Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. For qualitative researchers who need local transcription and strict data control, the open-source project GitHub - MSKazemi/yazses (published August 13, 2026) provides a fully offline, hold-to-talk dictation daemon and meeting transcription suite. According to the YazSes repository, the tool runs entirely on-device with no network calls by default, supporting dictation, file transcription, and speaker-labelled meeting capture while keeping audio under researcher control.
Key Takeaways
YazSes is a free, open-source, fully offline hold-to-talk dictation and transcription tool, as described in the GitHub - MSKazemi/yazses repository (published August 13, 2026).
- According to the YazSes README on August 13, 2026, YazSes runs entirely on-device: “No audio, no text, nothing leaves your machine by default.” (MSKazemi/yazses).
- According to the YazSes benchmarks (August 2026), median end-to-end decode latency is 1.6 s with the default `base.en` model and 0.9 s with `tiny.en` on modern laptop CPUs (MSKazemi/yazses).
- According to the YazSes benchmarks (August 2026), measured word error rate was 4.07% on LibriSpeech test-clean using a 13th-gen Core i7 with int8 fast-whisper models (MSKazemi/yazses).
- According to the YazSes documentation (August 2026), meeting diarization is an opt-in extra (~45 MB of models) and the install guidance reports an initial model and data download of roughly 1.1 GB (MSKazemi/yazses).
What happened and how YazSes works
Answer: YazSes packages CPU-based Whisper inference into a cross-platform, offline hold-to-talk and transcription daemon that types into any focused application and optionally diarizes meetings, according to the GitHub - MSKazemi/yazses repository (published August 13, 2026).
According to the YazSes docs, the core loop is: hold hotkey → record audio → VAD gate → faster-whisper transcribe (CPU int8) → optional cleanup/command routing → inject typed text or key sequence (MSKazemi/yazses).
According to the YazSes README (August 13, 2026), the speech model is provided by faster-whisper and the command routing uses a fast regex grammar with an optional ~0.5B SLM router for low-confidence utterances (MSKazemi/yazses).
Quote: "Hold a key, speak, and your words are typed into any app, " says the YazSes README (MSKazemi/yazses), and the project explicitly recommends it for confidential or air-gapped work where audio must not leave the machine.
Findings snapshot
| Date | Metric | Value | Implication for qualitative research |
|---|---|---|---|
| August 13, 2026 | Median decode latency (default base.en) | 1.6 s | Acceptable for near-real-time hold-to-talk note-taking and coding in interviews (MSKazemi/yazses). |
| August 13, 2026 | Median decode latency (tiny.en) | 0.9 s | Faster but lower-accuracy option useful for short live notes (MSKazemi/yazses). |
| August 13, 2026 | LibriSpeech test-clean WER | 4.07% (13th-gen Core i7) | Accuracy comparable to many CPU-based offline STT setups; verify on your mic and dialect (MSKazemi/yazses benchmarks). |
| August 2026 | Diarization extra model size | ~45 MB | Small extra download to enable speaker-labelled transcripts for multi-speaker interviews (MSKazemi/yazses). |
| August 2026 | Minimum RAM | 4 GB (8 GB comfortable) | Plan field laptops and VM specs accordingly for reliable offline transcription (MSKazemi/yazses). |
Implications for qualitative researchers
Answer: YazSes lets researchers keep raw audio and transcripts local while producing typed transcripts or speaker-labelled meeting notes on-device, enabling compliant handling of sensitive interviews, according to the YazSes repository (MSKazemi/yazses, August 13, 2026).
If your IRB or data policy requires that audio never leaves institutional systems, the YazSes model is directly relevant because the project documents a default offline flow and an explicit privacy statement that audio is deleted after transcription unless you choose to keep it (MSKazemi/yazses).
If your study uses many short in-person interviews or field notes, the reported median latency of 0.9–1.6 s (August 2026 benchmarks) means hold-to-talk capture is fast enough for live note-taking and incremental coding during interviews (MSKazemi/yazses).
