Create app.py

app.py

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+from transformers import pipeline
+from datasets import load_dataset
+import soundfile as sf
+import torch
+import gradio as gr
+
+# Text-to-speech pipeline
+synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+
+def synthesize_speech(text, speaker_id):
+    speaker_embedding = torch.tensor(embeddings_dataset[speaker_id]["xvector"]).unsqueeze(0)
+    speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding})
+    sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
+    return "speech.wav"
+
+# Define your interface
+iface = gr.Interface(
+    fn=synthesize_speech,
+    inputs=[gr.Textbox(label="Enter your text"), gr.Slider(minimum=0, maximum=len(embeddings_dataset)-1, label="Speaker ID")],
+    outputs="audio",
+    title="Text-to-Speech Synthesizer",
+    description="Type a text and choose a speaker to synthesize speech."
+)
+
+# Launch the app
+iface.launch()