Text To Speech logo

Text To Speech

Organization
sarvamai
text-to-speech

Write correct Sarvam Bulbul TTS code — bulbul:v4-flash persona speaker IDs (voice_language_style), REST, HTTP stream, WebSocket, pronunciation dictionaries, and v3/v4 traps (short names rejected on v4, param ranges). Use this skill when generating speech in an app with Python or JS/TS. For live TTS in chat via MCP, use sarvam-mcp instead.

Overview

Publishersarvamai
Repositoryskills
Skill nametext-to-speech
Stars
72
Forks
24
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sarvamai on GitHub. Read the source before you install it.

Installation

Install the Text To Speech AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sarvamai/skills/tree/main/text-to-speech
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sarvamai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/text-to-speech .claude/skills/text-to-speech
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Text To Speech in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Text To Speech on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Text To Speech is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Text-to-Speech — Bulbul

Live in-chat TTS → sarvam-mcp (sarvam_tools_tts_*). 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) SDK floor for the code below: Python sarvamai>=0.1.29, JS sarvamai@>=1.1.8. On older versions the TTS param is target_language_code — see Gotchas.

Model

bulbul:v4-flash — latest, use this for new integrations. Same REST / HTTP stream / WebSocket contract as v3. 200+ persona voices (222 IDs).

bulbul:v3 — what the API uses when model is omitted. Short names (shubh, priya). Still the right model for Malayalam and Odia, which have no v4 personas yet.

Speakers (v4 Flash)

IDs are lowercase voice_language_style: aparna_hi_customer, simran_en_customer, shubh_enhi_ads. Omit speaker and the default is shubh_enhi_ads. v2/v3 short names (shubh, priya, ishita, anushka) return 400.

The middle token is a language tag (hi, en, bn, gu, kn, mr, pa, ta, te) or enhi for English–Hindi code-mix. Use an _enhi_ persona when one utterance is genuinely mixed; a few English loanwords in Hindi script do not need it.

language_code still takes the usual BCP-47 values (hi-IN, en-IN, … including ml-IN and od-IN). There are no ml or od persona IDs — for those languages set model="bulbul:v3" and a v3 short name.

Use caseHindiEnglish
Customer careaparna_hi_customeraparna_en_companion
Salesaparna_hi_customer, shubh_hi_customersimran_en_sales
Collectionssimran_hi_recoveryshubh_en_recovery
Hinglish (one utterance)simran_enhi_customer—

Do not copy speaker="ishita" from the Voices “generate your own sample” snippet — that is a v3 short name and fails on v4-flash.

Coverage by language (live-verified 2026-10-06; one working persona each — pick others from the live list):

LanguageExample personaLanguageExample persona
en-INsimran_en_customer, sunny_en_socialmr-INishita_mr_conversational
hi-INaparna_hi_customer, shubh_hi_customergu-INbhavik_gu_conversation
hi-IN (Hinglish)shubh_enhi_ads (the default)kn-INchaitra_kn_conversation
bn-INroopa_bn_conversational, arnab_bn_conversationpa-INanand_pa_conversation
ta-INgokul_ta_narration, aravind_ta_adste-INkavitha_te_conversation

The roster changes. Sarvam renamed voices on 2026-10-06 (bappa_bn_conversation → arnab_bn_conversation, vetri_ta_ads/vetri_ta_suspense → aravind_ta_*), and the old IDs now 400. Do not hardcode IDs from memory or old blog posts. An unknown speaker returns 400 with the full live list, so the list is one bad request away. The API also lists 2 Assamese (_as_) personas that the docs catalog does not.

Quick Start (Python)

python
from sarvamai import SarvamAI
from sarvamai.play import save

client = SarvamAI()

response = client.text_to_speech.convert(
    text="नमस्ते, आप कैसे हैं?",
    language_code="hi-IN",
    model="bulbul:v4-flash",
    speaker="aparna_hi_customer",
    enable_preprocessing=True,
)
save(response, "output.wav")

# HTTP Stream (lower latency, binary audio)
chunks = []
for chunk in client.text_to_speech.convert_stream(
    text="Hello from Sarvam AI",
    language_code="en-IN",
    model="bulbul:v4-flash",
    speaker="simran_en_customer",
    output_audio_codec="wav",
):
    chunks.append(chunk)
audio = b"".join(chunks)

Quick Start (JavaScript/TypeScript)

typescript
import { SarvamAIClient } from "sarvamai";
import { writeFile } from "fs/promises";

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

// REST
const response = await client.textToSpeech.convert({
    text: "नमस्ते, आप कैसे हैं?",
    language_code: "hi-IN",
    model: "bulbul:v4-flash",
    speaker: "aparna_hi_customer",
    enable_preprocessing: true,
});

// HTTP Stream (lower latency, returns BinaryResponse)
const streamResponse = await client.textToSpeech.convertStream({
    text: "Hello from Sarvam AI",
    language_code: "en-IN",
    model: "bulbul:v4-flash",
    speaker: "simran_en_customer",
    output_audio_codec: "wav",
});
const bytes = await streamResponse.bytes();
await writeFile("output.wav", bytes);

