From e20f46b719c0066314f8b8b7413c3314dde27c6c Mon Sep 17 00:00:00 2001 From: daewook Date: Thu, 29 Jan 2026 11:30:00 +0900 Subject: [PATCH 1/2] Add streaming model switch + safe mode --- examples/run_voice_clone_service.sh | 84 +++ examples/run_voice_clone_stream_service.sh | 84 +++ examples/voice_clone_service.py | 564 ++++++++++++++++ examples/voice_clone_stream_service.md | 52 ++ examples/voice_clone_stream_service.py | 440 +++++++++++++ examples/voice_clone_stream_test.html | 613 ++++++++++++++++++ qwen_tts/inference/qwen3_tts_model.py | 386 ++++++++++- .../03f79f10717a47e39e186e7ee4e53027.pt | Bin 0 -> 199725 bytes .../3665c5834221470591e3c8fcb0de6e50.pt | Bin 0 -> 134893 bytes .../3a886347faee4fa6a4a5ad432a3dcab7.pt | Bin 0 -> 197741 bytes .../415c673dbc4647e788b92f3ab7318877.pt | Bin 0 -> 135405 bytes .../4c5eb5a3c84c4f4183b831ac5a7fe41a.pt | Bin 0 -> 148781 bytes .../4f47f8af90404b9e93649ce83b18af78.pt | Bin 0 -> 383661 bytes .../9a0aff0c1abe4f5e8d69b89f0b2271ce.pt | Bin 0 -> 199725 bytes .../e5641b080c9a40d7b434cd62bf9f61d3.pt | Bin 0 -> 77997 bytes voice_store/voices.json | 52 ++ 16 files changed, 2274 insertions(+), 1 deletion(-) create mode 100644 examples/run_voice_clone_service.sh create mode 100644 examples/run_voice_clone_stream_service.sh create mode 100644 examples/voice_clone_service.py create mode 100644 examples/voice_clone_stream_service.md create mode 100644 examples/voice_clone_stream_service.py create mode 100644 examples/voice_clone_stream_test.html create mode 100644 voice_store/prompts/03f79f10717a47e39e186e7ee4e53027.pt create mode 100644 voice_store/prompts/3665c5834221470591e3c8fcb0de6e50.pt create mode 100644 voice_store/prompts/3a886347faee4fa6a4a5ad432a3dcab7.pt create mode 100644 voice_store/prompts/415c673dbc4647e788b92f3ab7318877.pt create mode 100644 voice_store/prompts/4c5eb5a3c84c4f4183b831ac5a7fe41a.pt create mode 100644 voice_store/prompts/4f47f8af90404b9e93649ce83b18af78.pt create mode 100644 voice_store/prompts/9a0aff0c1abe4f5e8d69b89f0b2271ce.pt create mode 100644 voice_store/prompts/e5641b080c9a40d7b434cd62bf9f61d3.pt create mode 100644 voice_store/voices.json diff --git a/examples/run_voice_clone_service.sh b/examples/run_voice_clone_service.sh new file mode 100644 index 00000000..5f248eb8 --- /dev/null +++ b/examples/run_voice_clone_service.sh @@ -0,0 +1,84 @@ +#!/usr/bin/env bash +set -euo pipefail + +usage() { + cat <<'EOF' +Usage: run_voice_clone_service.sh [options] [-- extra-args] + +Options: + -g, --gpu ID GPU id to use (default: 4) + -p, --port PORT Port to bind (default: 8001) + -m, --model MODEL Model id or path (default: Qwen/Qwen3-TTS-12Hz-1.7B-Base) + -s, --voice-store DIR Voice store directory (default: /voice_store) + -d, --device DEVICE Device for device_map (default: cuda:0) + --ip IP Bind IP (default: 0.0.0.0) + -h, --help Show this help + +Env overrides: GPU_ID, PORT, MODEL, VOICE_STORE, DEVICE, IP, PYTHON_BIN +EOF +} + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)" + +GPU_ID="${GPU_ID:-4}" +PORT="${PORT:-8001}" +MODEL="${MODEL:-Qwen/Qwen3-TTS-12Hz-1.7B-Base}" +VOICE_STORE="${VOICE_STORE:-${PROJECT_ROOT}/voice_store}" +DEVICE="${DEVICE:-cuda:0}" +IP="${IP:-0.0.0.0}" +PYTHON_BIN="${PYTHON_BIN:-python3}" + +EXTRA_ARGS=() +while [[ $# -gt 0 ]]; do + case "$1" in + -g|--gpu) + GPU_ID="$2" + shift 2 + ;; + -p|--port) + PORT="$2" + shift 2 + ;; + -m|--model) + MODEL="$2" + shift 2 + ;; + -s|--voice-store) + VOICE_STORE="$2" + shift 2 + ;; + -d|--device) + DEVICE="$2" + shift 2 + ;; + --ip) + IP="$2" + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + --) + shift + EXTRA_ARGS+=("$@") + break + ;; + *) + EXTRA_ARGS+=("$1") + shift + ;; + esac +done + +mkdir -p "$VOICE_STORE" + +CUDA_VISIBLE_DEVICES="$GPU_ID" "$PYTHON_BIN" \ + "$SCRIPT_DIR/voice_clone_service.py" \ + --device "$DEVICE" \ + --ip "$IP" \ + --port "$PORT" \ + --model "$MODEL" \ + --voice-store "$VOICE_STORE" \ + "${EXTRA_ARGS[@]}" diff --git a/examples/run_voice_clone_stream_service.sh b/examples/run_voice_clone_stream_service.sh new file mode 100644 index 00000000..bb0336b9 --- /dev/null +++ b/examples/run_voice_clone_stream_service.sh @@ -0,0 +1,84 @@ +#!/usr/bin/env bash +set -euo pipefail + +usage() { + cat <<'EOF' +Usage: run_voice_clone_stream_service.sh [options] [-- extra-args] + +Options: + -g, --gpu ID GPU id to use (default: 4) + -p, --port PORT Port to bind (default: 8002) + -m, --model MODEL Model id or path (default: Qwen/Qwen3-TTS-12Hz-1.7B-Base) + -s, --voice-store DIR Voice store directory (default: /voice_store) + -d, --device DEVICE Device for device_map (default: cuda:0) + --ip IP Bind IP (default: 0.0.0.0) + -h, --help Show this help + +Env overrides: GPU_ID, PORT, MODEL, VOICE_STORE, DEVICE, IP, PYTHON_BIN +EOF +} + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)" + +GPU_ID="${GPU_ID:-5}" +PORT="${PORT:-8002}" +MODEL="${MODEL:-Qwen/Qwen3-TTS-12Hz-1.7B-Base}" +VOICE_STORE="${VOICE_STORE:-${PROJECT_ROOT}/voice_store}" +DEVICE="${DEVICE:-cuda:0}" +IP="${IP:-0.0.0.0}" +PYTHON_BIN="${PYTHON_BIN:-python3}" + +EXTRA_ARGS=() +while [[ $# -gt 0 ]]; do + case "$1" in + -g|--gpu) + GPU_ID="$2" + shift 2 + ;; + -p|--port) + PORT="$2" + shift 2 + ;; + -m|--model) + MODEL="$2" + shift 2 + ;; + -s|--voice-store) + VOICE_STORE="$2" + shift 2 + ;; + -d|--device) + DEVICE="$2" + shift 2 + ;; + --ip) + IP="$2" + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + --) + shift + EXTRA_ARGS+=("$@") + break + ;; + *) + EXTRA_ARGS+=("$1") + shift + ;; + esac +done + +mkdir -p "$VOICE_STORE" + +CUDA_VISIBLE_DEVICES="$GPU_ID" "$PYTHON_BIN" \ + "$SCRIPT_DIR/voice_clone_stream_service.py" \ + --device "$DEVICE" \ + --ip "$IP" \ + --port "$PORT" \ + --model "$MODEL" \ + --voice-store "$VOICE_STORE" \ + "${EXTRA_ARGS[@]}" diff --git a/examples/voice_clone_service.py b/examples/voice_clone_service.py new file mode 100644 index 00000000..5a9e57f0 --- /dev/null +++ b/examples/voice_clone_service.py @@ -0,0 +1,564 @@ +# coding=utf-8 +# Copyright 2026 The Alibaba Qwen team. +# SPDX-License-Identifier: Apache-2.0 +""" +Gradio service for reusable voice-clone prompts. +""" + +import argparse +import json +import threading +import time +import uuid +from dataclasses import asdict +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import gradio as gr +import numpy as np +import torch + +from qwen_tts import Qwen3TTSModel, VoiceClonePromptItem + + +DEFAULT_MODELS = [ + ("Base 1.7B", "Qwen/Qwen3-TTS-12Hz-1.7B-Base"), + ("Base 0.6B", "Qwen/Qwen3-TTS-12Hz-0.6B-Base"), +] + +MODEL_LOCK = threading.Lock() +MODEL_CACHE: Dict[Tuple[str, str, str, bool], Qwen3TTSModel] = {} +CURRENT_TTS: Optional[Qwen3TTSModel] = None +CURRENT_MODEL_ID: Optional[str] = None + +VOICE_INDEX_LOCK = threading.Lock() +VOICE_INDEX: Dict[str, Dict[str, Any]] = {} + +PROMPT_CACHE_LOCK = threading.Lock() +PROMPT_CACHE: Dict[str, Tuple[List[VoiceClonePromptItem], Dict[str, Any]]] = {} + +VOICE_STORE: Optional[Path] = None +PROMPT_DIR: Optional[Path] = None +INDEX_PATH: Optional[Path] = None + + +def _dtype_from_str(s: str) -> torch.dtype: + s = (s or "").strip().lower() + if s in ("bf16", "bfloat16"): + return torch.bfloat16 + if s in ("fp16", "float16", "half"): + return torch.float16 + if s in ("fp32", "float32"): + return torch.float32 + raise ValueError(f"Unsupported torch dtype: {s}. Use bfloat16/float16/float32.") + + +def _title_case_display(s: str) -> str: + s = (s or "").strip() + s = s.replace("_", " ") + return " ".join([w[:1].upper() + w[1:] if w else "" for w in s.split()]) + + +def _build_choices_and_map(items: Optional[List[str]]) -> Tuple[List[str], Dict[str, str]]: + if not items: + return [], {} + display = [_title_case_display(x) for x in items] + mapping = {d: r for d, r in zip(display, items)} + return display, mapping + + +def _collect_gen_kwargs(args: argparse.Namespace) -> Dict[str, Any]: + mapping = { + "max_new_tokens": args.max_new_tokens, + "temperature": args.temperature, + "top_k": args.top_k, + "top_p": args.top_p, + "repetition_penalty": args.repetition_penalty, + "subtalker_top_k": args.subtalker_top_k, + "subtalker_top_p": args.subtalker_top_p, + "subtalker_temperature": args.subtalker_temperature, + } + return {k: v for k, v in mapping.items() if v is not None} + + +def _normalize_audio(wav, eps=1e-12, clip=True): + x = np.asarray(wav) + + if np.issubdtype(x.dtype, np.integer): + info = np.iinfo(x.dtype) + if info.min < 0: + y = x.astype(np.float32) / max(abs(info.min), info.max) + else: + mid = (info.max + 1) / 2.0 + y = (x.astype(np.float32) - mid) / mid + elif np.issubdtype(x.dtype, np.floating): + y = x.astype(np.float32) + m = np.max(np.abs(y)) if y.size else 0.0 + if m <= 1.0 + 1e-6: + pass + else: + y = y / (m + eps) + else: + raise TypeError(f"Unsupported dtype: {x.dtype}") + + if clip: + y = np.clip(y, -1.0, 1.0) + if y.ndim > 1: + y = np.mean(y, axis=-1).astype(np.float32) + return y + + +def _audio_to_tuple(audio: Any) -> Optional[Tuple[np.ndarray, int]]: + if audio is None: + return None + + if isinstance(audio, tuple) and len(audio) == 2 and isinstance(audio[0], int): + sr, wav = audio + wav = _normalize_audio(wav) + return wav, int(sr) + + if isinstance(audio, dict) and "sampling_rate" in audio and "data" in audio: + sr = int(audio["sampling_rate"]) + wav = _normalize_audio(audio["data"]) + return wav, sr + + return None + + +def _wav_to_gradio_audio(wav: np.ndarray, sr: int) -> Tuple[int, np.ndarray]: + wav = np.asarray(wav, dtype=np.float32) + return sr, wav + + +def _build_model_choices(initial_model: str) -> Tuple[List[str], Dict[str, str], str]: + label_map = {label: model_id for label, model_id in DEFAULT_MODELS} + choices = list(label_map.keys()) + initial_label = None + for label, model_id in label_map.items(): + if model_id == initial_model: + initial_label = label + break + if initial_label is None: + custom_label = f"Custom ({initial_model})" + choices.insert(0, custom_label) + label_map[custom_label] = initial_model + initial_label = custom_label + return choices, label_map, initial_label + + +def _get_lang_choices(tts: Qwen3TTSModel) -> Tuple[List[str], Dict[str, str]]: + supported = [] + if callable(getattr(tts.model, "get_supported_languages", None)): + supported = tts.model.get_supported_languages() + supported = [x for x in (supported or []) if x] + if "Auto" not in supported: + supported = ["Auto"] + supported + choices, mapping = _build_choices_and_map(supported) + if "Auto" not in mapping: + mapping["Auto"] = "Auto" + if "Auto" not in choices: + choices.insert(0, "Auto") + return choices, mapping + + +def _init_voice_store(path: Path) -> None: + global VOICE_STORE, PROMPT_DIR, INDEX_PATH, VOICE_INDEX + VOICE_STORE = path + PROMPT_DIR = VOICE_STORE / "prompts" + PROMPT_DIR.mkdir(parents=True, exist_ok=True) + INDEX_PATH = VOICE_STORE / "voices.json" + VOICE_INDEX = _load_voice_index(INDEX_PATH) + + +def _load_voice_index(index_path: Path) -> Dict[str, Dict[str, Any]]: + if not index_path.exists(): + return {} + try: + payload = json.loads(index_path.read_text()) + except Exception: + return {} + voices = payload.get("voices", {}) + return voices if isinstance(voices, dict) else {} + + +def _save_voice_index(index_path: Path, voices: Dict[str, Dict[str, Any]]) -> None: + payload = {"voices": voices} + tmp_path = index_path.with_suffix(".tmp") + tmp_path.write_text(json.dumps(payload, ensure_ascii=True, indent=2)) + tmp_path.replace(index_path) + + +def _add_voice_meta(voice_id: str, meta: Dict[str, Any]) -> None: + with VOICE_INDEX_LOCK: + VOICE_INDEX[voice_id] = meta + _save_voice_index(INDEX_PATH, VOICE_INDEX) + + +def _build_voice_choices() -> Tuple[List[str], Dict[str, str]]: + with VOICE_INDEX_LOCK: + voices = dict(VOICE_INDEX) + items = list(voices.items()) + items.sort(key=lambda kv: kv[1].get("created_at", ""), reverse=True) + choices: List[str] = [] + mapping: Dict[str, str] = {} + for voice_id, meta in items: + name = meta.get("name") or voice_id[:8] + disp = f"{name} ({voice_id[:8]})" + choices.append(disp) + mapping[disp] = voice_id + return choices, mapping + + +def _torch_load_safe(path: Path) -> Any: + try: + return torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + return torch.load(path, map_location="cpu") + + +def _deserialize_prompt_items(payload: Dict[str, Any]) -> Tuple[List[VoiceClonePromptItem], Dict[str, Any]]: + items_raw = payload.get("items", None) + if not isinstance(items_raw, list) or len(items_raw) == 0: + raise ValueError("Empty or invalid prompt items.") + items: List[VoiceClonePromptItem] = [] + for d in items_raw: + if not isinstance(d, dict): + raise ValueError("Invalid prompt item format.") + ref_code = d.get("ref_code", None) + if ref_code is not None and not torch.is_tensor(ref_code): + ref_code = torch.tensor(ref_code) + ref_spk = d.get("ref_spk_embedding", None) + if ref_spk is None: + raise ValueError("Missing ref_spk_embedding in prompt item.") + if not torch.is_tensor(ref_spk): + ref_spk = torch.tensor(ref_spk) + items.append( + VoiceClonePromptItem( + ref_code=ref_code, + ref_spk_embedding=ref_spk, + x_vector_only_mode=bool(d.get("x_vector_only_mode", False)), + icl_mode=bool(d.get("icl_mode", not bool(d.get("x_vector_only_mode", False)))), + ref_text=d.get("ref_text", None), + ) + ) + meta = payload.get("meta", {}) + return items, meta if isinstance(meta, dict) else {} + + +def _load_prompt_items(voice_id: str) -> Tuple[List[VoiceClonePromptItem], Dict[str, Any]]: + with PROMPT_CACHE_LOCK: + cached = PROMPT_CACHE.get(voice_id) + if cached is not None: + return cached + + with VOICE_INDEX_LOCK: + meta = VOICE_INDEX.get(voice_id) + if not meta: + raise ValueError("Voice ID not found.") + rel_path = meta.get("path") + if not rel_path: + raise ValueError("Voice path missing.") + + payload = _torch_load_safe(VOICE_STORE / rel_path) + items, prompt_meta = _deserialize_prompt_items(payload) + + with PROMPT_CACHE_LOCK: + PROMPT_CACHE[voice_id] = (items, prompt_meta) + return items, prompt_meta + + +def _load_tts( + model_id: str, + device: str, + dtype: torch.dtype, + flash_attn: bool, + cache_models: bool, +) -> Qwen3TTSModel: + global CURRENT_TTS, CURRENT_MODEL_ID + key = (model_id, device, str(dtype), bool(flash_attn)) + if key in MODEL_CACHE: + CURRENT_TTS = MODEL_CACHE[key] + CURRENT_MODEL_ID = model_id + return CURRENT_TTS + + if not cache_models: + MODEL_CACHE.clear() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + attn_impl = "flash_attention_2" if flash_attn else None + tts = Qwen3TTSModel.from_pretrained( + model_id, + device_map=device, + dtype=dtype, + attn_implementation=attn_impl, + ) + if getattr(tts.model, "tts_model_type", None) != "base": + raise ValueError("Only Base models support voice cloning.") + + MODEL_CACHE[key] = tts + CURRENT_TTS = tts + CURRENT_MODEL_ID = model_id + return tts + + +def _get_current_tts() -> Qwen3TTSModel: + if CURRENT_TTS is None: + raise RuntimeError("Model not loaded.") + return CURRENT_TTS + + +def build_app(args: argparse.Namespace) -> gr.Blocks: + model_choices, model_label_map, initial_label = _build_model_choices(args.model) + + with MODEL_LOCK: + tts = _load_tts(args.model, args.device, _dtype_from_str(args.dtype), args.flash_attn, args.cache_models) + + lang_choices, lang_map = _get_lang_choices(tts) + voice_choices, voice_map = _build_voice_choices() + + gen_kwargs_default = _collect_gen_kwargs(args) + + def switch_model(model_label: str, cur_model: str, cur_lang_map: Dict[str, str]): + model_id = model_label_map.get(model_label, model_label) + try: + with MODEL_LOCK: + tts_local = _load_tts(model_id, args.device, _dtype_from_str(args.dtype), args.flash_attn, args.cache_models) + choices, mapping = _get_lang_choices(tts_local) + value = "Auto" if "Auto" in choices else (choices[0] if choices else None) + status = f"Loaded {model_id}" + return status, gr.update(choices=choices, value=value), model_id, mapping + except Exception as e: + return f"Failed to load model: {type(e).__name__}: {e}", gr.update(), cur_model, cur_lang_map + + def refresh_voices(): + choices, mapping = _build_voice_choices() + value = choices[0] if choices else None + return gr.update(choices=choices, value=value), mapping + + def save_voice(ref_audio, ref_text: str, use_xvec: bool, voice_name: str, cur_model: str): + try: + audio_tuple = _audio_to_tuple(ref_audio) + if audio_tuple is None: + return "Reference audio is required.", gr.update(), {} + if (not use_xvec) and (not ref_text or not ref_text.strip()): + return "Reference text is required when x-vector only is disabled.", gr.update(), {} + + tts_local = _get_current_tts() + with MODEL_LOCK: + items = tts_local.create_voice_clone_prompt( + ref_audio=audio_tuple, + ref_text=(ref_text.strip() if ref_text else None), + x_vector_only_mode=bool(use_xvec), + ) + + voice_id = uuid.uuid4().hex + name = (voice_name or "").strip() or voice_id[:8] + created_at = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) + rel_path = str(Path("prompts") / f"{voice_id}.pt") + payload = { + "items": [asdict(it) for it in items], + "meta": { + "voice_id": voice_id, + "name": name, + "created_at": created_at, + "model_id": cur_model, + }, + } + torch.save(payload, VOICE_STORE / rel_path) + + meta = { + "name": name, + "path": rel_path, + "created_at": created_at, + "model_id": cur_model, + } + _add_voice_meta(voice_id, meta) + + with PROMPT_CACHE_LOCK: + PROMPT_CACHE[voice_id] = (items, payload["meta"]) + + choices, mapping = _build_voice_choices() + value = choices[0] if choices else None + status = f"Saved voice '{name}' ({voice_id})." + return status, gr.update(choices=choices, value=value), mapping + except Exception as e: + return f"{type(e).__name__}: {e}", gr.update(), {} + + def run_tts( + voice_choice: str, + text: str, + lang_disp: str, + voice_map_local: Dict[str, str], + lang_map_local: Dict[str, str], + cur_model: str, + ): + try: + if not text or not text.strip(): + return None, "Target text is required." + if not voice_choice: + return None, "Please select a voice." + voice_id = voice_map_local.get(voice_choice) + if not voice_id: + return None, "Unknown voice selection." + + items, prompt_meta = _load_prompt_items(voice_id) + prompt_model = prompt_meta.get("model_id") + if prompt_model and prompt_model != cur_model: + return None, f"Voice created with {prompt_model}. Switch model to match." + + language = lang_map_local.get(lang_disp, "Auto") + tts_local = _get_current_tts() + with MODEL_LOCK: + wavs, sr = tts_local.generate_voice_clone( + text=text.strip(), + language=language, + voice_clone_prompt=items, + **gen_kwargs_default, + ) + return _wav_to_gradio_audio(wavs[0], sr), "Finished." + except Exception as e: + return None, f"{type(e).__name__}: {e}" + + theme = gr.themes.Soft(font=[gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]) + css = ".gradio-container {max-width: none !important;}" + + with gr.Blocks(theme=theme, css=css) as demo: + model_state = gr.State(args.model) + lang_state = gr.State(lang_map) + voice_map_state = gr.State(voice_map) + gr.Markdown( + """ +# Qwen3-TTS Voice Clone Service +- Create reusable voice prompts from reference audio +- Generate speech from stored voices +""" + ) + + with gr.Row(): + model_dd = gr.Dropdown( + label="Model", + choices=model_choices, + value=initial_label, + interactive=True, + ) + load_btn = gr.Button("Load Model", variant="primary") + model_status = gr.Textbox(label="Model Status", value=f"Loaded {args.model}", interactive=False) + + with gr.Tabs(): + with gr.Tab("Voice Provider"): + with gr.Row(): + with gr.Column(scale=2): + ref_audio = gr.Audio(label="Reference Audio", type="numpy") + ref_text = gr.Textbox(label="Reference Text", lines=2) + xvec_only = gr.Checkbox(label="Use x-vector only", value=False) + voice_name = gr.Textbox(label="Voice Name (optional)", lines=1) + save_btn = gr.Button("Create Voice", variant="primary") + with gr.Column(scale=3): + save_status = gr.Textbox(label="Status", lines=2) + + with gr.Tab("TTS"): + with gr.Row(): + with gr.Column(scale=2): + voice_dd = gr.Dropdown( + label="Voice", + choices=voice_choices, + value=(voice_choices[0] if voice_choices else None), + interactive=True, + ) + refresh_btn = gr.Button("Refresh Voice List") + text_in = gr.Textbox(label="Target Text", lines=4) + lang_in = gr.Dropdown( + label="Language", + choices=lang_choices, + value=("Auto" if "Auto" in lang_choices else (lang_choices[0] if lang_choices else None)), + interactive=True, + ) + gen_btn = gr.Button("Generate", variant="primary") + with gr.Column(scale=3): + audio_out = gr.Audio(label="Output Audio", type="numpy") + gen_status = gr.Textbox(label="Status", lines=2) + + load_btn.click( + switch_model, + inputs=[model_dd, model_state, lang_state], + outputs=[model_status, lang_in, model_state, lang_state], + ) + refresh_btn.click( + refresh_voices, + outputs=[voice_dd, voice_map_state], + ) + save_btn.click( + save_voice, + inputs=[ref_audio, ref_text, xvec_only, voice_name, model_state], + outputs=[save_status, voice_dd, voice_map_state], + ) + gen_btn.click( + run_tts, + inputs=[voice_dd, text_in, lang_in, voice_map_state, lang_state, model_state], + outputs=[audio_out, gen_status], + ) + + return demo + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="voice-clone-service", + description="Launch a reusable voice-clone Gradio service.", + formatter_class=argparse.RawTextHelpFormatter, + add_help=True, + ) + parser.add_argument( + "--model", + default="Qwen/Qwen3-TTS-12Hz-1.7B-Base", + help="Base model checkpoint path or HuggingFace repo id.", + ) + parser.add_argument("--device", default="cuda:0", help="Device for device_map, e.g. cpu, cuda:0.") + parser.add_argument( + "--dtype", + default="bfloat16", + choices=["bfloat16", "bf16", "float16", "fp16", "float32", "fp32"], + help="Torch dtype for loading the model (default: bfloat16).", + ) + parser.add_argument( + "--flash-attn/--no-flash-attn", + dest="flash_attn", + default=True, + action=argparse.BooleanOptionalAction, + help="Enable FlashAttention-2 (default: enabled).", + ) + parser.add_argument("--cache-models", action="store_true", help="Keep multiple models in memory.") + parser.add_argument("--voice-store", default="voice_store", help="Directory to store voice prompts.") + parser.add_argument("--ip", default="0.0.0.0", help="Server bind IP (default: 0.0.0.0).") + parser.add_argument("--port", type=int, default=8000, help="Server port (default: 8000).") + parser.add_argument("--concurrency", type=int, default=4, help="Gradio queue concurrency (default: 4).") + + parser.add_argument("--max-new-tokens", type=int, default=None, help="Max new tokens for generation (optional).") + parser.add_argument("--temperature", type=float, default=None, help="Sampling temperature (optional).") + parser.add_argument("--top-k", type=int, default=None, help="Top-k sampling (optional).") + parser.add_argument("--top-p", type=float, default=None, help="Top-p sampling (optional).") + parser.add_argument("--repetition-penalty", type=float, default=None, help="Repetition penalty (optional).") + parser.add_argument("--subtalker-top-k", type=int, default=None, help="Subtalker top-k (optional).") + parser.add_argument("--subtalker-top-p", type=float, default=None, help="Subtalker top-p (optional).") + parser.add_argument("--subtalker-temperature", type=float, default=None, help="Subtalker temperature (optional).") + + return parser + + +def main(argv=None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + + _init_voice_store(Path(args.voice_store)) + + demo = build_app(args) + demo.queue(default_concurrency_limit=int(args.concurrency)).launch( + server_name=args.ip, + server_port=args.port, + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/examples/voice_clone_stream_service.md b/examples/voice_clone_stream_service.md new file mode 100644 index 00000000..3b253931 --- /dev/null +++ b/examples/voice_clone_stream_service.md @@ -0,0 +1,52 @@ +# Voice Clone Streaming Service (SSE) + +This service streams voice-clone audio chunks as Server-Sent Events (SSE). + +Constraints +- Single-sample only (one `text`, one `voice_id` per request). +- Base 12Hz models only (e.g. `Qwen/Qwen3-TTS-12Hz-1.7B-Base`). +- SSE payload is base64-encoded `int16` PCM audio. + +Start +```bash +./Qwen3-TTS/examples/run_voice_clone_stream_service.sh +``` + +Test page +```text +http://localhost:8002/ +``` + +Request example +```bash +curl -N -X POST \ + -H "Content-Type: application/json" \ + -d '{ + "voice_id": "YOUR_VOICE_ID", + "text": "Hello, this is streaming TTS.", + "chunk_size": 8, + "left_context_size": 25 + }' \ + http://localhost:8002/voice/stream +``` + +Model switching +```bash +curl -X POST \ + -H "Content-Type: application/json" \ + -d '{"model_id":"Qwen/Qwen3-TTS-12Hz-0.6B-Base","safe_mode":true}' \ + http://localhost:8002/model/load +``` + +Available models from the voice store +```bash +curl http://localhost:8002/models +``` + +Notes +- Lower `chunk_size` yields faster first audio but more overhead. +- `left_context_size` improves continuity at chunk boundaries. +- FlashAttention is enabled by default; use `--no-flash-attn` to disable. +- TF32 matmul/cudnn is enabled by default on CUDA; use `--no-tf32` to disable. +- Model switching returns HTTP 409 if a stream is active. +- Safe mode disables sub-talker sampling for stability. diff --git a/examples/voice_clone_stream_service.py b/examples/voice_clone_stream_service.py new file mode 100644 index 00000000..251dacf3 --- /dev/null +++ b/examples/voice_clone_stream_service.py @@ -0,0 +1,440 @@ +# coding=utf-8 +# Copyright 2026 The Alibaba Qwen team. +# SPDX-License-Identifier: Apache-2.0 +""" +FastAPI service for streaming voice-clone audio chunks from stored prompts. +""" + +import argparse +import base64 +import gc +import json +import threading +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +from fastapi import FastAPI, HTTPException +from fastapi.responses import FileResponse, StreamingResponse +from pydantic import BaseModel + +from qwen_tts import Qwen3TTSModel, VoiceClonePromptItem + + +MODEL_LOCK = threading.Lock() +CURRENT_TTS: Optional[Qwen3TTSModel] = None +CURRENT_MODEL_ID: Optional[str] = None +CURRENT_CONFIG: Dict[str, Any] = { + "model": None, + "device": None, + "dtype": None, + "flash_attn": None, + "tf32": None, + "safe_mode": False, +} + +VOICE_INDEX_LOCK = threading.Lock() +VOICE_INDEX: Dict[str, Dict[str, Any]] = {} + +PROMPT_CACHE_LOCK = threading.Lock() +PROMPT_CACHE: