Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
+from datasets import load_dataset
+
+# Load model and processor
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+
+# Load a speaker embedding
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embedding = torch.tensor(embeddings_dataset[0]["xvector"]).unsqueeze(0)
+
+# TTS Function
+def text_to_speech(text):
+    inputs = processor(text=text, return_tensors="pt")
+    with torch.no_grad():
+        speech = model.generate_speech(inputs["input_ids"], speaker_embedding)
+    return speech.numpy()
+
+# Set up Gradio interface
+iface = gr.Interface(fn=text_to_speech, inputs="text", outputs="audio")
+iface.launch()