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README.md

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+---
+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: "cc-by-nc-4.0"
+tags:
+- bark
+- audio
+- 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.
+
+The following is additional information about the models released here. 
+
+## Model Usage
+
+```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.
+"""
+audio_array = generate_audio(text_prompt)
+
+# play text in notebook
+Audio(audio_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
+
+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). 

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