From d48e87c44fbca04b2669ca561d859532229aec60 Mon Sep 17 00:00:00 2001
From: Dryw Wade <dryw.wade@sparkfun.com>
Date: Wed, 25 Mar 2026 09:39:15 -0600
Subject: [PATCH 1/4] boards/SPARKFUN_THINGPLUS_ESP32C5: Add SparkFun Thing
 Plus ESP32-C5 img.

Signed-off-by: Thanh20VN <truongminhthanh0106@gmail.com>
---
 .../30678-Thing-Plus-ESP32-C5-Feature.jpg      | Bin 0 -> 112805 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 boards/SPARKFUN_THINGPLUS_ESP32C5/30678-Thing-Plus-ESP32-C5-Feature.jpg

diff --git a/boards/SPARKFUN_THINGPLUS_ESP32C5/30678-Thing-Plus-ESP32-C5-Feature.jpg b/boards/SPARKFUN_THINGPLUS_ESP32C5/30678-Thing-Plus-ESP32-C5-Feature.jpg
new file mode 100644
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From 35dc673c1af1dcc02a19bdbce6eed06253ecc588 Mon Sep 17 00:00:00 2001
From: thanh20VN <95450843+thanh20VN@users.noreply.github.com>
Date: Mon, 18 May 2026 11:20:04 +0000
Subject: [PATCH 2/4] stm32/boards: Add support for WEACT STM32WB55CGU6 board.

---
 boards/WEACT_STM32WB55CGU6/STM32WB55CGU6.jpg | Bin 0 -> 139558 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 boards/WEACT_STM32WB55CGU6/STM32WB55CGU6.jpg

diff --git a/boards/WEACT_STM32WB55CGU6/STM32WB55CGU6.jpg b/boards/WEACT_STM32WB55CGU6/STM32WB55CGU6.jpg
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HcmV?d00001


From a8e9df4446895f2c166eff95f402b527778cf13c Mon Sep 17 00:00:00 2001
From: thanh20VN <95450843+thanh20VN@users.noreply.github.com>
Date: Fri, 5 Jun 2026 21:04:34 +0700
Subject: [PATCH 3/4] Add files via upload

---
 gradio_main.py | 2005 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2005 insertions(+)
 create mode 100644 gradio_main.py

diff --git a/gradio_main.py b/gradio_main.py
new file mode 100644
index 0000000..32647dd
--- /dev/null
+++ b/gradio_main.py
@@ -0,0 +1,2005 @@
+import sys
+import io
+if sys.platform == "win32":
+    try:
+        sys.stdout.reconfigure(encoding="utf-8")
+        sys.stderr.reconfigure(encoding="utf-8")
+    except Exception:
+        sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
+        sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace")
+
+import gradio as gr
+print("⏳ Đang khởi động VieNeu-TTS... Vui lòng chờ...")
+import soundfile as sf
+import tempfile
+from vieneu import Vieneu
+import os
+import time
+import numpy as np
+import queue
+import threading
+import yaml
+import uuid
+from vieneu_utils.core_utils import split_text_into_chunks, join_audio_chunks, env_bool, get_silence_duration_v2
+from vieneu_utils.phonemize_text import phonemize_to_chunks
+from sea_g2p import Normalizer
+import gc
+
+from apps.ui_utils import (
+    _format_duration,
+    _split_estimate_status,
+    wrap_with_estimate,
+    cleanup_gpu_memory,
+    get_ref_text_cached,
+    on_codec_change,
+    validate_audio_duration,
+    on_custom_id_change
+)
+from apps.ui_constants import (
+    theme,
+    css,
+    head_html,
+    DEFAULT_TEXT_GPU,
+    DEFAULT_TEXT_TURBO
+)
+
+# --- CONSTANTS & CONFIG ---
+CONFIG_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "config.yaml")
+try:
+    with open(CONFIG_PATH, "r", encoding="utf-8") as f:
+        _config = yaml.safe_load(f) or {}
+except Exception as e:
+    raise RuntimeError(f"Không thể đọc config.yaml: {e}")
+
+BACKBONE_CONFIGS = _config.get("backbone_configs", {})
+CODEC_CONFIGS = _config.get("codec_configs", {})
+
+# Refilter and Simplify Configs per requirements
+HAS_GPU = False
+try:
+    import torch
+    HAS_GPU = torch.cuda.is_available() or (sys.platform == "darwin" and torch.backends.mps.is_available())
+except ImportError:
+    pass
+
+filtered_backbones = {}
+if HAS_GPU:
+    filtered_backbones["VieNeu-TTS-v2 (GPU)"] = {
+        "repo": "pnnbao-ump/VieNeu-TTS-v2",
+        "supports_streaming": False,
+        "description": "VieNeu-TTS Version 2 - hỗ trợ song ngữ (Anh-Việt) và chế độ podcast"
+    }
+    filtered_backbones["VieNeu-TTS (GPU)"] = {
+        "repo": "pnnbao-ump/VieNeu-TTS",
+        "supports_streaming": False,
+        "description": "VieNeu-TTS Version 1 - ổn định, production-ready"
+    }
+    filtered_backbones["VieNeu-TTS-0.3B-ngoc-huyen (GPU)"] = {
+        "repo": "pnnbao-ump/VieNeu-TTS-0.3B-ngoc-huyen",
+        "supports_streaming": False,
+        "description": "VieNeu-TTS-0.3B - Ngọc Huyền"
+    }
+
+filtered_backbones["VieNeu-TTS-v2 (CPU)"] = {
+    "repo": "pnnbao-ump/VieNeu-TTS-v2",
+    "gguf_filename": "VieNeu-TTS-v2-Q4-K-M.gguf",
+    "supports_streaming": False,
+    "description": "VieNeu-TTS-v2 (CPU) - GGUF Q4_K_M, hỗ trợ song ngữ & podcast"
+}
+
+filtered_backbones["VieNeu-TTS-v2-Turbo (CPU)"] = {
+    "repo": "pnnbao-ump/VieNeu-TTS-v2-Turbo-GGUF",
+    "supports_streaming": True,
+    "description": "VieNeu-TTS-v2-Turbo - Siêu nhanh, tối ưu tuyệt đối cho CPU & Thiết bị yếu"
+}
+
+BACKBONE_CONFIGS = filtered_backbones
+
+filtered_codecs = {
+    "NeuCodec (Distill)": {
+        "repo": "neuphonic/distill-neucodec",
+        "description": "Codec mặc định cho model GPU",
+        "use_preencoded": False
+    },
+    "NeuCodec (ONNX)": {
+        "repo": "neuphonic/neucodec-onnx-decoder-int8",
+        "description": "Codec siêu nhẹ, tối ưu cho CPU (ONNX)",
+        "use_preencoded": False
+    },
+    "VieNeu-Codec": {
+        "repo": "pnnbao-ump/VieNeu-Codec",
+        "description": "Codec tối ưu cho Turbo v2 (ONNX)",
+        "use_preencoded": False
+    }
+}
+CODEC_CONFIGS = filtered_codecs
+
+_text_settings = _config.get("text_settings", {})
+MAX_CHARS_PER_CHUNK = _text_settings.get("max_chars_per_chunk", 256)
+MAX_TOTAL_CHARS_STREAMING = _text_settings.get("max_total_chars_streaming", 3000)
+
+if not BACKBONE_CONFIGS or not CODEC_CONFIGS:
+    raise ValueError("config.yaml thiếu backbone_configs hoặc codec_configs")
+
+# --- 1. MODEL CONFIGURATION ---
+# Global model instance
+tts = None
+current_backbone = None
+current_codec = None
+model_loaded = False
+using_lmdeploy = False
+PRESET_VOICES_CACHE = []  # List of all voices (tuples or strings)
+CONV_VOICES_CACHE = []    # Filtered list for conversation (podcast=True)
+MAX_SPEAKERS = 8          # Max concurrent speakers in conversation tab
+
+# Normalizer (module-level singleton)
+_text_normalizer = Normalizer()
+
+def get_available_devices() -> list[str]:
+    """Get list of available devices for current platform."""
+    devices = ["Auto", "CPU"]
+    
+    try:
+        import torch
+        if sys.platform == "darwin" and torch.backends.mps.is_available():
+            devices.append("MPS")
+        elif torch.cuda.is_available():
+            devices.append("CUDA")
+    except ImportError:
+        pass
+
+    return devices
+
+def get_model_status_message() -> str:
+    """Reconstruct status message from global state"""
+    global model_loaded, tts, using_lmdeploy, current_backbone, current_codec
+    if not model_loaded or tts is None:
+        return "⏳ Chưa tải model."
+    
+    if "v2-Turbo" in (current_backbone or ""):
+        backend_name = "⚡ Turbo (v2)"
+    elif using_lmdeploy:
+        backend_name = "🚀 LMDeploy (Optimized)"
+    else:
+        backend_name = "📦 Standard"
+    
+    # We don't track the exact device strings perfectly in global state, so we estimate
+    try:
+        import torch
+        has_mps = torch.backends.mps.is_available()
+        has_cuda = torch.cuda.is_available()
+    except:
+        has_mps = has_cuda = False
+
+    device_info = "GPU (CUDA)" if (using_lmdeploy or "CUDA" in (current_backbone or "")) else ("MPS (Metal)" if has_mps else "Auto")
+    
+    if "v2-Turbo" in (current_backbone or ""):
+        codec_device = "GPU/MPS" if (has_cuda or has_mps) else "CPU"
+    elif "ONNX" in (current_codec or ""):
+        codec_device = "CPU"
+    else:
+        codec_device = "GPU/MPS" if (has_cuda or has_mps) else "CPU"
+
+    preencoded_note = ""    
+    opt_info = ""
+    if using_lmdeploy and hasattr(tts, 'get_optimization_stats'):
+        stats = tts.get_optimization_stats()
+        opt_info = (
+            f"\n\n🔧 Tối ưu hóa:"
+            f"\n  • Triton: {'✅' if stats['triton_enabled'] else '❌'}"
+            f"\n  • Max Batch Size (Default): {stats.get('max_batch_size', 'N/A')}"
+            f"\n  • Reference Cache: {stats['cached_references']} voices"
+            f"\n  • Prefix Caching: ❌"
+        )
+
+    return (
+        f"✅ Model đã tải thành công!\n\n"
+        f"🔧 Backend: {backend_name}\n"
+        f" Parrot: {current_backbone} on {device_info}\n"
+        f"🎵 Codec: {current_codec} on {codec_device}{preencoded_note}{opt_info}"
+    )
+
+def restore_ui_state():
+    """Update UI components based on persistence"""
+    global model_loaded
+    msg = get_model_status_message()
+    return (
+        msg, 
+        gr.update(interactive=model_loaded), # btn_generate
+        gr.update(interactive=model_loaded), # btn_generate_conv
+        gr.update(interactive=False)         # btn_stop
+    )
+
+def should_use_lmdeploy(backbone_choice: str, device_choice: str) -> bool:
+    """Determine if we should use LMDeploy backend."""
