speech-to-text

name	speech-to-text
description	Transcribe audio to text using Sarvam AI's Saarika model. Use when the user needs to convert speech to text, transcribe audio files, build voice interfaces, or process Indian language audio. Supports 11 Indian languages plus English with automatic language detection, code-mixing, speaker diarization, and word-level timestamps.
license	Apache-2.0
metadata	{"author":"sarvam-ai","version":"1.0","model":"saarika:v2.5"}

Speech-to-Text with Saarika

Saarika is Sarvam AI's speech recognition model optimized for Indian languages with support for code-mixing (Hindi-English etc.) and multi-speaker scenarios.

Installation

pip install sarvamai

Quick Start

from sarvamai import SarvamAI

client = SarvamAI()

response = client.speech_to_text.transcribe(
    file=open("audio.wav",
"rb"),
    model="saarika:v2.5",
    language_code="hi-IN"
)

print(response.transcript)

Supported Languages

Code	Language	Code	Language
hi-IN	Hindi	ta-IN	Tamil
bn-IN	Bengali	te-IN	Telugu
kn-IN	Kannada	ml-IN	Malayalam
mr-IN	Marathi	gu-IN	Gujarati
pa-IN	Punjabi	or-IN	Odia
en-IN	English (Indian)	auto	Auto-detect

API Options

REST API (≤30 seconds)

For short audio clips:

response = client.speech_to_text.transcribe(
    file=open("short_clip.wav",
"rb"),
    model="saarika:v2.5",
    language_code="auto",           # Auto-detect language
    with_timestamps=True,           # Word-level timestamps
    with_diarisation=True           # Speaker identification
)

print(response.transcript)
print(response.language_code)       # Detected language
print(response.words)               # Timestamped words
print(response.speaker_segments)    # Speaker turns

Batch API (≤1 hour)

For long recordings:

response = client.speech_to_text.transcribe_batch(
    file=open("long_recording.mp3",
"rb"),
    model="saarika:v2.5",
    language_code="hi-IN"
)

WebSocket Streaming (Real-time)

For live transcription. Audio must be sent as base64-encoded strings.

import asyncio
import base64
from sarvamai import AsyncSarvamAI

async def stream_audio():
    client = AsyncSarvamAI()

    async with client.speech_to_text_streaming.connect(
        language_code="hi-IN",
        model="saarika:v2.5",
        high_vad_sensitivity=True
    ) as ws:
        # Read and encode audio to base64
        with open("audio.wav",
"rb") as f:
            audio_base64 = base64.b64encode(f.read()).decode("utf-8")

        # Send base64 encoded audio
        await ws.transcribe(
            audio=audio_base64,
            encoding="audio/wav",
            sample_rate=16000
        )

        # Receive transcription
        response = await ws.recv()
        print(response)

asyncio.run(stream_audio())

WebSocket supported formats: wav, pcm_s16le, pcm_l16, pcm_raw only. MP3/AAC/OGG not supported for streaming.

JavaScript

import { SarvamAI } from 'sarvamai'
import fs from 'fs'

const client = new SarvamAI()

const response = await client.speechToText.transcribe({
  file: fs.createReadStream('audio.wav'),
  model: 'saarika:v2.5',
  languageCode: 'hi-IN',
  withTimestamps: true
})

console.log(response.transcript)

cURL

curl -X POST "https://api.sarvam.ai/speech-to-text" \
  -H "api-subscription-key: $SARVAM_API_KEY" \
  -F "file=@audio.wav" \
  -F "model=saarika:v2.5" \
  -F "language_code=hi-IN"

Parameters

Parameter	Type	Required	Description
file	File	Yes	Audio file (wav, mp3, flac, ogg, webm)
model	string	Yes	saarika:v2.5 or saarika:v2
language_code	string	Yes	BCP-47 code or auto
with_timestamps	bool	No	Return word timestamps
with_diarisation	bool	No	Enable speaker identification

Response

{
  "request_id": "abc123",
  "transcript": "नमस्ते, आप कैसे हैं?",
  "language_code": "hi-IN",
  "words": [
    {
      "word": "नमस्ते",
      "start": 0.0,
      "end": 0.5
    },
    {
      "word": "आप",
      "start": 0.6,
      "end": 0.8
    }
  ],
  "speaker_segments": [
    {
      "speaker": "SPEAKER_00",
      "start": 0.0,
      "end": 2.5
    }
  ]
}

See references/streaming.md for detailed WebSocket documentation.