Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. For qualitative researchers who must keep audio private, the open-source YazSes project offers an on-device, hold-to-talk dictation and transcription workflow that runs entirely on local CPU. According to the GitHub - MSKazemi/yazses README, YazSes “runs entirely on-device” and “No audio, no text, nothing leaves your machine by default, ” making it relevant for confidential interviews, air-gapped field work, and sensitive team meetings. This post explains what YazSes delivers, cites its accuracy and install metrics from August 2026, and maps concrete steps for getting offline transcripts into an AI-enabled qualitative analysis pipeline.
Key Takeaways
According to the GitHub - MSKazemi/yazses README, YazSes is a free, open-source, offline hold-to-talk dictation and transcription tool that types into any focused app and can produce speaker-labelled meeting transcripts locally.
- The YazSes README reports a median decode latency of 1.6 s with the default base.en model and 0.9 s with tiny.en, measured on modern laptop CPUs (benchmarks published in August 2026).
- The YazSes README reports a 4.07% WER on LibriSpeech test-clean measured on a 13th-gen Core i7 when running int8 on CPU with the base.en model (benchmarks page, August 2026).
- YazSes can export transcripts as txt, md, srt, vtt, or JSON and offers an optional diarization extra (~45 MB of models) for speaker-labelled meeting capture, according to the project documentation.
What Happened and How YazSes Works
What happened: the YazSes project published an offline, cross-platform hold-to-talk dictation and transcription tool that operates on CPU and injects typed text into the focused application, according to the GitHub - MSKazemi/yazses README.
How YazSes works: the README explains the core loop as hold hotkey → record audio → faster-whisper (int8 on CPU) → optional cleanup and command routing → typed output, and it emphasizes that “there is no network call at any step, no account, and no API key.”
Why it matters for researchers: the YazSes README states “No audio, no text, nothing leaves your machine by default, ” which directly addresses the data-residency and privacy constraints many qualitative projects impose.
"Hold a key, speak, release" is the user interaction described in the README for live dictation; the same repository documents file transcription and a Meeting Mode that can produce speaker-labelled transcripts and optional local-LLM minutes.
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| August 2026 | Median decode latency (default base.en) | 1.6 s | Acceptable latency for live hold-to-talk dictation in interviews and note-taking (per README benchmarks). |
| August 2026 | Median decode latency (tiny.en) | 0.9 s | Lower-latency option for faster feedback when highest accuracy is not required. |
| August 2026 | Word Error Rate (LibriSpeech test-clean, base.en) | 4.07% WER on 13th-gen Core i7 | Demonstrates competitive on-device accuracy for clean audio, useful when cloud STT is not allowed. |
| Project documentation (README) | Diarization extra model size | ~45 MB | Enables speaker-labelled meeting transcripts as an opt-in download rather than a default. |
| Installation docs | Model download / disk cost | ~1.1 GB install + 141 MB model noted | Plan for storage when provisioning field laptops or air-gapped machines. |
| Repository docs | Offline test-run time | ~30 seconds for full test-suite run | Developers and auditors can verify the tool fully offline in short time. |
Implications for Qualitative Researchers
YazSes makes offline transcription practical for confidential qualitative studies by keeping audio and text on-device, according to the GitHub - MSKazemi/yazses README.
Practical decision: use YazSes when fieldwork or institutional policy forbids cloud uploads, because the README explicitly states the default is fully offline operation and diarization and local minutes are opt-in extras.
Operational trade-offs: the README documents the trade-off that CPU-only faster-whisper may have slightly higher WER in noisy conditions than some cloud services, but the project provides benchmarks (4.07% WER on LibriSpeech test-clean with base.en) so teams can assess whether the accuracy-latency profile meets their study requirements.
Data-management note: the README says meeting audio is deleted after transcription unless you choose to keep it, so researchers should plan retention policies consistent with ethics approvals and participant consent.
How Evidano Helps
Problem: Offline transcripts are hard to synthesize at scale
Solution: Evidano ingests transcripts and open-ended responses then runs thematic, frequency, and cross-segment analyses to accelerate synthesis.
In practice: export YazSes transcripts as JSON, TXT, or MD (formats the README lists) and upload them to Evidano; Evidano will produce codebooks, frequency counts, and interactive visualizations so teams can move from raw text to findings faster. See the Evidano features page for relevant analysis tools.
Problem: Speaker-labelled meetings need attribution and segment-level comparison
Solution: Evidano supports speaker-tagged transcripts and cross-segment analysis, so you can compare themes by speaker, role, or demographic.
In practice: use YazSes diarization (opt-in per the README) to export speaker-labelled JSON, then import to Evidano to run cross-speaker thematic coding and to generate segment frequency tables for NVivo-style comparisons.
Problem: Secure, private workflows must avoid cloud STT
Solution: Evidano can work with on-device transcription outputs and offers encrypted storage and document-based AI chat over your analyses.
In practice: combine YazSes local transcription with Evidano ingestion and on-premises processing to preserve privacy while gaining AI-assisted synthesis. Learn more about Evidano speech support at Evidano Speech-to-Text and our data security approach.
FAQ: offline transcription for qualitative research
Can YazSes produce speaker-labelled transcripts for interviews and focus groups?
Yes, YazSes can produce speaker-labelled transcripts when you enable the diarization extra, according to the GitHub - MSKazemi/yazses README.
The README notes diarization is an opt-in download (~45 MB) and that speaker names come from voiceprints you enroll locally, which supports controlled attribution in analysis.
Is YazSes fully offline and safe for confidential research data?
Yes, the project README states “No audio, no text, nothing leaves your machine by default, ” which makes it suitable when cloud uploads are disallowed.
Researchers should still follow institutional ethics procedures: the README also documents audio retention behavior (audio deleted after transcription unless you keep it), so record retention and consent must be specified in study protocols.
How do I get YazSes transcripts into my qualitative analysis workflow?
Export YazSes transcripts as TXT/MD/JSON and import those files into your qualitative analysis platform, or upload them to Evidano for AI-enabled coding and visualization.
The YazSes README lists export formats and the Evidano features page describes supported import workflows for thematic and frequency analyses.
What accuracy and latency should I expect on a laptop?
Expect a median decode latency of about 1.6 s with base.en and about 0.9 s with tiny.en on modern laptop CPUs, as reported in the YazSes README (benchmarks, August 2026).
For clean audio the README reports a 4.07% WER on LibriSpeech test-clean using base.en on a 13th-gen Core i7; noisy field recordings will typically increase WER, so pilot-test in your environment.
Conclusion & Next Steps
YazSes provides a practical, privacy-first path to offline transcription for qualitative research, with published latency and accuracy benchmarks that let teams decide whether on-device STT meets their study needs, according to the GitHub - MSKazemi/yazses README.
Researchers should pilot YazSes in their recording conditions, export transcripts as JSON or TXT, and then run thematic and cross-segment analyses in an AI-enabled platform to shorten synthesis time.
If you want to pair on-device transcription with AI-assisted qualitative analysis and secure storage, upload your YazSes exports to Evidano to generate codebooks, thematic summaries, and visualizations quickly; Try Evidano for free.
Topics
- offline transcription for qualitative research
- offline voice dictation
- privacy-first transcription
- AI-enabled qualitative analysis
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