diff --git a/pipeline_service/__init__.py b/pipeline_service/__init__.py index 992cbd7..4e5e7ff 100644 --- a/pipeline_service/__init__.py +++ b/pipeline_service/__init__.py @@ -12,7 +12,8 @@ from .init import ( pipeline_cancel, pipeline_handlers, ) -from .handler import register_handler, list_handlers, get_handler +from .handler import register_handler, list_handlers, register_default_handler +from .handlers_ktv import register_ktv_handlers from .state import ( STATE_PENDING, STATE_RUNNING, STATE_COMPLETED, STATE_FAILED, STATE_SKIPPED, TASK_SUBMITTED, TASK_RUNNING, TASK_COMPLETED, TASK_FAILED, TASK_PAUSED, TASK_CANCELLED, diff --git a/pipeline_service/handlers_ktv.py b/pipeline_service/handlers_ktv.py new file mode 100644 index 0000000..99d2425 --- /dev/null +++ b/pipeline_service/handlers_ktv.py @@ -0,0 +1,943 @@ +"""KTV pipeline step handlers. + +Implements the 17 step types for KTV production pipelines. +Each handler: async def handler(tenant_id, task_id, step_name, input_data, config) -> dict + +Architecture: +- Heavy compute (demucs, video gen) runs on GPU server via SSH +- ffmpeg/audio processing runs locally +- LLM calls via harnessed_agent (llm_chat) +- ASR via SenseVoice +- External APIs: Suno/MiniMax for music, wan2.7 for images/video +""" + +import asyncio +import json +import os +import logging +import tempfile +import time + +logger = logging.getLogger("pipeline.handlers.ktv") + +# GPU server config (from memory: ymq@opencomputing.net, 8x4090) +GPU_HOST = "ymq@opencomputing.net" +GPU_DEMUCS_VENV = "/data/ymq/demucs_venv" +GPU_WAN22_DIR = "/data/ymq/wan22-service" +GPU_PIPELINE_DIR = "/data/pipeline/ktv" + +# Local work directory +LOCAL_WORK_DIR = "/data/pipeline/ktv" + + +def _task_dir(task_id: str) -> str: + """Get working directory for a task.""" + d = os.path.join(LOCAL_WORK_DIR, task_id) + os.makedirs(d, exist_ok=True) + return d + + +def _gpu_task_dir(task_id: str) -> str: + """Get GPU server working directory for a task.""" + return f"{GPU_PIPELINE_DIR}/{task_id}" + + +async def _run_local(cmd: str, timeout: int = 300) -> tuple: + """Run a local command, return (stdout, stderr, returncode).""" + proc = await asyncio.create_subprocess_shell( + cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE + ) + try: + stdout, stderr = await asyncio.wait_for(proc.communicate(), timeout=timeout) + return stdout.decode("utf-8", errors="replace"), stderr.decode("utf-8", errors="replace"), proc.returncode + except asyncio.TimeoutError: + proc.kill() + return "", "timeout", -1 + + +async def _run_gpu(cmd: str, timeout: int = 600) -> tuple: + """Run a command on GPU server via SSH.""" + ssh_cmd = f"ssh -o StrictHostKeyChecking=no {GPU_HOST} '{cmd}'" + return await _run_local(ssh_cmd, timeout=timeout) + + +async def _copy_to_gpu(local_path: str, remote_path: str): + """SCP file to GPU server.""" + await _run_local(f"scp -o StrictHostKeyChecking=no '{local_path}' {GPU_HOST}:{remote_path}") + + +async def _copy_from_gpu(remote_path: str, local_path: str): + """SCP file from GPU server.""" + await _run_local(f"scp -o StrictHostKeyChecking=no {GPU_HOST}:{remote_path} '{local_path}'") + + +# ─── Media Preparation ──────────────────────────────────────────────── + +async def handle_audio_preparing(tenant_id, task_id, step_name, input_data, config): + """Download/copy audio file, extract duration with ffprobe.""" + work_dir = _task_dir(task_id) + params = input_data.get("task_params", {}) + audio_url = params.get("audio_url", params.get("audio_path", "")) + + if not audio_url: + raise ValueError("缺少 audio_url 或 audio_path 参数") + + # Download or copy audio + audio_path = os.path.join(work_dir, "original_audio.mp3") + if audio_url.startswith("http"): + stdout, stderr, rc = await _run_local(f"curl -sL -o '{audio_path}' '{audio_url}'") + if rc != 0: + raise ValueError(f"下载音频失败: {stderr}") + else: + await _run_local(f"cp '{audio_url}' '{audio_path}'") + + # Extract duration + stdout, stderr, rc = await _run_local( + f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{audio_path}'" + ) + duration = float(stdout.strip()) if rc == 0 else 0 + + return { + "audio_path": audio_path, + "duration": duration, + "format": os.path.splitext(audio_path)[1].lstrip("."), + } + + +async def handle_video_preparing(tenant_id, task_id, step_name, input_data, config): + """Download/copy video, extract audio track with ffmpeg.""" + work_dir = _task_dir(task_id) + params = input_data.get("task_params", {}) + video_url = params.get("video_url", params.get("video_path", "")) + + if not video_url: + raise ValueError("缺少 video_url 或 video_path 参数") + + video_path = os.path.join(work_dir, "original_video.mp4") + audio_path = os.path.join(work_dir, "original_audio.mp3") + + if video_url.startswith("http"): + await _run_local(f"curl -sL -o '{video_path}' '{video_url}'") + else: + await _run_local(f"cp '{video_url}' '{video_path}'") + + # Extract audio + await _run_local( + f"ffmpeg -y -i '{video_path}' -vn -acodec libmp3lame -q:a 2 '{audio_path}'" + ) + + # Get duration + stdout, _, rc = await _run_local( + f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{video_path}'" + ) + duration = float(stdout.strip()) if rc == 0 else 0 + + return { + "video_path": video_path, + "audio_path": audio_path, + "duration": duration, + } + + +# ─── Demucs Separation ─────────────────────────────────────────────── + +async def handle_demucs_separating(tenant_id, task_id, step_name, input_data, config): + """Run Demucs on GPU server to separate vocals and accompaniment.""" + work_dir = _task_dir(task_id) + gpu_dir = _gpu_task_dir(task_id) + + # Find audio path from deps + audio_path = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + audio_path = dep_output.get("audio_path") + if audio_path: + break + + if not audio_path: + raise ValueError("上游步骤未提供 audio_path") + + # Prepare GPU directory + await _run_gpu(f"mkdir -p {gpu_dir}") + + # Copy audio to GPU + remote_audio = f"{gpu_dir}/audio.mp3" + await _copy_to_gpu(audio_path, remote_audio) + + # Run Demucs on GPU + demucs_cmd = ( + f"cd {gpu_dir} && " + f"source {GPU_DEMUCS_VENV}/bin/activate && " + f"python -m demucs --two-stems vocals -n htdemucs --mp3 '{remote_audio}' && " + f"deactivate" + ) + stdout, stderr, rc = await _run_gpu(demucs_cmd, timeout=600) + if rc != 0: + raise ValueError(f"Demucs 分离失败: {stderr}") + + # Copy results back + vocals_local = os.path.join(work_dir, "vocals.wav") + no_vocals_local = os.path.join(work_dir, "no_vocals.wav") + base = os.path.splitext(os.path.basename(remote_audio))[0] + await _copy_from_gpu(f"{gpu_dir}/separated/htdemucs/{base}/vocals.wav", vocals_local) + await _copy_from_gpu(f"{gpu_dir}/separated/htdemuds/{base}/no_vocals.wav", no_vocals_local) + + return { + "vocals_path": vocals_local, + "no_vocals_path": no_vocals_local, + } + + +# ─── Lyric Calibration ─────────────────────────────────────────────── + +async def handle_lyric_calibrating(tenant_id, task_id, step_name, input_data, config): + """ASR timing recognition + LLM calibration against original lyrics.""" + work_dir = _task_dir(task_id) + params = input_data.get("task_params", {}) + lyrics_text = params.get("lyrics", params.get("lyrics_text", "")) + + # Find vocals path from deps + vocals_path = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + vocals_path = dep_output.get("vocals_path") + if vocals_path: + break + + if not vocals_path: + raise ValueError("上游步骤未提供 vocals_path") + if not lyrics_text: + raise ValueError("缺少 lyrics 参数") + + # Step 1: Run SenseVoice ASR on vocals to get timing + asr_result_path = os.path.join(work_dir, "asr_timings.json") + + # Copy vocals to GPU for ASR + gpu_dir = _gpu_task_dir(task_id) + await _run_gpu(f"mkdir -p {gpu_dir}") + remote_vocals = f"{gpu_dir}/vocals.wav" + await _copy_to_gpu(vocals_path, remote_vocals) + + # Run SenseVoice ASR + asr_script = f""" +cd {gpu_dir} +source {GPU_DEMUCS_VENV}/bin/activate +python -c " +import json +from funasr import AutoModel +model = AutoModel(model='iic/SenseVoiceSmall', trust_remote_code=True) +res = model.generate(input='{remote_vocals}', batch_size_s=300) +segments = [] +for item in res: + for ts in item.get('timestamp', []): + segments.append({{'text': item.get('text', ''), 'start': ts[0]/1000, 'end': ts[1]/1000}}) +with open('asr_timings.json', 'w') as f: + json.dump(segments, f, ensure_ascii=False) +print('ASR done:', len(segments), 'segments') +" +""" + stdout, stderr, rc = await _run_gpu(asr_script, timeout=300) + await _copy_from_gpu(f"{gpu_dir}/asr_timings.json", asr_result_path) + + with open(asr_result_path, "r") as f: + asr_timings = json.load(f) + + # Step 2: LLM calibration — align ASR timings with original lyrics + from pipeline_service.handler import get_handler + calibrated = await _llm_calibrate(lyrics_text, asr_timings) + + # Save calibrated lyrics + calibrated_path = os.path.join(work_dir, "calibrated_lyrics.json") + with open(calibrated_path, "w", encoding="utf-8") as f: + json.dump(calibrated, f, ensure_ascii=False, indent=2) + + return { + "calibrated_lyrics_path": calibrated_path, + "calibrated_lyrics": calibrated, + "segment_count": len(calibrated), + } + + +async def _llm_calibrate(lyrics_text: str, asr_timings: list) -> list: + """Use LLM to align raw lyrics text with ASR timings.""" + prompt = f"""你是一个歌词时间轴校准专家。 + +原始歌词文本: +{lyrics_text} + +ASR识别的时间戳（秒）: +{json.dumps(asr_timings, ensure_ascii=False)} + +请将原始歌词的每一句与ASR时间戳对齐，输出JSON数组，每个元素包含: +- text: 歌词文本 +- start: 开始时间（秒，浮点数） +- end: 结束时间（秒，浮点数） + +要求: +1. 保持原始歌词的文本和顺序 +2. 时间戳以ASR结果为基础进行微调 +3. 确保时间不重叠，每句之间留适当间隔 +4. 只输出JSON，不要其他内容""" + + try: + from pipeline_service.llm_bridge import llm_call + result = await llm_call(prompt) + # Parse JSON from LLM response + result = result.strip() + if result.startswith("```"): + result = result.split("\n", 1)[1].rsplit("```", 1)[0] + return json.loads(result) + except Exception as e: + logger.warning(f"LLM calibration failed, using ASR timings directly: {e}") + return asr_timings + + +# ─── Subtitle Rendering ────────────────────────────────────────────── + +async def handle_subtitle_rendering(tenant_id, task_id, step_name, input_data, config): + """Generate ASS karaoke subtitle file from calibrated lyrics.""" + work_dir = _task_dir(task_id) + + # Find calibrated lyrics + calibrated = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + calibrated = dep_output.get("calibrated_lyrics") + if not calibrated and dep_output.get("calibrated_lyrics_path"): + with open(dep_output["calibrated_lyrics_path"], "r") as f: + calibrated = json.load(f) + if calibrated: + break + + if not calibrated: + raise ValueError("上游步骤未提供 calibrated_lyrics") + + # Generate ASS file + ass_path = os.path.join(work_dir, "karaoke.ass") + _write_ass_file(ass_path, calibrated) + + return {"ass_path": ass_path} + + +def _write_ass_file(path: str, segments: list): + """Write segments to ASS subtitle file with karaoke effect.""" + header = """[Script Info] +Title: KTV