built to be versatile and human
built to be versatile and human
built to be versatile and human
voice agents, ivr, customer support, dubbing. hear silk across every use case.
the models
the models
the models
mulberry
• starts speaking in 162 ms, faster than a blink.
• creates the voice you describe instantly.
muga
• 24 kHz studio-quality audio
• 6 built-in emotions (happy, sad, angry, excited, whisper, neutral)
mulberry
warm & conversational
• Starts speaking in 162 ms, faster than a blink.
• Creates the voice you describe instantly.
muga
velvet & calm
• 24 kHz studio-quality audio
• 6 built-in emotions (Happy, Sad, Angry, Excited, Whisper, Neutral)
mulberry
velvet & calm
• Starts speaking in 162 ms, faster than a blink.
• Creates the voice you describe instantly.
muga
warm & conversational
• 24 kHz studio-quality audio
• 6 built-in emotions (Happy, Sad, Angry, Excited, Whisper, Neutral)
integrate production-grade tts with a few lines of code.
get a key. send a request. receive 24 khz audio.
from rumikai import Rumik client = Rumik() # muga: expressive, steer with an inline tone tag audio = client.speech.create(text="[happy] Namaste! Kaise hain aap?", model="muga") audio.save("hello.wav") # mulberry: faster, steer with a description + preset speaker audio = client.speech.create( text="Hi there, how can I help you today?", model="mulberry", description="a warm 30s female voice, conversational pacing", speaker="speaker_2", ) audio.save("greeting.wav")
from rumikai import Rumik client = Rumik() # muga: expressive, steer with an inline tone tag audio = client.speech.create(text="[happy] Namaste! Kaise hain aap?", model="muga") audio.save("hello.wav") # mulberry: faster, steer with a description + preset speaker audio = client.speech.create( text="Hi there, how can I help you today?", model="mulberry", description="a warm 30s female voice, conversational pacing", speaker="speaker_2", ) audio.save("greeting.wav")
from rumikai import Rumik client = Rumik() # muga: expressive, steer with an inline tone tag audio = client.speech.create (text="[happy] Namaste! Kaise hain aap?", model="muga") audio.save("hello.wav") # mulberry: faster, steer with a description + preset speaker audio = client.speech.create( text="Hi there, how can I help you today?", model="mulberry", description="a warm 30s female voice, conversational pacing", speaker="speaker_2", ) audio.save("greeting.wav")
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
meet the teams already speaking through silk
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 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 students learn, quickly, on mobile, in simple english.
snaptv uses silk to give a voice to bite-sized lessons made for how students learn, quickly, on mobile, in simple english.
snap tv
snaptv uses silk to give a voice to bite-sized lessons made for how students learn, quickly, on mobile, in simple english.
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.
jee concepts are difficult enough. monk learning uses silk to turn dense explanations into clear, natural voice for aspirants preparing every day.
monk learning
faqs
faqs
faqs