+    # LMDeploy not supported on macOS
+    if sys.platform == "darwin":
+        return False
+
+    if "gguf" in backbone_choice.lower() or "v2-turbo" in backbone_choice.lower():
+        return False
+    
+    try:
+        import torch
+        if device_choice == "Auto":
+            has_gpu = torch.cuda.is_available()
+        elif device_choice == "CUDA":
+            has_gpu = torch.cuda.is_available()
+        else:
+            has_gpu = False
+        return has_gpu
+    except ImportError:
+        return False
+
+def load_model(backbone_choice: str, codec_choice: str, device_choice: str, 
+               force_lmdeploy: bool, custom_model_id: str = "", custom_base_model: str = "", 
+               custom_hf_token: str = ""):
+    """Load model with optimizations and max batch size control"""
+    global tts, current_backbone, current_codec, model_loaded, using_lmdeploy
+    lmdeploy_error_reason = None
+    model_loaded = False # Ensure we don't try to use a half-loaded model
+    
+    # Helper for slot updates (initially no change)
+    slot_no_updates = [gr.update()] * MAX_SPEAKERS
+
+    yield (
+        "⏳ Đang tải model với tối ưu hóa... Lưu ý: Quá trình này sẽ tốn thời gian. Vui lòng kiên nhẫn.",
+        gr.update(interactive=False), # btn_generate
+        gr.update(interactive=False), # btn_generate_conv
+        gr.update(interactive=False), # btn_load
+        gr.update(interactive=False), # btn_stop
+        gr.update(), # voice_select
+        gr.update(), gr.update(), gr.update(), gr.update(), # tab_p, tab_c, tab_sel, mode_state
+        gr.update(), # conv_tab
+        *slot_no_updates
+    )
+    
+    try:
+        # Cleanup before loading new model
+        if tts is not None:
+            tts = None # Reset instead of del to avoid NameError if load fails
+            cleanup_gpu_memory()
+        
+        # Prepare Backbone Config/Repo
+        custom_loading = False
+        is_merged_lora = False
+
+        if backbone_choice == "Custom Model":
+            custom_loading = True
+            if not custom_model_id or not custom_model_id.strip():
+                yield (
+                    "❌ Lỗi: Vui lòng nhập Model ID cho Custom Model.",
+                    gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True), gr.update(interactive=False), gr.update(),
+                    gr.update(), gr.update(), gr.update(), gr.update(),
+                    gr.update(), # conv_tab
+                    *slot_no_updates
+                )
+                return
+
+            # Check if it is a LoRA to merge
+            if "lora" in custom_model_id.lower():
+                # Merging mode
+                print(f"🔄 Detected LoRA in name. preparing merge with base: {custom_base_model}")
+                if custom_base_model not in BACKBONE_CONFIGS:
+                    yield (
+                        f"❌ Lỗi: Base Model '{custom_base_model}' không hợp lệ.",
+                        gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True), gr.update(interactive=False),
+                        gr.update(), gr.update(), gr.update(), gr.update(), gr.update(),
+                        gr.update(), # conv_tab
+                        *slot_no_updates
+                    )
+                    return
+                
+                base_config = BACKBONE_CONFIGS[custom_base_model]
+                backbone_config = {
+                    "repo": base_config["repo"], # Load base first
+                    "supports_streaming": base_config["supports_streaming"],
+                    "description": f"Custom Merged: {custom_model_id} + {custom_base_model}"
+                }
+                is_merged_lora = True
+            else:
+                # Normal custom model
+                backbone_config = {
+                    "repo": custom_model_id.strip(),
+                    "supports_streaming": False, # Assume false for unknown
+                    "description": f"Custom Model: {custom_model_id}"
+                }
+        else:
+            backbone_config = BACKBONE_CONFIGS[backbone_choice]
+            
+        codec_config = CODEC_CONFIGS[codec_choice]
+        use_lmdeploy = False
+        
+        # Override LMDeploy if custom
+        if custom_loading:
+             if "gguf" in backbone_config['repo'].lower() or "v2-turbo" in backbone_config['repo'].lower():
+                 # GGUF must use Standard/Turbo backend
+                 use_lmdeploy = False
+             elif is_merged_lora:
+                 # LoRA can use LMDeploy if we merge first (checked logic below) or Standard
+                 use_lmdeploy = force_lmdeploy and should_use_lmdeploy(custom_base_model, device_choice)
+             else:
+                 # Full custom model (e.g. finetune)
+                 use_lmdeploy = force_lmdeploy and should_use_lmdeploy("VieNeu-TTS (GPU)", device_choice) # Assume GPU compatible?
+        # Use LMDeploy only if Force LMDeploy is set and the model is compatible
+        # NOTE: For VieNeu-v2-Turbo, we handle LMDeploy inside TurboGPUVieNeuTTS class, 
+        # so we set use_lmdeploy = False here to avoid generic FastVieNeuTTS loading.
+        # NOTE: For custom_loading, the block above already decided use_lmdeploy correctly
+        # (e.g. False for GGUF repos). Do NOT override that decision here.
+        if "v2-Turbo" in backbone_choice:
+             should_use_generic_fast = False
+        elif custom_loading:
+             should_use_generic_fast = False  # already handled above per repo name
+        else:
+             should_use_generic_fast = force_lmdeploy and should_use_lmdeploy(backbone_choice, device_choice)
+             
+        if should_use_generic_fast:
+            use_lmdeploy = True
+        
+        if use_lmdeploy:
+            lmdeploy_error_reason = None
+            print(f"🚀 Using LMDeploy backend with optimizations")
+            
+            backbone_device = "cuda"
+            
+            if "ONNX" in codec_choice:
+                codec_device = "cpu"
+            else:
+                try:
+                    import torch
+                    codec_device = "cuda" if torch.cuda.is_available() else "cpu"
+                except ImportError:
+                    codec_device = "cpu"
+            
+            # Special handling for Custom LoRA + LMDeploy -> Merge & Save
+            target_backbone_repo = backbone_config["repo"]
+            
+            if custom_loading and is_merged_lora:
+                safe_name = custom_model_id.strip().replace("/", "_").replace("\\", "_").replace(":", "")
+                cache_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "merged_models_cache", safe_name)
+                target_backbone_repo = os.path.abspath(cache_dir)
+                
+                # Check if already merged (and voices.json exists)
+                if not os.path.exists(cache_dir) or not os.path.exists(os.path.join(cache_dir, "vocab.json")):
+                    print(f"🔄 Merging LoRA for LMDeploy optimization: {cache_dir}")
+                    if os.path.exists(cache_dir):
+                        print("   ⚠️ Detected incomplete cache, rebuilding...")
+                    yield (
+                         f"⏳ Đang merge và lưu model LoRA để tối ưu cho LMDeploy (thao tác này chỉ chạy một lần)...",
+                         gr.update(interactive=False),
+                         gr.update(interactive=False),
+                         gr.update(interactive=False),
+                         gr.update(interactive=False),
+                         gr.update(),
+                         gr.update(), gr.update(), gr.update(), gr.update(),
+                         gr.update(), # conv_tab
+                         *slot_no_updates
+                    )
+                    
+                    try:
+                        # Use GPU for merging if available for speed
+                        # We use the Base Model specified
+                        from vieneu.standard import VieNeuTTS
+                        base_repo = BACKBONE_CONFIGS[custom_base_model]["repo"]
+                        merge_device = "cuda" if torch.cuda.is_available() else "cpu"
+                        
+                        print(f"   • Loading base: {base_repo} ({merge_device})")
+                        temp_tts = VieNeuTTS(
+                            backbone_repo=base_repo,
+                            backbone_device=merge_device, 
+                            codec_repo=codec_config["repo"],
+                            codec_device="cpu", # Codec unused for merging, keep on CPU
+                            hf_token=custom_hf_token
+                        )
+                        
+                        print(f"   • Loading Adapter: {custom_model_id}")
+                        temp_tts.load_lora_adapter(custom_model_id.strip(), hf_token=custom_hf_token)
+                        
+                        print(f"   • Merging...")
+                        if hasattr(temp_tts.backbone, "merge_and_unload"):
+                            temp_tts.backbone = temp_tts.backbone.merge_and_unload()
+                        
+                        print(f"   • Saving to cache: {cache_dir}")
+                        temp_tts.backbone.save_pretrained(cache_dir)
+                        temp_tts.tokenizer.save_pretrained(cache_dir)
+                        
+                        # Fix for LMDeploy: Explicitly save legacy tokenizer files (vocab.json, merges.txt)
+                        # because LMDeploy/Transformers might default to slow tokenizer if fast one has issues,
+                        # and save_pretrained on fast tokenizer sometimes omits legacy files.
+                        try:
+                            print("   • Ensuring legacy tokenizer files...")
+                            from transformers import AutoTokenizer
+                            slow_tokenizer = AutoTokenizer.from_pretrained(base_repo, use_fast=False)
+                            slow_tokenizer.save_pretrained(cache_dir)
+                        except Exception as e:
+                            print(f"   ⚠️ Warning: Could not save slow tokenizer files: {e}")
+
+                        # Save voices.json to cache directory so FastVieNeuTTS can find it
+                        print(f"   • Saving voices definition...")
+                        import json
+                        voices_json_path = os.path.join(cache_dir, "voices.json")
+                        voices_content = {
+                             "meta": { "note": "Automatically generated during LoRA merge" },
+                             "default_voice": temp_tts._default_voice,
+                             "presets": temp_tts._preset_voices
+                        }
+                        with open(voices_json_path, 'w', encoding='utf-8') as f:
+                             json.dump(voices_content, f, ensure_ascii=False, indent=2)
+
+                        del temp_tts
+                        cleanup_gpu_memory()
+                        print("   ✅ Merge & Save successfully!")
+                        
+                    except Exception as e:
+                        import traceback
+                        traceback.print_exc()
+                        raise RuntimeError(f"Failed to merge & save LoRA for LMDeploy: {e}")
+
+            print(f"📦 Loading optimized model...")
+            print(f"   Backbone: {target_backbone_repo} on {backbone_device}")
+            print(f"   Codec: {codec_config['repo']} on {codec_device}")
+            print(f"   Triton: Enabled")
+            
+            try:
+                from vieneu.fast import FastVieNeuTTS
+                tts = FastVieNeuTTS(
+                    backbone_repo=target_backbone_repo,
+                    backbone_device=backbone_device,
+                    codec_repo=codec_config["repo"],
+                    codec_device=codec_device,
+                    memory_util=0.3,
+                    tp=1,
+                    enable_prefix_caching=False,
+                    enable_triton=True,
+                    hf_token=custom_hf_token
+                )
+                using_lmdeploy = True
+                
+                # Legacy caching removed
+                print(f"   ✅ Optimized backend initialized")
+                
+            except Exception as e:
+                import traceback
+                traceback.print_exc()
+                
+                error_str = str(e)
+                if "$env:CUDA_PATH" in error_str:
+                    lmdeploy_error_reason = "Không tìm thấy biến môi trường CUDA_PATH. Vui lòng cài đặt NVIDIA GPU Computing Toolkit."
+                else:
+                    lmdeploy_error_reason = f"{error_str}"
+                
+                yield (
+                    f"⚠️ LMDeploy Init Error: {lmdeploy_error_reason}. Đang loading model với backend mặc định - tốc độ chậm hơn so với lmdeploy...",
+                    gr.update(interactive=False),
+                    gr.update(interactive=False),
+                    gr.update(interactive=False),
+                    gr.update(interactive=False),
+                    gr.update(),
+                    gr.update(), gr.update(), gr.update(), gr.update(),
+                    gr.update(), # conv_tab
+                    *slot_no_updates
+                )
+                time.sleep(1)
+                use_lmdeploy = False
+                using_lmdeploy = False
+        
+        if not use_lmdeploy:
+            print(f"📦 Using original backend")
+
+            if device_choice == "Auto":
+                repo_lower = backbone_config['repo'].lower()
+                is_gguf_backbone = "gguf" in repo_lower
+
+                if is_gguf_backbone:
+                    # GGUF backbones (llama-cpp-python): Metal on Mac, CUDA on Windows/Linux
+                    if sys.platform == "darwin":
+                        backbone_device = "gpu"  # llama-cpp-python uses Metal via n_gpu_layers
+                    else:
+                        try:
+                            import torch
+                            backbone_device = "gpu" if torch.cuda.is_available() else "cpu"
+                        except ImportError:
+                            backbone_device = "cpu"
+                else:
+                    # PyTorch backbones (Standard, Turbo GPU): use native torch device
+                    try:
+                        import torch
+                        if sys.platform == "darwin":
+                            backbone_device = "mps" if torch.backends.mps.is_available() else "cpu"
+                        else:
+                            backbone_device = "cuda" if torch.cuda.is_available() else "cpu"
+                    except ImportError:
+                        backbone_device = "cpu"
+
+                # Codec device
+                if "ONNX" in codec_choice:
+                    codec_device = "cpu"
+                else:
+                    try:
+                        import torch
+                        if sys.platform == "darwin":
+                            codec_device = "mps" if torch.backends.mps.is_available() else "cpu"
+                        else:
+                            codec_device = "cuda" if torch.cuda.is_available() else "cpu"
+                    except ImportError:
+                        codec_device = "cpu"
+
+            elif device_choice == "MPS":
+                backbone_device = "mps"
+                codec_device = "mps" if "ONNX" not in codec_choice else "cpu"
+
+            else:
+                backbone_device = device_choice.lower()
+                codec_device = device_choice.lower()
+
+                if "ONNX" in codec_choice:
+                    codec_device = "cpu"
+
+            if "gguf" in backbone_config['repo'].lower() and backbone_device == "cuda":
+                # Only Llama-cpp (GGUF) uses the 'gpu' string for CUDA
+                backbone_device = "gpu"
+            
+            print(f"📦 Loading model...")
+            print(f"   Backbone: {backbone_config['repo']} on {backbone_device}")
+            print(f"   Codec: {codec_config['repo']} on {codec_device}")
+            
+            if "v2-Turbo" in backbone_choice:
+                # VieNeu v2 Turbo uses the dedicated backend
+                print("   ⚡ Mode: Turbo")
+                mode = "turbo_gpu" if "GPU" in backbone_choice else "turbo"
+                tts = Vieneu(
+                    mode=mode,
+                    backbone_repo=backbone_config["repo"],
+                    decoder_repo=codec_config["repo"],
+                    device=backbone_device,
+                    backend="lmdeploy" if force_lmdeploy and "GPU" in backbone_choice else "standard",
+                    hf_token=custom_hf_token
+                )
+            else:
+                from vieneu.standard import VieNeuTTS
+                tts = VieNeuTTS(
+                    backbone_repo=backbone_config["repo"],
+                    backbone_device=backbone_device,
+                    codec_repo=codec_config["repo"],
+                    codec_device=codec_device,
+                    hf_token=custom_hf_token,
+                    gguf_filename=backbone_config.get("gguf_filename")
+                )
+
+            # Perform LoRA Merge if needed (ONLY for Standard Backend)
+            # For LMDeploy, we handled it above by saving to disk
+            if is_merged_lora and custom_loading and not using_lmdeploy:
+                yield (
+                    f"🔄 Đang tải và merge LoRA adapter: {custom_model_id}...",
+                    gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False), gr.update(),
+                    gr.update(), gr.update(), gr.update(), gr.update(),
+                    gr.update(), # conv_tab
+                    *slot_no_updates
+                )
+                try:
+                    # 1. Load Adapter
+                    tts.load_lora_adapter(custom_model_id.strip(), hf_token=custom_hf_token)
+                    
+                    # 2. Merge and Unload
+                    # Check if backbone matches expected type for merge
+                    if hasattr(tts, 'backbone') and hasattr(tts.backbone, 'merge_and_unload'):
+                        print("   🔄 Merging LoRA into backbone...")
