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AudioLM for related training and inference code.EnCodec for a state-of-the-art implementation of a fantastic audio codec.nanoGPT for a dead-simple and blazing fast implementation of GPT-style models.Requests for future language support here or in the #forums channel on Discord. and to bias Bark toward male and female speakers, respectively.Please let us know if you find patterns that work particularly well on Discord! It can therefore generalize to arbitrary instructions beyond speech such as music lyrics, sound effects or other non-speech sounds.īelow is a list of some known non-speech sounds, but we are finding more every day. Different to previous approaches, the input text prompt is converted directly to audio without the intermediate use of phonemes. It is not a conventional TTS model, but instead a fully generative text-to-audio model capable of deviating in unexpected ways from any given script. It follows a GPT style architecture similar to AudioLM and Vall-E and a quantized Audio representation from EnCodec. ⚙️ Detailsīark is fully generative text-to-audio model devolved for research and demo purposes. If you don't have hardware available or if you want to play with bigger versions of our models, you can also sign up for early access to our model playground here. To use a smaller version of the models, which should fit into 8GB VRAM, set the environment flag SUNO_USE_SMALL_MODELS=True. The full version of Bark requires around 12GB of VRAM to hold everything on GPU at the same time. Details can be found in out tutorial sections here. For older GPUs or CPU you might want to consider using smaller models. On older GPUs, default colab, or CPU, inference time might be significantly slower. On enterprise GPUs and PyTorch nightly, Bark can generate audio in roughly real-time. 🛠️ Hardware and Inference Speedīark has been tested and works on both CPU and GPU ( pytorch 2.0+, CUDA 11.7 and CUDA 12.0). 💾 You can now use Bark with GPUs that have low VRAM (<4GB).Ĭd bark & pip install.

💬 Growing community support and access to new features here: We hope this resource helps you find useful prompts for your use cases! You can also join us on Discord, where the community actively shares useful prompts in the #audio-prompts channel. 📕 Long-form generation, voice consistency enhancements and other examples are now documented in a new notebooks section. We also added an option for a smaller version of Bark, which offers additional speed-up with the trade-off of slightly lower quality.

©️ Bark is now licensed under the MIT License, meaning it's now available for commercial use! Use at your own risk, and please act responsibly. Suno does not take responsibility for any output generated. It is not a conventional text-to-speech model but instead a fully generative text-to-audio model, which can deviate in unexpected ways from provided prompts. ⚠ Disclaimerīark was developed for research purposes. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use. The model can also produce nonverbal communications like laughing, sighing and crying. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. Bark is a transformer-based text-to-audio model created by Suno.
