---
library_name: transformers
tags:
- text-to-speech
- annotation
language:
- en
pipeline_tag: text-to-speech
inference: false
datasets:
- ylacombe/jenny-tts-tagged-v1
- reach-vb/jenny_tts_dataset
---



<img src="https://huggingface.co/datasets/parler-tts/images/resolve/main/thumbnail.png" alt="Parler Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>


# Parler-TTS Mini v1 - Jenny

<a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts_mini">
  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
</a>


* **Fine-tuning guide on Colab:**

<a target="_blank" href="https://github.com/ylacombe/scripts_and_notebooks/blob/main/Finetuning_Parler_TTS_v1_on_a_single_speaker_dataset.ipynb"> 
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> 
</a>

Fine-tuned version of **Parler-TTS Mini v1** on the [30-hours single-speaker high-quality Jenny (she's Irish ☘️) dataset](https://github.com/dioco-group/jenny-tts-dataset), suitable for training a TTS model.
Usage is more or less the same as Parler-TTS v1, just specify they keyword “Jenny” in the voice description:

## Usage


```sh
pip install git+https://github.com/huggingface/parler-tts.git
```

You can then use the model with the following inference snippet:

```py
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-mini-v1-jenny").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-mini-v1-jenny")

prompt = "Hey, how are you doing today? My name is Jenny, and I'm here to help you with any questions you have."
description = "Jenny speaks at an average pace with an animated delivery in a very confined sounding environment with clear audio quality."

input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
```

## Citation

If you found this repository useful, please consider citing this work and also the original Stability AI paper:

```
@misc{lacombe-etal-2024-parler-tts,
  author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
  title = {Parler-TTS},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/parler-tts}}
}
```

```
@misc{lyth2024natural,
      title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
      author={Dan Lyth and Simon King},
      year={2024},
      eprint={2402.01912},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}
```

## License

License - Attribution is required in software/websites/projects/interfaces (including voice interfaces) that generate audio in response to user action using this dataset. Atribution means: the voice must be referred to as "Jenny", and where at all practical, "Jenny (Dioco)". Attribution is not required when distributing the generated clips (although welcome). Commercial use is permitted. Don't do unfair things like claim the dataset is your own. No further restrictions apply.