+                        tts.backbone = tts.backbone.merge_and_unload()
+                        
+                        # Reset LoRA state so it behaves like a normal model
+                        tts._lora_loaded = False 
+                        tts._current_lora_repo = None
+                        print("   ✅ Merged successfully!")
+                    else:
+                        print("   ⚠️ Warning: Model does not support merge_and_unload, keeping adapter active.")
+                        
+                except Exception as e:
+                     raise RuntimeError(f"Failed to merge LoRA: {e}")
+
+            using_lmdeploy = False
+        
+        current_backbone = backbone_choice
+        current_codec = codec_choice
+        model_loaded = True
+        
+        # Success message with optimization info
+        backend_name = "🚀 LMDeploy (Optimized)" if using_lmdeploy else "📦 Standard"
+        device_info = "cuda" if use_lmdeploy else (backbone_device if not use_lmdeploy else "N/A")
+        
+        streaming_support = "✅ Có" if backbone_config['supports_streaming'] else "❌ Không"
+        preencoded_note = "\n⚠️ Codec này cần sử dụng pre-encoded codes (.pt files)" if codec_config['use_preencoded'] else ""
+        
+        opt_info = ""
+        if using_lmdeploy and hasattr(tts, 'get_optimization_stats'):
+            stats = tts.get_optimization_stats()
+            opt_info = (
+                f"\n\n🔧 Tối ưu hóa:"
+                f"\n  • Triton: {'✅' if stats['triton_enabled'] else '❌'}"
+                f"\n  • Max Batch Size (Default): {stats.get('max_batch_size', 'N/A')}"
+                f"\n  • Reference Cache: {stats['cached_references']} voices"
+                f"\n  • Prefix Caching: ❌"
+            )
+        
+        warning_msg = ""
+        if lmdeploy_error_reason:
+             warning_msg = (
+                 f"\n\n⚠️ **Cảnh báo:** Không thể kích hoạt LMDeploy (Optimized Backend) do lỗi sau:\n"
+                 f"👉 {lmdeploy_error_reason}\n"
+                 f"💡 Hệ thống đã tự động chuyển về chế độ Standard (chậm hơn)."
+             )
+
+        success_msg = get_model_status_message()
+        if warning_msg:
+            success_msg += warning_msg
+            
+        # Prepare voice update
+        try:
+            # Get voices with descriptions for UI from SDK
+            voices = tts.list_preset_voices()
+        except Exception:
+            voices = []
+
+        has_voices = len(voices) > 0
+        
+        if has_voices:
+            default_v = tts._default_voice
+            
+            # Helper to get values list
+            is_tuple = (len(voices) > 0 and isinstance(voices[0], tuple))
+            voice_values = [v[1] for v in voices] if is_tuple else voices
+            
+            if not default_v and voice_values:
+                 default_v = voice_values[0]
+
+            # Ensure default_v is in the list and selected correctly
+            if default_v and default_v not in voice_values:
+                if is_tuple:
+                    # Try to find a nice description if possible, else use ID
+                    voices.append((default_v, default_v))
+                else:
+                    voices.append(default_v)
+            
+            # Sort voices by name/label for better UX
+            if is_tuple:
+                voices.sort(key=lambda x: str(x[0]))
+            else:
+                voices.sort()
+
+            voice_update = gr.update(choices=voices, value=default_v, interactive=True)
+            
+            global PRESET_VOICES_CACHE, CONV_VOICES_CACHE
+            PRESET_VOICES_CACHE = voices
+            
+            # Filter voices for conversation tab (podcast=True)
+            # Handle both boolean True/False and string "True"/"False"
+            def _check_podcast(v_id):
+                val = tts._preset_voices.get(v_id, {}).get('podcast', True)
+                if isinstance(val, str):
+                    return val.strip().lower() == "true"
+                return bool(val)
+
+            CONV_VOICES_CACHE = [v for v in voices if _check_podcast(v[1])]
+            
+            slot_dd_update = gr.update(choices=CONV_VOICES_CACHE)
+            
+            # Show Standard Tabs
+            tab_p = gr.update(visible=True)
+            tab_c = gr.update(visible=True)
+            tab_sel = gr.update(selected="preset_mode")
+            mode_state = "preset_mode"
+        else:
+            # Missing voices.json case
+            msg = "⚠️ Không tìm thấy file voices.json. Vui lòng dùng Tab Voice Cloning."
+            voice_update = gr.update(choices=[msg], value=msg, interactive=False)
+            slot_dd_update = gr.update(choices=[])
+            
+            # Show Preset Tab (to see message) and Custom Tab
+            tab_p = gr.update(visible=True)
+            tab_c = gr.update(visible=True)
+            tab_sel = gr.update(selected="preset_mode")
+            mode_state = "preset_mode"
+
+        # Check if v2 for conversation tab
+        is_v2 = (backbone_choice == "VieNeu-TTS-v2 (GPU)" or backbone_choice == "VieNeu-TTS-v2 (CPU)")
+        conv_tab_update = gr.update(visible=is_v2)
+
+        # Update all MAX_SPEAKERS slot dropdowns
+        slot_updates = [slot_dd_update] * MAX_SPEAKERS
+
+        yield (
+            success_msg,
+            gr.update(interactive=True), # btn_generate
+            gr.update(interactive=True), # btn_generate_conv
+            gr.update(interactive=True), # btn_load
+            gr.update(interactive=False), # btn_stop
+            voice_update,
+            tab_p, tab_c, tab_sel, mode_state,
+            conv_tab_update,
+            *slot_updates
+        )
+        
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        model_loaded = False
+        using_lmdeploy = False
+
+        if "$env:CUDA_PATH" in str(e):
+            yield (
+                "❌ Lỗi khi tải model: Không tìm thấy biến môi trường CUDA_PATH. Vui lòng cài đặt NVIDIA GPU Computing Toolkit (https://developer.nvidia.com/cuda/toolkit)",
+                gr.update(interactive=False),
+                gr.update(interactive=False), # btn_generate_conv
+                gr.update(interactive=True), # btn_load
+                gr.update(interactive=False), # btn_stop
+                gr.update(), # voice_select
+                gr.update(), gr.update(), gr.update(), gr.update(),
+                gr.update(), # conv_tab
+                *slot_no_updates
+            )
+        else: 
+            yield (
+                f"❌ Lỗi khi tải model: {str(e)}",
+                gr.update(interactive=False),
+                gr.update(interactive=False),
+                gr.update(interactive=True),
+                gr.update(interactive=False),
+                gr.update(),
+                gr.update(), gr.update(), gr.update(), gr.update(),
+                gr.update(), # conv_tab
+                *slot_no_updates
+            )
+
+
+def resolve_voice_id(v_id: str) -> str:
+    """Robustly resolve voice ID, handling both display labels and internal IDs."""
+    if not v_id:
+        return v_id
+    
+    global PRESET_VOICES_CACHE
+    if not PRESET_VOICES_CACHE:
+        return v_id
+        
+    for item in PRESET_VOICES_CACHE:
+        if isinstance(item, (list, tuple)) and len(item) >= 2:
+            label, value = item[0], item[1]
+            if v_id == value or v_id == label:
+                return value
+        else:
+            if v_id == item:
+                return item
+            
+    return v_id
+
+# --- TEXT SAFETY HELPERS ---
+# Chặn input vượt context 2048 token của backbone.
+MAX_INPUT_TOKENS = _text_settings.get("max_input_tokens", 1800)
+
+
+class _ChunkProxy:
+    """Giữ nguyên các thuộc tính gốc của chunk, chỉ thay text."""
+
+    def __init__(self, base_chunk, text: str):
+        self._base_chunk = base_chunk
+        self.text = text
+
+    def __getattr__(self, name):
+        return getattr(self._base_chunk, name)
+
+
+def _get_model_tokenizer():
+    """Lấy tokenizer từ tts hiện tại nếu có."""
+    global tts
+    if tts is None:
+        return None
+
+    candidate_attrs = ("tokenizer", "llm_tokenizer", "text_tokenizer", "processor")
+    for attr in candidate_attrs:
+        tok = getattr(tts, attr, None)
+        if tok is not None:
+            return tok
+
+    backbone = getattr(tts, "backbone", None)
+    if backbone is not None:
+        for attr in candidate_attrs:
+            tok = getattr(backbone, attr, None)
+            if tok is not None:
+                return tok
+
+    return None
+
+
+def _count_tokens(tokenizer, text: str) -> int:
+    """Đếm token an toàn, fallback 0 nếu tokenizer lỗi."""
+    if tokenizer is None:
+        return 0
+
+    try:
+        encoded = tokenizer(text, add_special_tokens=False)
+        input_ids = getattr(encoded, "input_ids", None)
+
+        if input_ids is None and isinstance(encoded, dict):
+            input_ids = encoded.get("input_ids")
+
+        if input_ids is None:
+            return 0
+
+        if isinstance(input_ids, list) and input_ids and isinstance(input_ids[0], (list, tuple)):
+            return len(input_ids[0])
+
+        return len(input_ids)
+    except Exception:
+        return 0
+
+
+def _split_text_by_token_limit(text: str, tokenizer, max_tokens: int = MAX_INPUT_TOKENS):
+    """Cắt text theo token, giữ style cắt theo từ."""
+    text = (text or "").strip()
+    if not text:
+        return []
+
+    if tokenizer is None:
+        return [text]
+
+    words = text.split()
+    if not words:
+        return [text]
+
+    chunks = []
+    current_words = []
+
+    for word in words:
+        candidate = " ".join(current_words + [word]).strip()
+
+        if current_words and _count_tokens(tokenizer, candidate) > max_tokens:
+            chunks.append(" ".join(current_words).strip())
+            current_words = [word]
+        else:
+            current_words.append(word)
+
+    if current_words:
+        chunks.append(" ".join(current_words).strip())
+
+    return chunks or [text]
+
+
+def _build_safe_text_chunks(raw_text: str, max_chars: int, is_v2_turbo: bool):
+    """
+    Tạo chunk theo flow cũ của repo, rồi chặn thêm theo token.
+    - V2 Turbo: giữ object chunk gốc, chỉ thay .text nếu cần.
+    - Standard: giữ list str như cũ.
+    """
+    tokenizer = _get_model_tokenizer()
+
+    if is_v2_turbo:
+        base_chunks = phonemize_to_chunks(raw_text, max_chars=max_chars)
+        if tokenizer is None:
+            return base_chunks
+
+        safe_chunks = []
+        for chunk in base_chunks:
+            chunk_text = getattr(chunk, "text", str(chunk))
+            refined_texts = _split_text_by_token_limit(chunk_text, tokenizer)
+
+            if len(refined_texts) == 1 and refined_texts[0] == chunk_text:
+                safe_chunks.append(chunk)
+            else:
+                for refined in refined_texts:
+                    safe_chunks.append(_ChunkProxy(chunk, refined))
+
+        return safe_chunks
+
+    safe_chunks = []
+    for raw_chunk in split_text_into_chunks(raw_text, max_chars=max_chars):
+        normalized_chunk = _text_normalizer.normalize(raw_chunk)
+        refined_texts = _split_text_by_token_limit(normalized_chunk, tokenizer)
+
+        for refined in refined_texts:
+            safe_chunks.extend(split_text_into_chunks(refined, max_chars=max_chars))
+
+    return safe_chunks
+
+
+# --- 2. DATA & HELPERS ---
+
+def synthesize_speech(text: str, voice_choice: str, custom_audio, custom_text: str, 
+                      mode_tab: str, generation_mode: str, use_batch: bool, max_batch_size_run: int,
+                      temperature: float, max_chars_chunk: int, session_id: str = None):
+    """Synthesis with optimization support and max batch size control"""
+    global tts, current_backbone, current_codec, model_loaded, using_lmdeploy
+    
+    _STOP_EVENT.clear()  # Reset for new generation
+    
+    if not model_loaded or tts is None:
+        yield None, "⚠️ Vui lòng tải model trước!"
+        return
+    
+    if not text or text.strip() == "":
+        yield None, "⚠️ Vui lòng nhập văn bản!"
+        return
+    
+    raw_text = text.strip()
+    
+    codec_config = CODEC_CONFIGS[current_codec]
+    use_preencoded = codec_config['use_preencoded']
+    
+    
+    # Setup Reference
+    yield None, "📄 Đang xử lý Reference..."
+    
+    try:
+        ref_codes = None
+        ref_text_raw = ""
+        
+        if mode_tab == "preset_mode":
+            if not voice_choice:
+                raise ValueError("Vui lòng chọn giọng mẫu.")
