Text → audio
Describe what you need in natural language and land on timed moments inside long files.
Audio Search
Local text-to-audio and audio-to-audio search across private libraries. Find the moment inside a long recording—embeddings on disk, audio never uploaded.
Same engine. Two jobs: keep sensitive archives offline, and find the right moment in professional collections.
When the WAV cannot hit a SaaS embedder—investigations, regulated ops, air-gapped archives, clinical or financial voice.
SFX librarians, field and bioacoustics, production sound, podcast and radio archives that refuse cloud indexing.
Named libraries, watched folders, temporal hits, A/B compare, and export—built for large private collections, not casual cloud toys.
Describe what you need in natural language and land on timed moments inside long files.
Drop a sample—or a trimmed region—and find similar passages across your indexed libraries.
Local multimodal embeddings. No cloud embedding API. Optional GPU acceleration.
Index health, reconcile, chunk size and overlap—keep the archive current as files appear.
Compare hits, pin favorites into project bins, and export CSV or JSON when work leaves the app.
Automate indexing and search for ops pipelines that prefer a terminal.
If the archive cannot leave the building, the search engine should not either. Desktop app, on-device vectors, your disks—waveform and spectrogram inspection included.
Tell us about the library you cannot put in the cloud. We will show where local similarity search changes the workflow.
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