Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import scipy
+from transformers import VitsModel, AutoTokenizer
+
+# Load the model and tokenizer
+model = VitsModel.from_pretrained("kakao-enterprise/vits-ljs")
+tokenizer = AutoTokenizer.from_pretrained("kakao-enterprise/vits-ljs")
+
+def text_to_speech(text):
+    # Tokenize the text
+    inputs = tokenizer(text, return_tensors="pt")
+
+    # Generate audio
+    with torch.no_grad():
+        output = model(**inputs).waveform
+
+    # Convert to numpy array and save as WAV file
+    audio_array = output.cpu().numpy().squeeze()
+    audio_array /= 1.414
+    audio_array *= 32767
+    audio_array = audio_array.astype(np.int16)
+
+    # Save to WAV file
+    output_file = "output/output.wav"
+    scipy.io.wavfile.write(output_file, rate=model.config.sampling_rate, data=audio_array)
+
+    # Return the path to the WAV file
+    return output_file
+
+demo = gr.Interface(
+    text_to_speech,
+    gr.Textbox(label="Text to narrate"),
+    gr.Audio(label="Narrated audio"),
+    title="Text-to-Speech",
+    description="Enter text to generate audio narration",
+)
+
+demo.launch()