pip install ownvoice-cli

pip install ownvoice-cli

npx ownvoice-cli check

$ ownvoice check
[ownvoice check] PASS: PEFT LoRA injection succeeded against pocket-tts's flow_lm module (target_modules="all-linear").

$ ownvoice check
[ownvoice check] FAIL: PEFT LoRA injection failed against pocket-tts's flow_lm module structure: . Please post an honest blocker (this error plus the module tree above) as a comment on https://github.com/kyutai-labs/pocket-tts/issues/30 rather than working around it silently, that issue is exactly where this gap needs to be visible.

Module tree (for debugging / for the issue #30 blocker post):
: FlowLMModel
input_linear: Linear
transformer: StreamingTransformer
transformer.layers.0.self_attn.in_proj: Linear
transformer.layers.0.self_attn.out_proj: Linear
...

$ ownvoice train --voice-clips ./my-voice-clips
[ownvoice train] USABLE ADAPTER
Usable adapter (similarity 0.812 >= 0.75). Try it now:
  ownvoice infer --adapter ownvoice-adapter/adapter.safetensors --text "This is my own voice, trained with OwnVoice."

$ ownvoice train --voice-clips ./my-voice-clips
[ownvoice train] BELOW THRESHOLD
Below threshold (similarity 0.612 =1` | `10` | Number of training epochs. |
| `--lora-rank` | int, `>=1` | `8` | LoRA rank. |
| `--lora-alpha` | int, `>=1` | `16` | LoRA alpha. |
| `--lora-dropout` | float, `0.0`–`1.0` | `0.05` | LoRA dropout. |
| `--learning-rate` | float | `0.0001` | Optimizer learning rate. |
| `--eval-text` | string | `"This is my own voice, trained with OwnVoice."` | Sentence synthesized after training to score against the reference voice. |
| `--json` | flag | off | Print machine-readable JSON instead of human-readable text. |
| `--help` | flag | – | Show the help message and exit. |

### `ownvoice infer`

Generate speech in the trained voice from a saved adapter, and save it to a `.wav` file.

| Flag | Type | Default | Description |
|---|---|---|---|
| `--adapter` | path, required | – | Path to a trained `adapter.safetensors` file. |
| `--text` | string, required | – | Text to synthesize in the trained voice. |
| `--out` | path | `ownvoice-output.wav` | Output `.wav` file path. |
| `--reference-audio` | path | recorded reference | Override the reference clip OwnVoice recorded in `metadata.json` at train time. |
| `--json` | flag | off | Print machine-readable JSON instead of human-readable text. |
| `--help` | flag | – | Show the help message and exit. |

## Features

- **A free compatibility check before you spend anything on a GPU.** `ownvoice check` loads pocket-tts and dry-runs PEFT's LoRA injection against its real `flow_lm` module tree, CPU only, no training. On failure it prints the actual module tree instead of a stack trace, so a real blocker is reportable instead of silent.
- **An objective usable/not-usable signal, not a guess.** Every training run resamples the generated test utterance to 16kHz mono and scores it against your reference clips with [Resemblyzer](https://github.com/resemble-ai/Resemblyzer) cosine similarity. `0.75` or higher is labeled `USABLE ADAPTER`; anything lower is `BELOW THRESHOLD`, a labeled outcome and not a crash, exit code `0` either way.
- **Structured output on every subcommand.** `check`, `train`, and `infer` all accept `--json`, returning one machine-parseable object instead of colored terminal text: confirmed directly, `ownvoice check --json` returns `{"success": true, "message": "...", "module_tree": null}`.
- **Two files you keep, no server round-trip.** A finished training run writes `adapter.safetensors` (the trained weights, a few megabytes at the default `--lora-rank 8`) and `metadata.json` (the full training config, similarity score, per-epoch loss, and a timestamp) to disk. Load them back any time later with `ownvoice infer`, no network call required.
- **One base model, on purpose.** OwnVoice wraps pocket-tts only. There is no abstraction layer for a second base model, matching the codebase's own single-target-by-design architecture note: the LoRA injection path (`target_modules="all-linear"` against pocket-tts's real `flow_lm` layers) stays exact instead of generic.

## How It Works

`ownvoice/data.py` loads and validates the voice-clip directory. `ownvoice/train.py` loads pocket-tts's frozen base model, injects a LoRA adapter into its `flow_lm` transformer with PEFT (`target_modules="all-linear"`), runs the training loop, and saves the adapter plus a manifest. `ownvoice/infer.py` loads a saved adapter back onto the base model and generates speech. `ownvoice/score.py` resamples audio to 16kHz mono with `torchaudio.transforms.Resample` and scores speaker similarity with Resemblyzer.

OwnVoice is intentionally single-model: it wraps pocket-tts only, with no abstraction layer for a second base model, since none is in scope.

