WaveSpeedAI APIMmaudio V2

Mmaudio V2

Mmaudio V2

Playground

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MMAudio generates synchronized audio given video and/or text inputs. It can be combined with video models to get videos with audio.

Features

MMAudio Video-to-Audio Synthesis Model 🎵

A powerful video-to-audio synthesis model (based on MMAudio V2) that transforms visual content into rich, contextually appropriate audio. This model specializes in generating high-quality audio that matches the visual elements, actions, and environments in source videos while maintaining temporal consistency.

Implementation ✨

This Replicate deployment uses the MMAudio V2 model to provide advanced capabilities for video-to-audio synthesis, focusing on:

  • High-fidelity audio generation matching visual content
  • Real-time synchronization with video events
  • Environmental sound synthesis
  • Action-to-sound mapping

Model Description 🎧

The model employs the sophisticated deep learning architecture of MMAudio V2, designed specifically for video-to-audio synthesis. Using advanced neural networks and temporal analysis, it processes visual information to generate corresponding audio that naturally fits the content.

Key features:

  • 🎵 High-quality audio synthesis from video
  • 🎭 Context-aware sound generation
  • ⏱️ Precise temporal synchronization
  • 🌍 Rich environmental audio synthesis
  • 🎯 Accurate action-sound mapping
  • 🔄 Works with diverse video sources

Predictions Examples 🌟

The model excels at transformations like:

  • Converting silent films to audio-enhanced versions
  • Adding environmental sounds to nature videos
  • Generating appropriate sound effects for action sequences
  • Creating ambient audio for different settings
  • Synthesizing speech-like sounds for speaking figures

Limitations ⚠️

  • Processing time increases with video length
  • Complex acoustic environments may require additional processing
  • Output quality depends on input video clarity
  • Some unique sound effects may need specialized handling
  • Resource requirements scale with video complexity
  • Performance varies with rapid scene changes

Applications 🎯

MMAudio provides valuable solutions for:

  • Film and video post-production
  • Silent film restoration
  • Educational content enhancement
  • Gaming and VR sound design
  • Accessibility improvements
  • Content creation and editing

Ethical Considerations 📝

Important points to consider:

  • Respect original content rights
  • Maintain transparency about AI-generated audio
  • Consider potential misuse implications
  • Provide appropriate attribution
  • Follow content creation guidelines

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/mmaudio-v2" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "video": "https://d3gnftk2yhz9lr.wavespeed.ai/media/ec44bbf6abac4c25998dd2c4af1a46a7/videos/1744961424459636159_srROLJGD.mp4",
    "prompt": "Indian holy music",
    "negative_prompt": "",
    "num_inference_steps": 25,
    "duration": 8,
    "guidance_scale": 4.5,
    "mask_away_clip": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
videostringYes-The URL of the video to generate the audio for.
promptstringYes-The prompt to generate the audio for.
negative_promptstringNo-The negative prompt to generate the audio for.
num_inference_stepsintegerNo254 ~ 50The number of inference steps to perform.
durationintegerNo81 ~ 30The duration of the generated media in seconds.
guidance_scalenumberNo4.50 ~ 20The guidance scale to use for the generation.
mask_away_clipbooleanNofalse-Whether to mask away the clip.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Query Parameters

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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