Update README.md

README.md

@@ -903,115 +903,178 @@ configs:
     path: audio/mucs/telugu/train-*
 ---
 
-## Detailed Dataset Descriptions
+# Vaani ASR Benchmark: Comprehensive Evaluation of Indian Language Speech Recognition
 
-### Common Voice 12.0 (Mozilla Foundation)
-**Repository**: [mozilla-foundation/common_voice_12_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0)
+## About the Vaani ASR Benchmark
 
-The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 26,119 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines.
+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.
 
-The dataset currently consists of 17,127 validated hours in 104 languages, but more voices and languages are always added.
+### Why This Benchmark Matters
 
-**Languages**: Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
+**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:
 
-### IndicVoices (AI4Bharat)
-**Repository**: [AI4Bharat IndicVoices](https://ai4bharat.iitm.ac.in/datasets/indicvoices)
+- **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
 
-We present INDICVOICES, a dataset of natural and spontaneous speech containing a total of 12,000 hours of read (8%), extempore (76%) and conversational (15%) audio from 22,563 speakers covering 208 Indian districts and 22 languages. Of these 12,000 hours, 3,200 hours have already been transcribed, with a median of 122 hours per language. Through this paper, we share our journey of capturing the cultural, linguistic and demographic diversity of India to create a one-of-its-kind inclusive and representative dataset. More specifically, we share an open-source blueprint for data collection at scale comprising of standardised protocols, centralised tools, a repository of engaging questions, prompts and conversation scenarios spanning multiple domains and topics of interest, quality control mechanisms, comprehensive transcription guidelines and transcription tools. We hope that this open source blueprint will serve as a comprehensive starter kit for data collection efforts in other multilingual regions of the world. Using INDICVOICES, we build IndicASR, the first ASR model to support all the 22 languages listed in the 8th schedule of the Constitution of India.
+### What We Evaluate
 
-**Languages**: Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Santhali, Sindhi, Tamil, Telugu, and Urdu
+The benchmark evaluates ASR models across multiple dimensions:
 
-### FLEURS (Google)
-**Repository**: [google/fleurs](https://huggingface.co/datasets/google/fleurs)
+**🎯 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)
 
-FLEURS is the speech version of the FLoRes machine translation benchmark. We use 2,009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages.
+**📊 Multiple Test Sets**
+Our evaluation incorporates diverse, high-quality datasets:
 
-Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and "unit error rate" (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas:
+1. **FLEURS (Google)**: Multilingual speech corpus with 102 languages, providing ~10 hours per language with parallel sentences for robust cross-linguistic evaluation
 
-**South-Asia**: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu
-**South-East Asia**: Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese
+2. **Common Voice 12.0 (Mozilla)**: Community-contributed dataset with 26,119+ recorded hours across 104 languages, including rich demographic metadata (age, gender, accent)
 
-### Gramvaani Hindi ASR Benchmark
-**Repository**: [Gramvaani Hindi Dataset](https://aikosh.indiaai.gov.in/home/datasets/details/hindi_asr_benchmark_dataset_for_speech_recognition_gramvaani_hindi.html)
+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
 
-**Gramvaani Hindi ASR Benchmark Dataset for Speech Recognition**
-Hindi ASR (Automatic Speech Recognition) benchmark dataset from Bhashini for supporting the development of robust regional speech recognition systems.
+4. **Gramvaani Hindi Dataset**: Specialized Hindi ASR benchmark focusing on agriculture, healthcare, and general knowledge domains
 
-**About Dataset**
-This is a Hindi ASR benchmark dataset developed to evaluate and improve Automatic Speech Recognition (ASR) systems for the Hindi language. The dataset includes diverse and high-quality audio samples, focusing on topics such as agriculture, healthcare, and general knowledge. It serves as a critical resource for researchers and developers to build robust ASR models. Submitted by AI4Bharat, this dataset supports advancements in speech recognition technologies for regional languages.
+5. **MUCS 2021**: Multilingual and code-switching dataset with ~600 hours across 7 Indian languages, including Hindi-English and Bengali-English code-switching
 
-### MUCS 2021 Challenge Dataset
-**Repository**: [MUCS 2021](https://navana-tech.github.io/MUCS2021/data.html)
+6. **IndicTTS Database**: 10,000+ utterances per language across 22 Indian languages with both native and English content
 