Dict[str, Tuple[List[VoiceClonePromptItem], Dict[str, Any]]] = {} + +VOICE_STORE: Optional[Path] = None +PROMPT_DIR: Optional[Path] = None +INDEX_PATH: Optional[Path] = None +BASE_DIR = Path(__file__).resolve().parent +TEST_PAGE_PATH = BASE_DIR / "voice_clone_stream_test.html" + + +def _dtype_from_str(s: str) -> torch.dtype: + s = (s or "").strip().lower() + if s in ("bf16", "bfloat16"): + return torch.bfloat16 + if s in ("fp16", "float16", "half"): + return torch.float16 + if s in ("fp32", "float32"): + return torch.float32 + raise ValueError(f"Unsupported torch dtype: {s}. Use bfloat16/float16/float32.") + + +def _apply_tf32(enabled: bool) -> None: + if not torch.cuda.is_available(): + return + torch.backends.cuda.matmul.allow_tf32 = bool(enabled) + torch.backends.cudnn.allow_tf32 = bool(enabled) + if hasattr(torch, "set_float32_matmul_precision"): + torch.set_float32_matmul_precision("high") + + +def _set_current_config( + model_id: str, + device: str, + dtype: str, + flash_attn: bool, + tf32: bool, + safe_mode: bool, +) -> None: + global CURRENT_CONFIG + CURRENT_CONFIG = { + "model": model_id, + "device": device, + "dtype": dtype, + "flash_attn": bool(flash_attn), + "tf32": bool(tf32), + "safe_mode": bool(safe_mode), + } + + +def _clear_model() -> None: + global CURRENT_TTS, CURRENT_MODEL_ID + if CURRENT_TTS is None: + return + old = CURRENT_TTS + CURRENT_TTS = None + CURRENT_MODEL_ID = None + del old + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + gc.collect() + + +def _init_voice_store(path: Path) -> None: + global VOICE_STORE, PROMPT_DIR, INDEX_PATH, VOICE_INDEX + VOICE_STORE = path + PROMPT_DIR = VOICE_STORE / "prompts" + PROMPT_DIR.mkdir(parents=True, exist_ok=True) + INDEX_PATH = VOICE_STORE / "voices.json" + VOICE_INDEX = _load_voice_index(INDEX_PATH) + + +def _load_voice_index(index_path: Path) -> Dict[str, Dict[str, Any]]: + if not index_path.exists(): + return {} + try: + payload = json.loads(index_path.read_text()) + except Exception: + return {} + voices = payload.get("voices", {}) + return voices if isinstance(voices, dict) else {} + + +def _torch_load_safe(path: Path) -> Any: + try: + return torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + return torch.load(path, map_location="cpu") + + +def _deserialize_prompt_items(payload: Dict[str, Any]) -> Tuple[List[VoiceClonePromptItem], Dict[str, Any]]: + items_raw = payload.get("items", None) + if not isinstance(items_raw, list) or len(items_raw) == 0: + raise ValueError("Empty or invalid prompt items.") + items: List[VoiceClonePromptItem] = [] + for d in items_raw: + if not isinstance(d, dict): + raise ValueError("Invalid prompt item format.") + ref_code = d.get("ref_code", None) + if ref_code is not None and not torch.is_tensor(ref_code): + ref_code = torch.tensor(ref_code) + ref_spk = d.get("ref_spk_embedding", None) + if ref_spk is None: + raise ValueError("Missing ref_spk_embedding in prompt item.") + if not torch.is_tensor(ref_spk): + ref_spk = torch.tensor(ref_spk) + items.append( + VoiceClonePromptItem( + ref_code=ref_code, + ref_spk_embedding=ref_spk, + x_vector_only_mode=bool(d.get("x_vector_only_mode", False)), + icl_mode=bool(d.get("icl_mode", not bool(d.get("x_vector_only_mode", False)))), + ref_text=d.get("ref_text", None), + ) + ) + meta = payload.get("meta", {}) + return items, meta if isinstance(meta, dict) else {} + + +def _load_prompt_items(voice_id: str) -> Tuple[List[VoiceClonePromptItem], Dict[str, Any]]: + with PROMPT_CACHE_LOCK: + cached = PROMPT_CACHE.get(voice_id) + if cached is not None: + return cached + + with VOICE_INDEX_LOCK: + meta = VOICE_INDEX.get(voice_id) + if not meta: + raise ValueError("Voice ID not found.") + rel_path = meta.get("path") + if not rel_path: + raise ValueError("Voice path missing.") + + payload = _torch_load_safe(VOICE_STORE / rel_path) + items, prompt_meta = _deserialize_prompt_items(payload) + + with PROMPT_CACHE_LOCK: + PROMPT_CACHE[voice_id] = (items, prompt_meta) + return items, prompt_meta + + +def _validate_prompt_items(items: List[VoiceClonePromptItem], tts: Qwen3TTSModel) -> None: + vocab_size = int(tts.model.talker.config.vocab_size) + num_groups = int(tts.model.talker.config.num_code_groups) + for idx, item in enumerate(items): + ref_code = item.ref_code + if ref_code is None: + continue + if not torch.is_tensor(ref_code): + ref_code = torch.tensor(ref_code) + if ref_code.dim() != 2 or ref_code.shape[1] != num_groups: + raise ValueError( + f"ref_code shape mismatch for prompt[{idx}]: " + f"expected (*, {num_groups}), got {tuple(ref_code.shape)}." + ) + if ref_code.dtype not in (torch.int64, torch.int32, torch.int16, torch.uint8): + ref_code = ref_code.long() + if ref_code.numel() > 0: + min_id = int(ref_code.min().item()) + max_id = int(ref_code.max().item()) + if min_id < 0 or max_id >= vocab_size: + raise ValueError( + f"ref_code out of range for model vocab_size={vocab_size}: min={min_id}, max={max_id}. " + "Recreate the voice prompt with the same model." + ) + item.ref_code = ref_code + + +def _load_tts(model_id: str, device: str, dtype: torch.dtype, flash_attn: bool) -> Qwen3TTSModel: + global CURRENT_TTS, CURRENT_MODEL_ID + attn_impl = "flash_attention_2" if flash_attn else "eager" + tts = Qwen3TTSModel.from_pretrained( + model_id, + device_map=device, + dtype=dtype, + attn_implementation=attn_impl, + ) + if getattr(tts.model, "tts_model_type", None) != "base": + raise ValueError("Only Base models support voice cloning.") + CURRENT_TTS = tts + CURRENT_MODEL_ID = model_id + return tts + + +class StreamRequest(BaseModel): + voice_id: str + text: str + language: str = "Auto" + chunk_size: int = 8 + left_context_size: int = 25 + do_sample: Optional[bool] = None + top_k: Optional[int] = None + top_p: Optional[float] = None + temperature: Optional[float] = None + repetition_penalty: Optional[float] = None + subtalker_dosample: Optional[bool] = None + subtalker_top_k: Optional[int] = None + subtalker_top_p: Optional[float] = None + subtalker_temperature: Optional[float] = None + max_new_tokens: Optional[int] = None + + +class ModelLoadRequest(BaseModel): + model_id: str + device: Optional[str] = None + dtype: Optional[str] = None + flash_attn: Optional[bool] = None + tf32: Optional[bool] = None + clear_cache: bool = True + safe_mode: Optional[bool] = None + + +def build_app(args: argparse.Namespace) -> FastAPI: + _init_voice_store(Path(args.voice_store)) + _apply_tf32(bool(args.tf32)) + with MODEL_LOCK: + _load_tts(args.model, args.device, _dtype_from_str(args.dtype), args.flash_attn) + _set_current_config(args.model, args.device, args.dtype, args.flash_attn, args.tf32, False) + + app = FastAPI() + + @app.get("/") + def index(): + if not TEST_PAGE_PATH.exists(): + raise HTTPException(status_code=404, detail="Test page not found.") + return FileResponse(TEST_PAGE_PATH, media_type="text/html") + + @app.get("/health") + def health(): + return { + "status": "ok", + "model": CURRENT_MODEL_ID, + "device": CURRENT_CONFIG.get("device"), + "dtype": CURRENT_CONFIG.get("dtype"), + "flash_attn": CURRENT_CONFIG.get("flash_attn"), + "tf32": CURRENT_CONFIG.get("tf32"), + "safe_mode": CURRENT_CONFIG.get("safe_mode"), + } + + @app.get("/models") + def list_models(): + with VOICE_INDEX_LOCK: + voices = list(VOICE_INDEX.values()) + models = sorted({meta.get("model_id") for meta in voices if meta.get("model_id")}) + return {"models": models} + + @app.get("/voices") + def list_voices(): + with VOICE_INDEX_LOCK: + voices = dict(VOICE_INDEX) + return {"voices": voices} + + @app.post("/voice/stream") + def voice_stream(payload: StreamRequest): + if not payload.text.strip(): + raise HTTPException(status_code=400, detail="Target text is required.") + try: + items, prompt_meta = _load_prompt_items(payload.voice_id) + except Exception as e: + raise HTTPException(status_code=404, detail=str(e)) + + prompt_model = prompt_meta.get("model_id") + if prompt_model and prompt_model != CURRENT_MODEL_ID: + raise HTTPException(status_code=400, detail=f"Voice created with {prompt_model}. Switch model to match.") + tts = CURRENT_TTS + if tts is None: + raise HTTPException(status_code=500, detail="Model not loaded.") + try: + _validate_prompt_items(items, tts) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) + + gen_kwargs = { + "do_sample": payload.do_sample, + "top_k": payload.top_k, + "top_p": payload.top_p, + "temperature": payload.temperature, + "repetition_penalty": payload.repetition_penalty, + "subtalker_dosample": payload.subtalker_dosample, + "subtalker_top_k": payload.subtalker_top_k, + "subtalker_top_p": payload.subtalker_top_p, + "subtalker_temperature": payload.subtalker_temperature, + "max_new_tokens": payload.max_new_tokens, + } + gen_kwargs = {k: v for k, v in gen_kwargs.items() if v is not None} + if CURRENT_CONFIG.get("safe_mode"): + gen_kwargs.setdefault("subtalker_dosample", False) + + def _iter_events(): + header = {"sr": int(tts.model.speech_tokenizer.get_output_sample_rate()), "dtype": "int16"} + yield f"event:meta\ndata:{json.dumps(header)}\n\n" + chunk_idx = 0 + with MODEL_LOCK: + for wav_chunk, sr in tts.generate_voice_clone_stream( + text=payload.text, + language=payload.language, + voice_clone_prompt=items, + chunk_size=payload.chunk_size, + left_context_size=payload.left_context_size, + **gen_kwargs, + ): + pcm16 = (np.clip(wav_chunk, -1.0, 1.0) * 32767.0).astype(np.int16) + b64 = base64.b64encode(pcm16.tobytes()).decode("ascii") + data = {"i": chunk_idx, "sr": sr, "audio": b64} + yield f"data:{json.dumps(data)}\n\n" + chunk_idx += 1 + yield "event:end\ndata:done\n\n" + + headers = {"Cache-Control": "no-cache", "X-Accel-Buffering": "no"} + return StreamingResponse(_iter_events(), media_type="text/event-stream", headers=headers) + + @app.post("/model/load") + def load_model(payload: ModelLoadRequest): + model_id = (payload.model_id or "").strip() + if not model_id: + raise HTTPException(status_code=400, detail="model_id is required.") + if not MODEL_LOCK.acquire(blocking=False): + raise HTTPException(status_code=409, detail="Model is busy. Wait for streaming to finish.") + try: + current_device = CURRENT_CONFIG.get("device") or args.device + current_dtype = CURRENT_CONFIG.get("dtype") or args.dtype + current_flash = ( + CURRENT_CONFIG.get("flash_attn") if CURRENT_CONFIG.get("flash_attn") is not None else args.flash_attn + ) + current_tf32 = CURRENT_CONFIG.get("tf32") if CURRENT_CONFIG.get("tf32") is not None else args.tf32 + current_safe = CURRENT_CONFIG.get("safe_mode") if CURRENT_CONFIG.get("safe_mode") is not None else False + + device = payload.device or current_device + dtype_str = payload.dtype or current_dtype + flash_attn = payload.flash_attn if payload.flash_attn is not None else current_flash + tf32 = payload.tf32 if payload.tf32 is not None else current_tf32 + safe_mode = payload.safe_mode if payload.safe_mode is not None else current_safe + + _clear_model() + _apply_tf32(bool(tf32)) + _load_tts(model_id, device, _dtype_from_str(dtype_str), bool(flash_attn)) + _set_current_config(model_id, device, dtype_str, bool(flash_attn), bool(tf32), bool(safe_mode)) + if payload.clear_cache: + with PROMPT_CACHE_LOCK: + PROMPT_CACHE.clear() + return {"status": "ok", "model": CURRENT_MODEL_ID, "safe_mode": CURRENT_CONFIG.get("safe_mode")} + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) + finally: + MODEL_LOCK.release() + + return app + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="voice-clone-stream-service", + description="Launch a streaming voice-clone service (SSE).", + formatter_class=argparse.RawTextHelpFormatter, + add_help=True, + ) + parser.add_argument( + "--model", + default="Qwen/Qwen3-TTS-12Hz-1.7B-Base", + help="Base model checkpoint path or HuggingFace repo id.", + ) + parser.add_argument("--device", default="cuda:0", help="Device for device_map, e.g. cpu, cuda:0.") + parser.add_argument( + "--dtype", + default="float16", + choices=["bfloat16", "bf16", "float16", "fp16", "float32", "fp32"], + help="Torch dtype for loading the model (default: float16).", + ) + parser.add_argument( + "--flash-attn/--no-flash-attn", + dest="flash_attn", + default=True, + action=argparse.BooleanOptionalAction, + help="Enable FlashAttention-2 (default: enabled for streaming).", + ) + parser.add_argument( + "--tf32/--no-tf32", + dest="tf32", + default=True, + action=argparse.BooleanOptionalAction, + help="Enable TF32 matmul/cudnn on supported GPUs (default: enabled).", + ) + parser.add_argument("--voice-store", default="voice_store", help="Directory to store voice prompts.") + parser.add_argument("--ip", default="0.0.0.0", help="Server bind IP (default: 0.0.0.0).") + parser.add_argument("--port", type=int, default=8002, help="Server port (default: 8002).") + return parser + + +def main(argv=None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + + app = build_app(args) + import uvicorn + + uvicorn.run(app, host=args.ip, port=int(args.port), log_level="info") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/examples/voice_clone_stream_test.html b/examples/voice_clone_stream_test.html new file mode 100644 index 00000000..42bea2a5 --- /dev/null +++ b/examples/voice_clone_stream_test.html @@ -0,0 +1,613 @@ + + + + + + Voice Clone Streaming Test + + + +
+
+

