Spaces:
Running
on
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Running
on
Zero
Update gradio_app.py
Browse files- gradio_app.py +20 -23
gradio_app.py
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import
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import soundfile as sf
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import tempfile
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import torch
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from vieneu_tts import VieNeuTTS
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import os
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import time
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import threading
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import pickle
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import hashlib
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import numpy as np
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from pydub import AudioSegment
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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# --- KHỞI TẠO FASTAPI ---
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app = FastAPI()
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print("⏳ Đang khởi động VieNeu-TTS...")
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# --- 1. SETUP MODEL ---
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# Trên ZeroGPU, ban đầu có thể nó nhận là CPU, nhưng @spaces.GPU sẽ lo phần chuyển đổi sau
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"🖥️ Sử dụng thiết bị (Global): {device.upper()}")
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# Load Model
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try:
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tts = VieNeuTTS(
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backbone_repo="pnnbao-ump/VieNeu-TTS",
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backbone_device=device,
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@@ -81,7 +84,7 @@ VOICE_SAMPLES = {
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# --- 3. CORE LOGIC (Dùng chung cho cả API và UI) ---
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# QUAN TRỌNG:
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@spaces.GPU
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def core_synthesize(text, voice_choice, speed_factor):
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# Lấy thông tin giọng
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else:
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ref_codes = load_cache_from_disk(cache_key)
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if ref_codes is None:
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# Đảm bảo
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if torch.cuda.is_available():
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# (Usually VieNeuTTS handles this based on init, but we double check)
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pass
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ref_codes = tts.encode_reference(ref_audio_path)
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save_cache_to_disk(cache_key, ref_codes)
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reference_cache[cache_key] = ref_codes
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# Infer
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wav = tts.infer(text, ref_codes, ref_text_raw)
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# Speed
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# Hàm riêng cho Custom Voice cũng cần GPU
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@spaces.GPU
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def custom_synthesize_logic(text, ref_audio_path, ref_text_raw):
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ref_codes = tts.encode_reference(ref_audio_path)
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wav = tts.infer(text, ref_codes, ref_text_raw)
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return wav
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raise HTTPException(status_code=500, detail=str(e))
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# --- 5. GRADIO UI SETUP ---
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# Dùng theme Soft để tránh lỗi
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theme = gr.themes.Soft()
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# CSS
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css = ".container { max-width: 900px; margin: auto; }"
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def ui_synthesize(text, voice, custom_audio, custom_text, mode, speed):
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try:
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start = time.time()
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# Logic riêng cho UI (hỗ trợ custom voice)
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if mode == "custom_mode":
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wav = custom_synthesize_logic(text, custom_audio, custom_text)
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# (Bỏ qua speed control cho custom để code gọn)
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else:
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wav = core_synthesize(text, voice, speed)
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out_audio = gr.Audio(label="Kết quả", autoplay=True)
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out_status = gr.Textbox(label="Trạng thái")
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# Ẩn hiện mode
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mode_state = gr.Textbox(visible=False, value="preset_mode")
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tabs.children[0].select(lambda: "preset_mode", None, mode_state)
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tabs.children[1].select(lambda: "custom_mode", None, mode_state)
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btn.click(ui_synthesize, [inp_text, inp_voice, inp_audio, inp_ref_text, mode_state, inp_speed], [out_audio, out_status])
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# --- 6. MOUNT GRADIO VÀO FASTAPI ---
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# Đây là bước quan trọng nhất để chạy cả 2 cùng lúc
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app = gr.mount_gradio_app(app, demo, path="/")
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# --- 7. CHẠY SERVER ---
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if __name__ == "__main__":
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import uvicorn
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# 0.0.0.0 Mở port ra ngoài internet
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uvicorn.run(app, host="0.0.0.0", port=7860)
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import spaces # <--- QUAN TRỌNG: PHẢI ĐỂ DÒNG ĐẦU TIÊN
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import os
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import time
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import threading
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import pickle
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import hashlib
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import base64
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import io
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import tempfile
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import numpy as np
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# Các thư viện khác import sau spaces
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import torch
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import soundfile as sf
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from pydub import AudioSegment
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import gradio as gr
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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# Import thư viện nội bộ
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from vieneu_tts import VieNeuTTS
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# --- KHỞI TẠO FASTAPI ---
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app = FastAPI()
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print("⏳ Đang khởi động VieNeu-TTS...")
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# --- 1. SETUP MODEL ---
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"🖥️ Sử dụng thiết bị (Global): {device.upper()}")
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# Load Model
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try:
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print("📦 Đang tải model vào bộ nhớ...")
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tts = VieNeuTTS(
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backbone_repo="pnnbao-ump/VieNeu-TTS",
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backbone_device=device,
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# --- 3. CORE LOGIC (Dùng chung cho cả API và UI) ---
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# QUAN TRỌNG: Decorator GPU
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@spaces.GPU
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def core_synthesize(text, voice_choice, speed_factor):
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# Lấy thông tin giọng
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else:
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ref_codes = load_cache_from_disk(cache_key)
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if ref_codes is None:
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# Đảm bảo dọn dẹp bộ nhớ trước khi encode
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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ref_codes = tts.encode_reference(ref_audio_path)
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save_cache_to_disk(cache_key, ref_codes)
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reference_cache[cache_key] = ref_codes
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# Infer
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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wav = tts.infer(text, ref_codes, ref_text_raw)
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# Speed
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# Hàm riêng cho Custom Voice cũng cần GPU
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@spaces.GPU
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def custom_synthesize_logic(text, ref_audio_path, ref_text_raw):
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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ref_codes = tts.encode_reference(ref_audio_path)
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wav = tts.infer(text, ref_codes, ref_text_raw)
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return wav
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raise HTTPException(status_code=500, detail=str(e))
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# --- 5. GRADIO UI SETUP ---
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theme = gr.themes.Soft()
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css = ".container { max-width: 900px; margin: auto; }"
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def ui_synthesize(text, voice, custom_audio, custom_text, mode, speed):
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try:
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start = time.time()
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if mode == "custom_mode":
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wav = custom_synthesize_logic(text, custom_audio, custom_text)
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else:
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wav = core_synthesize(text, voice, speed)
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out_audio = gr.Audio(label="Kết quả", autoplay=True)
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out_status = gr.Textbox(label="Trạng thái")
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mode_state = gr.Textbox(visible=False, value="preset_mode")
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tabs.children[0].select(lambda: "preset_mode", None, mode_state)
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tabs.children[1].select(lambda: "custom_mode", None, mode_state)
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btn.click(ui_synthesize, [inp_text, inp_voice, inp_audio, inp_ref_text, mode_state, inp_speed], [out_audio, out_status])
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# --- 6. MOUNT GRADIO VÀO FASTAPI ---
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app = gr.mount_gradio_app(app, demo, path="/")
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# --- 7. CHẠY SERVER ---
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=7860)
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