Create app.py

app.py

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+from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5HifiGan
+import torch
+import gradio as gr
+
+# Load the TTS model and processor
+model_checkpoint = "ejazhabibdar/speecht5_finetuned_voxpopuli_nl"
+model = SpeechT5ForTextToSpeech.from_pretrained(model_checkpoint)
+processor = SpeechT5Processor.from_pretrained(model_checkpoint)
+
+# Load the vocoder for generating speech
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+# Set the device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+vocoder.to(device)
+
+# Set the model to evaluation mode
+model.eval()
+vocoder.eval()
+
+# Define the TTS function
+def text_to_speech(text):
+    # Preprocess the input text
+    inputs = processor(text=text, return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    # Generate speech
+    with torch.no_grad():
+        speech = model.generate_speech(inputs["input_ids"], vocoder=vocoder)
+
+    return speech.tolist()
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=text_to_speech,
+    inputs="text",
+    outputs="audio",
+    title="Text-to-Speech",
+    description="Enter the text and listen to the generated speech.",
+    theme="huggingface",
+)
+
+if __name__ == "__main__":
+    iface.launch()