Voice Clone Streaming Test

+

Stream from the SSE endpoint and hear chunks immediately.

+
+ +
+
+
+ + +
+
+ + +
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+ +
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+ +
+ + + +
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+ idle + sr: - +
+
+ +
+ + 

+      

+    

+
+    
+  
+
diff --git a/qwen_tts/inference/qwen3_tts_model.py b/qwen_tts/inference/qwen3_tts_model.py
index f4d33bf3..f9f9bde0 100644
--- a/qwen_tts/inference/qwen3_tts_model.py
+++ b/qwen_tts/inference/qwen3_tts_model.py
@@ -17,7 +17,7 @@
 import io
 import urllib.request
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 from urllib.parse import urlparse
 
 import librosa
@@ -632,6 +632,390 @@ def generate_voice_clone(
 
         return wavs_out, fs
 
+    @torch.inference_mode()
+    def generate_voice_clone_stream(
+        self,
+        text: str,
+        language: Optional[str] = None,
+        ref_audio: Optional[AudioLike] = None,
+        ref_text: Optional[str] = None,
+        x_vector_only_mode: bool = False,
+        voice_clone_prompt: Optional[Union[Dict[str, Any], List[VoiceClonePromptItem]]] = None,
+        non_streaming_mode: bool = False,
+        chunk_size: int = 8,
+        left_context_size: int = 25,
+        **kwargs,
+    ) -> Iterator[Tuple[np.ndarray, int]]:
+        """
+        Stream voice-clone audio chunks (single sample only).
+
+        This method yields (wav_chunk, sample_rate) tuples as soon as each chunk is decoded.
+        """
+        if self.model.tts_model_type != "base":
+            raise ValueError(
+                f"model with \ntokenizer_type: {self.model.tokenizer_type}\n"
+                f"tts_model_size: {self.model.tts_model_size}\n"
+                f"tts_model_type: {self.model.tts_model_type}\n"
+                "does not support generate_voice_clone_stream, Please check Model Card or Readme for more details."
+            )
+        if not isinstance(text, str):
+            raise ValueError("Streaming mode only supports a single text string.")
+        if chunk_size <= 0:
+            raise ValueError("chunk_size must be a positive integer.")
+        if left_context_size < 0:
+            raise ValueError("left_context_size must be >= 0.")
+
+        if voice_clone_prompt is None:
+            if ref_audio is None:
+                raise ValueError("Either `voice_clone_prompt` or `ref_audio` must be provided.")
+            prompt_items = self.create_voice_clone_prompt(
+                ref_audio=ref_audio,
+                ref_text=ref_text,
+                x_vector_only_mode=x_vector_only_mode,
+            )
+            if len(prompt_items) != 1:
+                raise ValueError("Streaming mode only supports a single prompt item.")
+            voice_clone_prompt_dict = self._prompt_items_to_voice_clone_prompt(prompt_items)
+            ref_text_for_ids = prompt_items[0].ref_text
+        else:
+            if isinstance(voice_clone_prompt, list):
+                if len(voice_clone_prompt) != 1:
+                    raise ValueError("Streaming mode only supports a single prompt item.")
+                voice_clone_prompt_dict = self._prompt_items_to_voice_clone_prompt(voice_clone_prompt)
+                ref_text_for_ids = voice_clone_prompt[0].ref_text
+            else:
+                voice_clone_prompt_dict = voice_clone_prompt
+                ref_text_for_ids = None
+
+        def _ensure_list_field(val):
+            if val is None:
+                return None
+            return val if isinstance(val, list) else [val]
+
+        voice_clone_prompt_dict = {
+            "ref_code": _ensure_list_field(voice_clone_prompt_dict.get("ref_code")),
+            "ref_spk_embedding": _ensure_list_field(voice_clone_prompt_dict.get("ref_spk_embedding")),
+            "x_vector_only_mode": _ensure_list_field(voice_clone_prompt_dict.get("x_vector_only_mode", False)),
+            "icl_mode": _ensure_list_field(voice_clone_prompt_dict.get("icl_mode", False)),
+        }
+
+        if language is None:
+            language = "Auto"
+        self._validate_languages([language])
+
+        input_ids = self._tokenize_texts([self._build_assistant_text(text)])
+        input_id = input_ids[0]
+
+        ref_id = None
+        if ref_text_for_ids:
+            ref_id = self._tokenize_texts([self._build_ref_text(ref_text_for_ids)])[0]
+
+        gen_kwargs = self._merge_generate_kwargs(**kwargs)
+
+        if self.model.speech_tokenizer.get_model_type() != "qwen3_tts_tokenizer_12hz":
+            raise ValueError("Streaming decode currently supports only 12Hz tokenizer models.")
+
+        decoder = self.model.speech_tokenizer.model.decoder
+        decoder_device = getattr(self.model.speech_tokenizer, "device", self.model.talker.device)
+        decode_upsample = int(getattr(decoder, "total_upsample", 0)) or int(
+            self.model.speech_tokenizer.get_decode_upsample_rate()
+        )
+
+        def _decode_codes(codes: torch.Tensor, drop_codes: int) -> Optional[np.ndarray]:
+            if codes.numel() == 0:
+                return None
+            codes_t = codes.transpose(0, 1).unsqueeze(0).to(decoder_device)
+            wav = decoder(codes_t).squeeze(0).squeeze(0)
+            if drop_codes > 0:
+                drop = drop_codes * decode_upsample
+                if drop < wav.shape[-1]:
+                    wav = wav[drop:]
+                else:
+                    wav = wav[:0]
+            return wav.detach().to(torch.float32).cpu().numpy()
+
+        suppress_token_ids = [
+            i
+            for i in range(self.model.config.talker_config.vocab_size - 1024, self.model.config.talker_config.vocab_size)
+            if i not in (self.model.config.talker_config.codec_eos_token_id,)
+        ]
+
+        def _apply_repetition_penalty(logits: torch.Tensor, prev_tokens: List[int], penalty: float) -> torch.Tensor:
+            if penalty is None or penalty == 1.0 or not prev_tokens:
+                return logits
+            for tok in set(prev_tokens):
+                if logits[tok] < 0:
+                    logits[tok] *= penalty
+                else:
+                    logits[tok] /= penalty
+            return logits
+
+        def _filter_top_k_top_p(logits: torch.Tensor, top_k: int, top_p: float) -> torch.Tensor:
+            if top_k and top_k > 0:
+                values, _ = torch.topk(logits, top_k)
+                min_keep = values[-1]
+                logits = torch.where(logits < min_keep, torch.tensor(-float("inf"), device=logits.device), logits)
+            if top_p is not None and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                probs = torch.softmax(sorted_logits, dim=-1)
+                cum_probs = probs.cumsum(dim=-1)
+                mask = cum_probs > top_p
+                if mask.numel() > 0:
+                    mask[0] = False
+                sorted_logits[mask] = -float("inf")
+                logits = torch.empty_like(logits).scatter_(0, sorted_indices, sorted_logits)
+            return logits
+
+        def _sample_next(logits: torch.Tensor, prev_tokens: List[int]) -> int:
+            logits = logits.clone()
+            if suppress_token_ids:
+                logits[suppress_token_ids] = -float("inf")
+            logits = _apply_repetition_penalty(logits, prev_tokens, float(gen_kwargs["repetition_penalty"]))
+            temperature = float(gen_kwargs["temperature"]) if gen_kwargs.get("temperature") is not None else 1.0
+            if temperature <= 0:
+                temperature = 1.0
+            logits = logits / temperature
+            logits = _filter_top_k_top_p(
+                logits,
+                int(gen_kwargs["top_k"]) if gen_kwargs.get("top_k") is not None else 0,
+                float(gen_kwargs["top_p"]) if gen_kwargs.get("top_p") is not None else 1.0,
+            )
+            if not bool(gen_kwargs.get("do_sample", True)):
+                return int(torch.argmax(logits).item())
+            probs = torch.softmax(logits.float(), dim=-1)
+            if not torch.isfinite(probs).all() or float(probs.sum().item()) <= 0:
+                return int(torch.argmax(logits).item())
+            return int(torch.multinomial(probs, num_samples=1).item())
+
+        voice_clone_spk_embeds = None
+        if voice_clone_prompt_dict.get("ref_spk_embedding") is not None:
+            voice_clone_spk_embeds = self.model.generate_speaker_prompt(voice_clone_prompt_dict)
+
+        if voice_clone_spk_embeds is None:
+            speaker_embed = None
+        else:
+            if voice_clone_prompt_dict["x_vector_only_mode"][0] or voice_clone_prompt_dict["icl_mode"][0]:
+                speaker_embed = voice_clone_spk_embeds[0]
+            else:
+                speaker_embed = None
+
+        if language.lower() == "auto":
+            language_id = None
+        else:
+            language_id = self.model.config.talker_config.codec_language_id[language.lower()]
+
+        tts_bos_embed, tts_eos_embed, tts_pad_embed = self.model.talker.text_projection(
+            self.model.talker.get_text_embeddings()(
+                torch.tensor(
+                    [[self.model.config.tts_bos_token_id, self.model.config.tts_eos_token_id, self.model.config.tts_pad_token_id]],
+                    device=self.model.talker.device,
+                    dtype=input_id.dtype,
+                )
+            )
+        ).chunk(3, dim=1)
+
+        if language_id is None:
+            codec_prefill_list = [[
+                self.model.config.talker_config.codec_nothink_id,
+                self.model.config.talker_config.codec_think_bos_id,
+                self.model.config.talker_config.codec_think_eos_id,
+            ]]
+        else:
+            codec_prefill_list = [[
+                self.model.config.talker_config.codec_think_id,
+                self.model.config.talker_config.codec_think_bos_id,
+                language_id,
+                self.model.config.talker_config.codec_think_eos_id,
+            ]]
+
+        codec_input_embedding_0 = self.model.talker.get_input_embeddings()(
+            torch.tensor(codec_prefill_list, device=self.model.talker.device, dtype=input_id.dtype)
+        )
+        codec_input_embedding_1 = self.model.talker.get_input_embeddings()(
+            torch.tensor(
+                [[self.model.config.talker_config.codec_pad_id, self.model.config.talker_config.codec_bos_id]],
+                device=self.model.talker.device,
+                dtype=input_id.dtype,
+            )
+        )
+        if speaker_embed is None:
+            codec_input_embedding = torch.cat([codec_input_embedding_0, codec_input_embedding_1], dim=1)
+        else:
+            codec_input_embedding = torch.cat(
+                [codec_input_embedding_0, speaker_embed.view(1, 1, -1), codec_input_embedding_1], dim=1
+            )
+
+        talker_input_embed_role = self.model.talker.text_projection(
+            self.model.talker.get_text_embeddings()(input_id[:, :3])
+        )
+        _talker_input_embed = torch.cat(
+            (tts_pad_embed.expand(-1, codec_input_embedding.shape[1] - 2, -1), tts_bos_embed),
+            dim=1,
+        ) + codec_input_embedding[:, :-1]
+        talker_input_embed = torch.cat((talker_input_embed_role, _talker_input_embed), dim=1)
+
+        ref_code_list = voice_clone_prompt_dict.get("ref_code")
+        ref_code_item = ref_code_list[0] if ref_code_list else None
+        if ref_code_item is not None and voice_clone_prompt_dict["icl_mode"][0]:
+            if ref_id is None:
+                raise ValueError("ref_text is required for ICL mode in streaming generation.")
+            icl_input_embed, trailing_text_hidden = self.model.generate_icl_prompt(
+                text_id=input_id[:, 3:-5],
+                ref_id=ref_id[:, 3:-2],
+                ref_code=ref_code_item.to(self.model.talker.device),
+                tts_pad_embed=tts_pad_embed,
+                tts_eos_embed=tts_eos_embed,
+                non_streaming_mode=non_streaming_mode,
+            )
+            talker_input_embed = torch.cat([talker_input_embed, icl_input_embed], dim=1)
+        else:
+            talker_input_embed = torch.cat(
+                [
+                    talker_input_embed,
+                    self.model.talker.text_projection(
+                        self.model.talker.get_text_embeddings()(input_id[:, 3:4])
+                    ) + codec_input_embedding[:, -1:],
+                ],
+                dim=1,
+            )
+            if non_streaming_mode:
+                talker_input_embed = talker_input_embed[:, :-1]
+                talker_input_embed = torch.cat(
+                    [
+                        talker_input_embed,
+                        torch.cat(
+                            (
+                                self.model.talker.text_projection(
+                                    self.model.talker.get_text_embeddings()(input_id[:, 3:-5])
+                                ),
+                                tts_eos_embed,
+                            ),
+                            dim=1,
+                        )
+                        + self.model.talker.get_input_embeddings()(
+                            torch.tensor(
+                                [[self.model.config.talker_config.codec_pad_id]] * (input_id[:, 3:-5].shape[1] + 1),
+                                device=self.model.talker.device,
+                                dtype=input_id.dtype,
+                            )
+                        ),
+                        tts_pad_embed
+                        + self.model.talker.get_input_embeddings()(
+                            torch.tensor(
+                                [[self.model.config.talker_config.codec_bos_id]],
+                                device=self.model.talker.device,
+                                dtype=input_id.dtype,
+                            )
+                        ),
+                    ],
+                    dim=1,
+                )
+                trailing_text_hidden = tts_pad_embed
+            else:
+                trailing_text_hidden = torch.cat(
+                    (
+                        self.model.talker.text_projection(
+                            self.model.talker.get_text_embeddings()(input_id[:, 4:-5])
+                        ),
+                        tts_eos_embed,
+                    ),
+                    dim=1,
+                )
+
+        prefill = self.model.talker(
+            inputs_embeds=talker_input_embed,
+            attention_mask=None,
+            use_cache=True,
+            output_hidden_states=True,
+            return_dict=True,
+            trailing_text_hidden=trailing_text_hidden,
+            tts_pad_embed=tts_pad_embed,
+            subtalker_dosample=bool(gen_kwargs.get("subtalker_dosample", True)),
+            subtalker_top_k=gen_kwargs.get("subtalker_top_k"),
+            subtalker_top_p=gen_kwargs.get("subtalker_top_p"),
+            subtalker_temperature=gen_kwargs.get("subtalker_temperature"),
+        )
+
+        next_token = _sample_next(prefill.logits[:, -1, :].squeeze(0), [])
+        past_key_values = prefill.past_key_values
+        past_hidden = prefill.past_hidden
+        generation_step = prefill.generation_step
+
+        ref_code = ref_code_item
+        context_codes = None
+        if ref_code is not None:
+            if left_context_size > 0:
+                context_codes = ref_code[-left_context_size:].to(decoder_device)
+            else:
+                context_codes = ref_code.to(decoder_device)
+
+        pending_codes: List[torch.Tensor] = []
+        prev_tokens: List[int] = []
+        max_new_tokens = int(gen_kwargs.get("max_new_tokens", 2048))
+        eos_token_id = int(self.model.config.talker_config.codec_eos_token_id)
+        sample_rate = int(self.model.speech_tokenizer.get_output_sample_rate())
+
+        for _ in range(max_new_tokens):
+            if next_token == eos_token_id:
+                break
+
+            output = self.model.talker(
+                input_ids=torch.tensor([[next_token]], device=self.model.talker.device),
+                attention_mask=None,
+                past_key_values=past_key_values,
+                use_cache=True,
+                output_hidden_states=True,
+                return_dict=True,
+                past_hidden=past_hidden,
+                trailing_text_hidden=trailing_text_hidden,
+                tts_pad_embed=tts_pad_embed,
+                generation_step=generation_step,
+                subtalker_dosample=bool(gen_kwargs.get("subtalker_dosample", True)),
+                subtalker_top_k=gen_kwargs.get("subtalker_top_k"),
+                subtalker_top_p=gen_kwargs.get("subtalker_top_p"),
+                subtalker_temperature=gen_kwargs.get("subtalker_temperature"),
+            )
+
+            codec_ids = output.hidden_states[1]
+            if codec_ids is None:
+                break
+            pending_codes.append(codec_ids.squeeze(0))
+            prev_tokens.append(int(next_token))
+
+            if len(pending_codes) >= chunk_size:
+                chunk = torch.stack(pending_codes[:chunk_size], dim=0)
+                pending_codes = pending_codes[chunk_size:]
+                if context_codes is not None and context_codes.numel() > 0:
+                    decode_codes = torch.cat([context_codes, chunk], dim=0)
+                    drop = int(context_codes.shape[0])
+                else:
+                    decode_codes = chunk
+                    drop = 0
+                wav_chunk = _decode_codes(decode_codes, drop)
+                if wav_chunk is not None and wav_chunk.size > 0:
+                    yield wav_chunk, sample_rate
+                if left_context_size > 0:
+                    context_codes = decode_codes[-left_context_size:]
+                else:
+                    context_codes = None
+
+            past_key_values = output.past_key_values
+            past_hidden = output.past_hidden
+            generation_step = output.generation_step
+            next_token = _sample_next(output.logits[:, -1, :].squeeze(0), prev_tokens)
+
+        if pending_codes:
+            chunk = torch.stack(pending_codes, dim=0)
+            if context_codes is not None and context_codes.numel() > 0:
+                decode_codes = torch.cat([context_codes, chunk], dim=0)
+                drop = int(context_codes.shape[0])
+            else:
+                decode_codes = chunk
+                drop = 0
+            wav_chunk = _decode_codes(decode_codes, drop)
+            if wav_chunk is not None and wav_chunk.size > 0:
+                yield wav_chunk, sample_rate
+
     # voice design model
     @torch.no_grad()
     def generate_voice_design(
diff --git a/voice_store/prompts/03f79f10717a47e39e186e7ee4e53027.pt b/voice_store/prompts/03f79f10717a47e39e186e7ee4e53027.pt
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diff --git a/voice_store/prompts/4f47f8af90404b9e93649ce83b18af78.pt b/voice_store/prompts/4f47f8af90404b9e93649ce83b18af78.pt
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diff --git a/voice_store/voices.json b/voice_store/voices.json
new file mode 100644
index 00000000..11278e3e
--- /dev/null
+++ b/voice_store/voices.json
@@ -0,0 +1,52 @@
+{
+  "voices": {
+    "9a0aff0c1abe4f5e8d69b89f0b2271ce": {
+      "name": "\uc548\uc131\uc7ac",
+      "path": "prompts/9a0aff0c1abe4f5e8d69b89f0b2271ce.pt",
+      "created_at": "2026-01-25T07:45:09Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    },
+    "03f79f10717a47e39e186e7ee4e53027": {
+      "name": "\uc548\uc131\uc7ac",
+      "path": "prompts/03f79f10717a47e39e186e7ee4e53027.pt",
+      "created_at": "2026-01-25T07:47:54Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    },
+    "4f47f8af90404b9e93649ce83b18af78": {
+      "name": "\uc548\uc131\uc7ac(long)",
+      "path": "prompts/4f47f8af90404b9e93649ce83b18af78.pt",
+      "created_at": "2026-01-26T00:47:11Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    },
+    "3665c5834221470591e3c8fcb0de6e50": {
+      "name": "3665c583",
+      "path": "prompts/3665c5834221470591e3c8fcb0de6e50.pt",
+      "created_at": "2026-01-27T11:13:14Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    },
+    "415c673dbc4647e788b92f3ab7318877": {
+      "name": "PuppyAI",
+      "path": "prompts/415c673dbc4647e788b92f3ab7318877.pt",
+      "created_at": "2026-01-27T11:15:38Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    },
+    "e5641b080c9a40d7b434cd62bf9f61d3": {
+      "name": "\ub6b1\uc774",
+      "path": "prompts/e5641b080c9a40d7b434cd62bf9f61d3.pt",
+      "created_at": "2026-01-27T11:54:25Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    },
+    "4c5eb5a3c84c4f4183b831ac5a7fe41a": {
+      "name": "Trump",
+      "path": "prompts/4c5eb5a3c84c4f4183b831ac5a7fe41a.pt",
+      "created_at": "2026-01-28T07:46:49Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
+    },
+    "3a886347faee4fa6a4a5ad432a3dcab7": {
+      "name": "\uc548\uc131\uc7ac(0.6B)",
+      "path": "prompts/3a886347faee4fa6a4a5ad432a3dcab7.pt",
+      "created_at": "2026-01-28T13:34:55Z",
+      "model_id": "Qwen/Qwen3-TTS-12Hz-0.6B-Base"
+    }
+  }
+}
\ No newline at end of file

