| | --- |
| | 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. |
