voxkitchen 0.2.1.dev38

A researcher-friendly, declarative speech data processing toolkit

VoxKitchen

Turn raw speech recordings into clean, inspectable training datasets.

VoxKitchen handles the repetitive audio prep around ASR, TTS, speaker analysis, and data cleaning: convert, segment, label, filter, and export from one Docker-backed YAML pipeline.

Status: Pre-alpha. APIs and Docker image contents may change between releases.

Use VoxKitchen when you want to:

• turn long recordings into ASR training data;
• prepare and inspect TTS datasets;
• diarize speakers, tag languages, or run speech quality checks;
• clean, filter, and package audio without maintaining one-off scripts.

Why VoxKitchen

Speech data preparation is usually a chain of fragile scripts: convert audio, split speech, denoise, transcribe, diarize, filter, and export. VoxKitchen makes that chain explicit and repeatable:

• Docker-first execution: prebuilt runtimes avoid local dependency conflicts.
• One YAML pipeline: define ingest, stages, filters, and output packs in one file.
• 51 built-in operators: audio prep, VAD, ASR, diarization, TTS, quality metrics, and packing.
• Resumable by design: every stage checkpoints under ./work.
• Inspectable outputs: reports, cut statistics, provenance, and per-stage errors.

Quick Start

Requirements:

• Docker
• Python 3.10+ for the lightweight vkit launcher

Install the vkit launcher from PyPI:

pipx install voxkitchen      # recommended — isolates the launcher
# or
pip install voxkitchen

This installs only the lightweight launcher and inspection commands (a few MB, no torch / ASR / TTS dependencies). All pipeline runtime dependencies stay inside the prebuilt Docker images.

Run the included demo with the smallest runtime image. No repository clone is required; the published image includes the demo pipeline and demo audio.

vkit docker pull --tag slim
vkit docker run --tag slim examples/pipelines/demo-no-asr.yaml
vkit inspect run ./work/demo-no-asr

vkit docker run writes run artifacts under ./work and exported datasets under ./output with your host user ID. It also mounts ./data automatically when that directory exists.

What You Can Build

Goal	Start with	Runtime image
Clean and filter raw speech audio	vkit init my-cleaning --template cleaning	slim
Build ASR training manifests	vkit init my-asr --template asr	asr
Analyze speakers and languages	vkit init my-speakers --template speaker	latest
Prepare TTS training data (quality gate)	vkit init my-tts --template tts	asr
Synthesize speech from text (built-in voices, 3-sec voice cloning)	see TTS Synthesis tutorial	tts
Voice cloning with Fish-Speech (44.1 kHz)	create a pipeline with tts_fish_speech	fish-speech

How It Works

A pipeline is a YAML file. Each stage reads a CutSet, writes a checkpoint, and passes the result to the next stage.

version: "0.1"
name: my-pipeline
work_dir: ./work/${name}-${run_id}

ingest:
  source: dir
  args:
    root: ./data
    recursive: true

stages:
  - name: resample
    op: resample
    args: { target_sr: 16000, target_channels: 1 }

  - name: vad
    op: silero_vad
    args: { threshold: 0.5 }

  - name: asr
    op: faster_whisper_asr
    args: { model: large-v3, compute_type: float16 }

  - name: filter
    op: quality_score_filter
    args:
      conditions: ["duration > 1", "duration < 30", "metrics.snr > 10"]

  - name: pack
    op: pack_jsonl

Interrupted runs resume from completed checkpoints.

Create A Project

vkit init my-project --template asr
cd my-project

# Put your audio files in ./data first.
vkit validate pipeline.yaml
vkit docker run --tag asr pipeline.yaml --dry-run
vkit docker run --tag asr pipeline.yaml
vkit inspect run work/

List templates:

vkit init --list-templates

Not sure which image a pipeline needs? Run:

vkit validate pipeline.yaml

It prints the recommended vkit docker pull --tag ... and vkit docker run --tag ... commands for that YAML.

Runtime Images

Every vkit docker command accepts --tag <name>:

Tag	Use when	GPU	Approx. size
slim	CPU-friendly cleaning, VAD, quality, pack, enhancement	no	~13 GB
asr	Faster-Whisper, FunASR, Qwen3-ASR, forced alignment	yes	~48 GB
diarize	Pyannote speaker diarization	yes	~32 GB
tts	Kokoro, ChatTTS, CosyVoice	yes	~44 GB
fish-speech	Fish-Speech isolated runtime	yes	~57 GB
latest	Mixed pipelines across ASR, diarization, TTS, or Fish-Speech	yes	~123 GB

Use latest when one pipeline mixes multiple runtime families, such as ASR plus diarization or ASR plus TTS. Otherwise, prefer the smallest image that contains the operators you need.

Useful checks:

vkit docker pull --tag asr
vkit docker doctor --tag asr --expect asr
vkit docker doctor --tag latest

Configuration

Some operators require API tokens. Create ./.env; vkit docker run passes it into the container automatically.

cp .env.example .env

Variable	Required by	Notes
HF_TOKEN	pyannote_diarize	Accept the pyannote model agreement on HuggingFace first.

Common Commands

vkit init <path> --template asr           # Scaffold a project
vkit validate pipeline.yaml               # Validate YAML and recommend an image
vkit docker run --tag asr pipeline.yaml --dry-run
vkit docker run --tag asr pipeline.yaml
vkit inspect run work/                    # Stage summary
vkit inspect cuts <cuts.jsonl.gz>          # CutSet statistics
vkit inspect errors work/                  # Per-stage failed cuts
vkit operators search <keyword>            # Find operators by name or summary
vkit operators --category quality          # List one category's operators
vkit schema export --out pipeline.schema.json  # Editor autocompletion for YAML
vkit recipes                               # List dataset recipes
vkit docker download --tag slim librispeech --root ./data/librispeech --subsets dev-clean
vkit docker doctor --tag latest            # Check image health

Documentation

• Getting Started
• Examples & Use Cases
• Pipeline YAML
• Recipes & Download
• CLI reference
• Operators reference
• Docker build guide
• Contributing

Agent Skill

The repo includes an agent-neutral VoxKitchen skill at skill/. Claude, Codex, and other SKILL.md-compatible agents can copy, symlink, or import that folder into their own skill search path. The skill follows the Docker-first vkit workflow in this README.

License

Apache 2.0. See LICENSE.