#INSTALL First change the variables in example.venv to the correct paths. The variables are explained below under "env" windows
python -m venv venv_bark
venv_bark/Scripts/activate.bat
git clone https://github.com/Anonym0us33/bark_storymaker
cd bark_storymaker
pip install -r freeze.txtlinux
change venv_bark/Scripts/activate.bat to source venv_bark/bin/activate
NOTE: freeze.txt is simple the output of one working environment. further steps may be required to install the pip requirements. Googleing the "Module not found" error usually gives the answer in the first result. I will make a setup script if there is demand for it.
There is also a tutorial on a popular video website: /watch?v=w41-MUfxIWo
#ENV
NOTE: these havn't been checked. you may have to alter them to fit the variables you are using.
leave as "/" or "" if unsure
BARK_PATH = "path/to/folder"
path for results
OUTPUT_PATH = "/audio"
path to activate.bat
VENV_PATH = "/venv_bark/Scripts/activate.bat"
path to activate on linux. TODO: implement
VENV_PATH_LINUX = "/venv_bark/bin/activate"
how ofter files are saved when using chunks.
Different to chunk size, set at runtime.
CHUNK_SAVE_FREQUENCY = 10
how ofter files are saved when using sentences.
Sentence mode uses a token counter to cut audio at the end of a sentence. Not reccomended on GPUs with <16GB VRAM. That means you need a RTX 3090, 4080 Ti or better to guarentee this will work.
SENTENCE_SAVE_FREQUENCY = 10
Not implemented. Extra value if you want to edit the code and test it.
TEST_SAVE_FREQUENCY = 1
Defaults to first GPU. Not tested but can delete from code or edit value to 0,1,2,3 etc if you have 2 or more GPUs. Older GPUs had cores counted individually so if you get this working with CUDA 11.4 or lower this may be useful.
CUDA_VISIBLE_DEVICES = 0
Full list of speakers can be displayed using commands from original github below. This is my selection of the best ones. You can edit it to any valid value.
BEST_SPEAKERS = en_speaker_9,it_speaker_9,ja_speaker_0,en_speaker_6,de_speaker_3
The file to be read when selecting option 2.
TEXT_FILE = input.txt
Strings for testing and default string.
TEST_TEXT = "one"
TEST_TEXT2 = "1"
TEST_TEXT3 = "10 ten."
Test string from original repo. env var unused.
POTATOS = ะะฐััะพัะตะปั,potato,potato
#adapted from official github https://github.com/suno-ai/bark
Examples โข Suno Studio Waitlist โข Updates โข How to Use โข Installation โข FAQ
Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use.
Bark was developed for research purposes. 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. Suno does not take responsibility for any output generated. Use at your own risk, and please act responsibly.
2023.05.01
-
ยฉ๏ธ Bark is now licensed under the MIT License, meaning it's now available for commercial use!
-
โก 2x speed-up on GPU. 10x speed-up on CPU. We also added an option for a smaller version of Bark, which offers additional speed-up with the trade-off of slightly lower quality.
-
๐ Long-form generation, voice consistency enhancements and other examples are now documented in a new notebooks section.
-
๐ฅ We created a voice prompt library. 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.
-
๐ฌ Growing community support and access to new features here:
-
๐พ You can now use Bark with GPUs that have low VRAM (<4GB).
2023.04.20
- ๐ถ Bark release!
from bark import SAMPLE_RATE, generate_audio, preload_models
from scipy.io.wavfile import write as write_wav
from IPython.display import Audio
# download and load all models
preload_models()
# generate audio from text
text_prompt = """
Hello, my name is Suno. And, uh โ and I like pizza. [laughs]
But I also have other interests such as playing tic tac toe.
"""
audio_array = generate_audio(text_prompt)
# save audio to disk
write_wav("bark_generation.wav", SAMPLE_RATE, audio_array)
# play text in notebook
Audio(audio_array, rate=SAMPLE_RATE)pizza.webm
Bark supports various languages out-of-the-box and automatically determines language from input text. When prompted with code-switched text, Bark will attempt to employ the native accent for the respective languages. English quality is best for the time being, and we expect other languages to further improve with scaling.
text_prompt = """
์ถ์์ ๋ด๊ฐ ๊ฐ์ฅ ์ข์ํ๋ ๋ช
์ ์ด๋ค. ๋๋ ๋ฉฐ์น ๋์ ํด์์ ์ทจํ๊ณ ์น๊ตฌ ๋ฐ ๊ฐ์กฑ๊ณผ ์๊ฐ์ ๋ณด๋ผ ์ ์์ต๋๋ค.
