rek

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Record your microphone, then transcribe locally with timestamps and speaker labels. Inference uses ONNX Runtime (with sherpa-onnx for ASR), with Core ML acceleration by default on macOS and CPU inference on Linux/Windows. There is no MLX, PyTorch, or CUDA dependency.

• Parakeet TDT 0.6B v2 INT8 transcribes English only.
• Nemotron 3 Diarization ONNX detects up to eight speakers directly, replacing both Pyannote segmentation and TitaNet embeddings/clustering. It runs in 27.2-second chunks with 3.2 seconds of look-ahead and an arrival-order speaker cache carried across the full recording. The NumPy/librosa frontend and cache follow the model's Transformers implementation; PyTorch is not needed. Speaker activity is thresholded at 0.5 on 10 ms frames, including overlapping speakers.

Nemotron uses the FP32 graph and its external weights (about 398 MB), pinned to revision 353b6f8ad2cac3580e982d7fbdf0a010786b0406. Both files are downloaded into the same cache directory so ONNX Runtime can resolve the external weights. The model is distributed under the OpenMDW 1.1 license.

The dependencies are intended for macOS, Linux, and Windows. This ONNX migration has not been runtime-tested, including on Linux or Windows.

Run

Install uv and FFmpeg (see below). From this checkout, install the command once:

uv tool install .

Then just give it a filename:

rek "Voice Memo.m4a"

No model, language, device, speaker-count, or tuning flags are required. The command downloads the models if needed, decodes and normalizes the audio, detects speakers, transcribes speech, and saves both text and JSON in a new recordings/<timestamp>/ directory under your current working directory. It prints the transcript and output location when finished. First use needs internet access for the model downloads. If your shell cannot find rek after installation, run uv tool update-shell and reopen your terminal.

You can also run directly from the checkout without installing the command:

uv run main.py "Voice Memo.m4a"

The defaults are:

These use the model's published settings and bounded inference windows; they are starting defaults, not benchmark-tuned settings for a particular machine.

Run rek without a filename to record your default microphone until Ctrl+C. Start speaking when you see “Recording”. Ctrl+C finishes saving the WAV and starts transcription. There is no duration limit or --seconds option.

The project uses Python 3.14; uv can install it automatically. Built-in model revisions are pinned. Later runs reuse the Hugging Face cache, and audio stays on your computer. To relocate the cache, set HF_HOME. For offline use, populate the cache first, then set HF_HUB_OFFLINE=1.

Overrides are optional, mainly for troubleshooting or changing the destination:

rek meeting.wav --output-dir recordings/meeting
rek meeting.wav --device cpu
rek --help

The older --input filename syntax remains supported.

--asr-model accepts a compatible sherpa-onnx Parakeet v2 INT8 Hugging Face repository or local directory containing encoder.int8.onnx, decoder.int8.onnx, joiner.int8.onnx, and tokens.txt. MLX repositories are no longer compatible.

M4A (AAC or Apple Lossless), WAV, FLAC, and other formats supported by libsndfile can be used as input. M4A decoding uses FFmpeg automatically, without changing the original file or creating an intermediate WAV. FFmpeg must be on PATH:

• macOS: brew install ffmpeg
• Debian/Ubuntu: sudo apt install ffmpeg
• Windows: winget install Gyan.FFmpeg (reopen your terminal after installation)

Stereo files are mixed to mono and resampled to 16 kHz. Parakeet v2 is English-only; --language en is optional, and other languages are rejected. Transcription starts after recording ends. Long audio is decoded in 30-second windows with two seconds of context on either side. Words are retained by their timestamp midpoint within each window, with timestamps relative to the full recording. Chunk boundaries can still affect recognition.

Output

Each run creates a new recordings/<timestamp>/ directory containing:

• recording.wav: 16 kHz mono audio (microphone mode only).
• transcript.txt: readable timestamped speaker turns, also printed to the terminal.
• transcript.json: transcript, individual word timestamps, raw speaker turns, model IDs, diarization graph filename, backends, requested device, and the original audio path.

