Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import pipeline
+
+# 1. Choose a TTS model from Hugging Face
+# You can later change this to another model, e.g. "suno/bark-small" if supported
+TTS_MODEL_ID = "facebook/mms-tts-eng"  # English TTS
+
+# 2. Create the TTS pipeline
+device = 0 if torch.cuda.is_available() else -1
+tts = pipeline("text-to-speech", model=TTS_MODEL_ID, device=device)
+
+def synthesize_tts(text):
+    if not text or text.strip() == "":
+        raise gr.Error("Please enter some text to synthesize.")
+    
+    # 3. Run the pipeline
+    out = tts(text)
+    # out["audio"] is a numpy array; out["sampling_rate"] is the sample rate
+    audio = (out["sampling_rate"], out["audio"])
+    return audio
+
+title = "Simple Text-to-Speech (TTS) Space"
+description = (
+    "Enter some English text and generate speech using a Hugging Face TTS model. "
+    "You can later replace the model with F5-TTS for voice cloning."
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+    
+    with gr.Row():
+        with gr.Column():
+            text_in = gr.Textbox(
+                lines=4,
+                label="Text to synthesize",
+                placeholder="Type some English text here..."
+            )
+            btn = gr.Button("Generate Speech")
+        with gr.Column():
+            audio_out = gr.Audio(label="Generated audio", type="numpy")
+    
+    btn.click(fn=synthesize_tts, inputs=text_in, outputs=audio_out)
+
+if __name__ == "__main__":
+    demo.launch()