-Recently, there is an increasing interest in multilingual automatic speech recognition (ASR) where a speech recognition system caters to multiple low resource languages by taking advantage of low amounts of labelled corpora in multiple languages. With multilingualism becoming common in today's world, there has been increasing interest in code-switching ASR as well. In code-switching, multiple languages are freely interchanged within a single sentence or between sentences. The success of low-resource multilingual and code-switching (MUCS) ASR often depends on the variety of languages in terms of their acoustics, linguistic characteristics as well as the amount of data available and how these are carefully considered in building the ASR system. In this MUCS 2021 challenge, we would like to focus on building MUCS ASR systems through two different subtasks related to a total of seven Indian languages, namely Hindi, Marathi, Odia, Tamil, Telugu, Gujarati and Bengali. For this purpose, we provide a total of ∼600 hours of transcribed speech data, comprising train and test sets, in these languages, including two code-switched language pairs, Hindi-English and Bengali-English. We also provide baseline recipes for both the subtasks with 30.73% and 32.45% word error rate on the MUCS test sets, respectively.
+7. **Kathbath (IndicSUPERB)**: 1,684 hours of labeled speech data across 12 Indian languages for comprehensive speech understanding evaluation
 
-**Index Terms**: Multilingual, Code-switching, low-resource
+### How We Evaluate
 
-### IndicTTS Database
-**Repository**: [IndicTTS Database](https://www.iitm.ac.in/donlab/indictts/database)
+**🔬 Rigorous Methodology**
+Our evaluation follows a standardized protocol ensuring fair and accurate assessment:
 
-A corpus of Indian languages covering 22 major languages of India. It comprises of 10,000+ spoken sentences/utterances each of native and english recorded by both Male and Female native speakers. Speech waveform files are available in .wav format along with the corresponding text. We hope that these recordings will be useful for researchers and speech technologists working on synthesis and recognition. The statistics given below include multiple speakers and genders for each language. Detailed statistics of the same is available.
+**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()
+```
 
-**Languages**: Assamese, Bengali, Bodo, Gujarati, Hindi, Kannada, Malayalam, Manipuri, Marathi, Odia, Punjabi, Rajasthani, Tamil, Telugu
+**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
 
-### Kathbath (IndicSUPERB)
-**Repository**: [IndicSUPERB](https://github.com/AI4Bharat/IndicSUPERB)
+### Language Coverage
 
-IndicSUPERB is a robust benchmark consisting of 6 speech language understanding (SLU) tasks across 12 Indian languages. The tasks include automatic speech recognition, automatic speaker verification, speech identification, query by example and keyword spotting. The IndicSUPERB also encompasses Kathbath dataset which has 1,684 hours of labelled speech data across 12 Indian Languages.
+**🗣️ Multilingual Support**
+The benchmark currently supports major Indian languages with plans for expansion:
 
-**Languages**: Kannada, Malayalam, Tamil, Telugu, Gujarati, Marathi, Bengali, Odia, Hindi, Punjabi, Sanskrit, Urdu
+**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.)
 
-## Citation
+### Dataset Characteristics
 
-If you use this dataset collection in your research, please cite the original datasets:
+**📈 Comprehensive Coverage**
+Our test datasets provide diverse evaluation scenarios:
 
-```bibtex
-@misc{commonvoice:2022,
-  title={Common Voice Corpus 12.0},
-  author={Mozilla},
-  year={2022},
-  url={https://commonvoice.mozilla.org/}
-}
+**Audio Quality Spectrum:**
+- Studio-quality recordings for controlled evaluation
+- Real-world recordings capturing natural speech variations
+- Telephonic and mobile recordings for practical applications
 
-@article{kaushal2023indicvoices,
-  title={IndicVoices: A Dataset of Natural and Spontaneous Speech in 22 Indian Languages},
-  author={Kaushal, Ashish and others},
-  journal={AI4Bharat},
-  year={2023}
-}
+**Speaker Diversity:**
+- **Demographics**: Balanced age, gender, and regional representation
+- **Accents**: Multiple dialectal variations within languages
+- **Speaking Styles**: Read speech, spontaneous speech, conversational audio
 
-@article{conneau2022fleurs,
-  title={FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
-  author={Conneau, Alexis and others},
-  journal={arXiv preprint},
-  year={2022}
-}
+**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
 
-@misc{gramvaani2023,
-  title={Gramvaani Hindi ASR Benchmark Dataset},
-  author={Bhashini},
-  year={2023},
-  url={https://aikosh.indiaai.gov.in/}
-}
+### Performance Analysis
 
-@inproceedings{mucs2021,
-  title={MUCS 2021: Multilingual and Code-switching ASR Challenges for Low Resource Indian Languages},
-  author={MUCS Challenge Organizers},
-  year={2021}
-}
+**📊 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
 
-@misc{indictts,
-  title={IndicTTS: Indian Language Text-to-Speech Database},
-  author={IIT Madras},
-  url={https://www.iitm.ac.in/donlab/indictts/database}
-}
+**🔍 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
 
-@article{javed2022indicsuperb,
-  title={IndicSUPERB: A Benchmark for Indian Language Speech Understanding},
-  author={Javed, Tahir and others},
-  journal={AI4Bharat},
-  year={2022}
+**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.