+            if "⚠️" in voice_choice:
+                raise ValueError("Không có giọng mẫu khả dụng. Vui lòng chuyển sang Tab Voice Cloning.")
+            
+            # Use SDK method - handles caching and JSON internally
+            v_id = resolve_voice_id(voice_choice)
+            voice_data = tts.get_preset_voice(v_id)
+            ref_codes = voice_data['codes']
+            ref_text_raw = voice_data['text']
+        
+        elif mode_tab == "custom_mode":
+            if custom_audio is None:
+                raise ValueError("Vui lòng upload file Audio mẫu (Reference Audio)!")
+            
+            is_turbo = "v2-Turbo" in (current_backbone or "")
+            if not is_turbo and (not custom_text or not custom_text.strip()):
+                raise ValueError("Vui lòng nhập nội dung văn bản của Audio mẫu (Reference Text)!")
+            
+            ref_text_raw = custom_text.strip() if custom_text else ""
+            ref_codes = tts.encode_reference(custom_audio)
+
+        # Ensure numpy for inference
+        if 'torch' in sys.modules:
+            import torch
+            if isinstance(ref_codes, torch.Tensor):
+                ref_codes = ref_codes.cpu().numpy()
+
+    except Exception as e:
+        yield None, f"❌ Lỗi xử lý Reference Audio: {str(e)}"
+        return
+    
+    # === STANDARD MODE ===
+    if generation_mode == "Standard (Một lần)":
+        backend_name = "LMDeploy" if using_lmdeploy else "Standard"
+
+        is_v2_turbo = "v2-Turbo" in (current_backbone or "")
+        text_chunks = _build_safe_text_chunks(raw_text, max_chars_chunk, is_v2_turbo)
+        total_chunks = len(text_chunks)
+
+        batch_info = " (Batch Mode)" if use_batch and using_lmdeploy and total_chunks > 1 else ""
+        
+        # Show batch size info
+        batch_size_info = ""
+        if use_batch and using_lmdeploy and hasattr(tts, 'max_batch_size'):
+            batch_size_info = f" [Max batch: {tts.max_batch_size}]"
+        
+        yield None, f"🚀 Bắt đầu tổng hợp {backend_name}{batch_info}{batch_size_info} ({total_chunks} đoạn)..."
+        
+        all_wavs = []
+        sr = 24000
+        
+        start_time = time.time()
+        
+        try:
+            if is_v2_turbo:
+                # Sequential processing with progress updates
+                total_chunks = len(text_chunks)
+                for i, chunk in enumerate(text_chunks):
+                    if _STOP_EVENT.is_set():
+                        yield None, "⏹️ Đã dừng tạo giọng nói."
+                        return
+                    yield None, f"⚡ Turbo v2: Đang xử lý đoạn {i+1}/{total_chunks}..."
+                    
+                    chunk_wav = tts.infer(
+                        chunk.text, 
+                        ref_codes=ref_codes, 
+                        temperature=temperature,
+                        max_chars=max_chars_chunk,
+                        skip_normalize=True,
+                        skip_phonemize=True
+                    )
+                    
+                    if chunk_wav is not None and len(chunk_wav) > 0:
+                        all_wavs.append(chunk_wav)
+                        # Add silence between Gradio-level chunks for Turbo
+                        if i < total_chunks - 1:
+                            sil_dur = get_silence_duration_v2(chunk)
+                            sil_wav = np.zeros(int(sr * sil_dur), dtype=np.float32)
+                            all_wavs.append(sil_wav)
+            
+            # Use batch processing if enabled and using LMDeploy (for v1)
+            elif use_batch and using_lmdeploy and hasattr(tts, 'infer_batch') and total_chunks > 1:
+                # Process in mini-batches to allow cancellation between batches
+                num_batches = (total_chunks + max_batch_size_run - 1) // max_batch_size_run
+                total_batch_duration = 0.0
+                completed_batches = 0
+                
+                for i in range(0, total_chunks, max_batch_size_run):
+                    if _STOP_EVENT.is_set():
+                        print("🛑 Synthesis stopped during batch processing.")
+                        yield None, "⏹️ Đã dừng tạo giọng nói."
+                        return
+                    
+                    batch_idx = i // max_batch_size_run
+                    estimate_info = ""
+                    if completed_batches > 0:
+                        average_batch_duration = total_batch_duration / completed_batches
+                        estimated_total = average_batch_duration * num_batches
+                        estimated_remaining = average_batch_duration * max(0, num_batches - batch_idx)
+                        estimate_info = (
+                            f" | Ước tính còn lại: {_format_duration(estimated_remaining)}"
+                            f" / tổng: {_format_duration(estimated_total)}"
+                        )
+                    yield None, f"⚡ Đang xử lý batch {batch_idx+1}/{num_batches} (đoạn {i+1}-{min(i+max_batch_size_run, total_chunks)}){estimate_info}..."
+                    
+                    current_batch = text_chunks[i : i + max_batch_size_run]
+                    batch_start_time = time.time()
+                    batch_wavs = tts.infer_batch(
+                        current_batch, 
+                        ref_codes=ref_codes, 
+                        ref_text=ref_text_raw,
+                        max_batch_size=max_batch_size_run,
+                        temperature=temperature,
+                        skip_normalize=True
+                    )
+                    batch_duration = time.time() - batch_start_time
+                    total_batch_duration += batch_duration
+                    completed_batches += 1
+                    average_batch_duration = total_batch_duration / completed_batches
+                    estimated_total = average_batch_duration * num_batches
+                    estimated_remaining = average_batch_duration * max(0, num_batches - completed_batches)
+                    for chunk_wav in batch_wavs:
+                        if chunk_wav is not None and len(chunk_wav) > 0:
+                            all_wavs.append(chunk_wav)
+                    yield None, (
+                        f"✅ Xong batch {batch_idx+1}/{num_batches} "
+                        f"(trung bình batch: {_format_duration(average_batch_duration)}, "
+                        f"ước tính còn lại: {_format_duration(estimated_remaining)}, "
+                        f"tổng: {_format_duration(estimated_total)})"
+                    )
+
+            else:
+                # Sequential processing (PyTorch or GGUF v1)
+                for i, chunk in enumerate(text_chunks):
+                    if _STOP_EVENT.is_set():
+                        yield None, "⏹️ Đã dừng tạo giọng nói."
+                        return
+                    yield None, f"⏳ Đang xử lý đoạn {i+1}/{total_chunks}..."
+                    chunk_wav = tts.infer(
+                        chunk, 
+                        ref_codes=ref_codes, 
+                        ref_text=ref_text_raw,
+                        temperature=temperature,
+                        max_chars=max_chars_chunk,
+                        skip_normalize=True
+                    )
+                    if chunk_wav is not None and len(chunk_wav) > 0:
+                        all_wavs.append(chunk_wav)
+            
+            if not all_wavs:
+                yield None, "❌ Không sinh được audio nào."
+                return
+            
+            yield None, "💾 Đang ghép file và lưu..."
+            
+            # Use utility function for joining with silence/crossfade
+            # Default silence=0.15s to match SDK
+            silence_p = 0.15 if not is_v2_turbo else 0.0 # Turbo adds silence internally
+            final_wav = join_audio_chunks(all_wavs, sr=sr, silence_p=silence_p)
+            
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+                sf.write(tmp.name, final_wav, sr)
+                output_path = tmp.name
+            
+            process_time = time.time() - start_time
+            backend_info = f" (Backend: {'LMDeploy 🚀' if using_lmdeploy else 'Standard 📦'})"
+            speed_info = f", Tốc độ: {len(final_wav)/sr/process_time:.2f}x realtime" if process_time > 0 else ""
+            
+            
+            yield output_path, f"✅ Hoàn tất! (Thời gian: {process_time:.2f}s{speed_info}){backend_info}"
+            
+            # Cleanup memory
+            if using_lmdeploy and hasattr(tts, 'cleanup_memory'):
+                tts.cleanup_memory()
+            
+            cleanup_gpu_memory()
+            
+        except Exception as e:
+            # Check for CUDA OOM specifically if torch is loaded
+            if 'torch' in sys.modules:
+                import torch
+                if isinstance(e, torch.cuda.OutOfMemoryError):
+                    cleanup_gpu_memory()
+                    yield None, (
+                        f"❌ GPU hết VRAM! Hãy thử:\n"
+                        f"• Giảm Max Batch Size (hiện tại: {tts.max_batch_size if hasattr(tts, 'max_batch_size') else 'N/A'})\n"
+                        f"• Giảm độ dài văn bản\n\n"
+                        f"Chi tiết: {str(e)}"
+                    )
+                    return
+            
+            import traceback
+            traceback.print_exc()
+            cleanup_gpu_memory()
+            yield None, f"❌ Lỗi Standard Mode: {str(e)}"
+            return
+    
+    # === STREAMING MODE ===
+    else:
+        sr = 24000
+        crossfade_samples = int(sr * 0.03)
+        audio_queue = queue.Queue(maxsize=100)
+        PRE_BUFFER_SIZE = 3
+        
+        end_event = threading.Event()
+        error_event = threading.Event()
+        error_msg = ""
+        
+        is_v2_turbo = "v2-Turbo" in (current_backbone or "")
+        text_chunks = _build_safe_text_chunks(raw_text, max_chars_chunk, is_v2_turbo)
+
+        def producer_thread():
+            nonlocal error_msg
+            try:
+                previous_tail = None
+                
+                for i, chunk_text in enumerate(text_chunks):
+                    if _STOP_EVENT.is_set():
+                        break
+                    
+                    if is_v2_turbo:
+                        stream_gen = tts.infer_stream(
+                            chunk_text.text,
+                            ref_codes=ref_codes, 
+                            temperature=temperature,
+                            max_chars=max_chars_chunk,
+                            skip_normalize=True,
+                            skip_phonemize=True,
+                            emotion_tag=""
+                        )
+                    else:
+                        stream_gen = tts.infer_stream(
+                            chunk_text, 
+                            ref_codes=ref_codes, 
+                            ref_text=ref_text_raw,
+                            temperature=temperature,
+                            max_chars=max_chars_chunk,
+                            skip_normalize=True,
+                            emotion_tag=""
+                        )
+                    
+                    for part_idx, audio_part in enumerate(stream_gen):
+                        if _STOP_EVENT.is_set():
+                            break
+                        if audio_part is None or len(audio_part) == 0:
+                            continue
+                        
+                        if previous_tail is not None and len(previous_tail) > 0:
+                            overlap = min(len(previous_tail), len(audio_part), crossfade_samples)
+                            if overlap > 0:
+                                fade_out = np.linspace(1.0, 0.0, overlap, dtype=np.float32)
+                                fade_in = np.linspace(0.0, 1.0, overlap, dtype=np.float32)
+                                
+                                blended = (audio_part[:overlap] * fade_in + 
+                                         previous_tail[-overlap:] * fade_out)
+                                
+                                processed = np.concatenate([
+                                    previous_tail[:-overlap] if len(previous_tail) > overlap else np.array([]),
+                                    blended,
+                                    audio_part[overlap:]
+                                ])
+                            else:
+                                processed = np.concatenate([previous_tail, audio_part])
+                            
+                            tail_size = min(crossfade_samples, len(processed))
+                            previous_tail = processed[-tail_size:].copy()
+                            output_chunk = processed[:-tail_size] if len(processed) > tail_size else processed
+                        else:
+                            tail_size = min(crossfade_samples, len(audio_part))
+                            previous_tail = audio_part[-tail_size:].copy()
+                            output_chunk = audio_part[:-tail_size] if len(audio_part) > tail_size else audio_part
+                        
+                        if len(output_chunk) > 0:
+                            audio_queue.put((sr, output_chunk))
+                            
+                    # Add silence between chunks for Turbo v2
+                    if is_v2_turbo and i < len(text_chunks) - 1:
+                        sil_dur = get_silence_duration_v2(chunk_text)
+                        sil_wav = np.zeros(int(sr * sil_dur), dtype=np.float32)
+                        audio_queue.put((sr, sil_wav))
+                
+                if previous_tail is not None and len(previous_tail) > 0:
+                    audio_queue.put((sr, previous_tail))
+                    
+            except Exception as e:
+                import traceback
+                traceback.print_exc()
+                error_msg = str(e)
+                error_event.set()
+            finally:
+                end_event.set()
+                audio_queue.put(None)
+        
+        threading.Thread(target=producer_thread, daemon=True).start()
+        
+        yield (sr, np.zeros(int(sr * 0.05))), "📄 Đang buffering..."
+        
+        pre_buffer = []
+        while len(pre_buffer) < PRE_BUFFER_SIZE:
+            try:
+                item = audio_queue.get(timeout=5.0)
+                if item is None:
+                    break
+                pre_buffer.append(item)
+            except queue.Empty:
+                if error_event.is_set():
+                    yield None, f"❌ Lỗi: {error_msg}"
+                    return
+                break
+        
+        full_audio_buffer = []
+        backend_info = "🚀 LMDeploy" if using_lmdeploy else "📦 Standard"
+        for sr, audio_data in pre_buffer:
+            full_audio_buffer.append(audio_data)
+            yield (sr, audio_data), f"🔊 Đang phát ({backend_info})..."
