Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from datasets import load_dataset
+import soundfile as sf
+import torch
+
+# Initialize the text-to-speech pipeline
+synthesiser = pipeline("text-to-speech", "umarigan/speecht5_tts_tr_v1.0")
+
+# Load the speaker embedding dataset
+embeddings_dataset = load_dataset("umarigan/turkish_voice_dataset_embedded", split="train")
+
+# Define the speech generation function
+def generate_speech(text, speaker_id):
+    speaker_embedding = torch.tensor(embeddings_dataset[speaker_id]["speaker_embeddings"]).unsqueeze(0)
+    speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding})
+    
+    # Save the generated audio to a file
+    sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
+    
+    # Return the audio file path to Gradio
+    return "speech.wav"
+
+# Define the Gradio interface
+inputs = [
+    gr.Textbox(label="Enter Text", placeholder="Bir berber bir berbere gel beraber bir berber kuralım demiş"),
+    gr.Number(label="Speaker ID", value=736, precision=0)
+]
+
+outputs = gr.Audio(label="Generated Speech")
+
+gr.Interface(fn=generate_speech, inputs=inputs, outputs=outputs, title="Turkish Text-to-Speech").launch()