From 308f9db91b775687c40f101e72722c77558161c4 Mon Sep 17 00:00:00 2001
From: daewook 
Date: Fri, 6 Feb 2026 09:25:40 +0900
Subject: [PATCH 2/2] Improve voice clone streaming stability

---
 examples/run_voice_clone_stream_service.sh    |   2 +-
 .../README.md}                                |  22 +-
 .../voice_clone_stream_service.py             | 141 +++++-
 .../voice_clone_stream_test.html              | 428 ++++++++++++++++--
 examples/voice_clone_service.py               |  91 +++-
 qwen_tts/inference/qwen3_tts_model.py         |  65 ++-
 qwen_tts/inference/qwen3_tts_tokenizer.py     |  27 +-
 7 files changed, 718 insertions(+), 58 deletions(-)
 rename examples/{voice_clone_stream_service.md => streaming/README.md} (75%)
 rename examples/{ => streaming}/voice_clone_stream_service.py (71%)
 rename examples/{ => streaming}/voice_clone_stream_test.html (56%)

diff --git a/examples/run_voice_clone_stream_service.sh b/examples/run_voice_clone_stream_service.sh
index bb0336b9..1359ae60 100644
--- a/examples/run_voice_clone_stream_service.sh
+++ b/examples/run_voice_clone_stream_service.sh
@@ -75,7 +75,7 @@ done
 mkdir -p "$VOICE_STORE"
 
 CUDA_VISIBLE_DEVICES="$GPU_ID" "$PYTHON_BIN" \
-  "$SCRIPT_DIR/voice_clone_stream_service.py" \
+  "$SCRIPT_DIR/streaming/voice_clone_stream_service.py" \
   --device "$DEVICE" \
   --ip "$IP" \
   --port "$PORT" \
diff --git a/examples/voice_clone_stream_service.md b/examples/streaming/README.md
similarity index 75%
rename from examples/voice_clone_stream_service.md
rename to examples/streaming/README.md
index 3b253931..0ead5033 100644
--- a/examples/voice_clone_stream_service.md
+++ b/examples/streaming/README.md
@@ -1,6 +1,10 @@
 # Voice Clone Streaming Service (SSE)
 
-This service streams voice-clone audio chunks as Server-Sent Events (SSE).
+# Streaming voice cloning service
+
+This service streams voice-clone audio chunks as Server-Sent Events (SSE) or binary PCM.
+
+Files live under `examples/streaming/`.
 
 Constraints
 - Single-sample only (one `text`, one `voice_id` per request).
@@ -30,6 +34,22 @@ curl -N -X POST \
   http://localhost:8002/voice/stream
 ```
 
+Binary PCM stream
+```text
+POST /voice/stream_pcm
+
+Binary protocol:
+- 12-byte header:
+  - magic: "QTS1" (4 bytes)
+  - sample_rate: uint32 LE
+  - sample_width_bytes: uint16 LE (2 for int16)
+  - channels: uint16 LE (1)
+- Then repeated frames:
+  - frame_len_bytes: uint32 LE
+  - raw PCM int16 bytes
+- End of stream: frame_len_bytes = 0
+```
+
 Model switching
 ```bash
 curl -X POST \
diff --git a/examples/voice_clone_stream_service.py b/examples/streaming/voice_clone_stream_service.py
similarity index 71%
rename from examples/voice_clone_stream_service.py
rename to examples/streaming/voice_clone_stream_service.py
index 251dacf3..9949e85b 100644
--- a/examples/voice_clone_stream_service.py
+++ b/examples/streaming/voice_clone_stream_service.py
@@ -23,6 +23,7 @@
 
 
 MODEL_LOCK = threading.Lock()
+STREAM_CANCEL_EVENT = threading.Event()
 CURRENT_TTS: Optional[Qwen3TTSModel] = None
 CURRENT_MODEL_ID: Optional[str] = None
 CURRENT_CONFIG: Dict[str, Any] = {
@@ -181,6 +182,7 @@ def _load_prompt_items(voice_id: str) -> Tuple[List[VoiceClonePromptItem], Dict[
 def _validate_prompt_items(items: List[VoiceClonePromptItem], tts: Qwen3TTSModel) -> None:
     vocab_size = int(tts.model.talker.config.vocab_size)
     num_groups = int(tts.model.talker.config.num_code_groups)
+    hidden_size = int(tts.model.talker.config.hidden_size)
     for idx, item in enumerate(items):
         ref_code = item.ref_code
         if ref_code is None:
@@ -201,9 +203,21 @@ def _validate_prompt_items(items: List[VoiceClonePromptItem], tts: Qwen3TTSModel
                 raise ValueError(
                     f"ref_code out of range for model vocab_size={vocab_size}: min={min_id}, max={max_id}. "
                     "Recreate the voice prompt with the same model."
-                )
+            )
         item.ref_code = ref_code
 
+        ref_spk = item.ref_spk_embedding
+        if ref_spk is None:
+            raise ValueError(f"Missing ref_spk_embedding in prompt[{idx}].")
+        if not torch.is_tensor(ref_spk):
+            ref_spk = torch.tensor(ref_spk)
+        if ref_spk.dim() != 1 or int(ref_spk.shape[0]) != hidden_size:
+            raise ValueError(
+                f"ref_spk_embedding size mismatch for prompt[{idx}]: expected {hidden_size}, got {tuple(ref_spk.shape)}. "
+                "Recreate the voice prompt with the same model."
+            )
+        item.ref_spk_embedding = ref_spk
+
 
 def _load_tts(model_id: str, device: str, dtype: torch.dtype, flash_attn: bool) -> Qwen3TTSModel:
     global CURRENT_TTS, CURRENT_MODEL_ID
@@ -247,6 +261,7 @@ class ModelLoadRequest(BaseModel):
     tf32: Optional[bool] = None
     clear_cache: bool = True
     safe_mode: Optional[bool] = None
+    wait_timeout_sec: Optional[float] = None
 
 
 def build_app(args: argparse.Namespace) -> FastAPI:
@@ -274,13 +289,14 @@ def health():
             "flash_attn": CURRENT_CONFIG.get("flash_attn"),
             "tf32": CURRENT_CONFIG.get("tf32"),
             "safe_mode": CURRENT_CONFIG.get("safe_mode"),
+            "busy": MODEL_LOCK.locked(),
         }
 
     @app.get("/models")
     def list_models():
         with VOICE_INDEX_LOCK:
             voices = list(VOICE_INDEX.values())
-        models = sorted({meta.get("model_id") for meta in voices if meta.get("model_id")})
+        models = sorted([meta["model_id"] for meta in voices if meta.get("model_id")])
         return {"models": models}
 
     @app.get("/voices")
@@ -304,6 +320,7 @@ def voice_stream(payload: StreamRequest):
         tts = CURRENT_TTS
         if tts is None:
             raise HTTPException(status_code=500, detail="Model not loaded.")
+        assert tts is not None
         try:
             _validate_prompt_items(items, tts)
         except ValueError as exc:
@@ -322,38 +339,137 @@ def voice_stream(payload: StreamRequest):
             "max_new_tokens": payload.max_new_tokens,
         }
         gen_kwargs = {k: v for k, v in gen_kwargs.items() if v is not None}
+        if CURRENT_MODEL_ID and "0.6B" in CURRENT_MODEL_ID:
+            gen_kwargs.setdefault("subtalker_dosample", False)
         if CURRENT_CONFIG.get("safe_mode"):
+            gen_kwargs.setdefault("do_sample", False)
             gen_kwargs.setdefault("subtalker_dosample", False)
 
