Transcribe recordings to text (offline)¶

yazses transcribe <file> turns an existing audio or video recording into a text file — a meeting, an interview, a voice memo, a lecture — entirely on your machine. It runs the same local faster-whisper engine as live dictation: no cloud, no account, no upload. With --diarize it also labels who said what.

This guide walks from the simplest case to a named, multi-speaker transcript.

1. The one-liner¶

yazses transcribe talk.mp3

This writes talk.txt next to the input. That's it — no daemon, no hotkey, no config change. The command accepts most common formats out of the box (wav, mp3, m4a, ogg, flac, opus, mp4, and other ffmpeg-decodable media); the audio is decoded to 16 kHz mono internally, so you don't convert anything first.

Choose the output path or format if you like:

yazses transcribe talk.mp3 -o notes.txt          # explicit output path
yazses transcribe lecture.mp3 --format srt        # subtitles with timestamps
yazses transcribe lecture.mp3 -f md               # Markdown (also: vtt, json)

srt and vtt carry per-cue timestamps (drop them into a video player); json is lossless — per-word timestamps plus speaker, for downstream tooling.

2. Pick the right model for the job¶

By default transcribe uses your configured [stt] model (often base.en, which is fast). For anything you care about — a real meeting, an accented speaker, background noise — a larger model is noticeably more accurate:

yazses transcribe interview.mp3 --model small.en

small.en roughly doubles or triples the runtime versus base.en but makes far fewer word errors. Rule of thumb: base.en for a quick gist, small.en (or larger) for a transcript you'll actually read or share.

Transcription is CPU-bound and proportional to the recording's length. A long recording (tens of minutes) can take several minutes on base.en and longer on small.en, so it's fine to leave it running in the background.

3. Other languages¶

To transcribe non-English audio and get English text back:

yazses transcribe note.fr.m4a --language translate

4. Tag who said what (diarization)¶

Speaker tagging is opt-in and needs a small one-time setup, because it uses a separate speaker model (sherpa-onnx — CPU-only, no PyTorch, no GPU, no account).

One-time: install the extra and fetch the models (~15 MB):

uv sync --extra diarization                 # or: pipx inject yazses sherpa-onnx
yazses transcribe any.m4a --download-models  # downloads, then exits

Then diarize:

yazses transcribe meeting.m4a --diarize

Each utterance is now prefixed with a label:

Speaker 1: Good morning, shall we start?
Speaker 2: Yes — I sent the doc earlier.

If diarization isn't available, --diarize degrades gracefully to a plain transcript and tells you why.

Give the speakers real names¶

If you know who's in the recording, name them. Two ways:

# Positional — names map to speakers in order of first appearance:
yazses transcribe meeting.wav --diarize --names "Alice,Bob,Carol"

# Explicit — map a specific detected speaker to a name (repeatable):
yazses transcribe meeting.wav --diarize --rename speaker_0=Alice --rename speaker_1=Bob

Help the model with the speaker count¶

Diarization auto-detects how many people are talking, but if you know the count, telling it usually improves the split:

yazses transcribe call.ogg --diarize --speakers 2

Use --min-speakers / --max-speakers to give a range instead of an exact number.

Let your own voice name itself¶

If you've enrolled a voiceprint (yazses enroll-voice), YazSes recognises your voice in the recording and labels it "You" automatically; everyone else stays Speaker N unless you name them.

5. A realistic accuracy note¶

Diarization on a long, casual multi-person recording is genuinely hard, and the result improves a lot with model size and a correct speaker count. Expect:

• Two distinct speakers to separate cleanly.
• A third speaker who joins late or talks little to sometimes get under-attributed (their lines folded into another speaker) on the fast model.

If a diarized transcript looks rough, re-run with --model small.en and either let the speaker count auto-detect or set it explicitly — both help. The transcript files you already have are never overwritten unless you reuse the same -o path.

Privacy¶

Everything here runs on your machine — the audio and the transcript never leave it. Speaker voiceprints are biometric data: they are stored encrypted, used only to label you, and no one is ever enrolled automatically. You are responsible for having permission to record and transcribe other people.

Quick reference¶

Full flag list: yazses transcribe -h, or the CLI reference.