dekun
379 lines
14 KiB
Python
379 lines
14 KiB
Python
"""
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Trading Studio — 自动化交易复盘视频配音系统
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Gradio Web 中控：音色锁定 → Whisper 识别 → Gemma4 润色 → ChatTTS 合成
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"""
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from __future__ import annotations
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import logging
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import shutil
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import sys
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import uuid
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from pathlib import Path
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import gradio as gr
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from config import (
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GIT_REPO_URL,
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HOST,
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MODEL_NAME,
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OLLAMA_URL,
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PORT,
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SPEAKER_EMB_PATH,
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UPLOAD_DIR,
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)
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from llm_service import check_ollama_health, polish_text
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from tts_service import generate_voice, save_fixed_speaker, speaker_is_ready
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from whisper_service import transcribe_audio
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# ---------------------------------------------------------------------------
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# 日志
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# ---------------------------------------------------------------------------
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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handlers=[
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logging.StreamHandler(sys.stdout),
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logging.FileHandler("trading_studio.log", encoding="utf-8"),
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],
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)
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logger = logging.getLogger("trading_studio")
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# ---------------------------------------------------------------------------
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# 全局 UI 状态（Gradio State）
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# ---------------------------------------------------------------------------
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# raw_transcript / polished_script 在流水线中传递
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def _save_upload(upload_file) -> str | None:
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"""将 Gradio 上传文件复制到本地 uploads 目录，返回持久化路径。"""
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if upload_file is None:
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return None
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src = Path(upload_file)
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if not src.exists():
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return None
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dest = UPLOAD_DIR / f"{uuid.uuid4().hex}_{src.name}"
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shutil.copy2(src, dest)
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return str(dest)
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# ---------------------------------------------------------------------------
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# 模块 1：音色锁定
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# ---------------------------------------------------------------------------
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def ui_lock_speaker(audio_file, sample_transcript: str) -> str:
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"""【音色锁定】从参考人声提取并保存 Speaker Embedding。"""
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path = _save_upload(audio_file)
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if not path:
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return "请上传 10-30 秒干净参考人声（wav/mp3 均可）。"
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ok, msg = save_fixed_speaker(path, sample_transcript or "")
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return msg if ok else f"❌ {msg}"
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def ui_speaker_status() -> str:
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"""刷新音色状态。"""
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ok, msg = speaker_is_ready()
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return f"✅ {msg}" if ok else f"⚠️ {msg}"
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# ---------------------------------------------------------------------------
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# 模块 2：音频极速识别
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# ---------------------------------------------------------------------------
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def ui_transcribe(audio_file) -> tuple[str, str]:
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"""【Whisper 识别】返回 (转写文本, 状态日志)。"""
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path = _save_upload(audio_file)
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if not path:
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return "", "请上传待识别的碎碎念录音。"
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ok, result = transcribe_audio(path)
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if ok:
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return result, f"✅ 识别完成，共 {len(result)} 字。"
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return "", f"❌ {result}"
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# ---------------------------------------------------------------------------
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# 模块 3：Gemma4 纪律审判
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# ---------------------------------------------------------------------------
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def ui_polish(raw_text: str) -> tuple[str, str]:
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"""【LLM 润色】对转写稿进行严厉自我反思式润色。"""
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if not raw_text or not raw_text.strip():
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return "", "请先完成语音识别或手动粘贴转写文本。"
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ok, result = polish_text(raw_text)
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if ok:
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return result, f"✅ Gemma4 润色完成，共 {len(result)} 字。"
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return "", f"❌ {result}"
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def ui_check_ollama() -> str:
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"""检测远程 Ollama 节点状态。"""
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ok, msg = check_ollama_health()
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return f"✅ {msg}" if ok else f"❌ {msg}"
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# ---------------------------------------------------------------------------
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# 模块 4：ChatTTS 音频合成
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# ---------------------------------------------------------------------------
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def ui_synthesize(polished_text: str) -> tuple[str | None, str]:
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"""【TTS 合成】生成最终 wav 配音文件。"""
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if not polished_text or not polished_text.strip():
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return None, "请先完成 Gemma4 润色。"
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ok, msg, wav_path = generate_voice(polished_text)
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if ok and wav_path:
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return wav_path, f"✅ {msg}"
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return None, f"❌ {msg}"
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# ---------------------------------------------------------------------------
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# 一键流水线
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# ---------------------------------------------------------------------------