"""
audio_array = generate_audio(text_prompt)suno_korean.webm
Note: since Bark recognizes languages automatically from input text, it is possible to use for example a german history prompt with english text. This usually leads to english audio with a german accent.
Bark can generate all types of audio, and, in principle, doesn't see a difference between speech and music. Sometimes Bark chooses to generate text as music, but you can help it out by adding music notes around your lyrics.
text_prompt = """
โช In the jungle, the mighty jungle, the lion barks tonight โช
"""
audio_array = generate_audio(text_prompt)lion.webm
Bark supports 100+ speaker presets across supported languages. You can browse the library of speaker presets here, or in the code. The community also often shares presets in Discord.
Bark tries to match the tone, pitch, emotion and prosody of a given preset, but does not currently support custom voice cloning. The model also attempts to preserve music, ambient noise, etc.
text_prompt = """
I have a silky smooth voice, and today I will tell you about
the exercise regimen of the common sloth.
"""
audio_array = generate_audio(text_prompt, history_prompt="v2/en_speaker_1")sloth.webm
By default, generate_audio works well with around 13 seconds of spoken text. For an example of how to do long-form generation, see this example notebook.
Click to toggle example long-form generations (from the example notebook)
dialog.webm
longform_advanced.webm
longform_basic.webm
pip install git+https://github.com/suno-ai/bark.git
or
git clone https://github.com/suno-ai/bark
cd bark && pip install .
Note: Do NOT use 'pip install bark'. It installs a different package, which is not managed by Suno.
Bark has been tested and works on both CPU and GPU (pytorch 2.0+, CUDA 11.7 and CUDA 12.0).
On enterprise GPUs and PyTorch nightly, Bark can generate audio in roughly real-time. On older GPUs, default colab, or CPU, inference time might be significantly slower. For older GPUs or CPU you might want to consider using smaller models. Details can be found in out tutorial sections here.
The full version of Bark requires around 12GB of VRAM to hold everything on GPU at the same time.
To use a smaller version of the models, which should fit into 8GB VRAM, set the environment flag SUNO_USE_SMALL_MODELS=True.
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.
Bark is fully generative tex-to-audio model devolved for research and demo purposes. It follows a GPT style architecture similar to AudioLM and Vall-E and a quantized Audio representation from EnCodec. 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. Different to previous approaches, the input text prompt is converted directly to audio without the intermediate use of phonemes. It can therefore generalize to arbitrary instructions beyond speech such as music lyrics, sound effects or other non-speech sounds.
Below is a list of some known non-speech sounds, but we are finding more every day. Please let us know if you find patterns that work particularly well on Discord!
[laughter][laughs][sighs][music][gasps][clears throat]โor...for hesitationsโชfor song lyrics- CAPITALIZATION for emphasis of a word
[MAN]and[WOMAN]to bias Bark toward male and female speakers, respectively
| Language | Status |
|---|---|
| English (en) | โ |
| German (de) | โ |
| Spanish (es) | โ |
| French (fr) | โ |
| Hindi (hi) | โ |
| Italian (it) | โ |
| Japanese (ja) | โ |
| Korean (ko) | โ |
| Polish (pl) | โ |
| Portuguese (pt) | โ |
| Russian (ru) | โ |
| Turkish (tr) | โ |
| Chinese, simplified (zh) | โ |
Requests for future language support here or in the #forums channel on Discord.
- nanoGPT for a dead-simple and blazing fast implementation of GPT-style models
- EnCodec for a state-of-the-art implementation of a fantastic audio codec
- AudioLM for related training and inference code
- Vall-E, AudioLM and many other ground-breaking papers that enabled the development of Bark
Bark is licensed under the MIT License.
Please contact us at bark@suno.ai to request access to a larger version of the model.
Weโre developing a playground for our models, including Bark.
If you are interested, you can sign up for early access here.
- Bark uses Hugging Face to download and store models. You can see find more info here.
- Bark is a GPT-style model. As such, it may take some creative liberties in its generations, resulting in higher-variance model outputs than traditional text-to-speech approaches.
- Bark supports 100+ speaker presets across supported languages. You can browse the library of speaker presets here. The community also shares presets in Discord. Bark also supports generating unique random voices that fit the input text. Bark does not currently support custom voice cloning.
- The full version of Bark requires around 12Gb of memory to hold everything on GPU at the same time. However, even smaller cards down to ~2Gb work with some additional settings. Simply add the following code snippet before your generation:
import os
os.environ["SUNO_OFFLOAD_CPU"] = True
os.environ["SUNO_USE_SMALL_MODELS"] = True