Create app.py

app.py

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+import streamlit as st
+import gradio as gr
+from speechbrain.pretrained import Tacotron2, HIFIGAN
+from scipy.io.wavfile import write
+
+# 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
+def text_to_speech(text):
+    mel_output, mel_length, alignment = tacotron2.encode_text(text)
+    waveforms, _, _ = hifi_gan.decode_batch(mel_output)
+    audio_path = "tts_output.wav"
+    write(audio_path, 22050, waveforms.squeeze(1).cpu().numpy())
+    return audio_path
+
+# Gradio interface for TTS
+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.")
+
+# Embed Gradio interface in Streamlit
+with st.spinner("Loading Gradio interface..."):
+    gr_interface.launch(share=False, inbrowser=True)