ARTPARK-IISc/Vaani-LID_v0

Model Card for Model ID

Indic Spoken Language Classifier supporting 42 languages.

Model Details

The model consists of a whisper-large-v3-turbo encoder followed by a pooled-attention module and finally a linear classifier layer. Trained end-to-end on ARTPARK-IISc/Vaani dataset, capable of classifying 42 Indic languages. The list of supported languages include:

["Surjapuri", 
"Marathi", 
"Assamese", 
"Haryanvi", 
"Halbi", 
"Malayalam", 
"Maithili", 
"Wancho", 
"Chhattisgarhi", 
"Punjabi", 
"Magahi", 
"Nepali", 
"Garhwali", 
"Garo", 
"Khortha", 
"Sumi", 
"Bajjika", 
"Marwari", 
"Telugu", 
"Nagamese", 
"Tulu", 
"Odia", 
"Urdu", 
"Kumaoni", 
"Kannada", 
"Tamil", 
"Sambalpuri", 
"Bengali", 
"Rajasthani", 
"English", 
"Malvani", 
"Chakma", 
"Surgujia", 
"Kokborok", 
"Khariboli", 
"Hindi", 
"Kurukh", 
"Angika", 
"Sadri", 
"Bhojpuri", 
"Konkani", 
"Gujarati"]

How to Get Started with the Model

from transformers import pipeline

pipe = pipeline(
    "audio-classification",
    model="ARTPARK-IISc/Vaani-LID_v0",
    trust_remote_code=True,
    # device=-1 # uncomment this line to run on cpu
)

out = pipe("path/to/16kHz/mono-channel/wav/file")
print(out)

Evaluation

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metrics:

• accuracy model-index: results:

  • task: type: speech-classification dataset: name: FLEURS split: test metrics:

    • name: Accuracy value: 0.71

    dataset: name: Kathbath split: test metrics:

    • name: Accuracy value: 0.63

    dataset: name: Vaani split: test metrics:

    • name: Accuracy value: 0.77

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