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def ui_full_pipeline(
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audio_file,
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skip_polish: bool,
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manual_raw: str,
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) -> tuple[str, str, str | None, str]:
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"""
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串联执行：识别 → 润色（可跳过）→ 合成。
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返回 (raw, polished, wav_path, log)
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"""
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logs: list[str] = []
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# Step 1: 识别
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if manual_raw and manual_raw.strip():
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raw = manual_raw.strip()
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logs.append(f"使用手动输入转写稿（{len(raw)} 字）。")
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else:
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path = _save_upload(audio_file)
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if not path:
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return "", "", None, "❌ 请上传录音或手动填写转写文本。"
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ok, result = transcribe_audio(path)
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if not ok:
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return "", "", None, f"❌ 识别失败: {result}"
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raw = result
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logs.append(f"✅ Whisper 识别完成（{len(raw)} 字）。")
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# Step 2: 润色
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if skip_polish:
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polished = raw
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logs.append("已跳过 LLM 润色，直接使用原文。")
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else:
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ok, result = polish_text(raw)
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if not ok:
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return raw, "", None, f"❌ 润色失败: {result}\n" + "\n".join(logs)
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polished = result
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logs.append(f"✅ Gemma4 润色完成（{len(polished)} 字）。")
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# Step 3: 合成
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ok, msg, wav_path = generate_voice(polished)
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if not ok:
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return raw, polished, None, f"❌ 合成失败: {msg}\n" + "\n".join(logs)
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logs.append(f"✅ {msg}")
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return raw, polished, wav_path, "\n".join(logs)
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# ---------------------------------------------------------------------------
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# Gradio 界面
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# ---------------------------------------------------------------------------
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CUSTOM_CSS = """
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/* 硬核暗黑科技风 */
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.gradio-container {
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background: linear-gradient(160deg, #0a0a0f 0%, #12121a 40%, #0d0d12 100%) !important;
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color: #c8c8d0 !important;
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}
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.dark-panel {
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border: 1px solid #2a2a35;
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border-radius: 8px;
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padding: 16px;
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background: rgba(18, 18, 26, 0.85);
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margin-bottom: 12px;
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}
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h1, h2, h3 { color: #e8e8f0 !important; letter-spacing: 0.05em; }
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.status-bar {
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font-family: 'Consolas', 'Monaco', monospace;
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font-size: 0.85em;
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color: #7a7a90;
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}
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footer { visibility: hidden; }
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"""
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def build_app() -> gr.Blocks:
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"""构建 Gradio 主界面。"""
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theme = gr.themes.Monochrome(
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primary_hue="slate",
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secondary_hue="gray",
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neutral_hue="slate",
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font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"],
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font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "Consolas", "monospace"],
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).set(
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body_background_fill="#0a0a0f",
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body_background_fill_dark="#0a0a0f",
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block_background_fill="#12121a",
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block_background_fill_dark="#12121a",
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block_border_color="#2a2a35",
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block_label_text_color="#9090a0",
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input_background_fill="#1a1a24",
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button_primary_background_fill="#3a3a50",
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button_primary_background_fill_hover="#4a4a60",
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)
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with gr.Blocks(
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title="Trading Studio | 交易复盘配音中控",
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theme=theme,
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css=CUSTOM_CSS,
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) as demo:
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gr.Markdown(
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f"""
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# ⚡ Trading Studio
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**本地量化交易复盘 → B 站配音生产流水线**
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`Whisper(CUDA)` → `Gemma4 @ {OLLAMA_URL}` → `ChatTTS(CUDA)`
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> 仓库: [{GIT_REPO_URL}]({GIT_REPO_URL})
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""",
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elem_classes=["dark-panel"],
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)
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with gr.Row():
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ollama_status = gr.Textbox(
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label="Ollama 节点",
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value=f"模型: {MODEL_NAME}",
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interactive=False,
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scale=3,
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elem_classes=["status-bar"],
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)
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speaker_status = gr.Textbox(
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label="音色状态",
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value="检测中...",
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interactive=False,
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scale=2,
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elem_classes=["status-bar"],
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)
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refresh_btn = gr.Button("🔄 刷新状态", scale=1)