+        if not MODEL_LOCK.acquire(blocking=False):
+            raise HTTPException(status_code=409, detail="Model is busy. Stop current stream first.")
+        STREAM_CANCEL_EVENT.clear()
+
         def _iter_events():
-            header = {"sr": int(tts.model.speech_tokenizer.get_output_sample_rate()), "dtype": "int16"}
-            yield f"event:meta\ndata:{json.dumps(header)}\n\n"
-            chunk_idx = 0
-            with MODEL_LOCK:
+            try:
+                speech_tokenizer = tts.model.speech_tokenizer
+                if speech_tokenizer is None:
+                    raise HTTPException(status_code=500, detail="Speech tokenizer missing.")
+                header = {"sr": int(speech_tokenizer.get_output_sample_rate()), "dtype": "int16"}
+                yield f"event:meta\ndata:{json.dumps(header)}\n\n"
+                chunk_idx = 0
                 for wav_chunk, sr in tts.generate_voice_clone_stream(
                     text=payload.text,
                     language=payload.language,
                     voice_clone_prompt=items,
                     chunk_size=payload.chunk_size,
                     left_context_size=payload.left_context_size,
+                    should_stop=STREAM_CANCEL_EVENT.is_set,
                     **gen_kwargs,
                 ):
+                    if STREAM_CANCEL_EVENT.is_set():
+                        break
                     pcm16 = (np.clip(wav_chunk, -1.0, 1.0) * 32767.0).astype(np.int16)
                     b64 = base64.b64encode(pcm16.tobytes()).decode("ascii")
                     data = {"i": chunk_idx, "sr": sr, "audio": b64}
                     yield f"data:{json.dumps(data)}\n\n"
                     chunk_idx += 1
-            yield "event:end\ndata:done\n\n"
+                yield "event:end\ndata:done\n\n"
+            finally:
+                MODEL_LOCK.release()
 
         headers = {"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}
         return StreamingResponse(_iter_events(), media_type="text/event-stream", headers=headers)
 
+    @app.post("/voice/stream_pcm")
+    def voice_stream_pcm(payload: StreamRequest):
+        if not payload.text.strip():
+            raise HTTPException(status_code=400, detail="Target text is required.")
+        try:
+            items, prompt_meta = _load_prompt_items(payload.voice_id)
+        except Exception as e:
+            raise HTTPException(status_code=404, detail=str(e))
+
+        prompt_model = prompt_meta.get("model_id")
+        if prompt_model and prompt_model != CURRENT_MODEL_ID:
+            raise HTTPException(status_code=400, detail=f"Voice created with {prompt_model}. Switch model to match.")
+        tts = CURRENT_TTS
+        if tts is None:
+            raise HTTPException(status_code=500, detail="Model not loaded.")
+        assert tts is not None
+        try:
+            _validate_prompt_items(items, tts)
+        except ValueError as exc:
+            raise HTTPException(status_code=400, detail=str(exc))
+
+        gen_kwargs = {
+            "do_sample": payload.do_sample,
+            "top_k": payload.top_k,
+            "top_p": payload.top_p,
+            "temperature": payload.temperature,
+            "repetition_penalty": payload.repetition_penalty,
+            "subtalker_dosample": payload.subtalker_dosample,
+            "subtalker_top_k": payload.subtalker_top_k,
+            "subtalker_top_p": payload.subtalker_top_p,
+            "subtalker_temperature": payload.subtalker_temperature,
+            "max_new_tokens": payload.max_new_tokens,
+        }
+        gen_kwargs = {k: v for k, v in gen_kwargs.items() if v is not None}
+        if CURRENT_MODEL_ID and "0.6B" in CURRENT_MODEL_ID:
+            gen_kwargs.setdefault("subtalker_dosample", False)
+        if CURRENT_CONFIG.get("safe_mode"):
+            gen_kwargs.setdefault("do_sample", False)
+            gen_kwargs.setdefault("subtalker_dosample", False)
+
+        if not MODEL_LOCK.acquire(blocking=False):
+            raise HTTPException(status_code=409, detail="Model is busy. Stop current stream first.")
+        STREAM_CANCEL_EVENT.clear()
+
+        def _iter_pcm():
+            try:
+                speech_tokenizer = tts.model.speech_tokenizer
+                if speech_tokenizer is None:
+                    raise HTTPException(status_code=500, detail="Speech tokenizer missing.")
+                sr = int(speech_tokenizer.get_output_sample_rate())
+                header = b"QTS1" + sr.to_bytes(4, "little") + (2).to_bytes(2, "little") + (1).to_bytes(2, "little")
+                yield header
+                for wav_chunk, _ in tts.generate_voice_clone_stream(
+                    text=payload.text,
+                    language=payload.language,
+                    voice_clone_prompt=items,
+                    chunk_size=payload.chunk_size,
+                    left_context_size=payload.left_context_size,
+                    should_stop=STREAM_CANCEL_EVENT.is_set,
+                    **gen_kwargs,
+                ):
+                    if STREAM_CANCEL_EVENT.is_set():
+                        break
+                    pcm16 = (np.clip(wav_chunk, -1.0, 1.0) * 32767.0).astype(np.int16)
+                    pcm_bytes = pcm16.tobytes()
+                    yield len(pcm_bytes).to_bytes(4, "little")
+                    if pcm_bytes:
+                        yield pcm_bytes
+                yield (0).to_bytes(4, "little")
+            finally:
+                MODEL_LOCK.release()
+
+        headers = {"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}
+        return StreamingResponse(_iter_pcm(), media_type="application/octet-stream", headers=headers)
+
     @app.post("/model/load")
     def load_model(payload: ModelLoadRequest):
         model_id = (payload.model_id or "").strip()
         if not model_id:
             raise HTTPException(status_code=400, detail="model_id is required.")
-        if not MODEL_LOCK.acquire(blocking=False):
+        wait_timeout = payload.wait_timeout_sec
+        if wait_timeout is None:
+            acquired = MODEL_LOCK.acquire(blocking=False)
+        else:
+            try:
+                timeout_value = max(0.0, float(wait_timeout))
+            except (TypeError, ValueError):
+                raise HTTPException(status_code=400, detail="wait_timeout_sec must be a number.")
+            acquired = MODEL_LOCK.acquire(timeout=timeout_value)
+        if not acquired:
             raise HTTPException(status_code=409, detail="Model is busy. Wait for streaming to finish.")
         try:
             current_device = CURRENT_CONFIG.get("device") or args.device
@@ -383,6 +499,11 @@ def load_model(payload: ModelLoadRequest):
         finally:
             MODEL_LOCK.release()
 
+    @app.post("/voice/stop")
+    def stop_voice_stream():
+        STREAM_CANCEL_EVENT.set()
+        return {"status": "ok"}
+
     return app
 
 
@@ -401,9 +522,9 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--device", default="cuda:0", help="Device for device_map, e.g. cpu, cuda:0.")
     parser.add_argument(
         "--dtype",
-        default="float16",
+        default="bfloat16",
         choices=["bfloat16", "bf16", "float16", "fp16", "float32", "fp32"],
-        help="Torch dtype for loading the model (default: float16).",
+        help="Torch dtype for loading the model (default: bfloat16).",
     )
     parser.add_argument(
         "--flash-attn/--no-flash-attn",
diff --git a/examples/voice_clone_stream_test.html b/examples/streaming/voice_clone_stream_test.html
similarity index 56%
rename from examples/voice_clone_stream_test.html
rename to examples/streaming/voice_clone_stream_test.html
index 42bea2a5..9246bd1a 100644
--- a/examples/voice_clone_stream_test.html
+++ b/examples/streaming/voice_clone_stream_test.html
@@ -234,17 +234,28 @@ 
Voice Clone Streaming Test

               Safe mode (stability)
             
           
-          

+          

+            
+          

+          

+            
+          

         
 
         

           

             
-            
+            
           

           

             
-            
+            
           

         

 
@@ -292,6 +303,8 @@ 
Voice Clone Streaming Test

       const voiceSelect = document.getElementById("voiceId");
       const modelSelect = document.getElementById("modelId");
       const safeModeInput = document.getElementById("safeMode");
+      const smoothModeInput = document.getElementById("smoothMode");
+      const binaryModeInput = document.getElementById("binaryMode");
       const loadModelBtn = document.getElementById("loadModel");
       const chunkSizeInput = document.getElementById("chunkSize");
       const leftContextInput = document.getElementById("leftContext");
@@ -309,7 +322,14 @@ 
Voice Clone Streaming Test

       let playheadTime = 0;
       let streamSampleRate = null;
       let drainTimer = null;
-      const prebufferSeconds = 0.5;
+      let voicesCache = {};
+      let prebufferSeconds = 0.5;
+      let pendingChunk = null;
+      let bufferTargetSec = 0.6;
+      const bufferMinSec = 0.3;
+      const bufferMaxSec = 1.4;
+      let rtfEma = null;
+      let lastChunkTs = null;
 
       const defaultBase = window.location.origin && window.location.origin !== "null"
         ? window.location.origin
@@ -323,6 +343,19 @@ 
Voice Clone Streaming Test

 
       function setStatus(msg) {
         statusEl.textContent = msg;
+        const busy = msg === "connecting" || msg === "streaming" || msg === "loading model";
+        loadModelBtn.disabled = busy;
+        loadBtn.disabled = busy;
+      }
+
+      async function sendStopSignal() {
+        const base = baseUrlInput.value.trim();
+        if (!base) return;
+        try {
+          await fetch(base + "/voice/stop", { method: "POST" });
+        } catch (err) {
+          log("Stop signal failed: " + err);
+        }
       }
 
       async function loadModels() {
@@ -331,7 +364,8 @@ 
Voice Clone Streaming Test

         try {
           const res = await fetch(base + "/models");
           const data = await res.json();
-          const models = data.models || [];
+          const rawModels = data.models || [];
+          const models = Array.from(new Set(rawModels));
           const current = modelSelect.value;
           modelSelect.innerHTML = "";
           for (const model of models) {
@@ -364,7 +398,7 @@ 
Voice Clone Streaming Test

         try {
           const res = await fetch(base + "/health");
           const data = await res.json();
-          if (data.model && !modelSelect.value) {
+          if (data.model) {
             const existing = Array.from(modelSelect.options).some((opt) => opt.value === data.model);
             if (!existing) {
               const opt = document.createElement("option");
@@ -372,7 +406,11 @@ 
Voice Clone Streaming Test

               opt.textContent = data.model;
               modelSelect.appendChild(opt);
             }
+            const prev = modelSelect.value;
             modelSelect.value = data.model;
+            if (prev !== data.model) {
+              log("Active model: " + data.model);
+            }
             await loadVoices();
           }
           if (typeof data.safe_mode === "boolean") {
@@ -391,6 +429,7 @@ 
Voice Clone Streaming Test

           const data = await res.json();
           voiceSelect.innerHTML = "";
           const voices = data.voices || {};
+          voicesCache = voices;
           const activeModel = (modelSelect.value || "").trim();
           for (const [id, meta] of Object.entries(voices)) {
             if (activeModel && meta.model_id && meta.model_id !== activeModel) {
@@ -423,22 +462,34 @@ 
Voice Clone Streaming Test

           return;
         }
         stopStream(true);
+        await sendStopSignal();
+        setStatus("loading model");
+        startBtn.disabled = true;
         try {
           const res = await fetch(base + "/model/load", {
             method: "POST",
             headers: { "Content-Type": "application/json" },
-            body: JSON.stringify({ model_id: modelId, safe_mode: safeModeInput.checked }),
+            body: JSON.stringify({
+              model_id: modelId,
+              safe_mode: safeModeInput.checked,
+              wait_timeout_sec: 30,
+            }),
           });
           if (!res.ok) {
             const errText = await res.text();
             log("Model load failed: " + res.status + " " + errText);
+            setStatus("idle");
             return;
           }
           const data = await res.json();
           log("Model loaded: " + (data.model || modelId));
           await loadVoices();
+          setStatus("idle");
         } catch (err) {
           log("Model load error: " + err);
+          setStatus("idle");
+        } finally {
+          startBtn.disabled = false;
         }
       }
 
@@ -447,10 +498,15 @@ 
Voice Clone Streaming Test

           controller.abort();
           controller = null;
         }
+        sendStopSignal();
         if (drainTimer) {
           clearTimeout(drainTimer);
           drainTimer = null;
         }
+        pendingChunk = null;
+        bufferTargetSec = 0.6;
+        rtfEma = null;
+        lastChunkTs = null;
         if (closeAudio && audioCtx) {
           audioCtx.close();
           audioCtx = null;
@@ -467,6 +523,7 @@ 
Voice Clone Streaming Test

 
       function scheduleAudio(samples, sr) {
         if (!audioCtx) return;
+        if (!samples || samples.length === 0) return;
         const buffer = audioCtx.createBuffer(1, samples.length, sr);
         buffer.getChannelData(0).set(samples);
         const source = audioCtx.createBufferSource();
@@ -494,6 +551,133 @@ 
Voice Clone Streaming Test

