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konkani text to speech that sounds human

turn konkani text into realistic speech that feels local, warm, and easy to follow.

built to be versatile and human

voice agents, ivr, customer support, dubbing. hear silk across every use case.

konkanitype ‹ to insert emotion tags

mulberry 1.6

konkanitype ‹ to insert emotion tags

mulberry 1.6

lower latency for real-time speech

in live speech, latency is the first thing users feel. mulberry returns first audio in 162 ms on the published benchmark

lower is better

162

mulberry

188

cartesia sonic-3

264

elevenlabs turbo v2.5

288

elevenlabs flash v2.5

313

deepgram aura-2

337

rime mist-v3

450

rime arcana

1,232

elevenlabs multilingual

2,295

openai tts-1-hd

generate konkani tts in three steps

add konkani text to speech with silk api and the official python sdk.

silk api

Create your key

Create your key

Create your key

grab a key from api keys in the playground. name it whatever you like.

choose a voice model

choose a voice model

choose a model

mulberry-1.6 covers all 22 languages. muga adds tone tags for hindi-english lines.

call the api

call the api

send the model and text, get a wav back. add audio_format for other formats.

from rumikai import Rumik

client = Rumik()  # reads RUMIK_API_KEY
audio = client.speech.create(
    text="Hello, what can I do for you?",
    model="mulberry-1.6",
    description="professional, Indian English accent, steady pace",
)
audio.save("speech.wav")

import requests

url = "[https://silk-api.rumik.ai/v1/tts](https://silk-api.rumik.ai/v1/tts)"

payload = {
    "model": "mulberry-1.6",
    "text": "Hello, what can I do for you?", 
    "description": "professional, British accent, steady pace", 
    "speaker": "aisha",
}
headers = {"Authorization": "Bearer <token>"}

response = requests.post(url, json=payload, headers=headers)

with open("speech.wav", "wb") as f:
    f.write(response.content)

import requests

url = "[https://silk-api.rumik.ai/v1/tts](https://silk-api.rumik.ai/v1/tts)"

payload = {
    "model": "mulberry-1.6",
    "text": "Hello, what can I do for you?", 
    "description": "professional, British accent, steady pace", 
    "speaker": "aisha",
}
headers = {"Authorization": "Bearer <token>"}

response = requests.post(url, json=payload, headers=headers)

with open("speech.wav", "wb") as f:
    f.write(response.content)

silk playground

paste your text
enter the konkani sentence you want to hear. keep punctuation, numbers, and mixed-language phrases exactly as you want them spoken.
choose a model
select the silk model for konkani, then adjust the available settings for the voice and delivery you need.
generate the audio
listen to the result, refine the text or settings when needed, and download the audio when the line is ready.

what makes konkani text to speech hard?

konkani speech has its own rules for pronunciation, rhythm, and written text. the voice has to carry the sentence as speech, with the right pauses, emphasis, and word shape.

script and spelling
turns multiple scripts, regional spellings, and dialect forms into clear speech without flattening the line.
pronunciation details
keeps nasal vowels, diphthongs, and konkani names clear while the voice holds its rhythm.
numbers and time
reads amounts, dates, and times the way people say them aloud.
mixed-language lines
keeps the same speaker through konkani-english phrases, local names, and product words.

where teams use konkani voice

konkani voice belongs in moments that need clarity, recall, and a human pace.

voice overs

turn scripts into audio for videos, product demos, presentations, ads, and social posts.

e-learning

create spoken lessons, onboarding modules, and mobile-first education content from written scripts.

customer support

create call flows, order updates, and support replies for real-time conversations.

products and apps

use voice in workflow tools, companion products, reading apps, and internal tools.

audiobooks and narration

turn chapters, explainers, and story scripts into narration people can listen to anywhere.

game dialogue

create character lines for games, stories, companion apps, and scripted scenes.

lower latency for real-time speech

in live speech, latency is the first thing users feel. mulberry returns first audio in 162 ms on the published benchmark

lower is better

162

mulberry

188

cartesia sonic-3

264

elevenlabs turbo v2.5

288

elevenlabs flash v2.5

313

deepgram aura-2

337

rime mist-v3

450

rime arcana

1,232

elevenlabs multilingual

2,295

openai tts-1-hd

what makes konkani text to speech hard?

konkani speech has its own rules for pronunciation, rhythm, and written text. the voice has to carry the sentence as speech, with the right pauses, emphasis, and word shape.

script and spelling
turns multiple scripts, regional spellings, and dialect forms into clear speech without flattening the line.
pronunciation details
keeps nasal vowels, diphthongs, and konkani names clear while the voice holds its rhythm.
numbers and time
reads amounts, dates, and times the way people say them aloud.
mixed-language lines
keeps the same speaker through konkani-english phrases, local names, and product words.

silk keeps the voice intact even through language changes

narrator

description: a male 40s british voice, low pitch, gravelly timbre, slow pacing, neutral, formal register, like a dramatic narrator. text: the door creaked open. nobody was there. and yet, something watched.

