sarvam-ai
Skill65 starsChanged 7 months agoMITView on GitHub
Indian AI toolkit powered by Sarvam AI — text-to-speech, speech-to-text, document intelligence, translation, transliteration, language detection, and chat completion across 23 Indian languages. Use when working with Indian languages, Hindi/Tamil/Bengali text, Sarvam AI, or when the user needs translation, transcription, or TTS for South Asian languages.
- Reads credentials
1 other project uses a version of this.
Adopted by
Most recently taken up by
- ImpactMojo/ImpactMojotheir own version, first seen Apr 12, 2026
Other versions1 project uses a version of this.
Word for word · 0
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Edited · 0
None found.
Wrote their own version · 1
- ImpactMojo/ImpactMojo★ 3same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 58
Batch workflow: create job → upload files → start → poll status (Accepted → Pending → Running → Completed) → download results.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Sarvam AI — Indian Language Toolkit
Comprehensive AI toolkit for 23 Indian languages: TTS, STT, Document Intelligence, Translation, Transliteration, Language Detection, and Chat.
Setup
- Get a free API key at https://dashboard.sarvam.ai
- Set environment variable:
export SARVAM_API_KEY="your-api-key"
Supported Languages
hi-IN Hindi, en-IN English, bn-IN Bengali, gu-IN Gujarati, kn-IN Kannada, ml-IN Malayalam, mr-IN Marathi, or-IN/od-IN Odia, pa-IN Punjabi, ta-IN Tamil, te-IN Telugu, ur-IN Urdu, as-IN Assamese, bodo-IN/brx-IN Bodo, doi-IN Dogri, ks-IN Kashmiri, kok-IN Konkani, mai-IN Maithili, mni-IN Manipuri, ne-IN Nepali, sa-IN Sanskrit, sat-IN Santali, sd-IN Sindhi
Text-to-Speech
python3 scripts/tts.py "नमस्ते, आप कैसे हैं?" --language hi-IN --speaker meera| Parameter | Default | Description |
text | — | Text to convert (max 2500 chars) |
--language | hi-IN | Language code |
--speaker | meera | Voice name |
--output | output.wav | Output file |
--sample-rate | 24000 | Audio sample rate |
Speakers — Female: Meera, Priya, Neha, Simran, Kavya, Ishita, Shreya, and more. Male: Shubh, Aditya, Rahul, Amit, Dev, Arjun, and more.
Speech-to-Text
Three modes: REST (quick, <30s), WebSocket (real-time streaming), Batch (long audio, diarization).
# REST — quick transcription
python3 scripts/speech_to_text.py rest audio.mp3
# WebSocket — real-time streaming
python3 scripts/speech_to_text.py websocket audio.wav
# Batch — multiple files with speaker diarization
python3 scripts/speech_to_text.py batch audio1.mp3 audio2.mp3 --diarization --num-speakers 3 --output-dir ./transcripts/Batch workflow: create job → upload files → start → poll status (Accepted → Pending → Running → Completed) → download results.
Formats: WAV, MP3, AAC, AIFF, OGG, OPUS, FLAC, MP4/M4A, AMR, WMA, WebM, PCM
Document Intelligence
Extract text from PDFs and images (JPEG/PNG).
python3 scripts/document_intelligence.py document.pdf --language hi-IN --format md
python3 scripts/document_intelligence.py --job-id <id> --download -o ./output/Formats: md (default), html, json. Max 200 MB, 500 pages.
Translation
# Auto-detect source, translate to Hindi
python3 scripts/text_processing.py translate "Hello, how are you?" --target hi-IN
# Mayura model with colloquial mode
python3 scripts/text_processing.py translate "What's up?" --target hi-IN --model mayura:v1 --mode modern-colloquialModels: sarvam-translate:v1 (23 languages), mayura:v1 (12 languages, supports modes and transliteration)
Modes (mayura only): formal, modern-colloquial, classic-colloquial, code-mixed
Transliteration
python3 scripts/text_processing.py transliterate "नमस्ते" --source hi-IN --target en-IN
python3 scripts/text_processing.py transliterate "namaste" --source en-IN --target hi-IN --spoken-formLanguage Detection
python3 scripts/text_processing.py detect "नमस्ते दुনিয়া"
# Output: Language: hi-IN, Script: DevaChat Completion
Two models: sarvam-105b (flagship, complex reasoning) and sarvam-m (efficient, general chat).
python3 scripts/text_processing.py chat "Explain quantum computing" --model sarvam-105b
python3 scripts/text_processing.py chat "What is the capital of India?" --model sarvam-m --temperature 0.8Resources
- Dashboard: https://dashboard.sarvam.ai
- Docs: https://docs.sarvam.ai
- Cookbook: https://github.com/sarvamai/sarvam-ai-cookbook
Does it work?
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