#!/usr/bin/env python3 """ Hugging Face Space 首页 - MOSS-TTSD 参考 fnlp/MOSS-TTSD Space 的实现,并结合本仓 UI 与文档做了增强: - 默认中文界面,保留简洁工作流 - 提供场景选择与一键加载 - 支持文本规范化选项 - 右侧提供简明的使用说明与文档链接 如需在本地运行本 Space 脚本: python hf_space/app.py """ import os import json import time import shutil import tempfile from typing import Optional, Tuple import gradio as gr import torch import torchaudio # HF Spaces GPU 调度 try: import spaces # 在HF空间中可用,本地不存在也不影响 except Exception: # noqa: BLE001 class _DummySpaces: # 兜底占位,以便本地运行不报错 def GPU(self, *args, **kwargs): # type: ignore[override] def deco(fn): return fn return deco spaces = _DummySpaces() # type: ignore from huggingface_hub import hf_hub_download # 复用本仓通用推理工具 from generation_utils import load_model, process_batch # ========================= # 配置 # ========================= SYSTEM_PROMPT = ( "You are a speech synthesizer that generates natural, realistic, and human-like conversational audio from dialogue text." ) # 场景配置映射 SCENARIO_CONFIG = { "科技播客_AI发展": { "title": "🤖 科技播客 - AI发展趋势", "description": "探讨人工智能的最新发展与未来趋势", "file": "scenarios/科技播客_AI发展.jsonl" }, "教育播客_学习方法": { "title": "📚 教育播客 - 高效学习方法", "description": "分享科学的学习方法与技巧", "file": "scenarios/教育播客_学习方法.jsonl" }, "生活播客_美食文化": { "title": "🍜 生活播客 - 美食文化探索", "description": "品味各地美食文化的魅力", "file": "scenarios/生活播客_美食文化.jsonl" }, "商业播客_创业经验": { "title": "💼 商业播客 - 创业经验分享", "description": "创业路上的经验教训与心得", "file": "scenarios/商业播客_创业经验.jsonl" }, "健康播客_运动健身": { "title": "🏃 健康播客 - 运动健身指南", "description": "科学健身与健康生活方式", "file": "scenarios/健康播客_运动健身.jsonl" }, "心理播客_情绪管理": { "title": "🧠 心理播客 - 情绪管理技巧", "description": "探索情绪管理与心理健康", "file": "scenarios/心理播客_情绪管理.jsonl" } } # 默认音频配置 DEFAULT_AUDIO_CONFIG = { "speaker1": { "audio": "examples/zh_spk1_moon.wav", "text": "周一到周五,每天早晨七点半到九点半的直播片段。言下之意呢,就是废话有点多,大家也别嫌弃,因为这都是直播间最真实的状态了。" }, "speaker2": { "audio": "examples/zh_spk2_moon.wav", "text": "如果大家想听到更丰富更及时的直播内容,记得在周一到周五准时进入直播间,和大家一起畅聊新消费新科技新趋势。" } } MODEL_PATH = "fnlp/MOSS-TTSD-v0.5" SPT_CONFIG_PATH = "XY_Tokenizer/config/xy_tokenizer_config.yaml" # 自动下载 XY_Tokenizer 权重到本地缓存(HF Space 会复用缓存) os.makedirs("XY_Tokenizer/weights", exist_ok=True) try: SPT_CHECKPOINT_PATH = hf_hub_download( repo_id="fnlp/XY_Tokenizer_TTSD_V0", filename="xy_tokenizer.ckpt", cache_dir="XY_Tokenizer/weights", ) except Exception as e: # noqa: BLE001 # 失败时保留占位路径,稍后初始化时再提示 print(f"⚠️ XY_Tokenizer 权重下载失败: {e}") SPT_CHECKPOINT_PATH = "XY_Tokenizer/weights/xy_tokenizer.ckpt" # 全局缓存 tokenizer = None model = None spt = None device = None # ========================= # 工具函数 # ========================= def get_scenario_examples(): """获取所有可用的场景示例,整合 JSON 文件和默认配置""" scenarios = {} # 加载 JSON 文件场景 for key, config in SCENARIO_CONFIG.items(): try: file_path = config["file"] print(f"🔍 检查场景文件: {file_path}") if os.path.exists(file_path): with open(file_path, "r", encoding="utf-8") as f: data = json.load(f) scenarios[config["title"]] = { "text": data.get("text", ""), "description": config["description"], "audio1": data.get("prompt_audio_speaker1", ""), "text1": data.get("prompt_text_speaker1", ""), "audio2": data.get("prompt_audio_speaker2", ""), "text2": data.get("prompt_text_speaker2", ""), "base_path": data.get("base_path", ""), } print(f"✅ 成功加载场景: {config['title']}") else: print(f"❌ 场景文件不存在: {file_path}") except Exception as e: print(f"⚠️ 加载场景 {key} 失败: {e}") # 添加默认示例(确保总有可用场景) scenarios["🎧 