Create app.py

app.py

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+import streamlit as st
+import transformers
+from transformers import VitsModel, AutoTokenizer
+import torch
+import numpy as np
+import io
+import soundfile as sf
+
+# Load model and tokenizer
+model = VitsModel.from_pretrained("facebook/mms-tts-eng")
+tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
+
+def generate_speech(text):
+  inputs = tokenizer(text, return_tensors="pt")
+
+  with torch.no_grad():
+    output = model(**inputs).waveform  # Corrected typo: waveform (not waveformform)
+
+  # Convert the waveform tensor to a NumPy array
+  waveform = output.squeeze().cpu().numpy()
+
+  # Convert the waveform to bytes
+  audio_bytes_io = io.BytesIO()
+  sf.write(audio_bytes_io, waveform, samplerate=22050, format='WAV')
+  audio_bytes_io.seek(0)
+
+  return audio_bytes_io
+
+st.title("Text-to-Speech Converter")
+st.write("Developed by Safwan Ahmad Saffi")
+st.write("Enter text below and click 'Generate Speech' to convert it to audio.")
+
+# Text input
+text_input = st.text_area("Text to convert:", "Some example text in the English language")
+
+if st.button("Generate Speech"):
+  if text_input:
+    st.write("Generating speech...")
+    audio_bytes_io = generate_speech(text_input)
+
+    # Display audio in Streamlit
+    st.audio(audio_bytes_io, format="audio/wav")
+  else:
+    st.write("Please enter some text.")