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refresh_btn.click(
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fn=lambda: (ui_check_ollama(), ui_speaker_status()),
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outputs=[ollama_status, speaker_status],
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)
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with gr.Tabs():
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# ---- Tab 1: 音色锁定 ----
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with gr.Tab("🎙️ 音色锁定"):
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gr.Markdown(
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"上传 **10-30 秒** 干净人声样本，系统将提取 Speaker Embedding "
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f"并保存至 `{SPEAKER_EMB_PATH.name}`，后续合成 100% 还原音色。"
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)
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with gr.Row():
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spk_audio = gr.Audio(
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label="参考人声（碎碎念盲录样本）",
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type="filepath",
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sources=["upload", "microphone"],
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)
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spk_transcript = gr.Textbox(
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label="参考音频精确转写（可选，提升还原度）",
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placeholder="尽量与参考音频内容完全一致...",
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lines=6,
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)
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lock_btn = gr.Button("🔒 锁定音色", variant="primary")
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lock_log = gr.Textbox(label="锁定结果", lines=4, interactive=False)
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lock_btn.click(ui_lock_speaker, [spk_audio, spk_transcript], lock_log)
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# ---- Tab 2: 分步操作 ----
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with gr.Tab("🔧 分步流水线"):
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with gr.Row():
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with gr.Column(scale=1):
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gr.Markdown("### Step 1 · 音频极速识别")
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rec_audio = gr.Audio(
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label="交易复盘碎碎念录音",
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type="filepath",
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sources=["upload", "microphone"],
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)
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transcribe_btn = gr.Button("⚡ Faster-Whisper 识别", variant="primary")
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transcribe_log = gr.Textbox(label="识别日志", lines=2, interactive=False)
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with gr.Column(scale=1):
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gr.Markdown("### Step 2 · Gemma4 纪律审判")
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raw_text = gr.Textbox(
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label="转写原文（可编辑）",
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lines=10,
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placeholder="识别结果将显示在此，也可手动粘贴...",
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)
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polish_btn = gr.Button("⚖️ 远程 Gemma4 严厉润色", variant="primary")
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polish_log = gr.Textbox(label="润色日志", lines=2, interactive=False)
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with gr.Column(scale=1):
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gr.Markdown("### Step 3 · ChatTTS 配音合成")
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polished_text = gr.Textbox(
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label="润色配音稿（可编辑）",
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lines=10,
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placeholder="润色结果将显示在此...",
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)
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synth_btn = gr.Button("🔊 合成配音 WAV", variant="primary")
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synth_log = gr.Textbox(label="合成日志", lines=2, interactive=False)
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output_audio = gr.Audio(label="成品配音", type="filepath")
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transcribe_btn.click(ui_transcribe, rec_audio, [raw_text, transcribe_log])
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polish_btn.click(ui_polish, raw_text, [polished_text, polish_log])
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synth_btn.click(ui_synthesize, polished_text, [output_audio, synth_log])
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# ---- Tab 3: 一键生产 ----
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with gr.Tab("🚀 一键生产"):
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gr.Markdown(
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"上传碎碎念录音，系统自动完成 **识别 → 润色 → 合成** 全流程。"
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)
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with gr.Row():
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pipe_audio = gr.Audio(
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label="复盘录音",
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type="filepath",
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sources=["upload", "microphone"],
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)
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pipe_manual = gr.Textbox(
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label="或手动输入转写（跳过识别）",
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lines=4,
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placeholder="若已有转写文本，可直接粘贴，留空则走 Whisper 识别",
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)
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skip_polish_cb = gr.Checkbox(
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label="跳过 Gemma4 润色（仅测试 TTS）",
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value=False,
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)
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pipeline_btn = gr.Button("▶ 启动全流程", variant="primary", size="lg")
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pipeline_log = gr.Textbox(label="流水线日志", lines=6, interactive=False)
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with gr.Row():
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pipe_raw = gr.Textbox(label="转写原文", lines=6)
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pipe_polished = gr.Textbox(label="润色稿", lines=6)
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pipe_output = gr.Audio(label="成品配音", type="filepath")
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pipeline_btn.click(
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ui_full_pipeline,
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[pipe_audio, skip_polish_cb, pipe_manual],
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[pipe_raw, pipe_polished, pipe_output, pipeline_log],
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)
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demo.load(
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fn=lambda: (ui_check_ollama(), ui_speaker_status()),
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outputs=[ollama_status, speaker_status],
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)
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return demo
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def main() -> None:
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"""主入口：启动 Gradio 服务。"""
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logger.info("Trading Studio 启动中... HOST=%s PORT=%s", HOST, PORT)
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app = build_app()
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app.launch(
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server_name=HOST,
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server_port=PORT,
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share=False,
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show_error=True,
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allowed_paths=[str(Path(__file__).resolve().parent / "outputs")],
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)
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if __name__ == "__main__":
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main()
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