Datasets:

prajjwal024
/

asr

dataset_info:
  - config_name: audio/FLEURS/assamese
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/FLEURS/bengali
    features:
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/FLEURS/gujarati
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/FLEURS/hindi
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/FLEURS/kannada
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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    download_size: 358502243
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  - config_name: audio/FLEURS/malayalam
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/FLEURS/marathi
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
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        num_examples: 1015
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  - config_name: audio/FLEURS/nepali
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/FLEURS/odia
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/FLEURS/punjabi
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/FLEURS/sindhi
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
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  - config_name: audio/FLEURS/tamil
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      - name: language
        dtype: string
      - name: transcript
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  - config_name: audio/FLEURS/telugu
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      - name: language
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      - name: transcript
        dtype: string
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  - config_name: audio/FLEURS/urdu
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        dtype: audio
      - name: language
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      - name: transcript
        dtype: string
    splits:
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  - config_name: audio/commonvoice/assamese
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/commonvoice/bengali
    features:
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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    download_size: 1684078386
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  - config_name: audio/commonvoice/hindi
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      - name: language
        dtype: string
      - name: transcript
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  - config_name: audio/commonvoice/malayalam
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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    download_size: 16021243
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  - config_name: audio/commonvoice/marathi
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
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  - config_name: audio/commonvoice/nepali
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      - name: language
        dtype: string
      - name: transcript
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  - config_name: audio/commonvoice/odia
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      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/commonvoice/punjabi
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      - name: language
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      - name: transcript
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      - name: train
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  - config_name: audio/commonvoice/tamil
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      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/commonvoice/urdu
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        dtype: audio
      - name: language
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      - name: transcript
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  - config_name: audio/gramvaani/hindi
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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      - name: train
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  - config_name: audio/indictts/bengali
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/indictts/gujarati
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      - name: language
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      - name: transcript
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      - name: train
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    download_size: 39721020
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  - config_name: audio/indictts/hindi
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      - name: language
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      - name: transcript
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    download_size: 22378518
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  - config_name: audio/indictts/kannada
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      - name: language
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      - name: transcript
        dtype: string
    splits:
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    download_size: 23441314
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  - config_name: audio/indictts/malayalam
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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    download_size: 17141988
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  - config_name: audio/indictts/marathi
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      - name: language
        dtype: string
      - name: transcript
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  - config_name: audio/indictts/odia
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
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  - config_name: audio/indictts/tamil
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
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        num_bytes: 35304113
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  - config_name: audio/indictts/telugu
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
        num_bytes: 48953745
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  - config_name: audio/kathbath/bengali
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/kathbath/gujarati
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      - name: language
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      - name: transcript
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    splits:
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  - config_name: audio/kathbath/kannada
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      - name: transcript
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  - config_name: audio/kathbath/marathi
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
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    splits:
      - name: train
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  - config_name: audio/kathbath/odia
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        dtype: audio
      - name: language
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      - name: transcript
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    splits:
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  - config_name: audio/kathbath/punjabi
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      - name: language
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      - name: transcript