+        
+        while True:
+            try:
+                item = audio_queue.get(timeout=0.05)
+                if item is None:
+                    break
+                sr, audio_data = item
+                full_audio_buffer.append(audio_data)
+                yield (sr, audio_data), f"🔊 Đang phát ({backend_info})..."
+            except queue.Empty:
+                if error_event.is_set():
+                    yield None, f"❌ Lỗi: {error_msg}"
+                    break
+                if end_event.is_set() and audio_queue.empty():
+                    break
+                continue
+        
+        if full_audio_buffer:
+            final_wav = np.concatenate(full_audio_buffer)
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+                sf.write(tmp.name, final_wav, sr)
+                
+                yield tmp.name, f"✅ Hoàn tất Streaming! ({backend_info})"
+            
+            # Cleanup memory
+            if using_lmdeploy and hasattr(tts, 'cleanup_memory'):
+                tts.cleanup_memory()
+            
+            cleanup_gpu_memory()
+
+synthesize_speech_with_estimate = wrap_with_estimate(synthesize_speech)
+
+def synthesize_conversation_with_empty_estimate(*args):
+    for audio_path, status in synthesize_conversation(*args):
+        yield audio_path, status, ""
+
+# --- CANCELLATION ---
+# threading.Event is a mutable object: never reassigned, always the same reference.
+# All threads share the exact same object — no scoping/serialization issues.
+_STOP_EVENT = threading.Event()
+
+# --- 3. CONVERSATION LOGIC ---
+
+def synthesize_conversation(
+    script_text: str,
+    *args
+):
+    """
+    Synthesizes multi-speaker conversation from a script.
+
+    Gradio passes speaker name boxes and voice dropdowns as individual positional args.
+    Layout: args[0..MAX_SPEAKERS-1] = speaker names, args[MAX_SPEAKERS..2*MAX_SPEAKERS-1] = voice IDs,
+    args[2*MAX_SPEAKERS] = silence_duration, args[2*MAX_SPEAKERS+1] = temperature,
+    args[2*MAX_SPEAKERS+2] = max_chars_chunk, args[2*MAX_SPEAKERS+3] = session_id
+    """
+    speaker_names     = list(args[:MAX_SPEAKERS])
+    speaker_voices    = list(args[MAX_SPEAKERS:MAX_SPEAKERS*2])
+    silence_duration  = args[MAX_SPEAKERS * 2]
+    temperature       = args[MAX_SPEAKERS * 2 + 1]
+    max_chars_chunk   = args[MAX_SPEAKERS * 2 + 2]
+    session_id        = args[MAX_SPEAKERS * 2 + 3] if len(args) > MAX_SPEAKERS * 2 + 3 else None
+
+    global tts, model_loaded, using_lmdeploy
+    
+    _STOP_EVENT.clear()
+    
+    if not model_loaded or tts is None:
+        yield None, "⚠️ Vui lòng tải model trước!"
+        return
+        
+    if not script_text or script_text.strip() == "":
+        yield None, "⚠️ Vui lòng nhập kịch bản hội thoại!"
+        return
+
+    # 1. Parse Script
+    lines = []
+    for line in script_text.strip().split('\n'):
+        if not line.strip(): continue
+        if ':' in line:
+            parts = line.split(':', 1)
+            lines.append({'speaker': parts[0].strip(), 'text': parts[1].strip()})
+        else:
+            if lines:
+                lines[-1]['text'] += " " + line.strip()
+            else:
+                lines.append({'speaker': 'Narrator', 'text': line.strip()})
+
+    if not lines:
+        yield None, "⚠️ Không tìm thấy lời thoại hợp lệ (định dạng Nhân vật: Lời thoại)!"
+        return
+
+    # 2. Build Speaker Mapping from individual slot components
+    mapping = {}
+    for name, voice in zip(speaker_names, speaker_voices):
+        name = str(name).strip() if name else ""
+        if not name: continue
+        # Use lowercase key for robust matching
+        v_id = resolve_voice_id(str(voice)) if voice else ""
+        mapping[name.lower()] = {
+            'type': 'Preset',
+            'voice': v_id,
+            'ref_text': ''
+        }
+
+
+    # 3. Process Each Line
+    all_wavs = []
+    sr = 24000
+    total_lines = len(lines)
+    
+    yield None, f"🎭 Đang khởi tạo hội thoại ({total_lines} câu)..."
+    
+    start_time = time.time()
+    
+    try:
+        for i, line in enumerate(lines):
+            if _STOP_EVENT.is_set():
+                yield None, "⏹️ Đã dừng hội thoại."
+                return
+            spk_name = line['speaker']
+            text = line['text']
+            
+            yield None, f"⏳ [{i+1}/{total_lines}] {spk_name}: {text[:30]}..."
+            
+            # Determine voice
+            ref_codes = None
+            ref_text_val = None
+            current_voice_obj = None
+            
+            # Case-insensitive lookup
+            config = mapping.get(spk_name.lower())
+            
+            if not config:
+                print(f"  ⚠️ Character '{spk_name}' not found in mapping. Fallback to default.")
+                # Fallback to default if speaker not mapped
+                try:
+                    # Get default voice data
+                    default_v_id = tts._default_voice
+                    if not default_v_id:
+                        dv_list = tts.list_preset_voices()
+                        if dv_list:
+                            first = dv_list[0]
+                            default_v_id = first[1] if isinstance(first, tuple) else first
+                    
+                    if default_v_id:
+                        current_voice_obj = tts.get_preset_voice(default_v_id)
+                        ref_codes = current_voice_obj['codes']
+                        ref_text_val = current_voice_obj['text']
+                except Exception as e:
+                    print(f"  ❌ Fallback failed: {e}")
+            else:
+                try:
+                    v_id = config['voice']
+                    if config['type'] == "Preset":
+                        current_voice_obj = tts.get_preset_voice(v_id)
+                        if current_voice_obj and 'codes' in current_voice_obj:
+                            ref_codes = current_voice_obj['codes']
+                            ref_text_val = current_voice_obj['text']
+                        else:
+                            print(f"  ❌ Could not find codes for voice '{v_id}'")
+                    else: # Custom
+                        if v_id and os.path.exists(v_id):
+                            ref_codes = tts.encode_reference(v_id)
+                            ref_text_val = config.get('ref_text', '')
+                            current_voice_obj = {'codes': ref_codes, 'text': ref_text_val}
+                            print(f"  🦜 Using custom voice for '{spk_name}'")
+                except Exception as e:
+                    print(f"  ❌ Lỗi nạp giọng cho {spk_name} (ID: {config.get('voice')}): {e}")
+            
+            # Ensure numpy for inference
+            if 'torch' in sys.modules:
+                import torch
+                if isinstance(ref_codes, torch.Tensor):
+                    ref_codes = ref_codes.cpu().numpy()
+
+            # Infer audio
+            try:
+                wav = tts.infer(
+                    text,
+                    voice=current_voice_obj, # Use full voice object
+                    ref_codes=ref_codes,     # Fallback if object not supported
+                    ref_text=ref_text_val,
+                    temperature=temperature,
+                    max_chars=max_chars_chunk,
+                    emotion_tag="<|emotion_0|>" # Emotion tag for conversation
+                )
+                
+                all_wavs.append(wav)
+                
+                # Add silence between turns
+                if i < total_lines - 1 and silence_duration > 0:
+                    silence_len = int(sr * silence_duration)
+                    silence = np.zeros(silence_len)
+                    all_wavs.append(silence)
+                    
+            except Exception as e:
+                print(f"❌ Lỗi tổng hợp câu {i+1}: {e}")
+                continue
+
+        if not all_wavs:
+            yield None, "❌ Không thể tạo được âm thanh nào!"
+            return
+
+        # 4. Merge and Output
+        yield None, "🪄 Đang ghép nối âm thanh..."
+        final_wav = np.concatenate(all_wavs)
+        
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+            sf.write(tmp.name, final_wav, sr)
+            elapsed = time.time() - start_time
+            yield tmp.name, f"✅ Hoàn tất hội thoại! ({total_lines} câu, xử lý trong {elapsed:.1f}s)"
+            
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        yield None, f"❌ Lỗi hệ thống: {str(e)}"
+
+def extract_speakers_from_script(script):
+    """Find unique speakers and return gr.update() lists for the 8 slot components."""
+    global CONV_VOICES_CACHE
+    if not script:
+        # Hide all slots
+        name_updates = [gr.update(value="", visible=False)] * MAX_SPEAKERS
+        dd_updates   = [gr.update(value=None, visible=False)] * MAX_SPEAKERS
+        row_updates  = [gr.update(visible=False)] * MAX_SPEAKERS
+        return name_updates + dd_updates + row_updates
+
+    speakers = []
+    seen = set()
+    for line in script.strip().split('\n'):
+        if ':' in line:
+            s = line.split(':', 1)[0].strip()
+            if s and s not in seen:
+                seen.add(s)
+                speakers.append(s)
+
+    # Auto-match each speaker name to a preset voice
+    def _best_match(name):
+        if not CONV_VOICES_CACHE:
+            return None
+        
+        name_l = name.lower()
+        
+        # 0. Manual overrides for specific common names
+        overrides = {
+            "phương": "Trúc Ly",
+            "dũng": "Thanh Bình",
+            "hùng": "Thái Sơn"
+        }
+        if name_l in overrides:
+            target = overrides[name_l].lower()
+            for v in CONV_VOICES_CACHE:
+                label, value = (v[0], v[1]) if isinstance(v, tuple) else (v, v)
+                if target in label.lower() or target in value.lower():
+                    return value
+
+        # 1. Try to find name in labels or values
+        for v in CONV_VOICES_CACHE:
+            label, value = (v[0], v[1]) if isinstance(v, tuple) else (v, v)
+            if name_l == label.lower() or name_l == value.lower():
+                return value
+        
+        # 2. Fuzzy match (contains)
+        for v in CONV_VOICES_CACHE:
+            label, value = (v[0], v[1]) if isinstance(v, tuple) else (v, v)
+            if name_l in label.lower() or name_l in value.lower() or label.lower() in name_l or value.lower() in name_l:
+                return value
+        
+        # 3. Default to first voice if no match
+        first_voice = CONV_VOICES_CACHE[0]
+        return first_voice[1] if isinstance(first_voice, tuple) else first_voice
+
+    name_updates, dd_updates, row_updates = [], [], []
+    for i in range(MAX_SPEAKERS):
+        if i < len(speakers):
+            name_updates.append(gr.update(value=speakers[i], visible=True))
+            dd_updates.append(gr.update(value=_best_match(speakers[i]), choices=CONV_VOICES_CACHE, visible=True))
+            row_updates.append(gr.update(visible=True))
+        else:
+            name_updates.append(gr.update(value="", visible=False))
+            dd_updates.append(gr.update(value=None, choices=CONV_VOICES_CACHE, visible=False))
+            row_updates.append(gr.update(visible=False))
+
+    return name_updates + dd_updates + row_updates
+
+EXAMPLES_LIST = [
+    ["Về miền Tây không chỉ để ngắm nhìn sông nước hữu tình, mà còn để cảm nhận tấm chân tình của người dân nơi đây.", "Vĩnh (nam miền Nam)"],
+    ["Hà Nội những ngày vào thu mang một vẻ đẹp trầm mặc và cổ kính đến lạ thường.", "Bình (nam miền Bắc)"],
+]
+
+with gr.Blocks(theme=theme, css=css, title="VieNeu-TTS", head=head_html) as demo:
+    # Session ID for cancellation tracking
+    session_id_state = gr.State("")
+
+    with gr.Column(elem_classes="container"):
+        gr.HTML("""
+<div class="header-box">
+    <h1 class="header-title">
+        <span class="header-icon">🦜</span>
+        <span class="gradient-text">VieNeu-TTS Studio</span>
+    </h1>
+    <div class="model-card-content">
+        <div class="model-card-item">
+            <strong>Models:</strong>
+            <a href="https://huggingface.co/pnnbao-ump/VieNeu-TTS" target="_blank" class="model-card-link">VieNeu-TTS</a>
+            <span>•</span>
+            <a href="https://huggingface.co/pnnbao-ump/VieNeu-TTS-v2" target="_blank" class="model-card-link">VieNeu-TTS-v2</a>
+        </div>
+        <div class="model-card-item">
+            <strong>Repository:</strong>
+            <a href="https://github.com/pnnbao97/VieNeu-TTS" target="_blank" class="model-card-link">GitHub</a>
+        </div>
+        <div class="model-card-item">
+            <strong>Tác giả:</strong>
+            <a href="https://www.facebook.com/pnnbao97" target="_blank" class="model-card-link">Phạm Nguyễn Ngọc Bảo</a>
+        </div>
+        <div class="model-card-item">
+            <strong>Discord:</strong>
+            <a href="https://discord.gg/yJt8kzjzWZ" target="_blank" class="model-card-link">Tham gia cộng đồng</a>
+        </div>
+    </div>
+</div>
+        """)
+        
+        # --- CONFIGURATION ---
+        with gr.Group():
+            with gr.Row():
+                # --- BACKBONE & CODEC DEFAULT LOGIC ---
+                if "VieNeu-TTS-v2 (GPU)" in BACKBONE_CONFIGS:
+                    default_backbone = "VieNeu-TTS-v2 (GPU)"
+                elif "VieNeu-TTS-v2-Turbo (CPU)" in BACKBONE_CONFIGS:
+                    default_backbone = "VieNeu-TTS-v2-Turbo (CPU)"
+                else:
+                    default_backbone = list(BACKBONE_CONFIGS.keys())[0]
+                
+                # Default parameters based on backbone
+                if "Turbo" in default_backbone:
+                    default_codec = "VieNeu-Codec"
+                    default_temp = 0.4
+                    default_text = DEFAULT_TEXT_TURBO
+                elif "(CPU)" in default_backbone:
+                    default_codec = "NeuCodec (ONNX)"
+                    default_temp = 0.7
+                    default_text = DEFAULT_TEXT_GPU
+                else:
+                    default_codec = "NeuCodec (Distill)" if "NeuCodec (Distill)" in CODEC_CONFIGS else list(CODEC_CONFIGS.keys())[0]
+                    default_temp = 0.7
+                    default_text = DEFAULT_TEXT_GPU
+
+                backbone_select = gr.Dropdown(
+                    list(BACKBONE_CONFIGS.keys()) + ["Custom Model"], 
+                    value=default_backbone, 
+                    label="🦜 Backbone"
+                )
+                codec_select = gr.Dropdown(
+                    list(CODEC_CONFIGS.keys()), 
+                    value=default_codec, 
+                    label="🎵 Codec",
+                    interactive=False
+                )
+                device_choice = gr.Radio(get_available_devices(), value="Auto", label="🖥️ Device")
+            
+            with gr.Row(visible=False) as custom_model_group:
+                custom_backbone_model_id = gr.Textbox(
+                    label="📦 Custom Model ID",
+                    placeholder="pnnbao-ump/VieNeu-TTS-0.3B-lora-ngoc-huyen",
+                    info="Nhập HuggingFace Repo ID hoặc đường dẫn local",
+                    scale=2
+                )
+                custom_backbone_hf_token = gr.Textbox(
+                    label="🔑 HF Token (nếu private)",
+                    placeholder="Để trống nếu repo public",
+                    type="password",
+                    info="Token để truy cập repo private",
+                    scale=1
+                )
+                base_model_choices = [k for k in BACKBONE_CONFIGS.keys() if "turbo" not in k.lower() and k != "Custom Model"]
+                custom_backbone_base_model = gr.Dropdown(
+                    base_model_choices,
+                    label="🔗 Base Model (cho LoRA)",
+                    value=base_model_choices[0] if base_model_choices else None,
+                    visible=False,
+                    info="Model gốc để merge với LoRA (GPU Only)",
+                    scale=1
+                )
+            
+            with gr.Row():
+                use_lmdeploy_cb = gr.Checkbox(
+                    value=True, 
+                    label="🚀 Optimize with LMDeploy (Khuyên dùng cho NVIDIA GPU)",
+                    info="Tick nếu bạn dùng GPU để tăng tốc độ tổng hợp đáng kể."