         return samples;
       }
 
+      function decodePcm16Bytes(bytes) {
+        const view = new DataView(bytes.buffer, bytes.byteOffset, bytes.byteLength);
+        const samples = new Float32Array(bytes.byteLength / 2);
+        for (let i = 0; i < samples.length; i++) {
+          samples[i] = view.getInt16(i * 2, true) / 32768;
+        }
+        return samples;
+      }
+
+      function concatUint8(a, b) {
+        if (!a || a.length === 0) return b || new Uint8Array(0);
+        if (!b || b.length === 0) return a;
+        const out = new Uint8Array(a.length + b.length);
+        out.set(a, 0);
+        out.set(b, a.length);
+        return out;
+      }
+
+      function parsePcmHeader(bytes) {
+        if (bytes.length < 12) return null;
+        const magic = String.fromCharCode(bytes[0], bytes[1], bytes[2], bytes[3]);
+        if (magic !== "QTS1") {
+          throw new Error("Invalid PCM stream header");
+        }
+        const view = new DataView(bytes.buffer, bytes.byteOffset, 12);
+        const sr = view.getUint32(4, true);
+        const sampleWidth = view.getUint16(8, true);
+        const channels = view.getUint16(10, true);
+        return { sr, sampleWidth, channels };
+      }
+
+      function concatFloat32(a, b) {
+        if (!a || a.length === 0) return b || new Float32Array(0);
+        if (!b || b.length === 0) return a;
+        const out = new Float32Array(a.length + b.length);
+        out.set(a, 0);
+        out.set(b, a.length);
+        return out;
+      }
+
+      function findLowEnergyIndex(samples, start, end, windowSize) {
+        const len = samples.length;
+        const s = Math.max(0, Math.min(start, len));
+        const e = Math.max(s + 1, Math.min(end, len));
+        if (e - s <= windowSize) return s;
+        const step = Math.max(1, Math.floor(windowSize / 2));
+        let bestIdx = s;
+        let bestEnergy = Infinity;
+        for (let i = s; i + windowSize <= e; i += step) {
+          let sum = 0;
+          for (let j = 0; j < windowSize; j++) {
+            const v = samples[i + j];
+            sum += v * v;
+          }
+          if (sum < bestEnergy) {
+            bestEnergy = sum;
+            bestIdx = i;
+          }
+        }
+        return bestIdx + Math.floor(windowSize / 2);
+      }
+
+      function crossfadeBoundary(left, right, sr) {
+        const fadeSamples = Math.max(1, Math.floor(sr * 0.018));
+        if (left.length < fadeSamples || right.length < fadeSamples) {
+          return { output: left, pending: right };
+        }
+        const tail = left.slice(left.length - fadeSamples);
+        const head = right.slice(0, fadeSamples);
+        const mixed = new Float32Array(fadeSamples);
+        const denom = Math.max(1, fadeSamples - 1);
+        for (let i = 0; i < fadeSamples; i++) {
+          const t = i / denom;
+          mixed[i] = tail[i] * (1 - t) + head[i] * t;
+        }
+        const output = concatFloat32(left.slice(0, left.length - fadeSamples), mixed);
+        const pending = right.slice(fadeSamples);
+        return { output, pending };
+      }
+
+      function emitBuffered(sr, forceFlush) {
+        if (!pendingChunk || pendingChunk.length === 0) return;
+
+        const fadeSamples = Math.max(1, Math.floor(sr * 0.018));
+        const windowSamples = Math.max(4, Math.floor(sr * 0.012));
+        const searchSamples = Math.max(fadeSamples * 2, Math.floor(sr * 0.12));
+        const tailSamples = Math.max(fadeSamples * 2, Math.floor(sr * 0.08));
+        const minBufferSamples = Math.max(fadeSamples * 2, Math.floor(sr * bufferTargetSec));
+
+        if (!forceFlush) {
+          if (pendingChunk.length < minBufferSamples + tailSamples + windowSamples) {
+            return;
+          }
+        }
+
+        if (forceFlush && pendingChunk.length <= fadeSamples * 2) {
+          scheduleAudio(pendingChunk, sr);
+          pendingChunk = null;
+          return;
+        }
+
+        const maxLen = pendingChunk.length;
+        const end = Math.max(windowSamples, maxLen - tailSamples);
+        const start = Math.max(0, end - searchSamples);
+        if (start >= end) {
+          scheduleAudio(pendingChunk, sr);
+          pendingChunk = null;
+          return;
+        }
+
+        let cutIdx = findLowEnergyIndex(pendingChunk, start, end, windowSamples);
+        cutIdx = Math.min(end, Math.max(minBufferSamples, cutIdx));
+        if (!forceFlush && cutIdx < minBufferSamples) {
+          return;
+        }
+
+        const left = pendingChunk.slice(0, cutIdx);
+        const right = pendingChunk.slice(cutIdx);
+        const stitched = crossfadeBoundary(left, right, sr);
+        scheduleAudio(stitched.output, sr);
+        pendingChunk = stitched.pending && stitched.pending.length ? stitched.pending : null;
+        if (forceFlush && pendingChunk && pendingChunk.length) {
+          scheduleAudio(pendingChunk, sr);
+          pendingChunk = null;
+        }
+      }
+
       function parseEventBlock(block) {
         let event = "message";
         const dataLines = [];
@@ -508,32 +692,108 @@ 
Voice Clone Streaming Test

         return { event, data: dataLines.join("\n").trim() };
       }
 
-      async function startStreaming() {
-        const base = baseUrlInput.value.trim();
-        const voiceId = voiceSelect.value;
-        const text = textInput.value.trim();
-        if (!base || !voiceId || !text) {
-          log("Missing base URL, voice, or text.");
+      async function startStreamingBinary(base, payload) {
+        controller = new AbortController();
+        const res = await fetch(base + "/voice/stream_pcm", {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify(payload),
+          signal: controller.signal,
+        });
+
+        if (!res.ok || !res.body) {
+          let errText = "";
+          try {
+            errText = await res.text();
+          } catch (err) {
+            errText = "";
+          }
+          log("Binary stream failed: " + res.status + (errText ? " " + errText : ""));
+          if (res.status === 404 || res.status === 405) {
+            log("Falling back to SSE stream.");
+            binaryModeInput.checked = false;
+            return startStreamingSse(base, payload);
+          }
+          stopStream(true);
           return;
         }
 
-        stopStream(false);
-        setStatus("connecting");
-        startBtn.disabled = true;
-        stopBtn.disabled = false;
+        setStatus("streaming");
+        const reader = res.body.getReader();
+        let buffer = new Uint8Array(0);
+        let headerParsed = false;
+        let endReceived = false;
 
-        audioCtx = new (window.AudioContext || window.webkitAudioContext)();
-        await audioCtx.resume();
-        playheadTime = audioCtx.currentTime + prebufferSeconds;
+        while (true) {
+          const { value, done } = await reader.read();
+          if (done) break;
+          if (value && value.length) {
+            buffer = concatUint8(buffer, value);
+          }
+          while (true) {
+            if (!headerParsed) {
+              const header = parsePcmHeader(buffer);
+              if (!header) break;
+              streamSampleRate = Number(header.sr);
+              srLabel.textContent = "sr: " + streamSampleRate;
+              log("pcm meta: sr=" + streamSampleRate);
+              if (header.sampleWidth !== 2 || header.channels !== 1) {
+                log("pcm format: " + header.sampleWidth + " bytes, ch=" + header.channels);
+              }
+              buffer = buffer.slice(12);
+              headerParsed = true;
+              continue;
+            }
+            if (buffer.length < 4) break;
+            const view = new DataView(buffer.buffer, buffer.byteOffset, buffer.byteLength);
+            const frameLen = view.getUint32(0, true);
+            if (frameLen === 0) {
+              buffer = buffer.slice(4);
+              endReceived = true;
+              break;
+            }
+            if (buffer.length < 4 + frameLen) break;
+            const frame = buffer.slice(4, 4 + frameLen);
+            buffer = buffer.slice(4 + frameLen);
+            const samples = decodePcm16Bytes(frame);
+            const sr = streamSampleRate || audioCtx.sampleRate;
+            const now = performance.now() / 1000;
+            if (lastChunkTs !== null) {
+              const dt = now - lastChunkTs;
+              const chunkSec = samples.length / Math.max(1, sr);
+              if (chunkSec > 0) {
+                const rtf = dt / chunkSec;
+                rtfEma = rtfEma === null ? rtf : (0.8 * rtfEma + 0.2 * rtf);
+                if (rtfEma > 1.05) {
+                  bufferTargetSec = Math.min(bufferMaxSec, bufferTargetSec + 0.08);
+                } else if (rtfEma < 0.9) {
+                  bufferTargetSec = Math.max(bufferMinSec, bufferTargetSec - 0.04);
+                }
+              }
+            }
+            lastChunkTs = now;
+            if (smoothModeInput.checked) {
+              pendingChunk = concatFloat32(pendingChunk, samples);
+              emitBuffered(sr, false);
+            } else {
+              scheduleAudio(samples, sr);
+            }
+          }
+          if (endReceived) break;
+        }
 
-        const payload = {
-          voice_id: voiceId,
-          text,
-          language: languageInput.value || "Auto",
-          chunk_size: Number(chunkSizeInput.value) || 8,
-          left_context_size: Number(leftContextInput.value) || 25,
-        };
+        setStatus("done");
+        if (smoothModeInput.checked) {
+          emitBuffered(streamSampleRate || audioCtx.sampleRate, true);
+        }
+        const remaining = Math.max(0, playheadTime - audioCtx.currentTime);
+        drainTimer = setTimeout(() => {
+          stopStream(false);
+          setStatus("done");
+        }, (remaining + 0.05) * 1000);
+      }
 
+      async function startStreamingSse(base, payload) {
         controller = new AbortController();
         const res = await fetch(base + "/voice/stream", {
           method: "POST",
@@ -543,7 +803,13 @@ 
Voice Clone Streaming Test

         });
 
         if (!res.ok || !res.body) {
-          log("Request failed: " + res.status);
+          let errText = "";
+          try {
+            errText = await res.text();
+          } catch (err) {
+            errText = "";
+          }
+          log("Request failed: " + res.status + (errText ? " " + errText : ""));
           stopStream(true);
           return;
         }
@@ -574,6 +840,9 @@ 
Voice Clone Streaming Test

               }
             } else if (event === "end") {
               setStatus("done");
+              if (smoothModeInput.checked) {
+                emitBuffered(streamSampleRate || audioCtx.sampleRate, true);
+              }
               const remaining = Math.max(0, playheadTime - audioCtx.currentTime);
               drainTimer = setTimeout(() => {
                 stopStream(false);
@@ -585,7 +854,30 @@ 
Voice Clone Streaming Test

                 const obj = JSON.parse(data);
                 const samples = decodePcm16(obj.audio);
                 const sr = streamSampleRate || obj.sr || audioCtx.sampleRate;
-                scheduleAudio(samples, sr);
+                const now = performance.now() / 1000;
+                if (lastChunkTs !== null) {
+                  const dt = now - lastChunkTs;
+                  const chunkSec = samples.length / Math.max(1, sr);
+                  if (chunkSec > 0) {
+                    const rtf = dt / chunkSec;
+                    rtfEma = rtfEma === null ? rtf : (0.8 * rtfEma + 0.2 * rtf);
+                    if (rtfEma > 1.05) {
+                      bufferTargetSec = Math.min(bufferMaxSec, bufferTargetSec + 0.08);
+                    } else if (rtfEma < 0.9) {
+                      bufferTargetSec = Math.max(bufferMinSec, bufferTargetSec - 0.04);
+                    }
+                  }
+                }
+                lastChunkTs = now;
+                if (smoothModeInput.checked) {
+                  pendingChunk = concatFloat32(pendingChunk, samples);
+                  emitBuffered(sr, false);
+                } else {
+                  scheduleAudio(samples, sr);
+                }
+                if (obj.i === 0) {
+                  log("audio chunk 0 received");
+                }
               } catch (err) {
                 log("chunk parse error: " + err);
               }
@@ -593,6 +885,9 @@ 
Voice Clone Streaming Test

           }
         }
         setStatus("done");
+        if (smoothModeInput.checked) {
+          emitBuffered(streamSampleRate || audioCtx.sampleRate, true);
+        }
         const remaining = Math.max(0, playheadTime - audioCtx.currentTime);
         drainTimer = setTimeout(() => {
           stopStream(false);
@@ -600,14 +895,83 @@ 
Voice Clone Streaming Test

         }, (remaining + 0.05) * 1000);
       }
 
+      async function startStreaming() {
+        const base = baseUrlInput.value.trim();
+        const voiceId = voiceSelect.value;
+        const text = textInput.value.trim();
+        if (!base || !voiceId || !text) {
+          log("Missing base URL, voice, or text.");
+          return;
+        }
+
+        stopStream(false);
+        setStatus("connecting");
+        startBtn.disabled = true;
+        stopBtn.disabled = false;
+
+        audioCtx = new (window.AudioContext || window.webkitAudioContext)();
+        await audioCtx.resume();
+        bufferTargetSec = smoothModeInput.checked ? 0.6 : bufferMinSec;
+        rtfEma = null;
+        lastChunkTs = null;
+        prebufferSeconds = smoothModeInput.checked ? bufferTargetSec : 0.5;
+        playheadTime = audioCtx.currentTime + prebufferSeconds;
+
+        const payload = {
+          voice_id: voiceId,
+          text,
+          language: languageInput.value || "Auto",
+          chunk_size: Number(chunkSizeInput.value) || 8,
+          left_context_size: Number(leftContextInput.value) || 25,
+        };
+
+        const voiceMeta = voicesCache[voiceId] || {};
+        const expectedModel = (voiceMeta.model_id || "").trim();
+        if (expectedModel) {
+          try {
+            const res = await fetch(base + "/health");
+            if (res.ok) {
+              const data = await res.json();
+              const currentModel = (data.model || "").trim();
+              if (currentModel && currentModel !== expectedModel) {
+                log(
+                  "Voice created with " + expectedModel + ". Server model is " + currentModel + ". Click Load model."
+                );
+                stopStream(true);
+                return;
+              }
+            }
+          } catch (err) {
+            log("Model check failed: " + err);
+          }
+        }
+        const modelHint = (voiceMeta.model_id || modelSelect.value || "").trim();
+        if (safeModeInput.checked) {
+          payload.do_sample = false;
+          payload.subtalker_dosample = false;
+        } else if (modelHint.includes("0.6B")) {
+          payload.subtalker_dosample = false;
+        }
+
+        if (binaryModeInput.checked) {
+          await startStreamingBinary(base, payload);
+        } else {
+          await startStreamingSse(base, payload);
+        }
+      }
+
       loadBtn.addEventListener("click", loadVoices);
       loadModelBtn.addEventListener("click", switchModel);
       startBtn.addEventListener("click", startStreaming);
       stopBtn.addEventListener("click", () => stopStream(true));
 
-      loadCurrentModel();
-      loadModels();
-      loadVoices();
+      async function initPage() {
+        await loadCurrentModel();
+        await loadModels();
+        await loadVoices();
+      }
+
+      initPage();
     
   
 
diff --git a/examples/voice_clone_service.py b/examples/voice_clone_service.py
index 5a9e57f0..44d5503c 100644
--- a/examples/voice_clone_service.py
+++ b/examples/voice_clone_service.py
@@ -194,7 +194,7 @@ def _add_voice_meta(voice_id: str, meta: Dict[str, Any]) -> None:
         _save_voice_index(INDEX_PATH, VOICE_INDEX)
 
 
-def _build_voice_choices() -> Tuple[List[str], Dict[str, str]]:
+def _build_voice_choices(model_id: Optional[str] = None) -> Tuple[List[str], Dict[str, str]]:
     with VOICE_INDEX_LOCK:
         voices = dict(VOICE_INDEX)
     items = list(voices.items())
@@ -202,6 +202,9 @@ def _build_voice_choices() -> Tuple[List[str], Dict[str, str]]:
     choices: List[str] = []
     mapping: Dict[str, str] = {}
     for voice_id, meta in items:
+        voice_model = meta.get("model_id")
+        if model_id and voice_model != model_id:
+            continue
         name = meta.get("name") or voice_id[:8]
         disp = f"{name} ({voice_id[:8]})"
         choices.append(disp)
@@ -267,6 +270,45 @@ def _load_prompt_items(voice_id: str) -> Tuple[List[VoiceClonePromptItem], Dict[
     return items, prompt_meta
 
