{"id":38551,"date":"2026-08-17T12:56:49","date_gmt":"2026-08-17T20:56:49","guid":{"rendered":"https:\/\/www.linquip.com\/blog\/?p=38551"},"modified":"2026-08-17T12:56:49","modified_gmt":"2026-08-17T20:56:49","slug":"privacy-conscious-whisper-alternatives-for-long-interviews","status":"publish","type":"post","link":"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/","title":{"rendered":"Best Whisper Alternatives for Long Interview Recordings: Privacy-First Tools That Also Produce Client Deliverables"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#Why_People_Choose_Whisper\" >Why People Choose Whisper<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#Where_Whisper_Reaches_Its_Limits\" >Where Whisper Reaches Its Limits<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#Who_This_Comparison_Is_For\" >Who This Comparison Is For<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#How_to_Evaluate_a_Whisper_Alternative\" >How to Evaluate a Whisper Alternative<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#Comparison_Table\" >Comparison Table<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#1_Notta\" >1. Notta<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#2_Speechmatics\" >2. Speechmatics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#3_Deepgram\" >3. Deepgram<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#4_AssemblyAI\" >4. AssemblyAI<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#5_Descript\" >5. Descript<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#6_Gladia\" >6. Gladia<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#When_Whisper_Is_Still_the_Better_Choice\" >When Whisper Is Still the Better Choice<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.linquip.com\/blog\/privacy-conscious-whisper-alternatives-for-long-interviews\/#Conclusion_Choosing_a_Privacy-First_Whisper_Alternative_That_Can_Ship_the_Final_Output\" >Conclusion: Choosing a Privacy-First Whisper Alternative That Can Ship the Final Output<\/a><\/li><\/ul><\/nav><\/div>\n<p><span style=\"font-weight: 400;\">Many teams adopt OpenAI Whisper because the open-source models can be run locally, which helps keep confidential interview audio on their own machines. For consultants and agencies who want that same privacy posture but also need tools that move past a raw transcript, Notta is often the most practical option: Privacy Mode enables local offline transcription, and Notta\u2019s cloud workflow can transform interviews into summaries, action items, and client-ready deliverables.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this article, \u201cWhisper\u201d mainly refers to OpenAI\u2019s open-source speech-to-text model used in a local setup. Privacy and data handling can differ when using the Whisper API or third-party Whisper apps, because audio may be processed off-device depending on the service.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Why_People_Choose_Whisper\"><\/span><b>Why People Choose Whisper<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Open-source and able to run locally. Teams can download models and transcribe on their own laptop, workstation, or infrastructure.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">More control over sensitive audio. In a true local Whisper workflow, interview recordings do not need to be uploaded to a vendor\u2019s cloud to get a transcript.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">No per-minute API bill when self-hosted. The model itself is free to run locally, but users still cover installation, compute, and ongoing maintenance.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Strong multilingual coverage and a broad ecosystem. Whisper supports many languages and has well-known community tools like whisper.cpp, Faster Whisper, and WhisperX.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Solid output for foundational transcription needs. Whisper can generate transcripts, timestamps, SRT\/VTT captions, and English translations from non-English audio.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ol>\n<h2><span class=\"ez-toc-section\" id=\"Where_Whisper_Reaches_Its_Limits\"><\/span><b>Where Whisper Reaches Its Limits<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Whisper is an ASR model, not a full interview or research workspace.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The baseline Whisper package does not include a complete, polished speaker-diarization pipeline out of the box.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It does not inherently produce summaries, action items, multi-interview synthesis, client reports, or other deliverables consultants routinely ship.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Local setups require comfort with installation, model choice, compute constraints, and upkeep. Long interviews can also demand chunking, alignment, and post-processing.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The privacy advantage is specific to running the open-source model locally. The Whisper API and many third-party \u201cWhisper-based\u201d apps may send audio to external services depending on their design.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Who_This_Comparison_Is_For\"><\/span><b>Who This Comparison Is For<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">This comparison is designed for consultants, agencies, and researchers working with long or sensitive interview recordings who want local control where it counts, but still need to convert multiple conversations into professional deliverables. The goal here is not only to find a model that might beat Whisper on accuracy. It is to keep privacy protections strong while also solving the downstream work Whisper does not cover.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That requires looking at two layers:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Privacy layer: Can you transcribe locally or offline for restricted or confidential recordings? Does audio ever leave the device? Is processing on-device, cloud, VPC, on-prem, or configurable? Are retention, deletion, and storage controls clearly documented? Do privacy features vary by plan, platform, language, or model? What outputs are available after transcription?<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Outcome layer: Can the tool convert interviews into speaker-aware records, themes, evidence snippets, summaries, briefs, reports, decision docs, and next actions?<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">People often start with Whisper because it can run locally and keep audio under their control. Notta is a compelling alternative for professionals who want a supported local offline transcription path, while also having a workflow that can turn long interviews into structured insights, client reports, decision briefs, and actionable next steps.