If you run multi-speaker focus groups, the diarization extra (~45 MB) gives speaker-labelled transcripts without cloud diarization, which simplifies annotation and segment-level coding while keeping data on-device (MSKazemi/yazses).
How Evidano helps
Problem: Local transcripts need systematic thematic analysis
Answer: Use Evidano to ingest YazSes transcripts and run thematic, frequency, and cross-segment analyses so you can move from raw text to coded insights quickly.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents, and Evidano ingests text and JSON transcripts to produce thematic code maps, co-occurrence networks, and segment cross-tabs.
Evidano can import speaker-labelled JSON or plain-text transcripts exported from YazSes for rapid theme extraction, which preserves the on-device privacy model because you control where you upload and store the transcripts.
Problem: Multiple languages, noisy transcripts, and corrections
Answer: Evidano supports translation and custom dictionaries so you can normalize multilingual transcripts and align researcher-corrected vocabulary.
Evidano's translation and custom-dictionary features complement YazSes's English-first models by allowing researchers to translate, normalize, and correct transcripts before running thematic extraction; see Evidano's transcription and translation tooling for integrations at Evidano speech-to-text and Evidano translation.
Problem: Secure collaboration and analysis
Answer: Evidano provides encrypted storage and analysis workflows so teams can annotate and synthesize transcripts under institutional controls.
Evidano's data handling is designed for research use: uploads are encrypted, and Evidano does not use customer data to train third-party models, which maps to the privacy goals that make YazSes attractive for sensitive work; learn more in Evidano's security overview at Evidano data-security.
FAQ: offline speech-to-text for qualitative research
Can YazSes run fully offline for field interviews?
Yes, YazSes runs fully offline on CPU and needs no cloud connection by default, according to the GitHub - MSKazemi/yazses repository (published August 13, 2026).
The project documents how to run it on Linux, macOS, and Windows and describes Docker/browser demo modes that also run with no network access for verification (MSKazemi/yazses).
Will offline models be accurate enough for verbatim transcripts?
It depends: YazSes reports a 4.07% WER on LibriSpeech test-clean using a 13th-gen Core i7 with int8 faster-whisper in August 2026, which indicates good baseline accuracy for clean speech in controlled settings (MSKazemi/yazses).
Research teams should validate accuracy on their own mics, dialects, and environments and consider local tuning or manual correction workflows before final analysis (MSKazemi/yazses).
How do I get speaker labels without sending audio to the cloud?
You can enable diarization as an opt-in extra in YazSes: the diarization model is an on-device download of about 45 MB as of August 2026, according to the YazSes docs (MSKazemi/yazses).
YazSes enrolls voiceprints locally and produces speaker-labelled transcripts that you can export as JSON for secure, local analysis (MSKazemi/yazses).
How should I integrate YazSes transcripts into an analysis workflow?
Answer: export YazSes transcripts (txt, md, or JSON) and ingest them into a qualitative analysis tool like Evidano for coding, cross-segment comparisons, and visualization.
Evidano ingests transcripts and supports thematic, frequency, and cross-segment analyses as well as AI chat over your documents; this combination preserves local capture while enabling efficient synthesis at scale.
Conclusion & Next Steps
YazSes (GitHub - MSKazemi/yazses) provides a practical, privacy-first way to capture interview and meeting audio entirely on-device, with reported median latencies of 0.9–1.6 s and published benchmarks including a 4.07% WER on LibriSpeech test-clean (MSKazemi/yazses, August 13, 2026).
For qualitative teams, the recommended next step is to pilot YazSes on the target hardware and recording conditions, export speaker-labelled transcripts as JSON, and run thematic and cross-segment analyses in Evidano.
If you want an end-to-end, research-focused workflow: test YazSes in your environment, then Try Evidano for free to ingest transcripts, run thematic coding, and generate segment-level insights.
Topics
- offline speech-to-text for qualitative research
- offline voice dictation for research
- on-device transcription
- privacy-first transcription
- speaker-labelled offline transcription
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