默认示例"] = { "text": ( "[S1]大家好,欢迎收听今天的节目,我是主播小雨。" "[S2]大家好,我是嘉宾阿明,很高兴和大家见面。" "[S1]今天我们要聊的话题非常有趣,相信大家会喜欢的。" "[S2]是的,让我们开始今天的精彩内容吧!" ), "description": "默认的示例对话,适合快速体验", "audio1": DEFAULT_AUDIO_CONFIG["speaker1"]["audio"], "text1": DEFAULT_AUDIO_CONFIG["speaker1"]["text"], "audio2": DEFAULT_AUDIO_CONFIG["speaker2"]["audio"], "text2": DEFAULT_AUDIO_CONFIG["speaker2"]["text"], "base_path": "", } print(f"📊 总共加载了 {len(scenarios)} 个场景") return scenarios def load_scenario_data(scenario_key: str): """加载场景数据,确保音频和文本一一对应""" if scenario_key not in SCENARIO_CONFIG: return None, None, None, None, None try: scenario_file = SCENARIO_CONFIG[scenario_key]["file"] if not os.path.exists(scenario_file): return None, None, None, None, None with open(scenario_file, "r", encoding="utf-8") as f: data = json.load(f) # 确保音频文件路径正确 audio1_path = data.get("prompt_audio_speaker1", "") audio2_path = data.get("prompt_audio_speaker2", "") if audio1_path and not audio1_path.startswith("/"): audio1_path = os.path.join(data.get("base_path", ""), audio1_path) if audio2_path and not audio2_path.startswith("/"): audio2_path = os.path.join(data.get("base_path", ""), audio2_path) return ( data.get("text", ""), audio1_path if os.path.exists(audio1_path) else None, data.get("prompt_text_speaker1", ""), audio2_path if os.path.exists(audio2_path) else None, data.get("prompt_text_speaker2", "") ) except Exception as e: print(f"❌ 加载场景失败: {e}") return None, None, None, None, None def load_default_audio(): """加载默认音频和文本""" audio1 = DEFAULT_AUDIO_CONFIG["speaker1"]["audio"] text1 = DEFAULT_AUDIO_CONFIG["speaker1"]["text"] audio2 = DEFAULT_AUDIO_CONFIG["speaker2"]["audio"] text2 = DEFAULT_AUDIO_CONFIG["speaker2"]["text"] # 默认对话文本 default_text = ( "[S1]大家好,欢迎收听今天的节目,我是主播小雨。" "[S2]大家好,我是嘉宾阿明,很高兴和大家见面。" "[S1]今天我们要聊的话题非常有趣,相信大家会喜欢的。" "[S2]是的,让我们开始今天的精彩内容吧!" ) return ( default_text, audio1 if os.path.exists(audio1) else None, text1, audio2 if os.path.exists(audio2) else None, text2 ) def initialize_model(): global tokenizer, model, spt, device if tokenizer is not None: return tokenizer, model, spt, device device = "cuda" if torch.cuda.is_available() else "cpu" print(f"🔧 初始化模型,设备: {device}") if not os.path.exists(SPT_CHECKPOINT_PATH): raise FileNotFoundError( "未找到 XY_Tokenizer 权重,请检查网络或手动放置到 XY_Tokenizer/weights/xy_tokenizer.ckpt" ) tokenizer, model, spt = load_model( MODEL_PATH, SPT_CONFIG_PATH, SPT_CHECKPOINT_PATH, ) model = model.to(device) spt = spt.to(device) # 合理限制生成长度,避免超时 try: model.generation_config.max_new_tokens = min( getattr(model.generation_config, "max_new_tokens", 4096), 4096 ) except Exception: # noqa: BLE001 pass print("✅ 模型初始化完成!") return tokenizer, model, spt, device # ========================= # 推理函数(供 UI 调用) # ========================= @spaces.GPU(duration=150) def generate_dialogue_audio( dialogue_text: str, speaker1_audio: Optional[str], speaker1_text: str, speaker2_audio: Optional[str], speaker2_text: str, use_normalize: bool, ) -> Tuple[Optional[str], str]: try: if not dialogue_text or not dialogue_text.strip(): return None, "❌ 请输入对话文本" # 允许只提供一个音频:会自动退化为单音频模式 if not speaker1_audio and not speaker2_audio: return None, "❌ 请上传至少一个参考音频文件" # 初始化模型 tokenizer, model, spt, device = initialize_model() # 根据输入拼装 item(process_batch 兼容单/双说话者) item = {"text": dialogue_text} if speaker1_audio and speaker2_audio: item.update( { "prompt_audio_speaker1": speaker1_audio, "prompt_text_speaker1": speaker1_text or "", "prompt_audio_speaker2": speaker2_audio, "prompt_text_speaker2": speaker2_text or "", } ) else: # 单音频模式 single_audio = speaker1_audio or speaker2_audio single_text = speaker1_text or