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--- |
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library_name: transformers |
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tags: |
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- text-to-speech |
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- annotation |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-to-speech |
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inference: false |
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datasets: |
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- parler-tts/mls_eng_10k |
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- blabble-io/libritts_r |
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- parler-tts/libritts_r_tags_tagged_10k_generated |
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- parler-tts/mls-eng-10k-tags_tagged_10k_generated |
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--- |
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<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'"/> |
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# Parler-TTS Mini v0.1 |
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<a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts_mini"> |
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<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> |
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</a> |
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**Parler-TTS Mini v0.1** is a lightweight text-to-speech (TTS) model, trained on 10.5K hours of audio data, that can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation). |
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It is the first release model from the [Parler-TTS](https://github.com/huggingface/parler-tts) project, which aims to provide the community with TTS training resources and dataset pre-processing code. |
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## Usage |
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Using Parler-TTS is as simple as "bonjour". Simply install the library once: |
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```sh |
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pip install git+https://github.com/huggingface/parler-tts.git |
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``` |
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You can then use the model with the following inference snippet: |
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```py |
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import torch |
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from parler_tts import ParlerTTSForConditionalGeneration |
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from transformers import AutoTokenizer |
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import soundfile as sf |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_mini_v0.1").to(device) |
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tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1") |
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prompt = "Hey, how are you doing today?" |
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description = "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast." |
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input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device) |
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prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) |
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generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids) |
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audio_arr = generation.cpu().numpy().squeeze() |
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sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate) |
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``` |
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**Tips**: |
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* Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise |
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* Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech |
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* The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt |
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## Motivation |
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Parler-TTS is a reproduction of work from the paper [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://www.text-description-to-speech.com) by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively. |
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Contrarily to other TTS models, Parler-TTS is a **fully open-source** release. All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models. |
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Parler-TTS was released alongside: |
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* [The Parler-TTS repository](https://github.com/huggingface/parler-tts) - you can train and fine-tuned your own version of the model. |
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* [The Data-Speech repository](https://github.com/huggingface/dataspeech) - a suite of utility scripts designed to annotate speech datasets. |
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* [The Parler-TTS organization](https://huggingface.co/parler-tts) - where you can find the annotated datasets as well as the future checkpoints. |
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## Citation |
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If you found this repository useful, please consider citing this work and also the original Stability AI paper: |
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``` |
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@misc{lacombe-etal-2024-parler-tts, |
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author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi}, |
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title = {Parler-TTS}, |
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year = {2024}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/huggingface/parler-tts}} |
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} |
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``` |
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``` |
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@misc{lyth2024natural, |
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title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations}, |
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author={Dan Lyth and Simon King}, |
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year={2024}, |
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eprint={2402.01912}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.SD} |
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} |
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``` |
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## License |
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This model is permissively licensed under the Apache 2.0 license. |
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