update ttsd

README.md

@@ -69,7 +69,7 @@ MOSS‑TTS Family is an open‑source **speech and sound generation model family
 When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline.
 
 - **MOSS‑TTS**: The flagship production model featuring high fidelity and optimal zero-shot voice cloning. It supports **long-speech generation**, **fine-grained control over Pinyin, phonemes, and duration**, as well as **multilingual/code-switched synthesis**.
-- **MOSS‑TTSD**: A spoken dialogue generation model for expressive, multi-speaker, and ultra-long dialogues. The new **v1.0 version** achieves **industry-leading performance on objective metrics** and **outperformed top closed-source models like Doubao and Gemini 2.5-pro** in subjective evaluations.
+- **MOSS‑TTSD**: A spoken dialogue generation model for expressive, multi-speaker, and ultra-long dialogues. The new **v1.0 version** achieves **industry-leading performance on objective metrics** and **outperformed top closed-source models like Doubao and Gemini 2.5-pro** in subjective evaluations. You can visit the [MOSS-TTSD repository](https://github.com/OpenMOSS/MOSS-TTSD) for details.
 - **MOSS‑VoiceGenerator**: An open-source voice design model capable of generating diverse voices and styles directly from text prompts, **without any reference speech**. It unifies voice design, style control, and synthesis, functioning independently or as a design layer for downstream TTS. Its performance **surpasses other top-tier voice design models in arena ratings**.
 - **MOSS‑TTS‑Realtime**: A multi-turn context-aware model for real-time voice agents. It uses incremental synthesis to ensure natural and coherent replies, making it **ideal for building low-latency voice agents when paired with text models**.
 - **MOSS‑SoundEffect**: A content creation model specialized in **sound effect generation** with wide category coverage and controllable duration. It generates audio for natural environments, urban scenes, biological sounds, human actions, and musical fragments, suitable for film, games, and interactive experiences.

README_zh.md

@@ -72,7 +72,7 @@ MOSS‑TTS 家族是由 [MOSI.AI](https://mosi.cn/#hero) 与 [OpenMOSS 团队](h
 当一段音频需要 **听起来像真实的人类**、**准确发音**、**在不同内容间切换说话风格**、**稳定持续数十分钟**，并且 **支持对话、角色扮演与实时交互** 时，单一 TTS 模型往往不足以胜任。**MOSS‑TTS 家族**将工作流拆分为 5 个可独立使用、亦可组合成完整管线的量产级模型。
 
 - **MOSS‑TTS**：MOSS‑TTS 是家族中的旗舰量产级 TTS 基础模型，**核心能力是高保真以及最优性能的零样本语音克隆**，支持**长文本长语音生成**、**拼音、音标与时长精细控制**，以及**多语种/中英混合合成**。它可作为大规模旁白、配音和语音产品的核心底座。
-- **MOSS‑TTSD**：MOSS‑TTSD 是对话语音生成模型，用于生成高表现力、多说话人、超长连续对话的音频。本次我们更新了全新的**v1.0版本**，相比于0.7版本，它在音色相似度，说话人切换准确率，词错误率等**客观指标上取得了业界最优的性能**，在竞技场主观评测中，也**战胜了豆包、Gemini2.5-pro**等顶尖闭源模型。
+- **MOSS‑TTSD**：MOSS‑TTSD 是对话语音生成模型，用于生成高表现力、多说话人、超长连续对话的音频。本次我们更新了全新的**v1.0版本**，相比于0.7版本，它在音色相似度，说话人切换准确率，词错误率等**客观指标上取得了业界最优的性能**，在竞技场主观评测中，也**战胜了豆包、Gemini2.5-pro**等顶尖闭源模型。详情请访问 [MOSS-TTSD 仓库](https://github.com/OpenMOSS/MOSS-TTSD)。
 - **MOSS‑VoiceGenerator**：MOSS‑VoiceGenerator 是开源音色设计模型，可从文本风格指令直接生成多样的说话人音色或风格，**无需参考音频**。它统一音色设计、风格控制与内容合成，可独立创作，也可作为下游 TTS 的音色设计层。模型性能在**竞技场评分上超过了其余等顶尖音色设计模型**。
 - **MOSS‑TTS‑Realtime**：MOSS‑TTS‑Realtime 是面向实时语音智能体的多轮上下文感知实时 TTS 模型。它结合多轮对话中的文本与历史语音信号进行低时延增量合成，使多轮回复保持连贯、自然且音色一致。**非常适合搭配文本模型构建低时延语音智能体**。
 - **MOSS‑SoundEffect**：MOSS‑SoundEffect 是面向内容制作的**音效生成**模型，具备广泛类别覆盖与可控时长能力。它能根据文本指令生成自然环境、城市场景、生物、人类动作与类音乐片段等音频，适用于影视、游戏、交互体验和数据合成。