+                )
+            
+            
+            gr.Markdown("""
+            💡 **Sử dụng Custom Model:** Chọn "Custom Model" để tải LoRA adapter hoặc bất kỳ model nào được finetune từ **VieNeu-TTS** hoặc **VieNeu-TTS-0.3B**.
+            """)
+            
+            gr.HTML("""
+            <div class="warning-banner">
+                <div class="warning-banner-title">
+                    🦜 Gợi ý tối ưu hiệu năng
+                </div>
+                <div class="warning-banner-grid">
+                    <div class="warning-banner-item">
+                        <strong>🐆 Hệ máy GPU</strong>
+                        <div class="warning-banner-content">
+                            Chế độ podcast và song ngữ Anh Việt đã được hỗ trợ bắt đầu từ phiên bản <b>VieNeu-TTS-v2</b>, tuy nhiên quá trình kiểm thử vẫn đang tiếp tục, có thể sẽ xảy ra lỗi không mong muốn, nếu có lỗi các bạn hãy thông báo với chúng tôi tại: https://discord.com/invite/yJt8kzjzWZ. Trong trường hợp bạn cần sự ổn định hãy sử dụng <b>VieNeu-TTS (GPU)</b>. 
+                        </div>
+                    </div>
+                    <div class="warning-banner-item" style="background: #dcfce7; border-color: #86efac;">
+                        <strong style="color: #15803d;">🐢 Hệ máy CPU</strong>
+                        <div class="warning-banner-content" style="color: #166534;">
+                            Mặc định là <b>VieNeu-TTS-v2-Turbo (CPU)</b> để tốc độ tổng hợp nhanh nhất có thể, tuy nhiên có hạn chế về chất lượng âm thanh. Trong trường hợp bạn cần chất lượng tốt nhất hãy sử dụng <b>VieNeu-TTS-v2 (CPU)</b>.
+                        </div>
+                    </div>
+                </div>
+                <div style="margin-top: 12px; font-size: 0.85rem; color: #92400e; border-top: 1px dashed #fcd34d; padding-top: 8px;">
+                    💡 <b>Mẹo:</b> Nếu máy bạn có GPU mà không thấy các phiên bản GPU hãy xem lại cách cài đặt uv sync --group gpu
+                </div>
+            </div>
+            """)
+
+            btn_load = gr.Button("🔄 Tải Model", variant="primary")
+            model_status = gr.Markdown("⏳ Chưa tải model.")
+        
+        with gr.Row(elem_classes="container"):
+            # --- INPUT ---
+            with gr.Column(scale=3):
+                with gr.Tabs() as main_input_tabs:
+                    # --- TAB 1: SINGLE SPEAKER ---
+                    with gr.Tab("🦜 Đọc truyện", id="single_tab") as single_tab:
+                        text_input = gr.Textbox(
+                            label=f"Văn bản",
+                            lines=8,
+                            value=default_text,
+                        )
+                        
+                        with gr.Tabs() as tabs:
+                            with gr.TabItem("👤 Preset", id="preset_mode") as tab_preset:
+                                voice_select = gr.Dropdown(choices=[], value=None, label="Giọng mẫu", allow_custom_value=True)
+                            
+                            with gr.TabItem("🦜 Voice Cloning", id="custom_mode") as tab_custom:
+                                with gr.Group(visible=True) as cloning_elements_group:
+                                    custom_audio = gr.Audio(label="Audio giọng mẫu (3-5 giây) (.wav)", type="filepath")
+                                    cloning_warning_msg = gr.Markdown(visible=False, elem_id="cloning-warning")
+                                    custom_text = gr.Textbox(label="Nội dung audio mẫu - vui lòng gõ đúng nội dung của audio mẫu - kể cả dấu câu vì model rất nhạy cảm với dấu câu (.,?!)")
+                                    gr.Examples(
+                                        examples=[
+                                            [os.path.join(os.path.dirname(os.path.dirname(__file__)), "examples", "audio_ref", "example.wav"), "Ví dụ 2. Tính trung bình của dãy số."],
+                                            [os.path.join(os.path.dirname(os.path.dirname(__file__)), "examples", "audio_ref", "example_2.wav"), "Trên thực tế, các nghi ngờ đã bắt đầu xuất hiện."],
+                                            [os.path.join(os.path.dirname(os.path.dirname(__file__)), "examples", "audio_ref", "example_3.wav"), "Cậu có nhìn thấy không?"],
+                                            [os.path.join(os.path.dirname(os.path.dirname(__file__)), "examples", "audio_ref", "example_4.wav"), "Tết là dịp mọi người háo hức đón chào một năm mới với nhiều hy vọng và mong ước."]
+                                        ],
+                                        inputs=[custom_audio, custom_text],
+                                        label="Ví dụ mẫu để thử nghiệm clone giọng"
+                                    )
+                                    
+                                    gr.Markdown("""
+                                    **💡 Mẹo nhỏ:** Nếu kết quả Zero-shot Voice Cloning chưa như ý, bạn hãy cân nhắc **Finetune (LoRA)** để đạt chất lượng tốt nhất. 
+                                    Hướng dẫn chi tiết có tại file: `finetune/README.md` hoặc xem trên [GitHub](https://github.com/pnnbao97/VieNeu-TTS/tree/main/finetune).
+                                    """)
+                        
+                        generation_mode = gr.Radio(
+                            ["Standard (Một lần)"],
+                            value="Standard (Một lần)",
+                            label="Chế độ sinh"
+                        )
+                        btn_generate = gr.Button("🎵 Bắt đầu", variant="primary", scale=2, interactive=False)
+
+                    # --- TAB 2: MULTI-SPEAKER CONVERSATION ---
+                    with gr.Tab("🎭 Hội thoại", id="conv_tab", visible=False) as conv_tab:
+                        conv_script_input = gr.Textbox(
+                            label="Kịch bản hội thoại",
+                            placeholder="Phương: Chào mọi người, mình là Phương...",
+                            lines=10,
+                            elem_classes="script-box",
+                            value='Phương: Chào mọi người, mình là Phương. Hôm nay team có một announcement cực lớn về VieNeu-TTS Version 2. Đồng hành cùng mình là anh Dũng và Hùng. Hi guys!\n\nDũng: Yo, chào cả nhà. Mình sẽ đi thẳng vào technical side của bản nâng cấp này để mọi người có cái nhìn deep hơn nhé.\n\nHùng: Chào mọi người. Thật sự V2 là một huge milestone. Nó phá vỡ rào cản của những công cụ đọc văn bản khô khan, hướng tới một sự natural communication đúng nghĩa.\n\nPhương: Correct! Và bất ngờ nhất là: nãy giờ mọi người đang nghe bản demo được tạo ra 100% bằng VieNeu-TTS V2 đấy. Tụi mình đều là sản phẩm của AI hết. Amazing, right?\n\nDũng: Đỉnh thật sự! Tiện đây Hùng share thêm về cái nội công bên trong của model này đi.\n\nHùng: Chắc chắn rồi. Model được train trên 10000 hours audio chất lượng cao, nên nó hỗ trợ code-switching Anh Việt cực mượt, tự nhiên như podcast. Đặc biệt, dự án này hoàn toàn open-source để cộng đồng cùng phát triển.\n\nDũng: Về hiệu năng thì khỏi bàn. Khi test trên GPU quốc dân RTX 3060, tốc độ sinh audio nhanh gấp 10 lần realtime. Và đừng lo, nếu bạn không có card đồ hỏa xịn, tụi mình có sẵn bản CPU version để ai cũng có thể tiếp cận được.\n\nPhương: Tốc độ cực nhanh, hỗ trợ đa nền tảng và hoàn toàn miễn phí. Mọi người hãy cùng trải nghiệm nhé!'
+                        )
+                        
+                        with gr.Row():
+                            btn_detect_speakers = gr.Button("🔍 Quét nhân vật", size="sm", variant="secondary")
+                            silence_slider = gr.Slider(minimum=0, maximum=3, value=0.1, step=0.1, label="⏱️ Khoảng lặng (giây)")
+
+                        gr.Markdown("### 🎭 Cấu hình giọng đọc")
+                        gr.Markdown("*Nhấn **Quét nhân vật** để tự động phát hiện và ánh xạ giọng đọc. Tải model trước để có danh sách giọng.*")
+
+                        # Pre-build MAX_SPEAKERS speaker slot rows
+                        speaker_name_boxes = []
+                        speaker_voice_dds  = []
+                        speaker_slot_rows  = []
+
+                        for _i in range(MAX_SPEAKERS):
+                            # Mặc định cho 3 nhân vật đầu tiên theo yêu cầu
+                            _default_name = ""
+                            _default_voice = None
+                            _row_visible = False
+                            
+                            if _i == 0:
+                                _default_name = "Phương"
+                                _default_voice = "Ly"
+                                _row_visible = True
+                            elif _i == 1:
+                                _default_name = "Dũng"
+                                _default_voice = "Binh"
+                                _row_visible = True
+                            elif _i == 2:
+                                _default_name = "Hùng"
+                                _default_voice = "Sơn"
+                                _row_visible = True
+                            elif _i < 2:
+                                _default_name = f"Nhân vật {_i+1}"
+                                _row_visible = True
+
+                            with gr.Row(visible=_row_visible) as _row:
+                                _name = gr.Textbox(
+                                    value=_default_name,
+                                    label="👤 Nhân vật",
+                                    interactive=False,
+                                    scale=1,
+                                    min_width=120
+                                )
+                                _dd = gr.Dropdown(
+                                    choices=PRESET_VOICES_CACHE,
+                                    value=_default_voice,
+                                    label="🎤 Giọng đọc",
+                                    interactive=True,
+                                    scale=3,
+                                    allow_custom_value=True
+                                )
+                            speaker_slot_rows.append(_row)
+                            speaker_name_boxes.append(_name)
+                            speaker_voice_dds.append(_dd)
+                        
+                        btn_generate_conv = gr.Button("🎭 Bắt đầu hội thoại", variant="primary", interactive=False)
+
+                # Global Generation Settings
+                with gr.Row():
+                    use_batch = gr.Checkbox(
+                        value=True, 
+                        label="⚡ Batch Processing",
+                        info="Xử lý nhiều đoạn cùng lúc (chỉ áp dụng khi sử dụng GPU và đã cài đặt LMDeploy)"
+                    )
+                    max_batch_size_run = gr.Slider(
+                        minimum=1, 
+                        maximum=16, 
+                        value=4, 
+                        step=1, 
+                        label="📊 Batch Size (Generation)",
+                        info="Số lượng đoạn văn bản xử lý cùng lúc. Giá trị cao = nhanh hơn nhưng tốn VRAM hơn. Giảm xuống nếu gặp lỗi Out of Memory."
