Create app.py

app.py

@@ -0,0 +1,41 @@
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+import torch
+import soundfile as sf
+import tempfile
+import streamlit as st
+from gtts import gTTS
+from io import BytesIO
+
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+def text_and_speak(text):
+  speaker_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+  speaker_embedding = torch.tensor(speaker_dataset[0]["xvector"]).unsqueeze(0)  # Use the first speaker as an example
+  inputs = processor(text=text, return_tensors="pt")
+  speech_audio = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
+  #sf.write("speech.wav", speech_audio.numpy(), samplerate=16000)
+  # Save the audio to a temporary file
+  with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
+    sf.write(temp_file.name, speech_audio.numpy(), samplerate=16000)
+    audio_path = temp_file.name
+
+  return audio_path
+def text_and_speak(text):
+    # Function to generate audio from text
+    tts = gTTS(text)
+    audio_file = BytesIO()
+    tts.write_to_fp(audio_file)
+    audio_file.seek(0)
+    return audio_file
+
+st.title("Text to Speech")
+input_text = st.text_area("Input text", height=200)
+if st.button("Generate Audio"):
+    if input_text.strip():
+        audio = text_and_speak(input_text)
+        st.audio(audio, format="audio/mp3", start_time=0)
+    else:
+        st.warning("Please enter some text to generate audio.")