 
+def _validate_prompt_items(items: List[VoiceClonePromptItem], tts: Qwen3TTSModel) -> None:
+    vocab_size = int(tts.model.talker.config.vocab_size)
+    num_groups = int(tts.model.talker.config.num_code_groups)
+    hidden_size = int(tts.model.talker.config.hidden_size)
+    for idx, item in enumerate(items):
+        ref_code = item.ref_code
+        if ref_code is not None:
+            if not torch.is_tensor(ref_code):
+                ref_code = torch.tensor(ref_code)
+            if ref_code.dim() != 2 or ref_code.shape[1] != num_groups:
+                raise ValueError(
+                    f"ref_code shape mismatch for prompt[{idx}]: "
+                    f"expected (*, {num_groups}), got {tuple(ref_code.shape)}."
+                )
+            if ref_code.dtype not in (torch.int64, torch.int32, torch.int16, torch.uint8):
+                ref_code = ref_code.long()
+            if ref_code.numel() > 0:
+                min_id = int(ref_code.min().item())
+                max_id = int(ref_code.max().item())
+                if min_id < 0 or max_id >= vocab_size:
+                    raise ValueError(
+                        f"ref_code out of range for model vocab_size={vocab_size}: min={min_id}, max={max_id}. "
+                        "Recreate the voice prompt with the same model."
+                    )
+            item.ref_code = ref_code
+
+        ref_spk = item.ref_spk_embedding
+        if ref_spk is None:
+            raise ValueError(f"Missing ref_spk_embedding in prompt[{idx}].")
+        if not torch.is_tensor(ref_spk):
+            ref_spk = torch.tensor(ref_spk)
+        if ref_spk.dim() != 1 or int(ref_spk.shape[0]) != hidden_size:
+            raise ValueError(
+                f"ref_spk_embedding size mismatch for prompt[{idx}]: expected {hidden_size}, got {tuple(ref_spk.shape)}. "
+                "Recreate the voice prompt with the same model."
+            )
+        item.ref_spk_embedding = ref_spk
+
+
 def _load_tts(
     model_id: str,
     device: str,
@@ -315,24 +357,45 @@ def build_app(args: argparse.Namespace) -> gr.Blocks:
         tts = _load_tts(args.model, args.device, _dtype_from_str(args.dtype), args.flash_attn, args.cache_models)
 
     lang_choices, lang_map = _get_lang_choices(tts)
-    voice_choices, voice_map = _build_voice_choices()
+    voice_choices, voice_map = _build_voice_choices(args.model)
 
     gen_kwargs_default = _collect_gen_kwargs(args)
 
-    def switch_model(model_label: str, cur_model: str, cur_lang_map: Dict[str, str]):
+    def switch_model(
+        model_label: str,
+        cur_model: str,
+        cur_lang_map: Dict[str, str],
+        cur_voice_map: Dict[str, str],
+    ):
         model_id = model_label_map.get(model_label, model_label)
         try:
             with MODEL_LOCK:
                 tts_local = _load_tts(model_id, args.device, _dtype_from_str(args.dtype), args.flash_attn, args.cache_models)
             choices, mapping = _get_lang_choices(tts_local)
             value = "Auto" if "Auto" in choices else (choices[0] if choices else None)
+            voice_choices, voice_map = _build_voice_choices(model_id)
+            voice_value = voice_choices[0] if voice_choices else None
             status = f"Loaded {model_id}"
-            return status, gr.update(choices=choices, value=value), model_id, mapping
+            return (
+                status,
+                gr.update(choices=choices, value=value),
+                model_id,
+                mapping,
+                gr.update(choices=voice_choices, value=voice_value),
+                voice_map,
+            )
         except Exception as e:
-            return f"Failed to load model: {type(e).__name__}: {e}", gr.update(), cur_model, cur_lang_map
+            return (
+                f"Failed to load model: {type(e).__name__}: {e}",
+                gr.update(),
+                cur_model,
+                cur_lang_map,
+                gr.update(),
+                cur_voice_map,
+            )
 
-    def refresh_voices():
-        choices, mapping = _build_voice_choices()
+    def refresh_voices(cur_model: str):
+        choices, mapping = _build_voice_choices(cur_model)
         value = choices[0] if choices else None
         return gr.update(choices=choices, value=value), mapping
 
@@ -378,7 +441,7 @@ def save_voice(ref_audio, ref_text: str, use_xvec: bool, voice_name: str, cur_mo
             with PROMPT_CACHE_LOCK:
                 PROMPT_CACHE[voice_id] = (items, payload["meta"])
 
-            choices, mapping = _build_voice_choices()
+            choices, mapping = _build_voice_choices(cur_model)
             value = choices[0] if choices else None
             status = f"Saved voice '{name}' ({voice_id})."
             return status, gr.update(choices=choices, value=value), mapping
@@ -407,8 +470,13 @@ def run_tts(
             if prompt_model and prompt_model != cur_model:
                 return None, f"Voice created with {prompt_model}. Switch model to match."
 
-            language = lang_map_local.get(lang_disp, "Auto")
             tts_local = _get_current_tts()
+            try:
+                _validate_prompt_items(items, tts_local)
+            except ValueError as exc:
+                return None, str(exc)
+
+            language = lang_map_local.get(lang_disp, "Auto")
             with MODEL_LOCK:
                 wavs, sr = tts_local.generate_voice_clone(
                     text=text.strip(),
@@ -481,11 +549,12 @@ def run_tts(
 
         load_btn.click(
             switch_model,
-            inputs=[model_dd, model_state, lang_state],
-            outputs=[model_status, lang_in, model_state, lang_state],
+            inputs=[model_dd, model_state, lang_state, voice_map_state],
+            outputs=[model_status, lang_in, model_state, lang_state, voice_dd, voice_map_state],
         )
         refresh_btn.click(
             refresh_voices,
+            inputs=[model_state],
             outputs=[voice_dd, voice_map_state],
         )
         save_btn.click(
diff --git a/qwen_tts/inference/qwen3_tts_model.py b/qwen_tts/inference/qwen3_tts_model.py
index f9f9bde0..42d660cd 100644
--- a/qwen_tts/inference/qwen3_tts_model.py
+++ b/qwen_tts/inference/qwen3_tts_model.py
@@ -17,7 +17,7 @@
 import io
 import urllib.request
 from dataclasses import dataclass
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union
 from urllib.parse import urlparse
 
 import librosa
@@ -72,6 +72,8 @@ def __init__(self, model: Qwen3TTSForConditionalGeneration, processor, generate_
         self.processor = processor
         self.generate_defaults = generate_defaults or {}
 
+        self.model.eval()
+
         self.device = getattr(model, "device", None)
         if self.device is None:
             try:
@@ -99,6 +101,7 @@ def from_pretrained(
                 HuggingFace repo id or local directory of the model.
             **kwargs:
                 Forwarded as-is into `AutoModel.from_pretrained(...)`.
+                `dtype` is accepted as an alias for `torch_dtype`.
                 Typical examples: device_map="cuda:0", dtype=torch.bfloat16, attn_implementation="flash_attention_2".
 
         Returns:
@@ -109,6 +112,13 @@ def from_pretrained(
         AutoModel.register(Qwen3TTSConfig, Qwen3TTSForConditionalGeneration)
         AutoProcessor.register(Qwen3TTSConfig, Qwen3TTSProcessor)
 
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        dtype = kwargs.pop("dtype", None)
+        if dtype is None and torch_dtype is not None:
+            dtype = torch_dtype
+        if dtype is not None:
+            kwargs["torch_dtype"] = cls._normalize_torch_dtype(dtype)
+
         model = AutoModel.from_pretrained(pretrained_model_name_or_path, **kwargs)
         if not isinstance(model, Qwen3TTSForConditionalGeneration):
             raise TypeError(
@@ -120,6 +130,24 @@ def from_pretrained(
         generate_defaults = model.generate_config
         return cls(model=model, processor=processor, generate_defaults=generate_defaults)
 
+    @staticmethod
+    def _normalize_torch_dtype(dtype: Any) -> torch.dtype:
+        if isinstance(dtype, torch.dtype):
+            return dtype
+        if isinstance(dtype, str):
+            key = dtype.strip().lower()
+            mapping = {
+                "bf16": torch.bfloat16,
+                "bfloat16": torch.bfloat16,
+                "fp16": torch.float16,
+                "float16": torch.float16,
+                "fp32": torch.float32,
+                "float32": torch.float32,
+            }
+            if key in mapping:
+                return mapping[key]
+        raise ValueError(f"Unsupported torch dtype: {dtype}")
+
     def _supported_languages_set(self) -> Optional[set]:
         langs = getattr(self.model, "get_supported_languages", None)
         if callable(langs):
@@ -476,6 +504,7 @@ def generate_voice_clone(
         x_vector_only_mode: Union[bool, List[bool]] = False,
         voice_clone_prompt: Optional[Union[Dict[str, Any], List[VoiceClonePromptItem]]] = None,
         non_streaming_mode: bool = False,
+        match_voice: bool = False,
         **kwargs,
     ) -> Tuple[List[np.ndarray], int]:
         """
@@ -533,6 +562,8 @@ def generate_voice_clone(
                 Temperature for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
             max_new_tokens:
                 Maximum number of new codec tokens to generate.
+            match_voice:
+                If True, enforce deterministic decoding for voice cloning unless sampling is explicitly requested.
             **kwargs:
                 Any other keyword arguments supported by HuggingFace Transformers `generate()` can be passed.
                 They will be forwarded to the underlying `Qwen3TTSForConditionalGeneration.generate(...)`.
@@ -599,6 +630,11 @@ def generate_voice_clone(
                     ref_ids.append(ref_tok)
 
         gen_kwargs = self._merge_generate_kwargs(**kwargs)
+        if match_voice:
+            if "do_sample" not in kwargs:
+                gen_kwargs["do_sample"] = False
+            if "subtalker_dosample" not in kwargs:
+                gen_kwargs["subtalker_dosample"] = False
 
         talker_codes_list, _ = self.model.generate(
             input_ids=input_ids,
@@ -644,12 +680,15 @@ def generate_voice_clone_stream(
         non_streaming_mode: bool = False,
         chunk_size: int = 8,
         left_context_size: int = 25,
+        match_voice: bool = False,
+        should_stop: Optional[Callable[[], bool]] = None,
         **kwargs,
     ) -> Iterator[Tuple[np.ndarray, int]]:
         """
         Stream voice-clone audio chunks (single sample only).
 
         This method yields (wav_chunk, sample_rate) tuples as soon as each chunk is decoded.
+        match_voice=True enforces deterministic decoding unless sampling is explicitly requested.
         """
         if self.model.tts_model_type != "base":
             raise ValueError(
@@ -711,6 +750,17 @@ def _ensure_list_field(val):
             ref_id = self._tokenize_texts([self._build_ref_text(ref_text_for_ids)])[0]
 
         gen_kwargs = self._merge_generate_kwargs(**kwargs)
+        if match_voice:
+            if "do_sample" not in kwargs:
+                gen_kwargs["do_sample"] = False
+            if "subtalker_dosample" not in kwargs:
+                gen_kwargs["subtalker_dosample"] = False
+
+        def _stop_requested() -> bool:
+            return bool(should_stop()) if should_stop is not None else False
+
+        if _stop_requested():
+            return
 
         if self.model.speech_tokenizer.get_model_type() != "qwen3_tts_tokenizer_12hz":
             raise ValueError("Streaming decode currently supports only 12Hz tokenizer models.")
@@ -936,6 +986,9 @@ def _sample_next(logits: torch.Tensor, prev_tokens: List[int]) -> int:
             subtalker_temperature=gen_kwargs.get("subtalker_temperature"),
         )
 
+        if _stop_requested():
+            return
+
         next_token = _sample_next(prefill.logits[:, -1, :].squeeze(0), [])
         past_key_values = prefill.past_key_values
         past_hidden = prefill.past_hidden
@@ -955,7 +1008,11 @@ def _sample_next(logits: torch.Tensor, prev_tokens: List[int]) -> int:
         eos_token_id = int(self.model.config.talker_config.codec_eos_token_id)
         sample_rate = int(self.model.speech_tokenizer.get_output_sample_rate())
 
+        stopped = False
         for _ in range(max_new_tokens):
+            if _stop_requested():
+                stopped = True
+                break
             if next_token == eos_token_id:
                 break
 
@@ -976,6 +1033,10 @@ def _sample_next(logits: torch.Tensor, prev_tokens: List[int]) -> int:
                 subtalker_temperature=gen_kwargs.get("subtalker_temperature"),
             )
 
+            if _stop_requested():
+                stopped = True
+                break
+
             codec_ids = output.hidden_states[1]
             if codec_ids is None:
                 break
@@ -1004,7 +1065,7 @@ def _sample_next(logits: torch.Tensor, prev_tokens: List[int]) -> int:
             generation_step = output.generation_step
             next_token = _sample_next(output.logits[:, -1, :].squeeze(0), prev_tokens)
 
-        if pending_codes:
+        if pending_codes and not stopped:
             chunk = torch.stack(pending_codes, dim=0)
             if context_codes is not None and context_codes.numel() > 0:
                 decode_codes = torch.cat([context_codes, chunk], dim=0)
diff --git a/qwen_tts/inference/qwen3_tts_tokenizer.py b/qwen_tts/inference/qwen3_tts_tokenizer.py
index eae969d9..5f0f7cee 100644
--- a/qwen_tts/inference/qwen3_tts_tokenizer.py
+++ b/qwen_tts/inference/qwen3_tts_tokenizer.py
@@ -84,6 +84,13 @@ def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "Qwen3
         AutoConfig.register("qwen3_tts_tokenizer_12hz", Qwen3TTSTokenizerV2Config)
         AutoModel.register(Qwen3TTSTokenizerV2Config, Qwen3TTSTokenizerV2Model)
 
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        dtype = kwargs.pop("dtype", None)
+        if dtype is None and torch_dtype is not None:
+            dtype = torch_dtype
+        if dtype is not None:
+            kwargs["torch_dtype"] = _normalize_torch_dtype(dtype)
+
         inst.feature_extractor = AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)
         inst.model = AutoModel.from_pretrained(pretrained_model_name_or_path, **kwargs)
         inst.config = inst.model.config
@@ -408,4 +415,22 @@ def get_decode_upsample_rate(self) -> int:
         Returns:
             int: Decode upsample rate.
         """
-        return int(self.model.get_decode_upsample_rate())
\ No newline at end of file
+        return int(self.model.get_decode_upsample_rate())
+
+
+def _normalize_torch_dtype(dtype: Union[str, torch.dtype]) -> torch.dtype:
+    if isinstance(dtype, torch.dtype):
+        return dtype
+    if isinstance(dtype, str):
+        key = dtype.strip().lower()
+        mapping = {
+            "bf16": torch.bfloat16,
+            "bfloat16": torch.bfloat16,
+            "fp16": torch.float16,
+            "float16": torch.float16,
+            "fp32": torch.float32,
+            "float32": torch.float32,
+        }
+        if key in mapping:
+            return mapping[key]
+    raise ValueError(f"Unsupported torch dtype: {dtype}")