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_to_Evaluate_a_Whisper_Alternative\"><\/span><b>How to Evaluate a Whisper Alternative<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Assess each option in this sequence:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Privacy and data governance. Can transcription happen fully on-device or offline? If it uses the cloud, where is data processed and stored? Are VPC or on-prem options available? Are retention and deletion policies explicit? Are the privacy features consistent across platforms and languages? What does the tool generate after transcription?<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Stability on long recordings. Some engines look great on short samples but lose consistency across 60 to 180 minute interviews with interruptions and shifting topics. Evaluate full-length performance, not only the first few minutes.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Speaker handling and diarization quality. Multi-speaker interviews demand accurate diarization and stable labeling, or editing time increases and summaries become less reliable.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Language and accent performance. If your interviews span regions, accents, or languages, you need consistent results across varied speakers, not only best-case accuracy.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Operational overhead. Self-hosting can be powerful, but it adds setup, maintenance, and troubleshooting. Consider who on your team will own the workflow.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Deliverables beyond the transcript. The transcript is rarely the end product. Check support for summaries, action items, cross-interview synthesis, and export formats clients expect.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who it fits best. Choose based on who must run it day to day and what the final outputs need to look like.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">The real question this comparison addresses is: which tools preserve the core reason people choose Whisper, local control, while also completing the work Whisper leaves for you?<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Comparison_Table\"><\/span><b>Comparison Table<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><b>\u00a0<\/b><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">Option<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Processing and limits<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Languages<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cost and setup<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Beyond the transcript<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Local OpenAI Whisper<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Local, self-hosted on Linux, macOS, or Windows. GPU optional; CPU is slower. Typical VRAM range: ~1 to 10 GB depending on model. No vendor-imposed duration limit<\/span><\/td>\n<td><span style=\"font-weight: 400;\">99; accuracy varies by language<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Low direct cost, higher setup effort. Open-source and free locally, no per-minute OpenAI fee. Users manage Python, PyTorch, FFmpeg, model files, and compute. Separate cloud whisper-1: $0.006\/min<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Produces transcripts and subtitle files. Multi-session synthesis and client deliverables require additional tools or custom workflows<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Notta Privacy Mode<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Local offline in Notta Desktop Pro. Unlimited local transcription usage; long sessions depend on device CPU, memory, storage, and app stability rather than the cloud plan&#8217;s five-hour cap<\/span><\/td>\n<td><span style=\"font-weight: 400;\">FunASR: auto-detect, Simplified Chinese, English, Japanese, Korean, Cantonese. Apple model: Simplified Chinese, English, Japanese, Korean, German, French, Spanish, Italian, Portuguese, Cantonese, Traditional Chinese<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Higher direct cost, lower setup complexity. Requires Notta Pro at $8.17\/month billed annually. Users download the local model in-app, no separate ASR environment required<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Audio and transcripts remain local. If users choose to use Notta cloud workflows separately, Brain can synthesize meetings and files into cross-session summaries and editable client deliverables<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Notta cloud transcription<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cloud processing through a meeting bot, standard Bot-Free, mobile, upload, and other capture paths. Up to five hours per recording on Pro and Business<\/span><\/td>\n<td><span style=\"font-weight: 400;\">58+ monolingual; 23 bilingual<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Pro: $8.17\/month annually with 1,800 minutes\/month. Business: $16.67\/month annually with unlimited transcription minutes<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Strong workflow layer: AI summaries and action items, plus cross-meeting and cross-file synthesis into reports, decision briefs, slides, tables, emails, and task lists<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Speechmatics<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cloud API; private or on-device enterprise options. Real-time sessions support 24+ hours; current batch cap requires confirmation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">56+<\/span><\/td>\n<td><span style=\"font-weight: 400;\">From $0.129\/audio hour<\/span><\/td>\n<td><span style=\"font-weight: 400;\">API output; a full cross-session deliverable workflow generally requires additional integration<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Deepgram<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cloud API; self-hosted enterprise option. No published duration cap; 2 GB per file<\/span><\/td>\n<td><span style=\"font-weight: 400;\">50+; depends on model<\/span><\/td>\n<td><span style=\"font-weight: 400;\">About $0.29\/audio hour for monolingual transcription<\/span><\/td>\n<td><span style=\"font-weight: 400;\">API output; full client deliverables typically require extra tooling or integrations<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">AssemblyAI<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cloud API; private or self-hosted enterprise options. Ten hours per file<\/span><\/td>\n<td><span style=\"font-weight: 400;\">99 with Universal-2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">From $0.15\/audio hour<\/span><\/td>\n<td><span style=\"font-weight: 400;\">API output; a complete cross-interview deliverable workflow requires integration work<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Descript<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cloud-based media editor. Up to fifteen hours per file<\/span><\/td>\n<td><span style=\"font-weight: 400;\">26; one language per file<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$16\/month billed annually, including ten media hours\/month<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Strong for editing and production; cross-session synthesis and client deliverables are not established in this review<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Gladia<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cloud API. Pre-recorded limit: 135 minutes; real-time limit: three hours<\/span><\/td>\n<td><span style=\"font-weight: 400;\">100+<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$0.61\/audio hour for asynchronous transcription<\/span><\/td>\n<td><span style=\"font-weight: 400;\">API output; a complete cross-session deliverable workflow generally needs additional systems<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"1_Notta\"><\/span><b>1. Notta<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Best for: Consultants, agencies, and researchers who want a supported local offline transcription option for sensitive interviews, plus an end-to-end workspace for turning conversations into client-ready deliverables.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Notta is a strong alternative to a Whisper-only workflow when privacy matters but the transcript is just the starting point. With Privacy Mode in Notta Desktop Pro, users can download a supported local model and transcribe recordings offline, whether they are local files or recordings captured on the device. Audio and transcript data are stored in a local workspace directory chosen by the user. Because availability can differ by OS, model, and language, teams should verify their specific requirements before starting a sensitive client engagement.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Privacy Mode sits alongside Notta\u2019s broader capture and processing options, designed for different interview contexts. For online interviews, users can bring a Notta Bot into supported meeting platforms, or use Notta Desktop to capture system audio and microphone input without adding a visible bot participant. It is important to distinguish standard Bot-Free recording from Privacy Mode: Bot-Free avoids introducing a bot into the attendee list, but the audio is still uploaded in encrypted form for cloud transcription. Privacy Mode is the path intended for local offline processing using a downloaded model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For in-person interviews, phone calls, and fieldwork, users can record via Notta\u2019s mobile apps or Notta Memo, a dedicated portable AI recorder. Notta also supports uploading existing audio and video for post-session processing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Notta\u2019s advantage becomes clearer after transcription. In the relevant Notta cloud workflows, teams can label or identify speakers, edit transcripts, produce summaries and action items, and synthesize findings across meetings and uploaded files. Using Notta Brain, teams can generate editable client deliverables such as executive summaries, decision briefs, structured reports, presentations, tables, email drafts, and task lists.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Why choose it instead of a local Whisper build:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A supported Privacy Mode for local offline transcription in eligible scenarios.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A productized interface rather than a DIY deployment and maintenance burden.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Multiple capture modes that better match real interview conditions.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Speaker support, transcript editing, summaries, and action items.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cross-interview and cross-file synthesis.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Deliverables that are editable, exportable, and shareable for client work.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Trade-offs:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Privacy Mode support depends on plan, platform, model, and language.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Standard Bot-Free capture is not the same as fully local processing.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Teams that want an open-source engine and full infrastructure control may still prefer running Whisper directly.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"2_Speechmatics\"><\/span><b>2. Speechmatics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Speechmatics is commonly evaluated when interview programs span geographies, accents, and multiple languages. It is offered as a cloud API, with private or on-device enterprise options available. For long-form work, Speechmatics highlights real-time sessions that can extend 24 hours or more, while any current batch-processing cap should be verified with the vendor. In long interviews, consistency across different voices and speech patterns can matter as much as best-case accuracy, making Speechmatics a frequent short-list candidate for international research.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For agencies doing global stakeholder interviews or multi-region research, Speechmatics can be a practical engine choice when you need broad language capability and dependable performance across diverse speakers.