## Setup-Time Benchmark vs Comparable Tools

| Tool | Time to first working setup | Notable design choice | Source |
|---|---|---|---|
| [kokoro-tts](https://github.com/nazdridoy/kokoro-tts) | under 2 minutes | `pip install git+...`, instant CLI synthesis, no fine-tuning | kokoro-tts README |
| [Unsloth](https://unsloth.ai) | under 1 minute to start a run | one-command training start (`uv pip install`) | Unsloth docs |
| [pocket-tts](https://github.com/kyutai-labs/pocket-tts) | seconds | `--voice ` zero-shot cloning, no training available | pocket-tts README |
| **OwnVoice** | under 2 minutes to a confirmed-working training environment | `ownvoice check`: free, instant, CPU-only PEFT-compatibility validation before spending anything on a GPU | this repo |

OwnVoice's own training run is real GPU time, honestly labeled and not hidden behind a fake progress bar, the same category norm Unsloth uses. What OwnVoice compresses to under two minutes is everything *before* that: confirming your environment actually works.

## Why OwnVoice Exists

pocket-tts is a genuinely good, MIT-licensed, CPU-capable local text-to-speech model from Kyutai. Its own maintainers have been clear that fine-tuning code isn't coming any time soon: on [issue #30](https://github.com/kyutai-labs/pocket-tts/issues/30), maintainer @vvolhejn wrote "We are not planning to release fine-tuning code for our TTS and STT models in the near future," and 18 people reacted to that thread asking for exactly this. OwnVoice is a small, standalone CLI that fills that specific gap: point it at a handful of your own voice recordings, and it trains a LoRA adapter you keep and run yourself.

It is not a hosted service, it has no billing, and it does not track usage. It is a training script, an inference script, and a scoring script, wired together behind three CLI commands.

## What OwnVoice Is Not

pocket-tts already ships zero-shot voice cloning out of the box: pass a `.wav` file to `--voice` (or call `get_state_for_audio_prompt()` from Python) and it clones that voice with no training step at all. If that is all you need, use pocket-tts directly, it is simpler and faster.

OwnVoice exists for a narrower case: baking a voice permanently into trained weights, so generation no longer depends on distributing or re-processing a reference audio clip at runtime, with (based on the training objective, not yet independently benchmarked at scale) more consistent output across many generations than a single-clip zero-shot embedding tends to produce. That is the specific gap the 18 reactors on issue #30 were describing, and it is the only thing OwnVoice adds on top of what pocket-tts already does well.

## Consent and Misuse

This tool clones a voice from audio you have the right to use. Do not clone someone else's voice, or a public figure's voice, without their explicit consent. OwnVoice ships no bulk-generation or auto-scaling feature in this version, keeping the blast radius of any single misuse case small.

## Implementation Status

This is a young, early-stage release. `ownvoice check`, the CLI argument parsing, voice-clip validation, the similarity scoring math, and the adapter/manifest save and load path are implemented and covered by the test suite (`pytest`). LoRA injection was verified structurally against pocket-tts's real source and then confirmed for real: `ownvoice check` was run against pocket-tts's actual downloaded weights, on CPU, and PEFT's `target_modules="all-linear"` injection genuinely succeeded. The full training and generation path has since been verified end to end for real too: a real 2-epoch LoRA training run against loaded pocket-tts weights produced a finite, non-NaN flow-matching loss, and the resulting adapter produced a real, non-silent generated `.wav` file via `ownvoice infer`. That validation surfaced two real gaps in the naive approach and fixed them: (1) pocket-tts's published, inference-only PyPI package does not actually expose a way to compute the training loss through `FlowLMModel.forward()` despite its own docstring claiming otherwise, so OwnVoice computes the flow-matching loss directly from `flow_lm`'s real submodules instead; (2) swapping `base_model.flow_lm` to the PEFT-wrapped model before calling `generate_audio()` breaks pocket-tts's internal KV-cache state lookup -- no swap is needed at all, since PEFT's LoRA injection already mutates `base_model.flow_lm` in place. One real, external limitation to know about: the publicly downloadable pocket-tts weights (`kyutai/pocket-tts-without-voice-cloning`) refuse a raw reference-clip path/URL outright; OwnVoice works around this by pre-loading and resampling the clip itself, but voice-cloning fidelity from that checkpoint is a known limitation of the base model, not an OwnVoice bug -- for kyutai's best-quality cloning weights, request gated access at [huggingface.co/kyutai/pocket-tts](https://huggingface.co/kyutai/pocket-tts). Run `ownvoice check` yourself and read the source before trusting any of it further, that is the right amount of skepticism for a project this early.