Example format:

[00:00:00.100 - 00:00:02.300] Speaker 1: Hello, everyone.
[00:00:02.500 - 00:00:03.800] Speaker 2: Hi there.

Speaker labels are anonymous and apply to one recording; they do not identify people by name. Parakeet's timestamped subword pieces are joined into complete words, preserving punctuation. Words are assigned to the speaker with the greatest total time overlap. Ties, missing word timestamps, and unmatched words are labelled Unknown. Token end times use TDT durations when available; otherwise they are estimated from the next token's start or the end of the chunk. Overlapping voices can reduce accuracy: this script does not separate mixed voices. When diarization detects no speech, transcription is skipped. Microphone audio is written to disk as you record; the completed recording is loaded into memory for inference. CPU inference can take longer than the recording.

Existing output directories are refused to prevent overwriting recordings. If model loading or inference fails, a completed microphone recording remains available; retry it with --input path/to/recording.wav. Ctrl+C during recording saves and transcribes the captured audio; Ctrl+C during model loading or transcription cancels processing and preserves the recording. Stopping before any audio arrives reports an empty recording instead of starting inference.

Devices and troubleshooting

--device auto requests the ONNX Runtime Core ML provider on macOS for both transcription and diarization, and CPU elsewhere. --device coreml, --device mps, and --device gpu explicitly request Core ML on macOS. --device cpu forces CPU on every platform. CUDA is not configured in this project.

Core ML can use Apple hardware acceleration; it is not PyTorch's MPS backend. Core ML chooses the compute hardware, and unsupported ONNX operations run on CPU. In particular, quantized Parakeet operations may remain on CPU: selecting Core ML does not guarantee that the whole model runs on the GPU or that inference is faster. The bundled macOS runtime includes Core ML support. Nemotron uses the Python ONNX Runtime package and falls back to CPU with a warning if Core ML is unavailable in that runtime. Custom sherpa-onnx runtimes without Core ML may also fall back to CPU. The JSON device field records the requested provider, not measured hardware usage. Core ML model compilation can add startup time. Use --device cpu if initialization fails or acceleration performs poorly. No acceleration benchmarks were run.

If microphone capture fails, check the system's default input device and grant microphone access to your terminal/app. macOS and Windows sounddevice wheels include PortAudio. Linux may need libportaudio2 and libsndfile1 installed using the system package manager. If a device does not support 16 kHz mono capture, record a file with another recorder and pass it with --input.

Development

uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run pyright
uv run python scripts/check_file_length.py

Tests use synthetic audio and mocked recording/inference; they do not access a microphone or download model weights. M4A integration tests generate real AAC and ALAC files and are skipped when FFmpeg is unavailable. Tests were updated for ONNX but deliberately not run during the migration. Ruff enforces annotations and a complexity limit of 10, Pyright checks types, and source files are limited to 500 lines.

Publishing

Version tags publish the rek distribution through GitHub Actions OIDC. No PyPI API token or password is stored in GitHub. Configure a PyPI Trusted Publisher with:

Create the pypi environment in the repository settings. For the first upload, register a pending publisher in your PyPI account's Publishing settings. See the PyPI Trusted Publishing guide.

To release, update version in pyproject.toml, run uv lock, and commit and push the changes. Create and push a matching version tag:

git tag -a v0.1.1 -m "Release rek 0.1.1"
git push origin v0.1.1

This example assumes version = "0.1.1" has already been committed. The workflow checks that the tag matches the package version, builds a wheel and source distribution, and publishes them from a separate job with id-token: write. Pushing a v* tag triggers publishing automatically; no GitHub release is required. Ordinary branch pushes do not publish. The tag must point to a commit containing this tag-triggered workflow; tags pushed before that change are not retriggered. Each new PyPI release needs a new version. The workflow does not run tests.

Once published, users can install and run the command with:

uv tool install rek
rek "Voice Memo.m4a"