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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq
+import soundfile as sf
+import numpy as np
+import tempfile
+
+
+# Tên model TTS
+MODEL_NAME = "hynt/F5-TTS-Vietnamese-100h"
+
+# Load model & tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float32,   # CPU Basic → dùng float32
+)
+
+# Đảm bảo chạy CPU
+device = torch.device("cpu")
+model = model.to(device)
+
+
+def tts_generate(text):
+    if not text.strip():
+        return None
+    
+    inputs = tokenizer(text, return_tensors="pt").to(device)
+
+    # Sinh audio
+    with torch.no_grad():
+        audio = model.generate(**inputs)
+
+    audio = audio.cpu().numpy().squeeze()
+
+    # Lưu tạm wav
+    tmp_path = tempfile.mktemp(suffix=".wav")
+    sf.write(tmp_path, audio, 22050)
+
+    return tmp_path
+
+
+### Gradio UI ###
+with gr.Blocks(title="TTS Vietnamese Free") as demo:
+    gr.Markdown("# 🇻🇳 Text-to-Speech Vietnamese (Free Version)\nNhập văn bản tiếng Việt và nhấn **Sinh giọng nói**:")
+
+    text_in = gr.Textbox(lines=5, label="Văn bản")
+    audio_out = gr.Audio(label="Kết quả", type="filepath")
+    btn = gr.Button("🎤 Sinh giọng nói")
+
+    btn.click(tts_generate, inputs=text_in, outputs=audio_out)
+
+demo.launch()

requirements.txt

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+gradio
+transformers
+torch
+soundfile
+numpy