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Audio Understanding - Speech Recognition (Clips)

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Recognize Speech in Each Clip of a Video List and Convert Them to Timed Transcripts

Split a video into scenes or highlights and you’re left with a pile of clips that still need captioning one by one. This node transcribes the whole list in a single pass — feed it the clips from an upstream splitter and get back a timed transcript for each one, timestamps preserved so captions line up exactly. It runs on OpenAI’s Whisper Large V3, the same state-of-the-art open-source recognizer used by the Video variant. Language auto-detects by default, or force English, Korean, Japanese, or Chines.

🏆 7.44 mean WER on the Open ASR Leaderboard, running at 145.51× real-time (RTFx)

What it does

This node takes a list of video clips (not a single video — a list of them, typically produced upstream by a scene-detection, highlight extraction, or clip-splitting node) and, for each clip, extracts the audio and produces a timed transcript. The output is per-clip, with timestamps preserved — so you know exactly when each phrase was spoken within each clip. Under the hood the model is the same OpenAI Whisper Large V3 used by the Video variant — the same 99-language coverage, the same accuracy, the same robustness to accents and noise. What makes this node different is the list-in, per-clip-timed-transcript-out shape, which is designed for downstream operations that need to line up captions, subtitles, or search markers against a set of segments. If you just have one video and want one transcript, use Audio Understanding - Speech Recognition (Video) instead.

Problem it solves

  • Batch clip transcription – Transcribe every clip in a list in one pass, without wiring separate speech recognition nodes per clip
  • Timed subtitle / caption generation – Get per-clip timestamps for driving subtitle tracks or on-screen captions in downstream video editing
  • Per-scene / per-highlight transcripts – When an upstream node has already split a video into scenes or highlights, this node fills each of them with a text transcript aligned by time
  • Searchable video segments – Make individual clips searchable by their transcript text, with timestamps
  • Multilingual clip lists – Auto language detection works per clip, so mixed-language clip lists are handled naturally
  • Robust to real-world audio – Same Whisper Large V3 strengths — background noise, accents, technical vocabulary

Input/Output

  • Input: A list of video clips (as produced by an upstream splitter / scene detector / highlight extractor node)
  • Output: A list of timed transcripts, one per input clip
    • Each transcript is timestamped so downstream nodes can align the text with the original video timeline (sentence-level or word-level timestamps depending on downstream needs)
  • Options:
    • Spoken language (dropdown, default Auto)
      • Auto — model detects the language from the audio itself (recommended for mixed or unknown-language content)
      • English — force English
      • Korean — force Korean
      • Japanese — force Japanese
      • Chinese — force Chinese
      • Note: under the hood the model supports 99 languages; the studio's picker is scoped to the four most-used explicit choices

Accuracy & Speed

Model
OpenAI Whisper Large V3 — state-of-the-art open-source speech recognition
Mean WER (Open ASR Leaderboard)
7.44 — among the strongest open ASR models
Real-Time Factor (RTFx)
145.51 — much faster than real-time on capable hardware
Improvement over Whisper V2
10 – 20% fewer errors across most languages
Languages
99 supported by the model; UI-exposed choices: Auto, English, Korean, Japanese, Chinese
Timing granularity
Sentence-level or word-level timestamps supported by the model
Model size
1.55B parameters
Throughput on clip lists
Whisper's chunked inference scales well across batched clips; typically much faster than real-time in aggregate

How the Clips variant compares

Speech Recognition (Clips)
Speech Recognition (Video)
Input
A list of video clips
A single video file
Output
A timed transcript per clip
One plain-text transcript
Best for
Per-scene / per-highlight captioning, subtitle alignment
One video → one transcript
Underlying model
Whisper Large V3
Whisper Large V3

Both run inside CNAPS Studio — for the single-video workflow use the sibling Audio Understanding - Speech Recognition (Video) node, or compare them side-by-side.

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Technical Details (for engineers)

Model Source

Compliance & Provenance

Provider
OpenAI (open-source)
Provider type
Specialized
License
Apache 2.0
EU AI Act risk class
Minimal Risk
Art. 50 transparency
Not applicable
Region availability
Available globally
Training data summary
Provider model card / training data

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