indicate 0.7.1

Transliterations to/from Indian languages

Indicate: Transliterate Indic Languages with PyTorch and LLMs

Indicate provides high-quality transliteration between Indic languages and English using both a traditional PyTorch model and state-of-the-art LLMs (Large Language Models).

🚀 Features

• 🧠 Dual Backend Support: Choose between the PyTorch model or LLM-based transliteration
• 🌍 Multi-Language: 12+ Indic languages (Hindi, Tamil, Telugu, Bengali, etc.)
• 🔄 Bidirectional: Supports both Indic→English and English→Indic transliteration
• 🛡️ Production Ready: Safe file handling, atomic writes, backup support
• 📊 Structured Output: Rich JSON format with metadata and error handling
• ⚡ Batch Processing: Efficient processing of large files with progress tracking

🎯 Supported Languages

Hindi • Tamil • Telugu • Bengali • Gujarati • Kannada • Malayalam • Punjabi • Marathi • Odia • Urdu • Sanskrit ↔ English

Install

We strongly recommend installing indicate inside a Python virtual environment (see venv documentation)

Requirements: Python 3.13+

pip install indicate

🔧 Quick Setup

For LLM-based transliteration (recommended):

pip install indicate

# Set your API key (choose one):
export OPENAI_API_KEY=your-key
export ANTHROPIC_API_KEY=your-key  
export GOOGLE_API_KEY=your-key

For the local model (no API key):

pip install indicate
# No API key needed. The PyTorch weights are downloaded once from Hugging Face
# (soodoku/indicate) on first transliterate and cached locally; tokenizers ship
# in the wheel. After the first run it works fully offline.

🎯 Usage

🧠 LLM-Based Transliteration (New!)

The LLM backend provides higher accuracy and supports all Indic languages:

# Simple transliteration (auto-detects Hindi)
indicate llm "राजशेखर चिंतालपति"
# Output: Rajashekar Chintalapati

# Specify languages explicitly  
indicate llm "முருகன்" --source tamil --target english
# Output: Murugan

# Between Indic languages
indicate llm "नमस्ते" --source hindi --target tamil  
# Output: நமஸ்தே

# Safe batch processing with structured JSON output
indicate llm --input names.txt --output results.json --format json --batch --backup

# Dry run to preview changes
indicate llm --input large_file.txt --dry-run

Python API:

from indicate import IndicLLMTransliterator

# Initialize for any language pair
transliterator = IndicLLMTransliterator('hindi', 'english')
result = transliterator.transliterate('राजशेखर चिंतालपति')
print(result)  # Output: Rajashekar Chintalapati

# Batch processing
texts = ["राजेश", "गौरव", "प्रिया"]
results = transliterator.transliterate_batch(texts)
print(results)  # ['Rajesh', 'Gaurav', 'Priya']

🤖 PyTorch Backend (Traditional)

Local offline models are available for Hindi (Devanagari) and Punjabi (Gurmukhi):

# Hindi to English using the local PyTorch model
indicate hindi2english "राजशेखर चिंतालपति"
# Output: rajshekhar chintapalati

# Punjabi (Gurmukhi) to English
indicate punjabi2english "ਰਵਿ ਸ਼ਰਮਾ"
# Output: ravi sharma

# From file
indicate hindi2english --input hindi.txt --output english.txt

# Batch processing
indicate hindi2english --input large_file.txt --batch

Python API:

from indicate import hindi2english, punjabi2english
print(hindi2english("हिंदी"))                # "hindi"
print(punjabi2english("ਰਵਿ"))                # "ravi"

# Top-k candidates (n > 1 returns a list)
print(hindi2english("नमस्ते", n=3))          # ["namaste", "namastey", "namste"]

# Batched (much faster for many inputs)
from indicate.hindi2english import HindiToEnglish
HindiToEnglish.transliterate_batch(["हिंदी", "मुंबई", "गौरव सूद"])
# -> ["hindi", "mumbai", "gaurav sood"]

📊 JSON Output Format

The LLM backend provides rich, structured output perfect for data processing:

{
  "metadata": {
    "source_language": "hindi",
    "target_language": "english", 
    "timestamp": "2024-12-09T12:00:00Z",
    "total_lines": 3,
    "successful_lines": 3,
    "failed_lines": 0,
    "encoding": "utf-8"
  },
  "results": [
    {
      "line_number": 1,
      "input_text": "राजेश कुमार",
      "output_text": "Rajesh Kumar", 
      "source_lang": "hindi",
      "target_lang": "english",
      "confidence": "high",
      "processing_time": 1.2,
      "timestamp": "2024-12-09T12:00:01Z"
    }
  ]
}

🛡️ Safety Features

• 🔒 Input/Output Validation: Prevents accidental file overwrites
• ⚛️ Atomic Writing: Safe file operations using temporary files
• 💾 Automatic Backups: Optional timestamped backups of existing files
• 🔄 Resume Support: Resume interrupted batch operations
• 👁️ Dry Run Mode: Preview operations before execution

🎛️ Advanced Usage

# Show few-shot examples being used
indicate llm --show-examples --source bengali --target english

# Resume interrupted batch job
indicate llm --input large_file.txt --output results.txt --resume

# Use specific LLM provider/model
indicate llm "text" --provider anthropic --model claude-3-opus

# Process JSON from previous results
indicate llm --input results.json --source english --target hindi

🔄 Backend Comparison

Feature	PyTorch Backend	LLM Backend
Languages	Hindi ↔ English only	12+ Indic languages ↔ English + Inter-Indic
Setup	No API key needed	Requires LLM API key
Speed	Very fast (local)	Moderate (API calls)
Accuracy	Good for common words	Excellent for all types
Cost	Free	Pay per API call
Offline	✅ Works offline	❌ Requires internet
Batch Processing	✅	✅ with safety features

🧪 Testing Locally

1. Clone and install:

   git clone https://github.com/in-rolls/indicate.git
   cd indicate
   uv sync  # or pip install -e .
   

2. Run tests:

   # All tests
   python -m pytest
   
   # Specific tests
   python -m pytest tests/test_llm_indic.py
   python -m pytest tests/test_file_safety.py
   

3. Test both backends:

   # PyTorch backend
   indicate hindi2english "हिंदी"
   
   # LLM backend (set API key first)
   export OPENAI_API_KEY=your-key
   indicate llm "हिंदी"
   

Data

The datasets used to train the model:

• Indian Election affidavits
• Google Dakshina dataset
• ESPN Cric Info for hindi version of the english scorecard
• IIT Bombay English-Hindi Corpus

Evaluation

The v2 models (trained on our data + the public Aksharantar corpus) are benchmarked against AI4Bharat IndicXlit — the same direction (native→Latin), the same test sets, the same metric (Top-1 exact-match, match-any-reference). Training is leakage-filtered so no eval word appears in it.

Model	Dakshina (gold)	Held-out-own names¹
Hindi → English	74.4% (IndicXlit 73.2%)	52.8% (IndicXlit 49.7%)
Punjabi → English	71.9% (IndicXlit 73.2%)	56.9% (IndicXlit 53.5%)

¹ Held-out slice of our own electoral/affidavit names — the cleanest comparison, since IndicXlit never trained on it. v2 matches or edges IndicXlit on the gold benchmark and beats it on the deployment domain. Primary metric is Top-1 exact-match; CER (character error rate) is the soft companion. Reproduce with training/eval.py and training/compare.py.

Below is the edit-distance distribution on the test set (0 = exact match):

Authors

Rajashekar Chintalapati and Gaurav Sood

Contributor Code of Conduct

The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.

License

The package is released under the MIT License.