---
license: openrail
language:
- nl
base_model:
- nari-labs/Dia-1.6B
pipeline_tag: text-to-speech
---
# Parkiet: Dutch Text-to-Speech (TTS)

![Parkiet](images/parkiet.png)

Open-weights Dutch TTS based on the [Parakeet](https://jordandarefsky.com/blog/2024/parakeet/) architecture, ported from [Dia](https://github.com/nari-labs/dia) to JAX for scalable training. A full walkthrough to train the model for your language on Google Cloud TPUs, can be found in the [TRAINING.md](TRAINING.md) doc.

**Parkiet creates highly realistic voices from text**. You can guide the audio output to control emotion and tone. The model also supports nonverbal sounds (currently only laughter), and up to four different speakers per prompt. Voice cloning is also supported. Here are some samples.

| Text | File |
|---|---|
| [S1] denk je dat je een open source model kan trainen met weinig geld en middelen? [S2] ja ik denk het wel. [S1] oh ja, hoe dan? [S2] nou kijk maar in de repo op Git Hub of Hugging Face. | [intro](https://github.com/pevers/parkiet/raw/refs/heads/main/samples/intro.mp3)
| [S1] hoeveel stemmen worden er ondersteund? [S2] nou, uhm, ik denk toch wel meer dan twee. [S3] ja, ja, d dat is het mooie aan dit model. [S4] ja klopt, het ondersteund tot vier verschillende stemmen per prompt. | [multi](https://github.com/pevers/parkiet/raw/refs/heads/main/samples/multi.mp3) 
| [S1] h h et is dus ook mogelijk, om eh ... uhm, heel veel t te st stotteren in een prompt. | [stutter](https://github.com/pevers/parkiet/raw/refs/heads/main/samples/stutter.mp3) |
| [S1] (laughs) luister, ik heb een mop, wat uhm, drinkt een webdesigner het liefst? [S2] nou ... ? [S1] Earl Grey (laughs) . [S2] (laughs) heel goed. | [laughs](https://github.com/pevers/parkiet/raw/refs/heads/main/samples/laughs.mp3)
| [S1] je hebt maar weinig audio nodig om een stem te clonen de rest van deze tekst is uitgesproken door een computer. [S2] wauw, dat klinkt wel erg goed. [S1] ja, ik hoop dat je er wat aan hebt. | [clone](https://github.com/pevers/parkiet/raw/refs/heads/main/samples/voice_out.mp3)


## Generation Guidelines

* Use `[S1]`, `[S2]`, `[S3]`, `[S4]` to indicate the different speakers. Always start with `[S1]` and always alternate between [S1] and [S2] (i.e. [S1]... [S1]... is not good).
* Prefer lower capital text prompts with punctuation. Write out digits as words. Even though the model should be able to handle some variety, it is better to stick close to the output of [WhisperD-NL](https://huggingface.co/pevers/whisperd-nl).
* Slowing down can be encouraged by using `...` in the prompt.
* Stuttering and disfluencies can be encouraged by using `uh`, `uhm`, `mmm`.
* Laughter can be added with the `(laughs)` tag. However, use it sparingly because the model quickly derails for too many events.
* Reduce hallucination by tuning the text prompts. The model can be brittle for unexpected events or tokens. Take a look at the example sentences and mimick the style.

## Quickstart

The JAX model has the best performance in terms of quality, but requires a bit more setup, and is (for the moment) a little bit slower. The model is also ported back to PyTorch. However, I suspect that due to small differences in the attention kernel between PyTorch and JAX, the PyTorch model hallucinates more and generates strange artifacts more than the JAX model.

```bash
uv sync # For CPU
uv sync --extra tpu # For TPU
uv sync --extra cuda # For CUDA

# Download the checkpoint
wget https://huggingface.co/pevers/parkiet/resolve/main/dia-nl-v1.zip?download=true -O weights/dia-nl-v1.zip

# Create the checkpoint folder and unzip
mkdir -p weights
unzip weights/dia-nl-v1.zip -d weights

# Run the inference demo
# NOTE: Inference can take a while because of JAX compilation. Subsequent calls will be cached and much faster. I'm working on some performance improvements.
uv run python src/parkiet/jax/inference.py
```

<details>

<summary>PyTorch</summary>

```bash
uv sync # For CPU
uv sync --extra cuda # For CUDA

wget https://huggingface.co/pevers/parkiet/resolve/main/dia-nl-v1.pth?download=true -O weights/dia-nl-v1.pth
uv run python src/parkiet/dia/inference.py
```

</details>

<details>

<summary>Dia Plug-and-Play Transformers</summary>

NOTE: Tune the `cfg_scale` option and temperature to reduce hallucinations.

```python
from transformers import AutoProcessor, DiaForConditionalGeneration

torch_device = "cuda"
model_checkpoint = "pevers/parkiet"

text = [
    "[S1] denk je dat je een open source model kan trainen met weinig geld en middelen? [S2] ja ik denk het wel. [S1] oh ja, hoe dan? [S2] nou kijk maar in de repo op Git Hub of Hugging Face."
]
processor = AutoProcessor.from_pretrained(model_checkpoint)
inputs = processor(text=text, padding=True, return_tensors="pt").to(torch_device)

model = DiaForConditionalGeneration.from_pretrained(model_checkpoint).to(torch_device)
outputs = model.generate(**inputs, max_new_tokens=3072, guidance_scale=3.0, temperature=1.8, top_p=0.90, top_k=50)

outputs = processor.batch_decode(outputs)
processor.save_audio(outputs, "example.mp3")
```

</details>

## ⚠️ Disclaimer
This project offers a high-fidelity speech generation model intended for research and educational use. The following uses are strictly forbidden:

* Identity Misuse: Do not produce audio resembling real individuals without permission.
* Deceptive Content: Do not use this model to generate misleading content (e.g. fake news).
* Illegal or Malicious Use: Do not use this model for activities that are illegal or intended to cause harm.
By using this model, you agree to uphold relevant legal standards and ethical responsibilities. We are not responsible for any misuse and firmly oppose any unethical usage of this technology.

## Training

For a full guide on data preparation, model conversion and the TPU setup to train this model for any language, see [TRAINING.md](TRAINING.md).

## Acknowledgements

* Darefsky for the excellent blog post on [Parakeet](https://jordandarefsky.com/blog/2024/parakeet/).
* [Dia](https://github.com/nari-labs/) for open-sourcing the PyTorch model.
* [Bas Büller](http://github.com/basbuller) for the endless discussions until it finally worked 🙌.
* [Google Cloud TPU Research Cloud Program](https://sites.research.google/trc/about/) for providing the computation resources.

## License

Repository code is licensed under the [MIT License](LICENSE). The TTS model itself is licensed as [RAIL-M](MODEL_LICENSE).