speech-to-text

name	speech-to-text
description	Write correct Sarvam Saaras STT code for 23 Indic languages — REST modes, Batch API with diarization, and WebSocket streaming gotchas. Use this skill when building transcription or voice apps in Python or JS/TS. For live transcription in chat via MCP, use sarvam-mcp instead.
license	Apache-2.0
metadata	{"author":"sarvam-ai","version":"3.3"}

Speech-to-Text — Saaras

Live in-chat transcription → sarvam-mcp (sarvam_tools_stt_*). This skill = SDK code.

[!IMPORTANT] Auth: api-subscription-key header — NOT Authorization: Bearer. Base URL: https://api.sarvam.ai (NOT /v1 — that prefix is only for the OpenAI-compatible chat endpoint)

Model

saaras:v3 — 23 languages, 5 output modes (transcribe, translate, verbatim, translit, codemix), auto language detection.

Quick Start (Python)

from sarvamai import SarvamAI
client = SarvamAI()

response = client.speech_to_text.transcribe(
    file=open("audio.wav", "rb"),
    model="saaras:v3",
    mode="transcribe"
)
print(response.transcript)

Quick Start (JavaScript/TypeScript)

import { SarvamAIClient } from "sarvamai";
import * as fs from "fs";

const client = new SarvamAIClient({ apiSubscriptionKey: "YOUR_SARVAM_API_KEY" });

const response = await client.speechToText.transcribe({
    file: fs.createReadStream("audio.wav"),
    model: "saaras:v3",
    mode: "transcribe"
});
console.log(response.transcript);

Batch API (Long Audio + Diarization)

job = client.speech_to_text_job.create_job(
    model="saaras:v3",
    mode="transcribe",
    language_code="hi-IN",
    with_diarization=True,
    num_speakers=2
)
job.upload_files(file_paths=["meeting.mp3"])
job.start()
job.wait_until_complete()
job.download_outputs(output_dir="./output")

Supports audio up to 2 hours per file, up to 20 files per job, up to 20 speakers (num_speakers), all 5 output modes.

WebSocket Streaming

import asyncio, base64
from sarvamai import AsyncSarvamAI

async def stream_audio():
    client = AsyncSarvamAI()
    async with client.speech_to_text_streaming.connect(
        model="saaras:v3",
        high_vad_sensitivity=True,
        flush_signal=True
    ) as ws:
        with open("audio.wav", "rb") as f:
            audio_base64 = base64.b64encode(f.read()).decode("utf-8")
        await ws.transcribe(audio=audio_base64, encoding="audio/wav", sample_rate=16000)
        await ws.flush()
        response = await ws.recv()
        print(response)

asyncio.run(stream_audio())

No fixed session duration limit — but the connection closes after 60 seconds of inactivity. Use sample_rate=8000 for telephony audio.

Gotchas

Gotcha	Detail
REST: 30s limit	Audio >30s fails. Use Batch API or WebSocket for longer files.
JS method name	client.speechToText.transcribe({...}) — camelCase, NOT speech_to_text. File via fs.createReadStream().
WebSocket codecs	Only wav, pcm_s16le, pcm_l16, pcm_raw. MP3/AAC/OGG NOT supported for streaming. PCM input is 16kHz only.
WebSocket audio	Must be base64-encoded. Use sample_rate=8000 for telephony audio.
WebSocket idle timeout	Connection closes after 60s of inactivity. For long-running sessions, send periodic silent (near-zero amplitude) audio chunks as keep-alive.
Flush signal	flush_signal=True + await ws.flush() forces immediate transcription boundary.
VAD events	vad_signals=True emits START_SPEECH/END_SPEECH events alongside transcripts. high_vad_sensitivity=True for automatic end-of-speech detection.
Short audio detection	Set language_code explicitly for audio <3 seconds — auto-detection needs more signal.

Full Docs

Fetch streaming protocol, batch API SDK examples, and codec details from:

• https://docs.sarvam.ai/llms.txt — comprehensive docs index
• STT Overview
• Streaming API
• Batch API + Diarization
• Rate Limits