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  - config_name: audio/kathbath/sanskrit
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      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
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  - config_name: audio/kathbath/tamil
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        dtype: audio
      - name: language
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      - name: transcript
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  - config_name: audio/kathbath/telugu
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/kathbath/urdu
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/mucs/gujarati
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      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
        num_bytes: 666434960.784
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  - config_name: audio/mucs/hindi
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      - name: language
        dtype: string
      - name: transcript
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      - name: train
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  - config_name: audio/mucs/marathi
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        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/mucs/odia
    features:
      - name: audio
        dtype: audio
      - name: language
        dtype: string
      - name: transcript
        dtype: string
    splits:
      - name: train
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  - config_name: audio/mucs/tamil
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      - name: language
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      - name: transcript
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  - config_name: audio/mucs/telugu
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      - name: language
        dtype: string
      - name: transcript
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configs:
  - config_name: FLEURS_assamese
    data_files:
      - split: train
        path: audio/FLEURS/assamese/train-*
  - config_name: FLEURS_bengali
    data_files:
      - split: train
        path: audio/FLEURS/bengali/train-*
  - config_name: FLEURS_gujarati
    data_files:
      - split: train
        path: audio/FLEURS/gujarati/train-*
  - config_name: FLEURS_hindi
    data_files:
      - split: train
        path: audio/FLEURS/hindi/train-*
  - config_name: FLEURS_kannada
    data_files:
      - split: train
        path: audio/FLEURS/kannada/train-*
  - config_name: FLEURS_malayalam
    data_files:
      - split: train
        path: audio/FLEURS/malayalam/train-*
  - config_name: FLEURS_marathi
    data_files:
      - split: train
        path: audio/FLEURS/marathi/train-*
  - config_name: FLEURS_nepali
    data_files:
      - split: train
        path: audio/FLEURS/nepali/train-*
  - config_name: FLEURS_odia
    data_files:
      - split: train
        path: audio/FLEURS/odia/train-*
  - config_name: FLEURS_punjabi
    data_files:
      - split: train
        path: audio/FLEURS/punjabi/train-*
  - config_name: FLEURS_sindhi
    data_files:
      - split: train
        path: audio/FLEURS/sindhi/train-*
  - config_name: FLEURS_tamil
    data_files:
      - split: train
        path: audio/FLEURS/tamil/train-*
  - config_name: FLEURS_telugu
    data_files:
      - split: train
        path: audio/FLEURS/telugu/train-*
  - config_name: FLEURS_urdu
    data_files:
      - split: train
        path: audio/FLEURS/urdu/train-*
  - config_name: commonvoice_assamese
    data_files:
      - split: train
        path: audio/commonvoice/assamese/train-*
  - config_name: commonvoice_bengali
    data_files:
      - split: train
        path: audio/commonvoice/bengali/train-*
  - config_name: commonvoice_hindi
    data_files:
      - split: train
        path: audio/commonvoice/hindi/train-*
  - config_name: commonvoice_malayalam
    data_files:
      - split: train
        path: audio/commonvoice/malayalam/train-*
  - config_name: commonvoice_marathi
    data_files:
      - split: train
        path: audio/commonvoice/marathi/train-*
  - config_name: commonvoice_nepali
    data_files:
      - split: train
        path: audio/commonvoice/nepali/train-*
  - config_name: commonvoice_odia
    data_files:
      - split: train
        path: audio/commonvoice/odia/train-*
  - config_name: commonvoice_punjabi
    data_files:
      - split: train
        path: audio/commonvoice/punjabi/train-*
  - config_name: commonvoice_tamil
    data_files:
      - split: train
        path: audio/commonvoice/tamil/train-*
  - config_name: commonvoice_urdu
    data_files:
      - split: train
        path: audio/commonvoice/urdu/train-*
  - config_name: gramvaani_hindi
    data_files:
      - split: train
        path: audio/gramvaani/hindi/train-*
  - config_name: indictts_bengali
    data_files:
      - split: train
        path: audio/indictts/bengali/train-*
  - config_name: indictts_gujarati
    data_files:
      - split: train
        path: audio/indictts/gujarati/train-*
  - config_name: indictts_hindi
    data_files:
      - split: train
        path: audio/indictts/hindi/train-*
  - config_name: indictts_kannada
    data_files:
      - split: train
        path: audio/indictts/kannada/train-*
  - config_name: indictts_malayalam
    data_files:
      - split: train
        path: audio/indictts/malayalam/train-*
  - config_name: indictts_marathi
    data_files:
      - split: train
        path: audio/indictts/marathi/train-*
  - config_name: indictts_odia
    data_files:
      - split: train
        path: audio/indictts/odia/train-*
  - config_name: indictts_tamil
    data_files:
      - split: train
        path: audio/indictts/tamil/train-*
  - config_name: indictts_telugu
    data_files:
      - split: train
        path: audio/indictts/telugu/train-*
  - config_name: kathbath_bengali
    data_files:
      - split: train
        path: audio/kathbath/bengali/train-*
  - config_name: kathbath_gujarati
    data_files:
      - split: train
        path: audio/kathbath/gujarati/train-*
  - config_name: kathbath_kannada
    data_files:
      - split: train
        path: audio/kathbath/kannada/train-*
  - config_name: kathbath_marathi
    data_files:
      - split: train
        path: audio/kathbath/marathi/train-*
  - config_name: kathbath_odia
    data_files:
      - split: train
        path: audio/kathbath/odia/train-*
  - config_name: kathbath_punjabi
    data_files:
      - split: train
        path: audio/kathbath/punjabi/train-*
  - config_name: kathbath_sanskrit
    data_files:
      - split: train
        path: audio/kathbath/sanskrit/train-*
  - config_name: kathbath_tamil
    data_files:
      - split: train
        path: audio/kathbath/tamil/train-*
  - config_name: kathbath_telugu
    data_files:
      - split: train
        path: audio/kathbath/telugu/train-*
  - config_name: kathbath_urdu
    data_files:
      - split: train
        path: audio/kathbath/urdu/train-*
  - config_name: mucs_gujarati
    data_files:
      - split: train
        path: audio/mucs/gujarati/train-*
  - config_name: mucs_hindi
    data_files:
      - split: train
        path: audio/mucs/hindi/train-*
  - config_name: mucs_marathi
    data_files:
      - split: train
        path: audio/mucs/marathi/train-*
  - config_name: mucs_odia
    data_files:
      - split: train
        path: audio/mucs/odia/train-*
  - config_name: mucs_tamil
    data_files:
      - split: train
        path: audio/mucs/tamil/train-*
  - config_name: mucs_telugu
    data_files:
      - split: train
        path: audio/mucs/telugu/train-*