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Features:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Wide language coverage and support for varied accents<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cloud API plus private or on-device enterprise deployment options<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Real-time and batch transcription modes<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Speaker diarization capabilities suitable for multi-person interviews<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Pros:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Strong candidate for multilingual and international interview work<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Useful when accent variation is common and you need consistent performance<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">On-device enterprise deployment offers an option for stricter data requirements<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Cons:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">More \u201cengine-first\u201d than \u201cworkflow-first\u201d for capture and deliverables<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Implementation details depend on your environment, and batch limits should be confirmed<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"3_Deepgram\"><\/span><b>3. Deepgram<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Deepgram is often considered by teams that prioritize speed, throughput, and deployment flexibility for large volumes of audio. It is primarily a cloud API, with a self-hosted option on enterprise plans. Deepgram does not publish a strict duration ceiling, but individual files are limited by size (2 GB per file). For long interview recordings, the appeal is efficient processing at scale and suitability for systems that repeatedly handle many hours of content.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For agencies with engineering support, Deepgram can be a strong Whisper alternative when interviews need to be processed in bulk and routed into internal tools such as knowledge bases, searchable archives, or analytics pipelines.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Features:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">APIs for batch and streaming transcription<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Self-hosted enterprise option for organizations with deployment constraints<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Diarization and timestamps for navigation through long recordings<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model and language options based on the use case<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Pros:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Well-suited for high-volume processing of long recordings<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Flexible for teams building repeatable, automated workflows<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Useful for rapid turnaround, including near real-time scenarios<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Cons:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Typically best when you have engineering resources available<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Turning transcripts into cross-session client deliverables usually requires additional tooling<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"4_AssemblyAI\"><\/span><b>4. AssemblyAI<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">AssemblyAI is frequently chosen when transcription is one component inside a broader software workflow. It is delivered as a cloud API, with private or self-hosted deployment options on enterprise plans, and supports files up to ten hours long. For long interviews, AssemblyAI can be a credible Whisper alternative because it is designed for programmatic processing at scale and offers features that help structure transcripts for downstream use.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For agencies, AssemblyAI is often most relevant when you are building custom research operations pipelines, data labeling workflows, or searchable interview repositories, rather than using an out-of-the-box interview workspace.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Features:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">API-centered transcription designed for application and product integration<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Enterprise options for private or self-hosted deployments<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Speaker diarization and timestamped output designed for long recordings<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Add-on intelligence features that support extraction and analysis workflows<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Pros:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Strong developer experience for embedding transcription into systems<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Structured outputs that support post-processing on long interviews<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Good fit for automation across many recordings, or for enterprise deployment requirements<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Cons:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Requires implementation effort to reach a polished end-user workflow<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cross-interview synthesis and client-ready deliverables generally require additional integration<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"5_Descript\"><\/span><b>5. Descript<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Descript is best known as a cloud-based media editor where transcripts act as the interface for editing audio and video. It supports files up to fifteen hours, though each file can only use one language. For long interview recordings, Descript can be especially helpful when the end product is edited media, such as a narrative cut, a podcast episode, highlight reels, or client-facing clips.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For consulting and research interviews, Descript can still be valuable, but it is most compelling when production and publishing are central to the workflow rather than primarily generating structured notes, summaries, and multi-interview synthesis. Cross-session synthesis and client deliverables beyond media editing are not established in the current review.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Features:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Transcript-driven audio and video editing workflow<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Speaker labeling and timeline-based editing controls<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Export options for edited media and text formats<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Collaboration tools for review and iteration<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Pros:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Excellent for turning long interviews into edited content and polished media outputs<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Editing workflow is approachable for many teams<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Useful when transcription and production need to happen in the same environment<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Cons:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">More tool than you need if the goal is mostly transcription plus summarization<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Not primarily built for high-volume, operations-style interview programs<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">One-language-per-file constraint limits multilingual interview workflows<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"6_Gladia\"><\/span><b>6. Gladia<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Gladia is a cloud API positioned for developers who want speech-to-text plus additional processing that can make transcripts easier to use. For long recordings, Gladia\u2019s limits matter: pre-recorded audio is capped at 135 minutes, and real-time sessions have a three-hour limit. Current materials do not indicate a self-hosted or on-device option. For interviews that fit within those constraints, Gladia can support workflows where you want structured metadata and enriched outputs that speed up review and analysis.