Update README.md

README.md

@@ -11,25 +11,21 @@ pipeline_tag: text-to-speech
 
 # Parler-TTS v0.1
 
-[[Paper we reproduce]](https://arxiv.org/abs/2402.01912)
-[[Models]](https://huggingface.co/parler-tts)
-[[Training Code]](training)
-[[Interactive Demo]](https://huggingface.co/spaces/parler-tts/parler_tts_mini)
 
-> [!IMPORTANT]
-> We're proud to release Parler-TTS v0.1, our first 300M-parameters Parler-TTS model, trained on 10.5K hours of audio data.
-
-Parler-TTS is a reproduction of the text-to-speech (TTS) model from the paper [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://www.text-description-to-speech.com)
-by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively. 
-
-Contrarily to standard TTS models, Parler-TTS allows you to directly describe the speaker characteristics with a simple text description where you can modulate gender, pitch, speaking style, accent, etc.
+Parler-TTS v0.1 is a lightweight text-to-speech (TTS) model, trained on 10.5K hours of audio data, that can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation)
 
 ## Usage
 
-> [!TIP]
+> [!IMPORTANT]
+> **NOTE:**
 > You can directly try it out in an interactive demo [here](https://huggingface.co/spaces/parler-tts/parler_tts_mini)!
 
-Using Parler-TTS is as simple as "bonjour". Simply use the following inference snippet.
+Using Parler-TTS is as simple as "bonjour". Simply install the library once:
+```sh
+pip install git+https://github.com/huggingface/parler-tts.git
+```
+
+ You can then use the model with the following inference snippet:
 
 ```py
 from parler_tts import ParlerTTSForConditionalGeneration
@@ -50,10 +46,23 @@ audio_arr = generation.cpu().numpy().squeeze()
 sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
 ```
 
+> [!TIP] Tips for ensuring good generation:
+> * Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise
+> * Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech
+> * The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt
 
-## Installation steps
+## Motivation
+
+Parler-TTS is a reproduction of work from the paper [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://www.text-description-to-speech.com) by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively. 
+
+Contrarily to other TTS models, Parler-TTS is a **fully open-source** release. All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models.
+Parler-TTS was released alongside:
+* [The Parler-TTS repository](https://github.com/huggingface/parler-tts) - you can train and fine-tuned your own version of the model.
+* [The Data-Speech repository](https://github.com/huggingface/dataspeech) - a suite of utility scripts designed to annotate speech datasets.
+* [The Parler-TTS organization](https://huggingface.co/parler-tts) - where you can find the annotated datasets as well as the future checkpoints.
+
+
+## License
+
+This model is permissively licensed under the Apache 2.0 license.
 
-Parler-TTS has light-weight dependencies and can be installed in one line:
-```sh
-pip install parler-tts
-```