init

app.py

@@ -1,39 +1,48 @@
+import os
+
 import gradio as gr
 import nltk
-import os
-os.system('python -m unidic download')
-from melo.api import TTS
 
-nltk.download('averaged_perceptron_tagger_eng')
+os.system("python -m unidic download")
+from melo.api import TTS  # noqa: E402
+
+nltk.download("averaged_perceptron_tagger_eng")
 
 # Get device
-device = 'auto'
-model = TTS(language='EN', device=device)
+device = "auto"
+model = TTS(language="EN", device=device)
 speaker_ids = model.hps.data.spk2id
 
 
-def inference(text: str, speed: float, speaker: str):
-    output_path = 'audio.wav'
+def inference(
+    text: str, speed: float, speaker: str, progress=gr.Progress(track_tqdm=True)
+):
+    output_path = "audio.wav"
     model.tts_to_file(text, speaker_ids[speaker], output_path, speed=speed)
     return output_path
 
 
 if __name__ == "__main__":
     demo = gr.Interface(
-        title='Text-to-Speech',
-        description='Convert English text to speech',
+        title="Text-to-Speech",
+        description="Convert English text to speech",
         fn=inference,
         inputs=[
-            gr.Textbox(label='Text to Synthesize'),
-            gr.Slider(minimum=0.5, maximum=3.0, value=1.0, label='Speed'),
+            gr.Textbox(label="Text to Synthesize"),
+            gr.Slider(minimum=0.5, maximum=3.0, value=1.0, label="Speed"),
             gr.Dropdown(
-                label='Speaker',
-                choices=['EN', 'EN-US', 'EN-BR', 'EN_INDIA', 'EN-AU', 'EN-Default'],
-                value='EN-US'
-            )
+                label="Speaker",
+                choices=["EN", "EN-US", "EN-BR", "EN_INDIA", "EN-AU", "EN-Default"],
+                value="EN-US",
+            ),
         ],
-        outputs=[
-            gr.Audio()
+        outputs=[gr.Audio()],
+        examples=[
+            [
+                "Hello, my name is Chi-ku-wa-bu. I am a text-to-speech system designed to assist you. How can I help you today?",
+                1.0,
+                "EN-US",
+            ],
         ],
     )
     demo.queue().launch()