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dataset_info:
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- name: train
num_bytes: 523709556.064
num_examples: 2609
download_size: 500876199
dataset_size: 523709556.064
- config_name: audio/mucs/telugu
features:
- name: audio
dtype: audio
- name: language
dtype: string
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 495266332.124
num_examples: 2549
download_size: 494383639
dataset_size: 495266332.124
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:
1. **FLEURS (Google)**: Multilingual speech corpus with 102 languages, providing ~10 hours per language with parallel sentences for robust cross-linguistic evaluation
2. **Common Voice 12.0 (Mozilla)**: Community-contributed dataset with 26,119+ recorded hours across 104 languages, including rich demographic metadata (age, gender, accent)
3. **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
4. **Gramvaani Hindi Dataset**: Specialized Hindi ASR benchmark focusing on agriculture, healthcare, and general knowledge domains
5. **MUCS 2021**: Multilingual and code-switching dataset with ~600 hours across 7 Indian languages, including Hindi-English and Bengali-English code-switching
6. **IndicTTS Database**: 10,000+ utterances per language across 22 Indian languages with both native and English content
7. **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:**
```python
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:
```bibtex
@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. |