+                    )
+                
+                with gr.Accordion("⚙️ Cài đặt nâng cao (Generation)", open=False):
+                    with gr.Row():
+                        temperature_slider = gr.Slider(
+                            minimum=0.1, maximum=1.5, value=default_temp, step=0.1,
+                            label="🌡️ Temperature", 
+                            info="Độ sáng tạo. Cao = đa dạng cảm xúc hơn nhưng dễ lỗi. Thấp = ổn định hơn."
+                        )
+                        max_chars_chunk_slider = gr.Slider(
+                            minimum=128, maximum=512, value=256, step=32,
+                            label="📝 Max Chars per Chunk",
+                            info="Độ dài tối đa mỗi đoạn xử lý."
+                        )
+                
+                # State to track current mode
+                current_mode_state = gr.State("preset_mode")
+                
+                with gr.Row():
+                    btn_stop = gr.Button("⏹️ Dừng", variant="stop", scale=1, interactive=False)
+            
+            # --- OUTPUT ---
+            with gr.Column(scale=2):
+                audio_output = gr.Audio(
+                    label="Kết quả",
+                    type="filepath",
+                    autoplay=True
+                )
+                with gr.Group():
+                    status_output = gr.Textbox(
+                        label="Trạng thái", 
+                        elem_classes="status-box",
+                        lines=2,
+                        max_lines=10,
+                        show_copy_button=True
+                    )
+                with gr.Group():
+                    estimate_output = gr.Textbox(
+                        label="Ước tính thời gian",
+                        elem_classes="estimate-box",
+                        lines=2,
+                        max_lines=4,
+                        show_copy_button=True
+                    )
+                gr.Markdown("<div style='text-align: center; color: #64748b; font-size: 0.8rem;'>🔒 Audio được đóng dấu bản quyền ẩn (Watermarker) để bảo mật và định danh AI.</div>")
+        
+        codec_select.change(
+            on_codec_change, 
+            inputs=[codec_select, current_mode_state], 
+            outputs=[tab_custom, tabs, current_mode_state]
+        )
+        
+        # Bind tab events to update state
+        tab_preset.select(lambda: "preset_mode", outputs=current_mode_state)
+        tab_custom.select(lambda: "custom_mode", outputs=current_mode_state)
+        
+        custom_audio.change(validate_audio_duration, inputs=[custom_audio], outputs=[cloning_warning_msg])
+        
+        # --- Custom Model Event Handlers ---
+
+        def on_backbone_change(choice):
+            is_custom = (choice == "Custom Model")
+            print(f"   🔄 Backbone changed to: {choice}")
+            
+            # 1. Device logic
+            # Allow hardware acceleration (MPS/CUDA/Auto) for all GPU models AND Turbo (GGUF) models
+            is_hw_accel_supported = "(GPU)" in choice or "v2-Turbo" in choice or is_custom
+            
+            if is_hw_accel_supported:
+                dev_choices = get_available_devices()
+                initial_dev = "Auto"
+            else:
+                dev_choices = ["CPU"]
+                initial_dev = "CPU"
+            
+            # 2. Parameter logic
+            if "Turbo" in choice:
+                codec_update = gr.update(value="VieNeu-Codec", interactive=False)
+                text_update = gr.update(value=DEFAULT_TEXT_TURBO)
+                temp_update = gr.update(value=0.4)
+            elif "(CPU)" in choice:
+                codec_update = gr.update(value="NeuCodec (ONNX)", interactive=False)
+                text_update = gr.update(value=DEFAULT_TEXT_GPU)
+                temp_update = gr.update(value=0.7)
+            else:
+                codec_update = gr.update(value="NeuCodec (Distill)", interactive=False)
+                text_update = gr.update(value=DEFAULT_TEXT_GPU)
+                temp_update = gr.update(value=0.7)
+                
+            return (
+                gr.update(visible=is_custom), 
+                codec_update, 
+                text_update, 
+                temp_update, 
+                gr.update(choices=dev_choices, value=initial_dev),
+                gr.update(visible=True)
+            )
+
+        backbone_select.change(
+            on_backbone_change,
+            inputs=[backbone_select],
+            outputs=[
+                custom_model_group, 
+                codec_select, 
+                text_input, 
+                temperature_slider, 
+                device_choice,
+                cloning_elements_group
+            ]
+        )
+        
+        custom_backbone_model_id.change(
+            on_custom_id_change,
+            inputs=[custom_backbone_model_id],
+            outputs=[custom_backbone_base_model, custom_audio, custom_text]
+        )
+
+        btn_load.click(
+            fn=load_model,
+            inputs=[backbone_select, codec_select, device_choice, use_lmdeploy_cb,
+                    custom_backbone_model_id, custom_backbone_base_model, custom_backbone_hf_token],
+            outputs=[model_status, btn_generate, btn_generate_conv, btn_load, btn_stop, voice_select,
+                     tab_preset, tab_custom, tabs, current_mode_state,
+                     conv_tab,
+                     *speaker_voice_dds]
+        )
+        
+        # --- Conversation Event Handlers ---
+        # Scan speakers → update all 8 slot rows/names/dropdowns
+        btn_detect_speakers.click(
+            fn=extract_speakers_from_script,
+            inputs=[conv_script_input],
+            outputs=speaker_name_boxes + speaker_voice_dds + speaker_slot_rows
+        )
+        
+        conv_gen_event = btn_generate_conv.click(
+            fn=synthesize_conversation_with_empty_estimate,
+            inputs=[conv_script_input,
+                    *speaker_name_boxes,
+                    *speaker_voice_dds,
+                    silence_slider, temperature_slider, max_chars_chunk_slider,
+                    session_id_state],
+            outputs=[audio_output, status_output, estimate_output]
+        )
+        btn_generate_conv.click(lambda: gr.update(interactive=True), outputs=btn_stop)
+        conv_gen_event.then(lambda: gr.update(interactive=False), outputs=btn_stop)
+
+        # --- Auto-adjust Temperature on Tab Switch ---
+        conv_tab.select(
+            fn=lambda: gr.update(value=1.0),
+            outputs=temperature_slider
+        )
+        single_tab.select(
+            fn=lambda: gr.update(value=default_temp),
+            outputs=temperature_slider
+        )
+        
+        # --- Standard Generation Handlers ---
+        gen_event = btn_generate.click(
+            fn=synthesize_speech_with_estimate,
+            inputs=[text_input, voice_select, custom_audio, custom_text, current_mode_state, 
+                    generation_mode, use_batch, max_batch_size_run,
+                    temperature_slider, max_chars_chunk_slider, session_id_state],
+            outputs=[audio_output, status_output, estimate_output]
+        )
+        btn_generate.click(lambda: gr.update(interactive=True), outputs=btn_stop)
+        gen_event.then(lambda: gr.update(interactive=False), outputs=btn_stop)
+
+        # --- Stop Button ---
+        def request_stop():
+            print("🛑 STOP REQUESTED via button click.")
+            _STOP_EVENT.set()
+            return None, "⏹️ Đã dừng tạo giọng nói.", "", gr.update(interactive=False)
+
+        # Handler: set stop event + update UI
+        # Note: We avoid cancels= here to prevent internal Gradio KeyError crashes,
+        # relying instead on the frequent _STOP_EVENT.is_set() checks in the code.
+        btn_stop.click(fn=request_stop, outputs=[audio_output, status_output, estimate_output, btn_stop])
+
+        # Persistence: Restore UI state on load
+        demo.load(
+            fn=restore_ui_state,
+            outputs=[model_status, btn_generate, btn_generate_conv, btn_stop]
+        )
+
+def main():
+    # Cho phép override từ biến môi trường (hữu ích cho Docker)
+    server_name = os.getenv("GRADIO_SERVER_NAME", "127.0.0.1")
+    server_port = int(os.getenv("GRADIO_SERVER_PORT", "7860"))
+
+    # Check running in Colab
+    is_on_colab = os.getenv("COLAB_RELEASE_TAG") is not None
+
+    # Default:
+    # - Colab: share=True (convenient)
+    # - Docker/local: share=False (safe)
+    share = env_bool("GRADIO_SHARE", default=is_on_colab)
+    
+    # If server_name is "0.0.0.0" and GRADIO_SHARE is not set, disable sharing
+    if server_name == "0.0.0.0" and os.getenv("GRADIO_SHARE") is None:
+        share = False
+
+    demo.queue().launch(server_name=server_name, server_port=server_port, share=share)
+
+if __name__ == "__main__":
+    main()

From 0999f0a3cb8bf08d74d2c421e0c0c5dccd0ebcd0 Mon Sep 17 00:00:00 2001
From: thanh20VN <95450843+thanh20VN@users.noreply.github.com>
Date: Fri, 5 Jun 2026 21:25:21 +0700
Subject: [PATCH 4/4] Create base.py

---
 base.py | 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 445 insertions(+)
 create mode 100644 base.py

diff --git a/base.py b/base.py
new file mode 100644
index 0000000..bbff50d
--- /dev/null
+++ b/base.py
@@ -0,0 +1,445 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Optional, Union, List, Dict, Any
+import json
+import numpy as np
+import logging
+from huggingface_hub import hf_hub_download
+from sea_g2p import Normalizer
+
+# Configure logging
+logger = logging.getLogger("Vieneu")
+
+class BaseVieneuTTS(ABC):
+    """
+    Abstract base class for VieNeu-TTS implementations.
+    Provides shared functionality for voice management and common operations.
+    """
+
+    def __init__(self, codec_repo: Optional[str] = None, codec_device: str = "cpu"):
+        self.sample_rate = 24_000
+        self.max_context = 4096
+        self.hop_length = 480
+
+        # Default streaming parameters
+        self.streaming_overlap_frames = 1
+        self.streaming_frames_per_chunk = 50
+        self.streaming_lookforward = 5
+        self.streaming_lookback = 50
+        self.streaming_stride_samples = self.streaming_frames_per_chunk * self.hop_length
+
+        self.assets_dir = Path(__file__).parent / "assets"
+        self._preset_voices: Dict[str, Any] = {}
+        self._default_voice: Optional[str] = None
+        self.normalizer = Normalizer()
+        self._ref_phoneme_cache: Dict[str, str] = {}
+
+        # Watermarker placeholder
+        self.watermarker = None
+        self._init_watermarker()
+
+        if codec_repo:
+            self._load_codec(codec_repo, codec_device)
+
+    def _load_codec(self, codec_repo: str, codec_device: str) -> None:
+        """Universal codec loader for all backends."""
+        logger.info(f"📦 Loading codec from: {codec_repo} on {codec_device} ...")
+
+        if any(x in codec_repo.lower() for x in ["onnx", "vieneu-codec"]) or codec_repo == "neuphonic/neucodec-onnx-decoder-int8":
+            if codec_device != "cpu":
+                logger.warning("⚠️ ONNX decoder only runs on CPU. Ignoring device selection.")
+            try:
+                from .utils import NeuCodecOnnx
+                self.codec = NeuCodecOnnx.from_pretrained(codec_repo)
+                self._is_onnx_codec = True
+                return
+            except Exception as e:
+                logger.warning(f"Failed to load standalone ONNX decoder: {e}. Trying via neucodec package...")
+                try:
+                    from neucodec import NeuCodecOnnxDecoder
+                    self.codec = NeuCodecOnnxDecoder.from_pretrained(codec_repo)
+                    self._is_onnx_codec = True
+                    return
+                except ImportError:
+                    raise ImportError(
+                        "The 'onnxruntime' package is required for ONNX decoder. \n"
+                        "Please install it via: pip install onnxruntime"
+                    ) from e
+
+        # For PyTorch codecs, check for torch first
+        try:
+            import torch
+            from neucodec import NeuCodec, DistillNeuCodec
+            
+            # Check MPS
+            if codec_device == "mps" and not torch.backends.mps.is_available():
+                logger.warning("⚠️ MPS not available for codec, falling back to CPU")
+                codec_device = "cpu"
+
+            if codec_repo == "neuphonic/neucodec":
+                self.codec = NeuCodec.from_pretrained(codec_repo)
+            elif codec_repo == "neuphonic/distill-neucodec":
+                self.codec = DistillNeuCodec.from_pretrained(codec_repo)
+            else:
+                raise ValueError(f"Unrecognized codec repository: {codec_repo}")
+
+            self.codec.eval().to(codec_device)
+        except ImportError:
+            raise ImportError(
+                f"Codec '{codec_repo}' requires PyTorch. \n"
+                "To remain lightweight in Remote mode, please use 'neuphonic/neucodec-onnx-decoder-int8'. \n"
+                "Or install torch via: pip install vieneu[gpu]"
+            )
+
+
+    def _init_watermarker(self) -> None:
+        """Initialize optional audio watermarker."""
