Uploaded model demo

app.py

@@ -1,4 +1,32 @@
+import io
+import numpy as np
 import streamlit as st
+from GPTTTS import GPTTTSTokenizer, GPTTTS
+import torch
+import torchaudio
+import tempfile
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load your GPTTTSTokenizer
+tokenizer = GPTTTSTokenizer()
+
+# Load the GPT-TTS model
+model = GPTTTS()
+
+st.title("GPT-TTS Demo")
+user_text = st.text_input("Enter text:")
+
+if user_text:
+    # Tokenize the input text
+    inputs = tokenizer(user_text)["input_ids"]
+
+    with st.spinner("Generating audio..."):
+        # Generate the audio tensor
+        with torch.no_grad():
+            audio = model(inputs)
+
+        # Save the audio tensor to a temporary file
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav:
+            torchaudio.save(tmp_wav.name, audio, sample_rate=model.sample_rate)
+
+            # Load the temporary file and play the audio
+            st.audio(tmp_wav.read(), format="audio/wav")