clis/moss_ttsd_app.py

@@ -25,6 +25,66 @@
 DEFAULT_MAX_NEW_TOKENS = 2000
 MIN_SPEAKERS = 1
 MAX_SPEAKERS = 5
+PRESET_REF_AUDIO_S1 = "assets/audio/reference_02_s1.wav"
+PRESET_REF_AUDIO_S2 = "assets/audio/reference_02_s2.wav"
+PRESET_PROMPT_TEXT_S1 = (
+    "[S1] In short, we embarked on a mission to make America great again for all Americans."
+)
+PRESET_PROMPT_TEXT_S2 = (
+    "[S2] NVIDIA reinvented computing for the first time after 60 years. In fact, Erwin at IBM knows quite "
+    "well that the computer has largely been the same since the 60s."
+)
+PRESET_DIALOGUE_TEXT = (
+    "[S1] Listen, let's talk business. China. I'm hearing things.\n"
+    "People are saying they're catching up. Fast. What's the real scoop?\n"
+    "Their AI, is it a threat?\n"
+    "[S2] Well, the pace of innovation there is extraordinary, honestly.\n"
+    "They have the researchers, and they have the drive.\n"
+    "[S1] Extraordinary? I don't like that. I want us to be extraordinary.\n"
+    "Are they winning?\n"
+    "[S2] I wouldn't say winning, but their progress is very promising.\n"
+    "They are building massive clusters. They're very determined.\n"
+    "[S1] Promising. There it is. I hate that word.\n"
+    "When China is promising, it means we're losing.\n"
+    "It's a disaster, Jensen. A total disaster."
+)
+PRESET_EXAMPLES = [
+    {
+        "name": "Quick Start | reference_02_s1/s2",
+        "speaker_count": 2,
+        "s1_audio": PRESET_REF_AUDIO_S1,
+        "s1_prompt": PRESET_PROMPT_TEXT_S1,
+        "s2_audio": PRESET_REF_AUDIO_S2,
+        "s2_prompt": PRESET_PROMPT_TEXT_S2,
+        "dialogue_text": PRESET_DIALOGUE_TEXT,
+    }
+]
+PRESET_DISPLAY_FIELDS = [
+    ("Speaker Count", "speaker_count"),
+    ("S1 Reference Audio (Optional)", "s1_audio"),
+    ("S1 Prompt Text (Required with reference audio)", "s1_prompt"),
+    ("S2 Reference Audio (Optional)", "s2_audio"),
+    ("S2 Prompt Text (Required with reference audio)", "s2_prompt"),
+    ("Dialogue Text", "dialogue_text"),
+]
+
+
+def _build_preset_table_rows():
+    rows = []
+    row_to_preset = []
+    for preset_idx, preset in enumerate(PRESET_EXAMPLES):
+        for field_name, field_key in PRESET_DISPLAY_FIELDS:
+            value = str(preset.get(field_key, ""))
+            if field_key == "dialogue_text":
+                value = value.replace("\n", " ").strip()
+                if len(value) > 120:
+                    value = value[:120] + " ..."
+            rows.append([field_name, value])
+            row_to_preset.append(preset_idx)
+    return rows, row_to_preset
+
+
+PRESET_TABLE_ROWS, PRESET_TABLE_ROW_TO_PRESET = _build_preset_table_rows()
 
 
 def resolve_attn_implementation(requested: str, device: torch.device, dtype: torch.dtype) -> str | None:
@@ -197,6 +257,59 @@ def update_speaker_panels(speaker_count: int):
     return [gr.update(visible=(idx < count)) for idx in range(MAX_SPEAKERS)]
 
 
+def apply_preset_selection(evt: gr.SelectData):
+    if evt is None or evt.index is None:
+        return (
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            *[gr.update() for _ in range(MAX_SPEAKERS)],
+        )
+
+    if isinstance(evt.index, (tuple, list)):
+        row_idx = int(evt.index[0])
+    else:
+        row_idx = int(evt.index)
+
+    if row_idx < 0 or row_idx >= len(PRESET_TABLE_ROW_TO_PRESET):
+        return (
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            *[gr.update() for _ in range(MAX_SPEAKERS)],
+        )
+
+    preset_idx = PRESET_TABLE_ROW_TO_PRESET[row_idx]
+    if preset_idx < 0 or preset_idx >= len(PRESET_EXAMPLES):
+        return (
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            gr.update(),
+            *[gr.update() for _ in range(MAX_SPEAKERS)],
+        )
+
+    preset = PRESET_EXAMPLES[preset_idx]
+    panel_updates = update_speaker_panels(int(preset["speaker_count"]))
+    return (
+        gr.update(value=int(preset["speaker_count"])),
+        gr.update(value=str(preset["s1_audio"])),
+        gr.update(value=str(preset["s1_prompt"])),
+        gr.update(value=str(preset["s2_audio"])),
+        gr.update(value=str(preset["s2_prompt"])),
+        gr.update(value=str(preset["dialogue_text"])),
+        *panel_updates,
+    )
+
+
 def _merge_consecutive_speaker_tags(text: str) -> str:
     segments = re.split(r"(?=\[S\d+\])", text)
     if not segments:
@@ -593,12 +706,34 @@ def build_demo(args: argparse.Namespace):
                 output_audio = gr.Audio(label="Output Audio", type="numpy", elem_id="output_audio")
                 gr.HTML("", elem_id="output_audio_spacer")
                 status = gr.Textbox(label="Status", lines=4, interactive=False, elem_id="output_status")
+                preset_examples = gr.Dataframe(
+                    headers=["Field", "Value (click any row to fill inputs)"],
+                    value=PRESET_TABLE_ROWS,
+                    datatype=["str", "str"],
+                    row_count=(len(PRESET_TABLE_ROWS), "fixed"),
+                    col_count=(2, "fixed"),
+                    interactive=False,
+                    wrap=True,
+                    label="Preset Examples",
+                )
 
         speaker_count.change(
             fn=update_speaker_panels,
             inputs=[speaker_count],
             outputs=speaker_panels,
         )
+        preset_examples.select(
+            fn=apply_preset_selection,
+            outputs=[
+                speaker_count,
+                speaker_refs[0],
+                speaker_prompts[0],
+                speaker_refs[1],
+                speaker_prompts[1],
+                dialogue_text,
+                *speaker_panels,
+            ],
+        )
 
         run_btn.click(
             fn=lambda speaker_count, *inputs: run_inference(
@@ -673,4 +808,4 @@ def main() -> None:
 
 
 if __name__ == "__main__":
-    main()
+    main()