| --- |
| library_name: transformers |
| tags: |
| - text-to-speech |
| - annotation |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-to-speech |
| inference: false |
| datasets: |
| - parler-tts/mls_eng |
| - parler-tts/libritts_r_filtered |
| - parler-tts/libritts-r-filtered-speaker-descriptions |
| - parler-tts/mls-eng-speaker-descriptions |
| --- |
| |
| <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 |
|
|
| <a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts"> |
| <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> |
| </a> |
|
|
| **Parler-TTS Mini v1** is a lightweight text-to-speech (TTS) model, trained on 45K 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). |
|
|
| With [Parler-TTS Large v1](https://huggingface.co/parler-tts/parler-tts-large-v1), this is the second set of models published as part of 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. |
|
|
| ## 📖 Quick Index |
| * [👨💻 Installation](#👨💻-installation) |
| * [🎲 Using a random voice](#🎲-random-voice) |
| * [🎯 Using a specific speaker](#🎯-using-a-specific-speaker) |
| * [Motivation](#motivation) |
| * [Optimizing inference](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md) |
|
|
| ## 🛠️ Usage |
|
|
| ### 👨💻 Installation |
|
|
| Using Parler-TTS is as simple as "bonjour". Simply install the library once: |
|
|
| ```sh |
| pip install git+https://github.com/huggingface/parler-tts.git |
| ``` |
|
|
| ### 🎲 Random voice |
|
|
|
|
| **Parler-TTS** has been trained to generate speech with features that can be controlled with a simple text prompt, for example: |
|
|
| ```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-tts-mini-v1").to(device) |
| tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1") |
| |
| prompt = "Hey, how are you doing today?" |
| description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up." |
| |
| 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) |
| ``` |
|
|
| ### 🎯 Using a specific speaker |
|
|
| To ensure speaker consistency across generations, this checkpoint was also trained on 34 speakers, characterized by name (e.g. Jon, Lea, Gary, Jenna, Mike, Laura). |
|
|
| To take advantage of this, simply adapt your text description to specify which speaker to use: `Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise.` |
|
|
| ```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-tts-mini-v1").to(device) |
| tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1") |
| |
| prompt = "Hey, how are you doing today?" |
| description = "Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise." |
| |
| 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) |
| ``` |
|
|
| **Tips**: |
| * We've set up an [inference guide](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md) to make generation faster. Think SDPA, torch.compile, batching and streaming! |
| * Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise |
| * Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech |
| * The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt |
|
|
| ## Motivation |
|
|
| 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. |
|
|
| 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. |
| Parler-TTS was released alongside: |
| * [The Parler-TTS repository](https://github.com/huggingface/parler-tts) - you can train and fine-tuned your own version of the model. |
| * [The Data-Speech repository](https://github.com/huggingface/dataspeech) - a suite of utility scripts designed to annotate speech datasets. |
| * [The Parler-TTS organization](https://huggingface.co/parler-tts) - where you can find the annotated datasets as well as the future checkpoints. |
|
|
| ## 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 |
|
|
| This model is permissively licensed under the Apache 2.0 license. |
