Silence Detector.
- each::labs
- Text to Text
- $0.0006 per processing second
- ~35 sec ortalama
Silence Detector scans a video or audio track and returns the exact timestamps of every silent span, ready for cutting, splitting, or editing.
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Silence Detector is useful when this specific operation fits between source preparation and delivery. These scenarios show how it can support different parts…
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Keep an untouched source file and verify its duration, dimensions and streams before choosing edit settings. Make one small representative request, inspect the…
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Silence Detector scans a video or audio track and returns the exact timestamps of every silent span, ready for cutting, splitting, or editing.
Overview
Silence Detector Overview
Silence Detector scans a video or audio track and returns the exact timestamps of every silent span, ready for cutting, splitting, or editing.
The input is an audio URL, and the result identifies detected silent spans.
Silence Detector is available through each::labs in the Video-API family. Required fields are audio.
The operation uses the supplied source to perform its specific task. It can be placed between source preparation and final delivery without combining unrelated changes into the same request.
Use the Silence Detector API when you need this transformation or analysis as a repeatable step in an application or creative pipeline. The sections below distinguish the required material, adjustable controls and checks that matter before delivery.
Capabilities
Capabilities
- The input is an audio URL, and the result identifies detected silent spans.
- audio: URL of the audio file to analyze for silence.
- Produces a text result for the next stage of your workflow.
- Runs the requested operation on the supplied input rather than requiring a manual editing session.
- Can be used as a focused processing step in an each::labs application.
Use cases
Use Cases for Silence Detector
Silence Detector is useful when this specific operation fits between source preparation and delivery. These scenarios show how it can support different parts of a working pipeline.
Creators
Find gaps in a podcast. Use a representative source or a concise brief, then inspect the result before preparing a larger set of assets.
Design and marketing teams
Prepare candidate edit markers. Compare the result with the intended message, composition and destination. Keep reviewable versions of the source and output.
Developers
Inspect a recording before a separate silence-removal pass. Inspect the actual returned value before connecting it to another step, and keep the chosen request settings with the result.
Start with a clear definition of the desired result and use the exposed controls to reach it. Keep any additional generation, editing or application logic in separate steps so that the effect of this operation remains easy to review.
Tips & tricks
Tips and Tricks
Keep an untouched source file and verify its duration, dimensions and streams before choosing edit settings. Make one small representative request, inspect the returned asset, and use the same settings only when they fit the remaining material.
Choose a representative source for the first pass. Keep the original material and record the request values so that a successful result can be reproduced as part of your wider editing or processing sequence.
Set the named operation controls directly. This entry has no general creative-prompt interface; supplying an invented prompt field will not replace a required source or numeric setting.
Compare one deliberate adjustment at a time. Inspect the part of the result affected by that change, then check the rest of the asset or response for unwanted side effects before reusing the settings.
Technical spec
Technical Specifications
- Result type: text.
- Required fields: audio.
audio: URL of the audio file to analyze for silence.
Values must use the types and accepted options listed in the parameter reference. A displayed default does not remove a required-field requirement. Choose a supported output setting for this entry rather than borrowing a preset from a related model.
Things to be aware of
Things to Be Aware Of
Check the processed asset in the actual destination player or editor. Review the opening, closing, sound, crop and any transition points relevant to this operation instead of assuming a successful job proves the edit is correct.
Optional settings are not necessarily interchangeable with those on another entry in the same family. Use the accepted names, types and values shown for this operation.
For media-based inputs, supply a directly retrievable file URL rather than a page containing the asset. Keep the source available while processing. Check the final content in the destination player, editor or application, not only the request status.
Key considerations
Key Considerations
Silence is signal-dependent; quiet speech and background noise can affect useful boundaries.
Public estimate: $0.0006 per processing second.
Prepare audio before starting. Keep these settings aligned with the intended delivery, especially when a later step expects a particular type or format.
Run a representative request before scaling to a larger collection. Judge the result against the source and the brief, and account for all generated outputs or usage-sensitive options when estimating a batch.
Limitations
Limitations
This entry supports the operation and controls described here; it does not provide a general interface for unrelated transformations.
Unsupported parameters do not establish additional capabilities. Source quality and the chosen settings constrain what a processing step can deliver. Keep the source and check the result before replacing a final asset.
Bu model hakkında sorular
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Silence Detector scans a video or audio track and returns the start and end timestamps of every silent section as structured data. On the each::labs Video API it works as a fast analysis step, giving you precise silence markers without altering the original file.
Use Silence Detector to find dead air in interviews, podcasts, and talking-head footage, then feed the timestamps into trimming or splitting steps. It is ideal for editors and automated pipelines that need to tighten recordings or locate speech segments quickly.
Silence Detector returns JSON with the timing of each silent span rather than a new video, so nothing is re-encoded. The results plug directly into cutting or segmentation tools, letting you automate clean edits based on real audio content.