WebSocket Streaming

python
import asyncio
from sarvamai import AsyncSarvamAI

async def tts_stream():
    client = AsyncSarvamAI()
    async with client.text_to_speech_streaming.connect(model="bulbul:v4-flash") as ws:
        await ws.configure(
            target_language_code="hi-IN",
            speaker="aparna_hi_customer",
            output_audio_codec="wav",
        )
        await ws.convert("Your text here")
        await ws.flush()
        async for message in ws:
            pass  # base64 audio chunks

asyncio.run(tts_stream())

Character Limits

MethodMax Text
REST (convert)2,500 chars
HTTP Stream (convert_stream)3,500 chars
Legacy inputs array (raw REST)500 chars per item — send text instead to get the 2,500 limit
WebSocket2,500 chars/msg (keep <500 for lowest latency; send many messages per connection)

Gotchas

GotchaDetail
Set model or you get v3Omitting model selects bulbul:v3. v3 short names then work; v4 persona IDs do not. New code sets model="bulbul:v4-flash".
v3/v2 short names on v4shubh, priya, ishita, anushka, etc. are rejected. Use voice_language_style. Unknown IDs 400 with the live speaker list.
language_code is version-gatedPython >=0.1.29 and JS >=1.1.8 (both 2026-08-03) take language_code; earlier versions take target_language_code. Hard rename — no alias, no deprecation shim, and the JSON body key changed too, so the wrong name raises TypeError before any request goes out. Check with pip show sarvamai / npm ls sarvamai if you hit that.
WebSocket was not renamedPython ws.configure() still takes target_language_code (mapped to language_code on the wire). Docs that pass language_code= into ws.configure() raise TypeError. JS configureConnection() takes language_code.
JS method nameclient.textToSpeech.convert({...}) and .convertStream({...}) — camelCase. Stream returns BinaryResponse with .stream(), .bytes(), .blob().
pitch / loudness rangespitch −0.5–0.5 (default 0), loudness 0.1–2.5 (default 1), pace 0.5–2.0. Out-of-range values return 400. Bulbul v3 used to reject pitch / loudness; as of 2026-10-06 it accepts them too (live-verified). v2-only ranges in the model-page limits table do not apply.
Streaming codec defaultHTTP stream and WebSocket do not default to WAV. Set output_audio_codec (wav or mp3; also flac / aac on HTTP stream). opus can appear in the schema and currently errors on /text-to-speech.
Sample ratespeech_sample_rate 8000, 16000, 24000 and 48000 verified on v4-flash REST (the returned WAV matches the requested rate).
Sample rate >24kHz32kHz, 44.1kHz, 48kHz only via REST, not streaming. Omitted → 22050 Hz.
REST responseBase64-encoded audio in response.audios[0]. Use sarvamai.play.save() or base64.b64decode(). Writing the raw base64 string to disk is not playable audio.
Speaker must match modelThe mismatch fails both ways: a v3 short name on bulbul:v4-flash and a v4 persona on bulbul:v3 both return 400. Pair model and speaker explicitly.
No SSMLSSML markup is NOT supported. Use pace (and on v4, pitch / loudness) plus sentence-boundary chunking.
Use native scriptRomanized Indic input ("Aapka order confirm ho gaya hai") degrades quality. Write Indic words in native script; keep English loanwords in Latin ("आपका order confirm हो गया है"). With enable_preprocessing=true, do not hand-expand numbers, dates, or currency.
ml-IN / od-IN have no v4 personasCodes are accepted. The catalog has no Malayalam or Odia IDs. Use bulbul:v3 for those languages.
Pronunciation dictionaryPass dict_id on v4-flash the same way as v3 (10 dicts/user, 100 words/dict). Create via Python client.pronunciation_dictionary.create(file=f). JS SDK upload is broken (missing multipart Content-Type) — use raw fetch + FormData with an explicit Blob type. The pronunciation-page limits row that says “v3 only” is stale; the examples on that page send dict_id with bulbul:v4-flash.

Full Docs

Fetch the voice catalog, ranked picks, streaming protocol, and codec options from:

Frequently asked questions

What does the Text To Speech AI skill do?

Write correct Sarvam Bulbul TTS code — bulbul:v4-flash persona speaker IDs (voice_language_style), REST, HTTP stream, WebSocket, pronunciation dictionaries, and v3/v4 traps (short names rejected on v4, param ranges). Use this skill when generating speech in an app with Python or JS/TS. For live TTS in chat via MCP, use sarvam-mcp instead.

Why use Text To Speech on TypingMind?

Because you install it once and use it with any model. Text To Speech is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Text To Speech in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sarvamai/skills/tree/main/text-to-speech. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Text To Speech?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Text To Speech?

As many as you like. As long as a model supports skills, you can use Text To Speech with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Text To Speech AI skill free?

Yes. It is published on GitHub by sarvamai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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