Create README.md (#12)

- Create README.md (803565c0a51ec4b9aa70d16dbfbcbb68e8234818)

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.
+
+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:
+
+<a target="_blank" href="https://colab.research.google.com/drive/1eJfA2XUa-mXwdMy7DoYKVYHI1iTd9Vkt?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+* Hugging Face Colab:
+
+<a target="_blank" href="https://colab.research.google.com/drive/1dWWkZzvu7L9Bunq9zvD-W02RFUXoW-Pd?usp=sharing"> 
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> 
+</a>
+
+* Hugging Face Demo:
+
+<a target="_blank" href="https://huggingface.co/spaces/suno/bark">
+  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
+</a>
+
+
+## 🤗 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) from main:
+
+```
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+2. Run the following Python code to generate speech samples:
+
+```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_speech(**inputs, do_sample=True)
+```
+
+3. 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).
+
+## 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).