Create main.py

main.py

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+import streamlit as st
+from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+import torch
+import soundfile as sf
+import os
+
+# Function to generate speech using the pipeline method
+def generate_speech_pipeline(text, speaker_embedding):
+    synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")
+    speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding})
+    return speech["audio"], speech["sampling_rate"]
+
+# Function to generate speech using the processor + generate method
+def generate_speech_processor(text, speaker_embedding):
+    processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+    model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+    inputs = processor(text=text, return_tensors="pt")
+    speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
+    return speech.numpy(), 16000
+
+def main():
+    st.title("Text-to-Speech with SpeechT5")
+    
+    st.write("Enter the text you want to convert to speech:")
+
+    text = st.text_area("Text", "Hello, my dog is cooler than you!")
+    
+    if st.button("Generate Speech"):
+        st.write("Generating speech...")
+
+        # Load xvector containing speaker's voice characteristics from a dataset
+        embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+        speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+        # Choose the method to generate speech
+        method = st.selectbox("Choose the method for generating speech", ["Pipeline", "Processor + Generate"])
+
+        if method == "Pipeline":
+            audio, samplerate = generate_speech_pipeline(text, speaker_embedding)
+        else:
+            audio, samplerate = generate_speech_processor(text, speaker_embedding)
+
+        # Save and play the generated speech
+        output_path = "speech.wav"
+        sf.write(output_path, audio, samplerate=samplerate)
+        st.audio(output_path)
+
+if __name__ == "__main__":
+    main()