Create app.py

app.py

@@ -1,13 +1,12 @@
-import streamlit as st
-import gradio as gr
 from speechbrain.pretrained import Tacotron2, HIFIGAN
 from scipy.io.wavfile import write
+import gradio as gr
 
 # Load TTS and vocoder models
 tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tts")
 hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
 
-# Function to generate speech from text
+# TTS function
 def text_to_speech(text):
     mel_output, mel_length, alignment = tacotron2.encode_text(text)
     waveforms, _, _ = hifi_gan.decode_batch(mel_output)
@@ -15,17 +14,8 @@ def text_to_speech(text):
     write(audio_path, 22050, waveforms.squeeze(1).cpu().numpy())
     return audio_path
 
-# Gradio interface for TTS
+# Gradio interface
 def chatbot_response(text):
-    audio_path = text_to_speech(text)
-    return audio_path
-
-gr_interface = gr.Interface(fn=chatbot_response, inputs="text", outputs="audio")
-
-# Streamlit app setup
-st.title("Text-to-Speech Chatbot")
-st.write("This application converts text into speech using SpeechBrain.")
+    return text_to_speech(text)
 
-# Embed Gradio interface in Streamlit
-with st.spinner("Loading Gradio interface..."):
-    gr_interface.launch(share=False, inbrowser=True)
+gr.Interface(fn=chatbot_response, inputs="text", outputs="audio").launch()