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You can use local AI to ask focused questions about an interview transcript without sending its text to a remote AI service—but only if both the transcription and analysis stages are configured to run locally. MacWhisper documents transcript chat with local Ollama or LM Studio models; LocalWhisper documents searchable local transcription history. Neither feature, by itself, establishes reliable semantic search across a large research repository.
Choose the kind of search you need
“Search transcripts” can mean different things. A tool might let you ask questions about one open transcript, find a saved recording in local history, or retrieve relevant passages across many interviews. Those capabilities are not interchangeable.
- One transcript: MacWhisper documents an assistant workflow for asking questions about an opened transcription using Ollama or LM Studio. MacWhisper’s provider setup guide explains configuration.
- Saved transcription history: LocalWhisper says its local history can be searched. That is a history search feature, not proof of semantic retrieval across a research corpus. See LocalWhisper’s getting-started documentation.
- Many interviews at once: The cited product documentation does not verify a complete, corpus-wide local semantic search workflow. Treat that as a separate indexing and retrieval problem rather than assuming transcript chat covers it.
Set up local AI to question a transcript
MacWhisper’s documented setup supports local providers Ollama and LM Studio. The provider must be configured for the AI feature you use; installing a local transcription app alone does not ensure that every processing step stays on your device.
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- Install and run Ollama, then download or otherwise install the model you intend to use.
- In MacWhisper, open the AI Services settings and add Ollama as a provider.
- Keep the default local base address if you have not changed Ollama’s configuration. If you have changed it, use the address for your running local service.
- Refresh the model list and select the model you installed.
- Open a transcription and use the assistant or chat feature to ask a focused question about its contents. MacWhisper describes the assistant in its Assistant guide.
Using LM Studio
- Load your chosen model in LM Studio.
- Start LM Studio’s local server.
- Add LM Studio as the AI provider in MacWhisper, then select the model and use the assistant on an opened transcription.
For the exact provider controls and current configuration details, consult MacWhisper’s provider setup guide. These workflows support questioning a transcript; they do not demonstrate an independently verified index that searches all interviews at once.
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Keep each stage inside the intended privacy boundary
Local processing is a property of each configured step, not a blanket guarantee attached to an app. MacWhisper says transcription is local by default, but documents exceptions: cloud transcription sends audio to the selected provider, DeepL translation sends transcript text to DeepL, and remote AI providers receive the transcript and prompt. Its privacy guide and provider guide describe these distinctions.
Before processing sensitive interviews, check which provider each feature will use. If local-only handling is required, verify both the transcription provider and the AI provider, and avoid cloud transcription, remote AI, or external translation for that material. A local service may still make network connections for downloads or other configured features.
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Ollama’s privacy policy, last updated March 2026, says prompts and responses processed locally are not collected or accessed by Ollama; cloud-hosted models are a different case. This is the provider’s policy statement, not an independent technical audit. Read the Ollama privacy policy and assess it alongside your organization’s rules, consent commitments, and retention requirements.
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LocalWhisper says its transcription history is saved locally and encrypted, and that the app works offline after a Whisper model is downloaded on first launch. These are vendor claims, not an independent security assessment. Its getting-started documentation describes the feature.
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Use AI as a way to find evidence, not certify findings
A transcript assistant can help identify candidate passages or organize material, but its response is not a substitute for checking the source. Use a repeatable evidence workflow:
- Check permission and policy. Confirm that participant consent, retention requirements, and organizational rules allow the transcript to be processed by the chosen local tools.
- Keep stable source files and identifiers. Use an interview ID and preserve useful metadata such as date, speaker labels, and timestamps. MacWhisper’s command-line documentation describes timestamped and speaker-detection output, along with JSON and segment formats: MacWhisper Command-Line Tool.
- Ask a narrow question. For example, ask which passages describe difficulty completing a particular task, rather than asking the model to summarize every theme in an entire research program.
- Verify every candidate passage. Return to the original transcript, confirm the wording and speaker, and check the timestamp before using the passage as evidence. Distinguish a participant’s statement from the model’s interpretation.
- Separate discovery from synthesis. Use the assistant to locate material; make and document research judgments against the source transcripts and your study context.
Plan for long transcripts and larger collections
When one transcript is too long
MacWhisper warns that large transcripts can exceed a provider’s context or request limit. If a request fails or produces an incomplete answer, its support guidance suggests trying a model with a larger context window or shortening the transcript or prompt. A larger context window may help with input capacity, but it does not establish that an answer is complete or accurate; validate findings against the source.
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When you need to search many interviews
Do not assume that asking one chat model to read an entire collection is a dependable search system. The documented workflows here cover chat with an opened transcript and searching local transcription history, not validated corpus-wide semantic retrieval. For a large repository, choose and assess an indexing and retrieval approach separately, with privacy, source traceability, and validation requirements defined before use.
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MacWhisper’s command-line tool can help produce consistent files for later processing, including batch transcription and structured output with timestamps or speaker detection. Its documentation does not establish that the CLI itself searches semantically across transcripts.
What local transcript search can—and cannot—promise
Local AI can make it easier to ask questions of a transcript while keeping analysis on-device when the selected providers really are local. The practical boundary is important: transcript chat and searchable history are documented features, while reliable retrieval across a large research corpus is not established by those capabilities alone. Treat model output as a lead to source evidence, and check both the configured data path and the underlying passage before relying on it.
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