style

app.py

@@ -325,16 +325,15 @@ with gr.Blocks(css=css) as block:
         <p><a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> is a training and inference library for
         high-fidelity text-to-speech (TTS) models. Two models are demonstrated here, <a href="https://huggingface.co/parler-tts/parler_tts_mini_v0.1"> Parler-TTS Mini v0.1</a>, 
         is the first iteration model trained using 10k hours of narrated audiobooks, and <a href="https://huggingface.co/ylacombe/parler-tts-mini-jenny-30H"> Parler-TTS Jenny</a>,
-        a model fine-tuned on the <a href="https://huggingface.co/datasets/reach-vb/jenny_tts_dataset"> Jenny dataset</a>.</p>
-         
-        <p>Both models generates high-quality speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).</p>
+        a model fine-tuned on the <a href="https://huggingface.co/datasets/reach-vb/jenny_tts_dataset"> Jenny dataset</a>.
+        Both models generates high-quality speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).</p>
 
         <p>Tips for ensuring good generation:
         <ul>
             <li>Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise</li>
+            <li>When using the fine-tuned model, include the term "Jenny" to pick out her voice</li>
             <li>Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech</li>
             <li>The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt</li>
-            <li>Include the term "Jenny" when using the fine-tuned Jenny model to pick out her voice</li>
         </ul>
         </p>
         """