---
language:
- en
- de
- es
- fr
- hi
- it
- ja
- ko
- pl
- pt
- ru
- tr
- zh
thumbnail: >-
https://user-images.githubusercontent.com/5068315/230698495-cbb1ced9-c911-4c9a-941d-a1a4a1286ac6.png
library: bark
license: mit
tags:
- bark
- audio
- text-to-speech
duplicated_from: ylacombe/bark-small
pipeline_tag: text-to-speech
---
# Bark
Bark is a transformer-based text-to-audio model created by [Suno](https://www.suno.ai).
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 ready for inference.
The original github repo and model card can be found [here](https://github.com/suno-ai/bark).
This model is meant for research purposes only.
The model output is not censored and the authors do not endorse the opinions in the generated content.
Use at your own risk.
Two checkpoints are released:
- [**small** (this checkpoint)](https://huggingface.co./suno/bark-small)
- [large](https://huggingface.co./suno/bark)
## Example
Try out Bark yourself!
* Bark Colab:
* Hugging Face Colab:
* Hugging Face Demo:
## 🤗 Transformers Usage
You can run Bark locally with the 🤗 Transformers library from version 4.31.0 onwards.
1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy:
```
pip install --upgrade pip
pip install --upgrade transformers scipy
```
2. Run inference via the `Text-to-Speech` (TTS) pipeline. You can infer the bark model via the TTS pipeline in just a few lines of code!
```python
from transformers import pipeline
import scipy
synthesiser = pipeline("text-to-speech", "suno/bark-small")
speech = synthesiser("Hello, my dog is cooler than you!", forward_params={"do_sample": True})
scipy.io.wavfile.write("bark_out.wav", rate=speech["sampling_rate"], data=speech["audio"])
```
3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 24 kHz speech waveform for more fine-grained control.
```python
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("suno/bark-small")
model = AutoModel.from_pretrained("suno/bark-small")
inputs = processor(
text=["Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe."],
return_tensors="pt",
)
speech_values = model.generate(**inputs, do_sample=True)
```
4. Listen to the speech samples either in an ipynb notebook:
```python
from IPython.display import Audio
sampling_rate = model.generation_config.sample_rate
Audio(speech_values.cpu().numpy().squeeze(), rate=sampling_rate)
```
Or save them as a `.wav` file using a third-party library, e.g. `scipy`:
```python
import scipy
sampling_rate = model.config.sample_rate
scipy.io.wavfile.write("bark_out.wav", rate=sampling_rate, data=speech_values.cpu().numpy().squeeze())
```
For more details on using the Bark model for inference using the 🤗 Transformers library, refer to the [Bark docs](https://huggingface.co./docs/transformers/model_doc/bark).
### Optimization tips
Refers to this [blog post](https://huggingface.co./blog/optimizing-bark#benchmark-results) to find out more about the following methods and a benchmark of their benefits.
#### Get significant speed-ups:
**Using 🤗 Better Transformer**
Better Transformer is an 🤗 Optimum feature that performs kernel fusion under the hood. You can gain 20% to 30% in speed with zero performance degradation. It only requires one line of code to export the model to 🤗 Better Transformer:
```python
model = model.to_bettertransformer()
```
Note that 🤗 Optimum must be installed before using this feature. [Here's how to install it.](https://huggingface.co./docs/optimum/installation)
**Using Flash Attention 2**
Flash Attention 2 is an even faster, optimized version of the previous optimization.
```python
model = BarkModel.from_pretrained("suno/bark-small", torch_dtype=torch.float16, use_flash_attention_2=True).to(device)
```
Make sure to load your model in half-precision (e.g. `torch.float16``) and to [install](https://github.com/Dao-AILab/flash-attention#installation-and-features) the latest version of Flash Attention 2.
**Note:** Flash Attention 2 is only available on newer GPUs, refer to 🤗 Better Transformer in case your GPU don't support it.
#### Reduce memory footprint:
**Using half-precision**
You can speed up inference and reduce memory footprint by 50% simply by loading the model in half-precision (e.g. `torch.float16``).
**Using CPU offload**
Bark is made up of 4 sub-models, which are called up sequentially during audio generation. In other words, while one sub-model is in use, the other sub-models are idle.
If you're using a CUDA device, a simple solution to benefit from an 80% reduction in memory footprint is to offload the GPU's submodels when they're idle. This operation is called CPU offloading. You can use it with one line of code.
```python
model.enable_cpu_offload()
```
Note that 🤗 Accelerate must be installed before using this feature. [Here's how to install it.](https://huggingface.co./docs/accelerate/basic_tutorials/install)
## Suno Usage
You can also run Bark locally through the original [Bark library]((https://github.com/suno-ai/bark):
1. First install the [`bark` library](https://github.com/suno-ai/bark)
3. Run the following Python code:
```python
from bark import SAMPLE_RATE, generate_audio, preload_models
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 — and I like pizza. [laughs]
But I also have other interests such as playing tic tac toe.
"""
speech_array = generate_audio(text_prompt)
# play text in notebook
Audio(speech_array, rate=SAMPLE_RATE)
```
[pizza.webm](https://user-images.githubusercontent.com/5068315/230490503-417e688d-5115-4eee-9550-b46a2b465ee3.webm)
To save `audio_array` as a WAV file:
```python
from scipy.io.wavfile import write as write_wav
write_wav("/path/to/audio.wav", SAMPLE_RATE, audio_array)
```
## Model Details
The following is additional information about the models released here.
Bark is a series of three transformer models that turn text into audio.
### Text to semantic tokens
- Input: text, tokenized with [BERT tokenizer from Hugging Face](https://huggingface.co./docs/transformers/model_doc/bert#transformers.BertTokenizer)
- Output: semantic tokens that encode the audio to be generated
### Semantic to coarse tokens
- Input: semantic tokens
- Output: tokens from the first two codebooks of the [EnCodec Codec](https://github.com/facebookresearch/encodec) from facebook
### Coarse to fine tokens
- Input: the first two codebooks from EnCodec
- Output: 8 codebooks from EnCodec
### Architecture
| Model | Parameters | Attention | Output Vocab size |
|:-------------------------:|:----------:|------------|:-----------------:|
| Text to semantic tokens | 80/300 M | Causal | 10,000 |
| Semantic to coarse tokens | 80/300 M | Causal | 2x 1,024 |
| Coarse to fine tokens | 80/300 M | Non-causal | 6x 1,024 |
### Release date
April 2023
## Broader Implications
We anticipate that this model's text to audio capabilities can be used to improve accessbility tools in a variety of languages.
While we hope that this release will enable users to express their creativity and build applications that are a force
for good, we acknowledge that any text to audio model has the potential for dual use. While it is not straightforward
to voice clone known people with Bark, it can still be used for nefarious purposes. To further reduce the chances of unintended use of Bark,
we also release a simple classifier to detect Bark-generated audio with high accuracy (see notebooks section of the main repository).
## License
Bark is licensed under the [MIT License](https://github.com/suno-ai/bark/blob/main/LICENSE), meaning it's available for commercial use.