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The cloud for

video & audio 

AI
Leading product teams use Sieve's APIs, tools, and infrastructure to ship
AI-powered capabilities faster, together.
Get Started for FreeBook a Demo
Watch the demo (3 min)
Deploy custom appsAccelerate your R&D
Empower your team of developers, designers, and PMs to build custom AI apps with
full ownership & control.
Many ready-to-use models and apps
State-of-the art models in just a few lines of code, and a curated set of
production-ready apps for video and audio.
Featured apps

autocrop

public
Smart, automatic cropping of a video to a given aspect ratio based on subject
detection and speaker tracking.
Featured
functionbuilt 4h ago


video_transcript_analyzer

public
Given a video or audio, generate a title, chapters, summary and tags
Featured
functionbuilt a month ago


video_retalking

public
Sync lips in a video to any audio
Featured
functionbuilt 20d ago


speech_transcriber

public
Fast, high quality speech transcription with word-level timestamps and
translation capabilities
Featured
functionbuilt 2 months ago


audio_enhancement

public
Remove background noise from audio and upsample it.
Featured
functionbuilt a month ago


dis_background_remover

public
Remove background from image and video
Featured
functionbuilt 3 months ago
Explore more apps
import sieve audio = sieve.Audio(path="noisy_audio.wav") enhancer =
sieve.functions.get("sieve/audio_enhancement") cleaned = enhancer.run(audio)

import sieve

audio = sieve.Audio(path="noisy_audio.wav")
enhancer = sieve.functions.get("sieve/audio_enhancement")
cleaned = enhancer.run(audio)


Build complex AI apps
What if you could import your favorite models like they were utilities? With
Sieve, you can combine many models together in just a few lines of code.
import sieve @sieve.function(name="video_dubbing", system_packages=["ffmpeg"])
def video_dubbing(source_video: sieve.Video, language: str): transcriber =
sieve.function.get("sieve/speech_transcriber") translator =
sieve.function.get("sieve/seamless_text2text") tts =
sieve.function.get("sieve/xtts-v1") lipsyncer =
sieve.function.get("sieve/video_retalking") # Extract audio from video import
subprocess audio_path = 'temp.wav' subprocess.run( ["ffmpeg", "-i",
source_video.path, audio_path, "-y"] ) # transcribe audio transcript =
list(transcriber.run(sieve.Audio(path=audio_path))) text =
transcript_to_text(transcript) # Translate text translated_text =
translator.run(text, "eng", language) # Generate new audio from translated text
target_audio = tts.run(source_audio, language, translated_text) # Combine audio
and video with Retalker return lipsyncer.run(source_video, target_audio)

import sieve

@sieve.function(name="video_dubbing", system_packages=["ffmpeg"])
def video_dubbing(source_video: sieve.Video, language: str):
  transcriber = sieve.function.get("sieve/speech_transcriber")
  translator = sieve.function.get("sieve/seamless_text2text")
  tts = sieve.function.get("sieve/xtts-v1")
  lipsyncer = sieve.function.get("sieve/video_retalking")

  # Extract audio from video
  import subprocess
  audio_path = 'temp.wav'
  subprocess.run(
    ["ffmpeg", "-i", source_video.path, audio_path, "-y"]
  )
  
  # transcribe audio
  transcript = list(transcriber.run(sieve.Audio(path=audio_path)))
  text = transcript_to_text(transcript)
  
  # Translate text
  translated_text = translator.run(text, "eng", language)
  
  # Generate new audio from translated text
  target_audio = tts.run(source_audio, language, translated_text)
  
  # Combine audio and video with Retalker
  return lipsyncer.run(source_video, target_audio)


Collaborative playgrounds
Each app comes with an auto-generated playground to share with your team. PMs
and designers can experiment just as easily as engineers.

Deploy custom models with ease
Define your dependencies and compute type in-code, and deploy with a single
command.
import sieve @sieve.function( name="my_model", gpu=True,
python_packages=["torch==1.8.1"] ) def my_model(video: sieve.Video): # do stuff

import sieve

@sieve.function(
  name="my_model",
  gpu=True,
  python_packages=["torch==1.8.1"]
)
def my_model(video: sieve.Video):
  # do stuff



sieve deploy

sieve deploy


Infrastructure that just works
Fast, scalable infrastructure without the hassle. No AWS account required.

Run at any scale

We built Sieve to automatically scale as your traffic increases with zero extra
configuration.

Stop worrying about Docker, CUDA, and GPUs

Package models with a simple Python decorator and deploy instantly.

Logs and metrics

A full-featured observability stack so you have full visibility of what’s
happening under the hood.

Flexible, compute-based pricing

Pay only for what you use, by the second. Gain full control over your costs.
Built for your use case

Work with the experts
We're a team of machine learning experts from places like Berkeley, NVIDIA,
Apple, Snapchat, Microsoft, and Scale AI. Work with us to support your custom
use case and integrate Sieve into your existing pipeline.

Schedule a demo

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