+        try:
+            import perth
+            self.watermarker = perth.PerthImplicitWatermarker()
+            logger.info("🔒 Audio watermarking initialized (Perth)")
+        except (ImportError, AttributeError):
+            self.watermarker = None
+
+    def _load_voices(self, backbone_repo: Optional[str], hf_token: Optional[str] = None, clear_existing: bool = False) -> None:
+        """Unified voice loading for Local and Remote paths."""
+        if not backbone_repo:
+            return
+
+        path_obj = Path(backbone_repo)
+        if path_obj.exists():
+            # Local Path (Dir or File)
+            if path_obj.is_dir():
+                json_path = path_obj / "voices.json"
+            else:
+                json_path = path_obj.parent / "voices.json"
+
+            if json_path.exists():
+                self._load_voices_from_file(json_path, clear_existing=clear_existing)
+            else:
+                if clear_existing:
+                     self._preset_voices.clear()
+                logger.warning(f"Validation Warning: Local path '{backbone_repo}' missing 'voices.json'.")
+                logger.warning(f"Falling back to Custom Voice Cloning mode.")
+        else:
+            # Remote Repo
+            if clear_existing:
+                self._preset_voices.clear()
+
+            try:
+                self._load_voices_from_repo(backbone_repo, hf_token)
+            except Exception as e:
+                logger.warning(f"Could not load voices from repo '{backbone_repo}': {e}")
+                logger.warning(f"Falling back to Custom Voice Cloning mode.")
+
+    def _load_voices_from_file(self, file_path: Path, clear_existing: bool = False) -> None:
+        """Load voices from a local JSON file."""
+        try:
+            if not file_path.exists():
+                logger.error(f"Voice file not found: {file_path}")
+                return
+
+            with open(file_path, 'r', encoding='utf-8') as f:
+                try:
+                    data = json.load(f)
+                except json.JSONDecodeError as e:
+                    logger.error(f"Invalid JSON in voice file {file_path}: {e}")
+                    return
+
+            if "presets" in data:
+                if clear_existing:
+                    self._preset_voices.clear()
+                    logger.info("🧹 Cleared existing voices for replacement")
+
+                # Merge into existing presets
+                self._preset_voices.update(data["presets"])
+                logger.info(f"📢 Loaded {len(data['presets'])} voices from {file_path.name}")
+
+            # Update default voice if provided
+            if "default_voice" in data and data["default_voice"]:
+                self._default_voice = data["default_voice"]
+
+        except Exception as e:
+            logger.error(f"Failed to load voices from {file_path}: {e}")
+
+    def _load_voices_from_repo(self, repo_id: str, hf_token: Optional[str] = None) -> None:
+        """Download and load voices.json from a HuggingFace repo."""
+        voices_file = None
+        try:
+            # 1. Try normal download (checks for updates from server)
+            voices_file = hf_hub_download(
+                repo_id=repo_id,
+                filename="voices.json",
+                token=hf_token,
+                repo_type="model"
+            )
+        except Exception:
+            # 2. Network error? Try to use cached version if available
+            logger.warning(f"Network check failed for voices.json. Trying local cache...")
+            try:
+                voices_file = hf_hub_download(
+                    repo_id=repo_id,
+                    filename="voices.json",
+                    token=hf_token,
+                    repo_type="model",
+                    local_files_only=True
+                )
+                logger.info(f"✅ Using cached voices.json")
+            except Exception:
+                # 3. No cache available either
+                pass
+
+        if voices_file:
+            self._load_voices_from_file(Path(voices_file))
+        else:
+            logger.warning(f"Repository '{repo_id}' is missing 'voices.json'. Falling back to Custom Voice mode.")
+
+    def list_preset_voices(self) -> List[tuple[str, str]]:
+        """List available preset voices as (description, id)."""
+        return [
+            (v.get("description", k) if isinstance(v, dict) else str(v), k)
+            for k, v in self._preset_voices.items()
+        ]
+
+    def get_preset_voice(self, voice_name: Optional[str] = None) -> Dict[str, Any]:
+        """
+        Get reference codes and text for a preset voice.
+
+        Args:
+            voice_name: Name of voice. If None, uses default_voice.
+
+        Returns:
+            dict: { 'codes': Union[np.ndarray, 'torch.Tensor'], 'text': str }
+        """
+        if voice_name is None:
+            voice_name = self._default_voice
+            if voice_name is None:
+                if self._preset_voices:
+                    voice_name = next(iter(self._preset_voices))
+                else:
+                    raise ValueError("No voice specified and no preset voices available.")
+
+        if voice_name not in self._preset_voices:
+            raise ValueError(f"Voice '{voice_name}' not found. Available: {self.list_preset_voices()}")
+
+        voice_data = self._preset_voices[voice_name]
+        codes = voice_data["codes"]
+        
+        # Only convert to torch if explicitly requested or if we're not in turbo mode
+        if isinstance(codes, list):
+            if codes and isinstance(codes[0], float):
+                codes = np.array(codes, dtype=np.float32)
+            else:
+                # Là integer token sequence (Standard mode)
+                try:
+                    import torch
+                    codes = torch.tensor(codes, dtype=torch.long)
+                except ImportError:
+                    codes = np.array(codes, dtype=np.int64)
+
+        return {"codes": codes, "text": voice_data["text"]}
+
+    def get_ref_phonemes(self, ref_text: str) -> str:
+        """
+        Get phonemized version of reference text, using cache if available.
+        """
+        if ref_text not in self._ref_phoneme_cache:
+            from vieneu_utils.phonemize_text import phonemize_with_dict
+            self._ref_phoneme_cache[ref_text] = phonemize_with_dict(ref_text)
+        return self._ref_phoneme_cache[ref_text]
+
+    def save(self, audio: np.ndarray, output_path: Union[str, Path]) -> None:
+        """Save audio waveform to a file."""
+        import soundfile as sf
+        sf.write(str(output_path), audio, self.sample_rate)
+
+    def encode_reference(self, ref_audio_path: Union[str, Path]) -> Union[np.ndarray, 'torch.Tensor']:
+        """
+        Encode reference audio to codes.
+
+        Args:
+            ref_audio_path: Path to the reference audio file.
+
+        Returns:
+            Union[np.ndarray, torch.Tensor]: Encoded codes.
+        """
+        import librosa
+        wav, _ = librosa.load(ref_audio_path, sr=16000, mono=True)
+        
+        # If we have an ONNX encoder or specialized turbo encoder, handle it here
+        # For now, default backends still use torch
+        try:
+            import torch
+            wav_tensor = torch.from_numpy(wav).float().unsqueeze(0).unsqueeze(0)  # [1, 1, T]
+
+            # Ensure device and dtype compatibility
+            if hasattr(self.codec, "device"):
+                wav_tensor = wav_tensor.to(self.codec.device)
+
+            with torch.no_grad():
+                ref_codes = self.codec.encode_code(audio_or_path=wav_tensor).squeeze(0).squeeze(0)
+            return ref_codes
+        except ImportError:
+            raise ImportError("Torch is required for encode_reference in the current backend. Please install torch or use a backend that supports standalone encoding.")
+
+    def _decode(self, codes_str: str) -> np.ndarray:
+        """
+        Decode speech tokens to audio waveform.
+
+        Args:
+            codes_str: String containing speech tokens.
+
+        Returns:
+            np.ndarray: Decoded audio waveform.
+        """
+        from .utils import extract_speech_ids
+        speech_ids = extract_speech_ids(codes_str)
+
+        if len(speech_ids) == 0:
+            raise ValueError("No valid speech tokens found in the output.")
+
+        # Onnx decode
+        if getattr(self, "_is_onnx_codec", False):
+            codes = np.array(speech_ids, dtype=np.int32)[np.newaxis, np.newaxis, :]
+            recon = self.codec.decode_code(codes)
+        # Torch decode
+        else:
+            try:
+                import torch
+                with torch.no_grad():
+                    codes = torch.tensor(speech_ids, dtype=torch.long)[None, None, :]
+                    if hasattr(self.codec, "device"):
+                        codes = codes.to(self.codec.device)
+
+                    recon = self.codec.decode_code(codes)
+                    if hasattr(recon, "cpu"):
+                        recon = recon.cpu()
+                    if hasattr(recon, "numpy"):
+                        recon = recon.numpy()
+            except ImportError:
+                raise ImportError("Torch is required for the current codec backend. Please install torch or use an ONNX-based codec.")
+
+
+        return recon[0, 0, :]
+
+    def _resolve_ref_voice(
+        self,
+        voice: Optional[Dict[str, Any]] = None,
+        ref_audio: Optional[Union[str, Path]] = None,
+        ref_codes: Optional[Union[np.ndarray, 'torch.Tensor']] = None,
+        ref_text: Optional[str] = None
+    ) -> tuple[Union[np.ndarray, 'torch.Tensor'], str]:
+        """Resolve reference voice codes and text."""
+        if voice is not None:
+            ref_codes = voice.get('codes', ref_codes)
+            ref_text = voice.get('text', ref_text)
+
+        if ref_audio is not None and ref_codes is None:
+            ref_codes = self.encode_reference(ref_audio)
+        elif self._default_voice and (ref_codes is None or ref_text is None):
+            try:
+                voice_data = self.get_preset_voice(None)
+                ref_codes = voice_data['codes']
+                ref_text = voice_data['text']
+            except Exception:
+                pass
+
+        if ref_codes is None or ref_text is None:
+            raise ValueError("Must provide either 'voice' dict or both 'ref_codes' and 'ref_text'.")
+
+        return ref_codes, ref_text
+
+    def _apply_watermark(self, wav: np.ndarray) -> np.ndarray:
+        """Apply watermark to audio if enabled."""
+        if self.watermarker:
+            return self.watermarker.apply_watermark(wav, sample_rate=self.sample_rate)
+        return wav
+
+    def to_list(self, codes: Any) -> List[int]:
+        """Convert reference codes (Tensor, Array, List) to a Python list of integers."""
+        if isinstance(codes, list):
+            return codes
+        if isinstance(codes, np.ndarray):
+            return codes.flatten().tolist()
+
+        # Check for torch without importing it at module level
+        try:
+            import torch
+            if isinstance(codes, torch.Tensor):
+                return codes.flatten().tolist()
+        except ImportError:
+            pass
+
+        # Fallback for other array-like types
+        if hasattr(codes, "tolist"):
+            return codes.flatten().tolist() if hasattr(codes, "flatten") else codes.tolist()
+
+        return list(codes)
+
+    def _format_prompt(
+        self,
+        ref_codes: Any,
+        ref_text: str,
+        input_text: str,
+        ref_phonemes: Optional[str] = None,
+        input_phonemes: Optional[str] = None,
+        use_chat_format: bool = False,
+        emotion_tag: Optional[str] = None
+    ) -> str:
+        """
+        Format the prompt for the TTS model.
+        Common implementation for LMDeploy (Fast) and Remote backends.
+        Standard backend uses a specialized chat template via tokenizer.
+
+        Args:
+            use_chat_format: If True, wraps the prompt with chat-style user/assistant
+                             tokens (used by VieNeu-TTS GPU model). If False (default),
+                             returns a compact prompt without those wrappers.
+        """
+        ref_codes_list = self.to_list(ref_codes)
+
+        # Import inside method to avoid potential circular dependencies between
+        # base TTS and phonemization utilities.
+        from vieneu_utils.phonemize_text import phonemize_with_dict
+
+        ref_text_phones = ref_phonemes if ref_phonemes else self.get_ref_phonemes(ref_text)
+        input_text_phones = input_phonemes if input_phonemes else phonemize_with_dict(input_text, skip_normalize=True)
+        codes_str = "".join([f"<|speech_{idx}|>" for idx in ref_codes_list])
+
+        emotion_prefix = emotion_tag if emotion_tag else ""
+
+        if use_chat_format:
+            return (
+                f"user: Convert the text to speech:<|TEXT_PROMPT_START|>{emotion_prefix}{ref_text_phones} {input_text_phones}"
+                f"<|TEXT_PROMPT_END|>\nassistant:<|SPEECH_GENERATION_START|>{codes_str}"
+            )
+        return (
+            f"<|TEXT_PROMPT_START|>{emotion_prefix}{ref_text_phones} {input_text_phones}"
+            f"<|TEXT_PROMPT_END|><|SPEECH_GENERATION_START|>{codes_str}"
+        )
+
+    @abstractmethod
+    def infer(self, text: str, apply_watermark: bool = True, **kwargs: Any) -> np.ndarray:
+        """Main inference method for single text."""
+        pass
+
+    @abstractmethod
+    def infer_batch(self, texts: List[str], apply_watermark: bool = True, **kwargs: Any) -> List[np.ndarray]:
+        """Main inference method for batch processing."""
+        pass
+
+    def close(self) -> None:
+        """Release resources."""
+        pass
+
+    def __enter__(self) -> 'BaseVieneuTTS':
+        return self
+
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
+        self.close()
+
+    def __del__(self):
+        try:
+            self.close()
+        except Exception:
+            pass