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Agencies typically look at Gladia when building customized research pipelines, such as automated tagging, searchable libraries, or integrations with internal tools.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Features:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">API-first transcription designed for batch workflows<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Options intended for transcript enrichment and workflow automation<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Structured outputs that support downstream analysis and indexing<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integrations oriented toward developer-led implementations<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Pros:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Good fit for building custom processing pipelines for interview content<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Helpful when you want outputs beyond plain text<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Designed for repeatable automation across multiple recordings<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Cons:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Not a turnkey solution for non-technical teams<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Interview capture and client deliverables often require additional tooling<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pre-recorded files over 135 minutes must be split prior to processing<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"When_Whisper_Is_Still_the_Better_Choice\"><\/span><b>When Whisper Is Still the Better Choice<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Local Whisper remains a strong choice for users who prefer an open-source speech-recognition engine and full control over the stack, are comfortable installing and maintaining the environment, and mainly need outputs like transcripts, timestamps, translations, or subtitles.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Notta is generally a better workflow fit when teams want to reduce operational burden, capture interviews in multiple ways, synthesize across conversations, and produce professional deliverables.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><b>Why are long interview recordings harder than short clips?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Long interviews introduce more variation: changing audio quality, interruptions, overlapping speech, multiple speakers, and topic shifts. Over time, diarization and consistent labeling become more important, and small errors can compound into more editing work.<\/span><\/p>\n<h3><b>Do you need a meeting bot for long-form interview transcription?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Not necessarily. Some teams like a meeting bot for live online sessions, but other situations call for bot-free capture during the interview or a supported local offline option afterward. Having multiple capture paths makes it easier to match real interview constraints.<\/span><\/p>\n<h3><b>What is the difference between offline transcription and uploading a recording later?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Offline transcription means the audio is processed locally on your device, such as with Notta Desktop Pro\u2019s Privacy Mode using a downloaded model, without sending the audio to the cloud. Recording first and uploading later is a different workflow: once uploaded, transcription happens in the cloud even if the capture happened offline.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion_Choosing_a_Privacy-First_Whisper_Alternative_That_Can_Ship_the_Final_Output\"><\/span><b>Conclusion: Choosing a Privacy-First Whisper Alternative That Can Ship the Final Output<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Whisper continues to be a strong option for teams that want an open-source transcription engine, full control over local deployment, and outputs like transcripts, timestamps, or subtitles. It is especially attractive when the technical setup is acceptable and the transcript is the primary deliverable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In many consulting and agency engagements, the work continues well beyond transcription. Sensitive interviews may require a supported local offline route, and the broader project still needs themes, decisions, briefs, reports, and next actions. Notta is particularly well suited to that combination: Privacy Mode supports local offline transcription in eligible scenarios, and the wider Notta workspace helps convert interviews and source material into editable deliverables that clients can actually use.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Many teams adopt OpenAI Whisper because the open-source models can be run locally, which helps keep confidential interview audio on their own machines. For consultants and agencies who want that same privacy posture but also need tools that move past a raw transcript, Notta is often the most practical option: Privacy Mode enables local offline &#8230;<\/p>\n","protected":false},"author":14,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-38551","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts\/38551","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/comments?post=38551"}],"version-history":[{"count":2,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts\/38551\/revisions"}],"predecessor-version":[{"id":38553,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/posts\/38551\/revisions\/38553"}],"wp:attachment":[{"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/media?parent=38551"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/categories?post=38551"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.linquip.com\/blog\/wp-json\/wp\/v2\/tags?post=38551"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}