Metadata-Version: 2.4
Name: voxkitchen
Version: 0.3.0
Summary: A researcher-friendly, declarative speech data processing toolkit
Project-URL: Homepage, https://github.com/XqFeng-Josie/VoxKitchen
Project-URL: Documentation, https://voxkitchen.readthedocs.io
Project-URL: Repository, https://github.com/XqFeng-Josie/VoxKitchen
Project-URL: Issues, https://github.com/XqFeng-Josie/VoxKitchen/issues
Project-URL: Changelog, https://github.com/XqFeng-Josie/VoxKitchen/blob/main/CHANGELOG.md
Author: VoxKitchen contributors
License: Copyright 2026 VoxKitchen contributors
        
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License-File: LICENSE
Keywords: asr,audio,dataset,diarization,pipeline,speaker,speech,tts,vad,voice
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Multimedia :: Sound/Audio
Classifier: Topic :: Scientific/Engineering
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Description-Content-Type: text/markdown

<p align="center">
  <img src="https://raw.githubusercontent.com/XqFeng-Josie/VoxKitchen/main/voxkitchen_logo.svg" width="360" alt="VoxKitchen logo">
</p>

<h1 align="center">VoxKitchen</h1>

<p align="center">
  <strong>Turn raw speech recordings into clean, inspectable training datasets.</strong>
</p>

<p align="center">
  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.
</p>

<p align="center">
  <a href="https://github.com/XqFeng-Josie/VoxKitchen/actions/workflows/ci.yml"><img src="https://github.com/XqFeng-Josie/VoxKitchen/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
  <a href="https://pypi.org/project/voxkitchen/"><img src="https://img.shields.io/pypi/v/voxkitchen.svg" alt="PyPI"></a>
  <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.10%2B-blue" alt="Python"></a>
  <img src="https://img.shields.io/badge/runtime-Docker--first-2496ED" alt="Docker-first">
  <img src="https://img.shields.io/badge/operators-51-brightgreen" alt="51 operators">
  <a href="https://github.com/XqFeng-Josie/VoxKitchen/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License"></a>
</p>

> **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:

```bash
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.

```bash
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 in a built-in voice | see [Speaker TTS tutorial](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/tutorials/tts-speaker.md) | `tts` |
| Clone a voice from a 3–10 s reference | see [Voice Cloning & TTS tutorial](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/tutorials/tts-voice-cloning.md) | `tts` or `fish-speech` |

## How It Works

![VoxKitchen pipeline overview](https://raw.githubusercontent.com/XqFeng-Josie/VoxKitchen/main/pipeline.png)

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

```yaml
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

```bash
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:

```bash
vkit init --list-templates
```

Not sure which image a pipeline needs? Run:

```bash
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:

```bash
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.

```bash
cp .env.example .env
```

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

## Common Commands

```bash
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](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/getting-started.md)
- [Examples & Use Cases](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/examples.md)
- [Pipeline YAML](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/reference/pipeline-yaml.md)
- [Recipes & Download](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/reference/recipes.md)
- [CLI reference](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/reference/cli.md)
- [Operators reference](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/reference/operators.md)
- [Docker build guide](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/docs/docker-build.md)
- [Contributing](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/CONTRIBUTING.md)

## Agent Skill

The repo includes an agent-neutral VoxKitchen skill at [skill/](https://github.com/XqFeng-Josie/VoxKitchen/tree/main/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](https://github.com/XqFeng-Josie/VoxKitchen/blob/main/LICENSE).