0:00 / 0:00

podcast host

description: a female 30s hindi voice, normal pitch, smooth timbre, conversational pacing, energetic, casual register, like a podcast host. text: आज का episode थोड़ा अलग है। एक minute के लिए सीधा बैठ जाओ।

0:00 / 0:00

support

description: a female 30s indian voice, normal pitch, warm timbre, conversational pacing, neutral, neutral register, like a customer support agent. text: मैं आपकी help के लिए यहाँ हूँ। एक minute, मैं check करती हूँ।

0:00 / 0:00

streamer

description: a male 20s american voice, high pitch, smooth timbre, very fast pacing, excited, casual register, like a streamer reacting live. text: oh my god, did you see that play? that was insane!

0:00 / 0:00

generate tts in 22 languages

build voices for indian languages, and accents from the same speech stack

languages

english hindi tamil telugu bengali

showing 6 of 22 supported languages.

english accents

american british middle eastern asian-american indian

generate tts in 22 languages

build voices for indian languages, and accents from the same speech stack

languages

english hindi tamil telugu bengali

english accents

american british middle eastern asian-american indian

where teams use konkani voice

konkani voice belongs in moments that need clarity, recall, and a human pace.

voice overs

turn scripts into audio for videos, product demos, presentations, ads, and social posts.

e-learning

create spoken lessons, onboarding modules, and mobile-first education content from written scripts.

customer support

create call flows, order updates, and support replies for real-time conversations.

products and apps

use voice in workflow tools, companion products, reading apps, and internal tools.

audiobooks and narration

turn chapters, explainers, and story scripts into narration people can listen to anywhere.

game dialogue

create character lines for games, stories, companion apps, and scripted scenes.

meet the teams already speaking through silk

curvet put mulberry and muga directly inside its ai workflow canvas. within two days, teams generated voices across education, design, crm and enterprise workflows.

100 + hours in 2 days

curvet ai

snaptv uses silk to give a voice to bite-sized lessons made for how india learns, quickly, on mobile, in simple hindi and easy english.

snap tv

jee concepts are difficult enough. monk learning uses silk to turn dense explanations into clear, natural voice for aspirants preparing every day.

monk learning

meet the teams already speaking through silk

curvet put mulberry and muga directly inside its ai workflow canvas. within two days, teams generated voices across education, design, crm and enterprise workflows.

100 + hours in 2 days

curvet ai

snaptv uses silk to give a voice to bite-sized lessons made for how india learns, quickly, on mobile, in simple hindi and easy english.

snap tv

jee concepts are difficult enough. monk learning uses silk to turn dense explanations into clear, natural voice for aspirants preparing every day.

monk learning

frequently asked questions

paste your konkani text into the rumik playground, choose a model, and generate the audio. you can use the playground for quick testing and then move the same script into silk api when you need it inside a product.

yes. rumik gives you free playground credits to test konkani tts with your own text before moving it into a product or workflow.

install the official python sdk with pip install rumik-ai and then store your api key. send konkani text through the rumikai client with a model and voice description.

silk mulberry 1.6 is the best fit for konkani tts when you need fast multilingual speech, voice descriptions, and low-latency speech.

yes. rumik supports konkani-english code-switching so a mixed line can keep the same speaker style across the switch.

yes. you can use rumik konkani tts in commercial products, apps, and content, subject to your plan and rumik's usage terms. check the current terms before shipping paid or high-volume usage.

start building with rumik's tts api

join us

we are a close-knit group of researchers, engineers, and designers working on the hardest problems in ai.

join us

we are a close-knit group of researchers, engineers, and designers working on the hardest problems in ai.

join us

we are a close-knit group of researchers, engineers, and designers working on the hardest problems in ai.

2026 - rumik intelligence private limited

konkani text to speech that sounds human

built to be versatile and human

built to be versatile and human

built to be versatile and human

lower latency for real-time speech

generate konkani tts in three steps

generate konkani tts in three steps

generate konkani tts in three steps

silk api

silk api

silk api

silk playground

silk playground

silk playground

paste your text

choose a model

generate the audio

what makes konkani text to speech hard?

script and spelling

pronunciation details

numbers and time

mixed-language lines

where teams use konkani voice

script and spelling

pronunciation details

numbers and time

mixed-language lines

silk keeps the voice intact even through language changes

silk keeps the voice intact even through language changes

silk keeps the voice intact even through language changes

generate tts in 22 languages

meet the teams already speaking through silk

meet the teams already speaking through silk

meet the teams already speaking through silk

frequently asked questions

frequently asked questions

frequently asked questions

start building with rumik's tts api

start building with rumik's tts api

we are a close-knit group of researchers, engineers, and designers working on the hardest problems in ai.

text to speech

text to speech

playground

playground

ai glossary

ai glossary

api keys

api keys

voice agents

voice agents

pricing

pricing

documentation

documentation

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support

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we are a close-knit group of researchers, engineers, and designers working on the hardest problems in ai.

text to speech

text to speech