Vaani ASR Benchmark: Comprehensive Evaluation of Indian Language Speech Recognition

About the Vaani ASR Benchmark

The Vaani ASR Benchmark is a comprehensive evaluation framework designed to assess the performance of Automatic Speech Recognition (ASR) models across multiple Indian languages. This benchmark addresses the critical need for standardized evaluation of ASR systems in the linguistically diverse Indian subcontinent, where over 700 languages are spoken with 22 official languages recognized by the Constitution.

Why This Benchmark Matters

Addressing the Indian Language Gap: While significant progress has been made in ASR for high-resource languages like English and Mandarin, Indian languages have remained underrepresented in speech recognition research. The Vaani benchmark fills this critical gap by providing:

Standardized Evaluation: Consistent metrics and methodology across different models and languages
Diverse Linguistic Coverage: Support for major Indian languages including Hindi, Tamil, Telugu, Kannada, Bengali, and more
Real-world Applicability: Evaluation datasets that reflect actual usage scenarios across India
Research Acceleration: A common platform for researchers to compare and improve their ASR models

What We Evaluate

The benchmark evaluates ASR models across multiple dimensions:

🎯 Primary Metrics

Word Error Rate (WER): Percentage of words incorrectly recognized (lower is better)
Character Error Rate (CER): Percentage of characters incorrectly recognized (lower is better)

📊 Multiple Test Sets Our evaluation incorporates diverse, high-quality datasets:

FLEURS (Google): Multilingual speech corpus with 102 languages, providing ~10 hours per language with parallel sentences for robust cross-linguistic evaluation
Common Voice 12.0 (Mozilla): Community-contributed dataset with 26,119+ recorded hours across 104 languages, including rich demographic metadata (age, gender, accent)
IndicVoices (AI4Bharat): 12,000 hours of natural Indian speech covering 22 languages with diverse content:
- Read speech (8%)
- Extempore speech (76%)
- Conversational speech (15%)
- 22,563 speakers across 208 Indian districts
Gramvaani Hindi Dataset: Specialized Hindi ASR benchmark focusing on agriculture, healthcare, and general knowledge domains
MUCS 2021: Multilingual and code-switching dataset with ~600 hours across 7 Indian languages, including Hindi-English and Bengali-English code-switching
IndicTTS Database: 10,000+ utterances per language across 22 Indian languages with both native and English content
Kathbath (IndicSUPERB): 1,684 hours of labeled speech data across 12 Indian languages for comprehensive speech understanding evaluation

How We Evaluate

🔬 Rigorous Methodology Our evaluation follows a standardized protocol ensuring fair and accurate assessment:

Text Preprocessing Pipeline:

def clean(text):
    # Remove annotations and markup
    text = re.sub(r'{[^}]*}','',text)           # Remove {annotations}
    text = re.sub("[([].*?[)]]", "", text)      # Remove [brackets] and (parentheses)
    text = re.sub('<[^>]+>', '', text)          # Remove HTML/XML tags
    
    # Normalize punctuation
    text = text.replace("।", " ").replace("|", " ").replace("-", " ")\
        .replace(".", " ").replace(",", " ").replace("I", " ")\
        .replace('\n', ' ')
    
    # Normalize spacing
    text = re.sub(' +', ' ', text)
    return text.strip()

Error Rate Calculation:

Uses industry-standard jiwer library for accurate WER/CER computation
Identical preprocessing applied to both reference and hypothesis texts
Results scaled to percentage (0-100) with 2-decimal precision
Handles edge cases and missing data appropriately

Language Coverage

🗣️ Multilingual Support The benchmark currently supports major Indian languages with plans for expansion:

Currently Supported:

Indo-Aryan: Hindi, Bengali, Marathi, Gujarati, Punjabi, Urdu, Assamese, Odia, Nepali
Dravidian: Tamil, Telugu, Kannada, Malayalam
Tibeto-Burman: Manipuri, Bodo
Others: Sanskrit, Santhali

Planned Expansion:

Additional regional languages and dialects
Tribal and minority languages
Code-switching scenarios (Hindi-English, Tamil-English, etc.)

Dataset Characteristics

📈 Comprehensive Coverage Our test datasets provide diverse evaluation scenarios:

Audio Quality Spectrum:

Studio-quality recordings for controlled evaluation
Real-world recordings capturing natural speech variations
Telephonic and mobile recordings for practical applications

Speaker Diversity:

Demographics: Balanced age, gender, and regional representation
Accents: Multiple dialectal variations within languages
Speaking Styles: Read speech, spontaneous speech, conversational audio

Content Variety:

Domains: News, agriculture, healthcare, education, general knowledge
Speech Types: Formal presentations, casual conversations, prompted responses
Acoustic Conditions: Clean studio, noisy environments, multiple speakers

Performance Analysis

📊 Detailed Metrics

AVG WER/CER: Simple average across all test datasets
Language-specific Performance: Individual language breakdowns
Dataset-specific Analysis: Performance variations across different test sets
Statistical Significance: Confidence intervals and significance testing

🔍 Interactive Exploration

Metric Selector: Switch between WER and CER views
Language Filtering: Focus on specific languages or language families
Dataset Comparison: Compare model performance across different test sets
Trend Analysis: Track model improvements over time

Research Impact

🎯 Advancing Indian Language ASR The Vaani benchmark serves multiple stakeholders:

For Researchers:

Standardized evaluation platform for model comparison
Comprehensive datasets for training and testing
Open-source evaluation code for reproducibility

For Industry:

Performance benchmarks for commercial ASR systems
Quality assurance metrics for product development
Market readiness assessment for Indian language applications

For Society:

Enabling voice interfaces in local languages
Supporting digital inclusion across linguistic communities
Preserving and promoting linguistic diversity through technology

Technical Implementation

🛠️ Robust Infrastructure

Scalable Evaluation: Automated pipeline handling large-scale model evaluation
Reproducible Results: Version-controlled datasets and evaluation scripts
Quality Assurance: Multiple validation checkpoints and error detection
Open Source: Full transparency in methodology and implementation

Future Roadmap

🚀 Continuous Enhancement

Dataset Expansion: Adding more languages and domains
Metric Refinement: Incorporating semantic and contextual evaluation measures
Real-time Evaluation: Support for streaming ASR model assessment
Community Integration: Enabling community contributions and model submissions

Citation

If you use this benchmark in your research, please cite:

@misc{vaani_asr_benchmark_2024,
  title={Vaani ASR Benchmark: Comprehensive Evaluation Framework for Indian Language Speech Recognition},
  author={Vaani Team},
  year={2024},
  url={https://vaani.iisc.ac.in}
}

For individual datasets used in the benchmark, please also cite the original sources as provided in our dataset documentation.