Audio Classification
Transformers
ONNX
NeMo
Safetensors
anuvani_lid
audio
speech
language-identification
spoken-language-id
lid
indic
conformer
custom_code
Instructions to use CIIL/Anuvani-LID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIIL/Anuvani-LID with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="CIIL/Anuvani-LID", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("CIIL/Anuvani-LID", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download samples/README.md from CIIL/Anuvani-LID: direct link, hf CLI and curl.
- Browser
- Download file 327 Bytes
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https://huggingface.co/CIIL/Anuvani-LID/resolve/main/samples/README.md
- Command line
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hf download hf://CIIL/Anuvani-LID/samples/README.md
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curl -L -o README.md https://huggingface.co/CIIL/Anuvani-LID/resolve/main/samples/README.md
327 Bytes
| # Demo clips | |
| Five ~6–8 s speech clips (16 kHz mono), one per language, for a quick check of the model (see the model card). | |
| Source: **FLEURS** (`google/fleurs`, validation split), Conneau et al. 2022 — licensed **CC-BY-4.0**. | |
| Files: `hi_in` Hindi · `ta_in` Tamil · `bn_in` Bengali · `te_in` Telugu · `mr_in` Marathi. | |