## FAQ

**What is OwnVoice, and why not just use pocket-tts by itself?**
OwnVoice trains a LoRA adapter for [pocket-tts](https://github.com/kyutai-labs/pocket-tts) and saves it to your own disk as `adapter.safetensors` plus `metadata.json`. It exists because pocket-tts's own maintainers have said fine-tuning code is not on their near-term roadmap (see [issue #30](https://github.com/kyutai-labs/pocket-tts/issues/30)). Once you have a trained adapter, you never need OwnVoice again to use it: `ownvoice infer` just loads the adapter back onto the base model.

**How is this different from pocket-tts's own built-in `--voice ` zero-shot cloning?**
pocket-tts already clones a voice from a single reference clip with no training step, `--voice ` at the CLI or `get_state_for_audio_prompt()` in Python. OwnVoice trades that speed for a permanently trained adapter, so generation no longer depends on carrying around a reference clip at runtime, with (based on the training objective, not yet independently benchmarked at scale) more consistent output across repeated generations than a single-clip zero-shot embedding tends to give. If zero-shot is enough for your use case, use pocket-tts directly, it is simpler and faster.

**What do I need to install it, and does it run on Apple Silicon or a CPU-only machine?**
Python 3.11 or newer, then `pip install ownvoice-cli`. `ownvoice check` and `ownvoice infer` need no GPU at all and run fine on Apple Silicon or a CPU-only machine, matching pocket-tts's own CPU-capable design. `ownvoice train` runs on CPU too, it just takes longer per epoch; install the CUDA build of PyTorch first if you have an NVIDIA GPU and want training to go faster.

**How does OwnVoice compare to kokoro-tts and Unsloth?**
[kokoro-tts](https://github.com/nazdridoy/kokoro-tts) gets you synthesizing speech in under 2 minutes but has no fine-tuning step at all. [Unsloth](https://unsloth.ai) gets a training run started in under a minute but is a general LLM fine-tuning framework, not TTS-specific. OwnVoice is narrower than either: one base model (pocket-tts only), one job (a voice adapter), plus a free `ownvoice check` step that confirms PEFT's LoRA injection actually works against your environment before you spend anything on a GPU, a check neither of those tools has an equivalent of.

**My training run finished but printed "BELOW THRESHOLD", is that a bug?**
No. It is a labeled outcome, not a crash, `ownvoice train` exits `0` either way. Below the 0.75 cosine-similarity bar, the adapter is still saved to disk and OwnVoice tells you plainly to try more or cleaner voice clips, more epochs, or a higher `--lora-rank`, then re-run. Only two things actually fail the command with a non-zero exit: no usable clips to load, or a caught PEFT-injection failure.

**Can I use OwnVoice, and the adapters it produces, commercially?**
OwnVoice's own code is MIT (see [LICENSE](https://github.com/RudrenduPaul/ownvoice/blob/main/LICENSE)). pocket-tts's code package is MIT too, but the model weights OwnVoice actually downloads and trains against, [`kyutai/pocket-tts-without-voice-cloning`](https://huggingface.co/kyutai/pocket-tts-without-voice-cloning) and the gated [`kyutai/pocket-tts`](https://huggingface.co/kyutai/pocket-tts), are licensed CC-BY-4.0, not MIT. CC-BY-4.0 permits commercial use but requires attribution to Kyutai. Since any adapter you train is derived from those weights, check that attribution requirement before shipping a commercial product built on it.

**Whose voice can I actually clone with this?**
Only your own, or someone else's with their explicit, checked consent, never a public figure's voice without it. See [Consent and Misuse](#consent-and-misuse) above. OwnVoice ships no bulk-generation or auto-scaling feature in this version, which keeps the blast radius of any single misuse case small.

## MCP Server

OwnVoice ships a [Model Context Protocol](https://modelcontextprotocol.io) server, so an MCP-compatible agent can drive `ownvoice check` / `train` / `infer` directly over stdio instead of shelling out and parsing text itself.

Add it to an MCP client's config (for example, Claude Desktop's `claude_desktop_config.json`):

The server exposes a single tool, `run(args: list[str]) -> dict`, that shells out to the real `ownvoice` CLI with the given argv and returns its result as structured JSON, so a caller gets the exact same behavior the human-facing CLI has, including `--json` mode. Example call: `run(args=["check", "--json"])` returns `{"result": {"success": true, "message": "...", "module_tree": null}}`. A non-zero exit, a launch failure, or a subprocess timeout is always returned as `{"error": "..."}` rather than raised.

## Contributing

Issues and PRs welcome, MIT licensed throughout. If you want to help close the actual gap this project targets, the most useful contribution is upstream: a lightweight LoRA-adapter training script contributed back to [kyutai-labs/pocket-tts](https://github.com/kyutai-labs/pocket-tts) itself, discussed on [issue #30](https://github.com/kyutai-labs/pocket-tts/issues/30).

## License

MIT. See [LICENSE](https://github.com/RudrenduPaul/ownvoice/blob/main/LICENSE).