiKumarAdapala/indic-transcribe-core-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManiKumarAdapala/indic-transcribe-core-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ManiKumarAdapala/indic-transcribe-core-8bit", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ManiKumarAdapala/indic-transcribe-core-8bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b3598c1bbfc6cdff6bf1da16aa2c137a6f6a0ac3ef0d1181480ab0613f7a135
- Size of remote file:
- 133 kB
- SHA256:
- d937fed7f88a7ae26960be72ba4b79b7018d69e66805ba2a8c39909ba8f11a69
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