Create app.py

app.py

@@ -0,0 +1,17 @@
+from transformers import pipeline
+from datasets import load_dataset
+import soundfile as sf
+import torch
+synthetiser =pipeline('text-to-speech', model='microsoft/speecht5_tts')
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+speech = synthetiser("Hello everyone!", forward_params={"speaker_embeddings": speaker_embedding})
+sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
+
+import gradio as gr
+def text_to_speech(text):
+  speech = synthetiser(text)
+  return(speech['audio'])
+
+demo_text_to_speech = gr.Interface(text_to_speech, title="Text to speech converter", description="Enter a text here!", inputs='text', outputs='audio')
+demo_text_to_speech.launch()