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natural text to speech for e-learning

help learners stay with the lesson through a clear warm voice that makes dense text easier to follow

lesson voices built for clarity

lesson voices built for clarity

lesson voices built for clarity

turn dense text into a clear lesson voice with 12 voice presets and steady pacing

type ‹ to insert emotion tags

built for real production work

built for real production work

built for real production work

online courses
silk's text to speech for video lessons, course intros and module recaps.
employee training
voice overs for onboarding videos, compliance modules and safety walkthroughs.
customer education
product tutorials, help center videos and onboarding lessons for your users.
study material
revision notes, lecture summaries and exam prep you can listen to instead of read.

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

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 production-ready e-learning narration in three steps

generate production-ready e-learning narration in three steps

generate production-ready e-learning narration in three steps

call silk from your product workflow, or open the playground to hear the voice before shipping

silk api

silk api

silk api

  1. Create your key

  1. Create your key

  1. Create your key

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

  1. choose a voice model

  1. choose a voice model

  1. choose a model

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

  1. call the api

  1. 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

silk playground

silk playground

  1. paste your text

    enter the english sentence you want to hear. keep punctuation, numbers, and mixed-language phrases exactly as you want them spoken.

  2. choose a model

    select the silk model for english, then adjust the available settings for the voice and delivery you need.

  3. generate the audio

    listen to the result, refine the text or settings when needed, and download the audio when the line is ready.

e-learning

e-learning across languages and accents

e-learning across languages and accents

e-learning

build speech for 22 indian languages, english accents, and regional delivery from the same silk stack

e-learning across languages and accents

build speech for 22 indian languages, english accents, and regional delivery from the same silk stack

where ai lesson audio falls short

where ai lesson audio falls short

where ai lesson audio falls short

course content needs a voice that gives learners time to understand the point

problemwhat goes wronghow silk helps
too fast to learnthe voice moves through definitions and steps before the learner can catch up.set a calmer pace for dense lesson text, especially where the learner needs a second to process the idea.
misreads key termsacronyms, product terms, and mixed-language examples come out wrong.silk text to speech handles indian languages and code-mixed speech closer to how learners hear it in real classes.
no teacher warmththe voice is clear, but it feels like a system reading slides.guide the voice toward a warmer teaching style, so the lesson feels less like a readout.

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

Image (9) (no background)

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

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.

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

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.

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

Image (9) (no background)

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

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

frequently asked questions

frequently asked questions

use text to speech when a lesson works better as audio than another block of screen text. silk turns lesson copy into a clear voice that learners can follow in course videos, training modules, or study material.
yes, text to speech can be used for online courses. silk's text to speech models help course creators generate lesson narration that sounds warm and steady, so the voice supports the teaching instead of distracting from it.
silk lets you guide pace, pauses, and emphasis, so definitions and step-by-step explanations do not rush past the learner.
yes, silk text to speech can pronounce acronyms and technical terms. for uncommon words, test a short line in the silk playground and adjust the spelling or cue before generating the full lesson.
yes. with the silk api, you can send the revised lesson text and generate a fresh audio file from the same workflow. this keeps course audio easier to update when the source material changes.
yes, silk text to speech can generate training audio across 22 indian languages and code-mixed learning content. workplace lessons can sound closer to real classroom or training-room speech instead of a stiff translated readout.

explore more voice use cases

where teams use english voice

explore more voice use cases

start building with rumik's tts api

start building with rumik's tts api

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2026 - rumik

join us

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

company

contact

contact

careers

careers

research

research

ira

ira

privacy policy

privacy policy

terms

terms

api

playground

playground

text to speech

text to speech

api keys

api keys

agents

agents

pricing

pricing

docs

docs

support

support

community

X

linkedin

discord

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2026 - rumik intelligence private limited