speaker2_text or "" item.update({"prompt_audio": single_audio, "prompt_text": single_text}) # 执行合成 actual_texts_data, audio_results = process_batch( batch_items=[item], tokenizer=tokenizer, model=model, spt=spt, device=device, system_prompt=SYSTEM_PROMPT, start_idx=0, use_normalize=use_normalize, ) if not audio_results or audio_results[0] is None: return None, "❌ 音频生成失败" audio_result = audio_results[0] out_path = tempfile.NamedTemporaryFile(suffix=".wav", delete=False).name torchaudio.save(out_path, audio_result["audio_data"], audio_result["sample_rate"]) # type: ignore[index] status = ( f"✅ 生成成功!\n\n" f"📊 音频信息:\n" f"- 采样率: {audio_result['sample_rate']} Hz\n" f"- 时长: {audio_result['audio_data'].shape[-1] / audio_result['sample_rate']:.2f} 秒\n" f"- 通道数: {audio_result['audio_data'].shape[0]}\n\n" f"📝 文本处理:\n" f"- 是否规范化: {use_normalize}\n" ) return out_path, status except Exception as e: # noqa: BLE001 import traceback return None, f"❌ 生成出错: {e}\n\n{traceback.format_exc()}" # ========================= # UI 构建 # ========================= def create_space_ui() -> gr.Blocks: custom_css = """ .gradio-container { max-width: 1400px !important; margin: 0 auto !important; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; } .header { text-align: center; margin-bottom: 2rem; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 2.5rem; border-radius: 20px; color: white; box-shadow: 0 10px 30px rgba(0,0,0,0.2); } .header h1 { font-size: 2.5rem; margin-bottom: 0.5rem; font-weight: 700; } .header p { font-size: 1.2rem; opacity: 0.9; margin: 0; } .section { background: #f8fafc; padding: 1.5rem; border-radius: 15px; border: 1px solid #e2e8f0; margin-bottom: 1rem; box-shadow: 0 2px 10px rgba(0,0,0,0.05); } .quick-btn { background: linear-gradient(45deg, #FF6B6B, #4ECDC4) !important; border: none !important; color: white !important; font-weight: 600 !important; border-radius: 10px !important; transition: all 0.3s ease !important; } .quick-btn:hover { transform: translateY(-2px) !important; box-shadow: 0 5px 15px rgba(0,0,0,0.2) !important; } .generate-btn { background: linear-gradient(45deg, #667eea, #764ba2) !important; border: none !important; color: white !important; font-weight: 700 !important; font-size: 1.1rem !important; border-radius: 15px !important; padding: 1rem 2rem !important; width: 100% !important; transition: all 0.3s ease !important; } .generate-btn:hover { transform: translateY(-3px) !important; box-shadow: 0 8px 25px rgba(102, 126, 234, 0.4) !important; } .speaker-section { background: linear-gradient(135deg, #667eea15, #764ba215); padding: 1.5rem; border-radius: 15px; border: 2px solid #667eea20; } """ with gr.Blocks(css=custom_css, title="🎙️ MOSS-TTSD | Hugging Face Space", theme=gr.themes.Soft()) as demo: gr.HTML( """

🎙️ MOSS-TTSD 对话语音合成

零样本双说话者对话合成 · 默认中文界面 · 一键加载场景

""" ) with gr.Row(): # 左侧:输入 with gr.Column(scale=3): with gr.Group(): gr.Markdown("### 📝 对话文本") dialogue_text = gr.TextArea( label="", lines=6, placeholder="请输入对话内容,使用[S1]/[S2]标记不同说话者...", value=( "[S1]大家好,欢迎收听今天的《AI前沿》播客。" "[S2]你好,我是嘉宾阿明。" "[S1]今天我们来聊聊最新的语音合成技术,特别是MOSS-TTSD这个项目。" "[S2]是的,这个开源项目确实很有意思,它能生成非常自然的对话音频。" ), ) with gr.Group(): gr.Markdown("### 🚀 快速操作") # 获取场景选项,设置第一个为默认值 scenario_choices = list(get_scenario_examples().keys()) default_scenario = scenario_choices[0] if scenario_choices else None scenario_dropdown = gr.Dropdown( choices=scenario_choices, value=default_scenario, label="🎭 选择场景", info="选择一个预设场景,自动填充对话文本和参考音频" ) with gr.Row(): btn_load_scenario = gr.Button("📥 加载场景", variant="secondary") btn_load_default = gr.Button("🎧 默认音频", variant="secondary") with gr.Row(): with gr.Group(): gr.Markdown("### 🎵 说话者1 (女声)") speaker1_audio = gr.Audio(label="参考音频", type="filepath") speaker1_text = gr.TextArea( label="参考文本", lines=2, placeholder="请输入与参考音频内容完全匹配的文本..." ) with gr.Group(): gr.Markdown("### 🎵 说话者2 (男声)") speaker2_audio = gr.Audio(label="参考音频", type="filepath") speaker2_text = gr.TextArea( label="参考文本", lines=2, placeholder="请输入与参考音频内容完全匹配的文本..." ) with gr.Group(): gr.Markdown("### ⚙️ 设置") with gr.Row(): use_normalize = gr.Checkbox(label="✅ 文本标准化(推荐)", value=True) btn_generate = gr.Button("🎬 开始合成", variant="primary") # 右侧:输出与说明 with gr.Column(scale=2): with gr.Group(): gr.Markdown("### 🎧 生成结果") output_audio = gr.Audio(label="生成的音频", type="filepath") status_info = gr.TextArea(label="状态信息", lines=12, interactive=False) with gr.Group(): gr.Markdown("### 📚 使用说明") gr.Markdown( """ **🎯 快速开始:** 1. 选择场景并点击"加载场景",或自己输入对话文本 2. 上传两个参考音频(分别对应说话者1和说话者2) 3. 输入与参考音频完全匹配的参考文本 4. 勾选"文本标准化"(推荐) 5. 点击"开始合成" **📝 格式要求:** - 使用 `[S1]`/`[S2]` 标记不同说话者 - 参考文本需与参考音频内容完全匹配 - 支持上传两个参考音频(双说话者)或一个(单说话者) **🎵 音频建议:** - 格式: WAV, MP3, FLAC - 时长: 10-30秒最佳 - 质量: 清晰无背景噪音 - 语速: 自然正常语速 **💡 提示:** - 文本标准化开启可提升质量(数字、标点等处理更稳定) - 文本尽量短句、自然口语化 - 生成时间根据文本长度而定,请耐心等待 """ ) # ===== 交互逻辑 ===== def on_load_scenario(name: str): """加载选中的场景,包括文本和音频""" if not name or name.strip() == "": gr.Warning("⚠️ 请先选择一个场景") return gr.update(), gr.update(), gr.update(), gr.update(), gr.update() scenarios = get_scenario_examples() if name not in scenarios: gr.Error(f"❌ 场景不存在: {name}") return gr.update(), gr.update(), gr.update(), gr.update(), gr.update() try: scenario = scenarios[name] # 处理音频路径 audio1_path = None audio2_path = None if scenario.get("audio1"): audio1_full = scenario["audio1"] if scenario.get("base_path") and not audio1_full.startswith("/"): audio1_full = os.path.join(scenario["base_path"], audio1_full) if os.path.exists(audio1_full): audio1_path = audio1_full else: print(f"⚠️ 音频文件不存在: {audio1_full}") if scenario.get("audio2"): audio2_full = scenario["audio2"] if scenario.get("base_path") and not audio2_full.startswith("/"): audio2_full = os.path.join(scenario["base_path"], audio2_full) if os.path.exists(audio2_full): audio2_path = audio2_full else: print(f"⚠️ 音频文件不存在: {audio2_full}") gr.Info(f"✅ 成功加载场景: {name}") return ( scenario.get("text", ""), audio1_path, scenario.get("text1", ""), audio2_path, scenario.get("text2", "") ) except Exception as e: gr.Error(f"❌ 加载场景时出错: {str(e)}") return gr.update(), gr.update(), gr.update(), gr.update(), gr.update() def on_load_default(): """加载默认音频和文本""" try: result = load_default_audio() gr.Info("✅ 成功加载默认音频和文本") return result except Exception as e: gr.Error(f"❌ 加载默认音频时出错: {str(e)}") return gr.update(), gr.update(), gr.update(), gr.update(), gr.update() btn_load_scenario.click( fn=on_load_scenario, inputs=[scenario_dropdown], outputs=[dialogue_text, speaker1_audio, speaker1_text, speaker2_audio, speaker2_text], ) btn_load_default.click( fn=on_load_default, outputs=[dialogue_text, speaker1_audio, speaker1_text, speaker2_audio, speaker2_text], ) btn_generate.click( fn=generate_dialogue_audio, inputs=[dialogue_text, speaker1_audio, speaker1_text, speaker2_audio, speaker2_text, use_normalize], outputs=[output_audio, status_info], show_progress=True, ) return demo # 供 HF Spaces 直接加载 demo = create_space_ui() def main(): demo.queue(max_size=16).launch() if __name__ == "__main__": main()