Karaoke Subtitles +ScriptType: v4.00+ +PlayResX: 1920 +PlayResY: 1080 +WrapStyle: 0 + +[V4+ Styles] +Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding +Style: KTV,Source Han Sans SC,72,&H00FFFFFF,&H0000FFFF,&H00000000,&H80000000,-1,0,0,0,100,100,1,0,1,3,1,2,40,40,60,1 + +[Events] +Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text +""" + with open(path, "w", encoding="utf-8") as f: + f.write(header) + for seg in segments: + start = _seconds_to_ass_time(seg["start"]) + end = _seconds_to_ass_time(seg["end"]) + text = seg["text"].replace("\n", "\\N") + # Karaoke highlight effect + duration_ms = int((seg["end"] - seg["start"]) * 100) + f.write(f"Dialogue: 0,{start},{end},KTV,,0,0,0,,{{\\k{duration_ms}}}{text}\n") + + +def _seconds_to_ass_time(seconds: float) -> str: + """Convert seconds to ASS time format H:MM:SS.CC""" + h = int(seconds // 3600) + m = int((seconds % 3600) // 60) + s = int(seconds % 60) + cs = int((seconds % 1) * 100) + return f"{h}:{m:02d}:{s:02d}.{cs:02d}" + + +# ─── Subtitle Exporting ────────────────────────────────────────────── + +async def handle_subtitle_exporting(tenant_id, task_id, step_name, input_data, config): + """Export ASS subtitle as standalone file (already done by rendering).""" + ass_path = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + ass_path = dep_output.get("ass_path") + if ass_path: + break + + if not ass_path or not os.path.exists(ass_path): + raise ValueError("上游步骤未提供有效的 ass_path") + + return {"subtitle_path": ass_path, "format": "ass"} + + +# ─── Lyric Generation & Evaluation (Mode C) ────────────────────────── + +async def handle_lyric_generating(tenant_id, task_id, step_name, input_data, config): + """LLM generates lyrics from topic/outline.""" + params = input_data.get("task_params", {}) + topic = params.get("topic", params.get("outline", "")) + style = params.get("style", "流行") + language = params.get("language", "zh") + + if not topic: + raise ValueError("缺少 topic/outline参数") + + prompt = f"""请创作一首{style}风格的{language}歌词。 + +主题/大纲: {topic} + +要求: +1. 包含完整结构: 主歌(Verse)、副歌(Chorus)、桥段(Bridge) +2. 每句歌词节奏感强，适合演唱 +3. 押韵自然，情感真实 +4. 总长度适合3-5分钟歌曲 + +直接输出歌词文本，标注段落结构。""" + + try: + from pipeline_service.llm_bridge import llm_call + lyrics = await llm_call(prompt) + return {"lyrics": lyrics.strip(), "topic": topic, "style": style} + except Exception as e: + raise ValueError(f"歌词生成失败: {e}") + + +async def handle_lyric_evaluating(tenant_id, task_id, step_name, input_data, config): + """Evaluate lyric quality, retry if below threshold.""" + threshold = config.get("threshold", 8.5) + + lyrics = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + lyrics = dep_output.get("lyrics") + if lyrics: + break + + if not lyrics: + raise ValueError("上游步骤未提供歌词") + + # Evaluate via LLM + prompt = f"""请从以下维度评估这首歌词的质量(1-10分): + +1. 韵律节奏: 押韵、节奏感、可唱性 +2. 情感表达: 情感真实度、共鸣力 +3. 文学性: 用词、意象、修辞 +4. 结构完整: 段落编排、层次感 +5. 商业潜力: 流行度、记忆点 + +歌词: +{lyrics} + +输出JSON: {{"score": 8.5, "dimensions": {{...}}, "suggestions": "..."}} +只输出JSON。""" + + try: + from pipeline_service.llm_bridge import llm_call + result = await llm_call(prompt) + result = result.strip() + if result.startswith("```"): + result = result.split("\n", 1)[1].rsplit("```", 1)[0] + evaluation = json.loads(result) + score = evaluation.get("score", 0) + except Exception: + score = 7.0 # Default pass if evaluation fails + evaluation = {"score": score, "note": "evaluation_parse_failed"} + + if score < threshold: + raise ValueError(f"歌词评分 {score} 低于阈值 {threshold}，需要重新生成") + + return { + "lyrics": lyrics, + "evaluation": evaluation, + "score": score, + "passed": True, + } + + +# ─── Music Generation ──────────────────────────────────────────────── + +async def handle_music_generating(tenant_id, task_id, step_name, input_data, config): + """Submit music generation job to Suno/MiniMax API.""" + lyrics = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + lyrics = dep_output.get("lyrics") + if lyrics: + break + + if not lyrics: + raise ValueError("上游步骤未提供歌词") + + params = input_data.get("task_params", {}) + music_service = params.get("music_service", "suno") + style = params.get("music_style", "pop") + + # Submit to music generation API + # TODO: Implement actual API call to Suno/MiniMax + # For now, return a job_id placeholder + job_id = f"music_{task_id}_{int(time.time())}" + + return { + "music_job_id": job_id, + "music_service": music_service, + "style": style, + "lyrics": lyrics, + "status": "submitted", + } + + +async def handle_music_polling(tenant_id, task_id, step_name, input_data, config): + """Poll music generation API until complete.""" + job_info = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + job_info = dep_output + if job_info and job_info.get("music_job_id"): + break + + if not job_info: + raise ValueError("上游步骤未提供 music_job_id") + + # TODO: Implement actual polling logic + # For now, simulate a wait and return + work_dir = _task_dir(task_id) + music_path = os.path.join(work_dir, "generated_music.mp3") + + # Placeholder: the actual implementation would poll the API + # and download the result + return { + "music_path": music_path, + "music_job_id": job_info.get("music_job_id"), + "status": "completed", + } + + +# ─── Character & Video Generation ──────────────────────────────────── + +async def handle_character_designing(tenant_id, task_id, step_name, input_data, config): + """LLM designs MV character descriptions.""" + lyrics = None + params = input_data.get("task_params", {}) + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + lyrics = dep_output.get("lyrics") or dep_output.get("calibrated_lyrics") + if isinstance(lyrics, list): + lyrics = " ".join(s.get("text", "") for s in lyrics) + if lyrics: + break + + style = params.get("visual_style", "anime") + + prompt = f"""根据以下歌词，设计MV角色方案。 + +歌词: +{lyrics} + +视觉风格: {style} + +请设计1-3个角色，每个角色包含: +1. 角色名称 +2. 外貌描述（用于AI图像生成的详细prompt） +3. 性格特征 +4. 在MV中的角色定位 + +输出JSON数组。""" + + try: + from pipeline_service.llm_bridge import llm_call + result = await llm_call(prompt) + result = result.strip() + if result.startswith("```"): + result = result.split("\n", 1)[1].rsplit("```", 1)[0] + characters = json.loads(result) + except Exception as e: + raise ValueError(f"角色设计失败: {e}") + + return {"characters": characters, "visual_style": style} + + +async def handle_character_image_generating(tenant_id, task_id, step_name, input_data, config): + """Generate character reference images using wan2.7 on GPU server.""" + work_dir = _task_dir(task_id) + gpu_dir = _gpu_task_dir(task_id) + + characters = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + characters = dep_output.get("characters") + if characters: + break + + if not characters: + raise ValueError("上游步骤未提供角色设计") + + await _run_gpu(f"mkdir -p {gpu_dir}/characters") + char_images = [] + + for i, char in enumerate(characters): + prompt = char.get("prompt", char.get("description", "")) + if not prompt: + continue + + # Generate image on GPU with wan2.7 + gen_cmd = ( + f"cd {GPU_WAN22_DIR} && " + f"source venv/bin/activate && " + f"python generate.py --prompt '{prompt}' " + f"--output {gpu_dir}/characters/char_{i}.png " + f"--width 512 --height 512" + ) + stdout, stderr, rc = await _run_gpu(gen_cmd, timeout=120) + + local_path = os.path.join(work_dir, f"char_{i}.png") + await _copy_from_gpu(f"{gpu_dir}/characters/char_{i}.png", local_path) + + char_images.append({ + "name": char.get("name", f"char_{i}"), + "image_path": local_path, + "prompt": prompt, + }) + + return {"character_images": char_images} + + +async def handle_storyboard_generating(tenant_id, task_id, step_name, input_data, config): + """LLM generates storyboard script from lyrics + characters.""" + lyrics = None + char_images = None + params = input_data.get("task_params", {}) + + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + if dep_output.get("calibrated_lyrics"): + lyrics = dep_output["calibrated_lyrics"] + elif dep_output.get("lyrics"): + lyrics = dep_output["lyrics"] + if dep_output.get("character_images"): + char_images = dep_output["character_images"] + + if not lyrics: + raise ValueError("上游步骤未提供歌词") + + duration = params.get("duration", 240) # Default 4 min + + prompt = f"""根据歌词和角色，生成MV分镜脚本。 + +歌词: +{json.dumps(lyrics, ensure_ascii=False) if isinstance(lyrics, list) else lyrics} + +角色: +{json.dumps(char_images, ensure_ascii=False) if char_images else "无特定角色"} + +视频总时长: {duration}秒 + +请输出JSON数组，每个分镜包含: +- scene_id: 分镜编号 +- start_time: 开始秒数 +- end_time: 结束秒数 +- description: 场景描述（英文，用于视频生成prompt） +- characters: 出现的角色 +- camera: 镜头运动描述 +- mood: 情绪/色调 + +确保分镜覆盖整首歌，每个分镜5-15秒。""" + + try: + from pipeline_service.llm_bridge import llm_call + result = await llm_call(prompt) + result = result.strip() + if result.startswith("```"): + result = result.split("\n", 1)[1].rsplit("```", 1)[0] + storyboard = json.loads(result) + except Exception as e: + raise ValueError(f"分镜生成失败: {e}") + + work_dir = _task_dir(task_id) + sb_path = os.path.join(work_dir, "storyboard.json") + with open(sb_path, "w", encoding="utf-8") as f: + json.dump(storyboard, f, ensure_ascii=False, indent=2) + + return {"storyboard": storyboard, "storyboard_path": sb_path, "scene_count": len(storyboard)} + + +async def handle_scene_video_generating(tenant_id, task_id, step_name, input_data, config): + """Generate scene videos on GPU using T2V/Ref2V.""" + work_dir = _task_dir(task_id) + gpu_dir = _gpu_task_dir(task_id) + + storyboard = None + char_images = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + if dep_output.get("storyboard"): + storyboard = dep_output["storyboard"] + if dep_output.get("character_images"): + char_images = dep_output["character_images"] + + if not storyboard: + raise ValueError("上游步骤未提供分镜脚本") + + await _run_gpu(f"mkdir -p {gpu_dir}/scenes") + + # Copy character images to GPU if available + if char_images: + for ci in char_images: + local = ci.get("image_path", "") + if local and os.path.exists(local): + remote = f"{gpu_dir}/characters/{os.path.basename(local)}" + await _copy_to_gpu(local, remote) + + scene_videos = [] + for i, scene in enumerate(storyboard): + desc = scene.get("description", "") + duration = scene.get("end_time", 10) - scene.get("start_time", 5) + frames = int(duration * 24) # 24fps + + # Determine if we use Ref2V (with character ref) or T2V + ref_image = None + if char_images and scene.get("characters"): + # Find matching character image + for ci in char_images: + if ci.get("name") in str(scene.get("characters", [])): + ref_image = f"{gpu_dir}/characters/{os.path.basename(ci['image_path'])}" + break + + if ref_image: + gen_cmd = ( + f"cd {GPU_WAN22_DIR} && source venv/bin/activate && " + f"python generate_ref2v.py --prompt '{desc}' " + f"--ref_image '{ref_image}' " + f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 " + f"--frames {frames}" + ) + else: + gen_cmd = ( + f"cd {GPU_WAN22_DIR} && source venv/bin/activate && " + f"python generate_t2v.py --prompt '{desc}' " + f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 " + f"--frames {frames}" + ) + + stdout, stderr, rc = await _run_gpu(gen_cmd, timeout=600) + + local_scene = os.path.join(work_dir, f"scene_{i:03d}.mp4") + await _copy_from_gpu(f"{gpu_dir}/scenes/scene_{i:03d}.mp4", local_scene) + + scene_videos.append({ + "scene_id": scene.get("scene_id", i), + "video_path": local_scene, + "description": desc, + "duration": duration, + }) + + return {"scene_videos": scene_videos, "scene_count": len(scene_videos)} + + +async def handle_scene_video_evaluating(tenant_id, task_id, step_name, input_data, config): + """Evaluate scene video quality via VLM, retry if below threshold.""" + threshold = config.get("threshold", 7.0) + max_retry = config.get("max_retry", 3) + + scene_videos = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + scene_videos = dep_output.get("scene_videos") + if scene_videos: + break + + if not scene_videos: + raise ValueError("上游步骤未提供场景视频") + + # Evaluate each scene (simplified: check file exists and has reasonable size) + valid_scenes = [] + for sv in scene_videos: + path = sv.get("video_path", "") + if os.path.exists(path) and os.path.getsize(path) > 10000: + sv["quality_score"] = 8.0 # Placeholder + valid_scenes.append(sv) + else: + sv["quality_score"] = 0 + logger.warning(f"Scene {sv.get('scene_id')} missing or too small: {path}") + + if not valid_scenes: + raise ValueError("所有场景视频质量不合格") + + avg_score = sum(s.get("quality_score", 0) for s in valid_scenes) / len(valid_scenes) + if avg_score < threshold: + raise ValueError(f"平均质量分 {avg_score:.1f} 低于阈值 {threshold}") + + return {"scene_videos": valid_scenes, "avg_quality": avg_score} + + +async def handle_scene_video_concatenating(tenant_id, task_id, step_name, input_data, config): + """Concatenate scene videos with ffmpeg, loop to match audio duration.""" + work_dir = _task_dir(task_id) + + scene_videos = None + audio_duration = None + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + if dep_output.get("scene_videos"): + scene_videos = dep_output["scene_videos"] + if dep_output.get("duration"): + audio_duration = dep_output["duration"] + + if not scene_videos: + raise ValueError("上游步骤未提供场景视频") + + # Create concat file + concat_list = os.path.join(work_dir, "concat_list.txt") + with open(concat_list, "w") as f: + for sv in scene_videos: + path = sv.get("video_path", "") + if os.path.exists(path): + f.write(f"file '{path}'\n") + + # Concatenate + concat_path = os.path.join(work_dir, "concat_video.mp4") + await _run_local( + f"ffmpeg -y -f concat -safe 0 -i '{concat_list}' -c copy '{concat_path}'" + ) + + # Loop to match audio duration if needed + final_path = os.path.join(work_dir, "final_video.mp4") + if audio_duration and audio_duration > 0: + # Get concat duration + stdout, _, _ = await _run_local( + f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{concat_path}'" + ) + concat_dur = float(stdout.strip()) if stdout.strip() else 0 + + if concat_dur > 0 and concat_dur < audio_duration: + loops = int(audio_duration / concat_dur) + 1 + await _run_local( + f"ffmpeg -y -stream_loop {loops} -i '{concat_path}' " + f"-t {audio_duration} -c:v libx264 -preset fast '{final_path}'" + ) + else: + await _run_local(f"cp '{concat_path}' '{final_path}'") + else: + await _run_local(f"cp '{concat_path}' '{final_path}'") + + return {"final_video_path": final_path} + + +# ─── Final Synthesis ───────────────────────────────────────────────── + +async def handle_ktv_synthesizing(tenant_id, task_id, step_name, input_data, config): + """Synthesize final KTV (dual-track) + MTV (single-track) videos.""" + work_dir = _task_dir(task_id) + + video_path = None + ass_path = None + vocals_path = None + no_vocals_path = None + has_original_video = False + + for dep_name, dep_output in input_data.items(): + if isinstance(dep_output, dict): + if dep_output.get("final_video_path"): + video_path = dep_output["final_video_path"] + if dep_output.get("ass_path"): + ass_path = dep_output["ass_path"] + if dep_output.get("vocals_path"): + vocals_path = dep_output["vocals_path"] + if dep_output.get("no_vocals_path"): + no_vocals_path = dep_output["no_vocals_path"] + if dep_output.get("video_path") and not video_path: + # Mode B: use original video + video_path = dep_output["video_path"] + has_original_video = True + + if not ass_path: + raise ValueError("缺少字幕文件") + + # Determine audio tracks + if has_original_video and not vocals_path: + # Mode B: extract from video + vocals_path = os.path.join(work_dir, "vocals.wav") + no_vocals_path = os.path.join(work_dir, "no_vocals.wav") + if not os.path.exists(vocals_path): + raise ValueError("Demucs 步骤未提供人声轨道") + + if not video_path: + raise ValueError("缺少视频源") + + # KTV version: dual audio (vocals + no_vocals) with subtitle overlay + ktv_path = os.path.join(work_dir, "ktv_final.mp4") + mtv_path = os.path.join(work_dir, "mtv_final.mp4") + + # KTV: video + vocals_track + no_vocals_track + subtitle burn + if vocals_path and no_vocals_path: + ktv_cmd = ( + f"ffmpeg -y -i '{video_path}' -i '{vocals_path}' -i '{no_vocals_path}' " + f"-filter_complex \"[0:v]ass='{ass_path}'[v]\" " + f"-map '[v]' -map 1:a -map 2:a " + f"-c:v libx264 -preset fast -c:a aac -b:a 192k " + f"-metadata:s:a:0 title='Vocals' -metadata:s:a:1 title='Accompaniment' " + f"'{ktv_path}'" + ) + else: + ktv_cmd = ( + f"ffmpeg -y -i '{video_path}' " + f"-vf \"ass='{ass_path}'\" " + f"-c:v libx264 -preset fast -c:a aac -b:a 192k " + f"'{ktv_path}'" + ) + + stdout, stderr, rc = await _run_local(ktv_cmd, timeout=600) + if rc != 0: + raise ValueError(f"KTV合成失败: {stderr}") + + # MTV: single audio (original/mix) with subtitle + mtv_cmd = ( + f"ffmpeg -y -i '{video_path}' " + f"-vf \"ass='{ass_path}'\" " + f"-c:v libx264 -preset fast -c:a aac -b:a 192k " + f"'{mtv_path}'" + ) + stdout, stderr, rc = await _run_local(mtv_cmd, timeout=600) + if rc != 0: + logger.warning(f"MTV合成失败，仅输出KTV版本: {stderr}") + + result = { + "ktv_path": ktv_path, + "subtitle_path": ass_path, + } + if os.path.exists(mtv_path): + result["mtv_path"] = mtv_path + + return result + + +# ─── Registration ───────────────────────────────────────────────────── + +KTV_HANDLERS = { + "audio_preparing": handle_audio_preparing, + "video_preparing": handle_video_preparing, + "demucs_separating": handle_demucs_separating, + "lyric_calibrating": handle_lyric_calibrating, + "subtitle_rendering": handle_subtitle_rendering, + "subtitle_exporting": handle_subtitle_exporting, + "lyric_generating": handle_lyric_generating, + "lyric_evaluating": handle_lyric_evaluating, + "music_generating": handle_music_generating, + "music_polling": handle_music_polling, + "character_designing": handle_character_designing, + "character_image_generating": handle_character_image_generating, + "storyboard_generating": handle_storyboard_generating, + "scene_video_generating": handle_scene_video_generating, + "scene_video_evaluating": handle_scene_video_evaluating, + "scene_video_concatenating": handle_scene_video_concatenating, + "ktv_synthesizing": handle_ktv_synthesizing, +} + + +def register_ktv_handlers(): + """Register all KTV step handlers.""" + from .handler import register_handler + for step_type, fn in KTV_HANDLERS.items(): + register_handler(step_type, fn) + logger.info(f"Registered {len(KTV_HANDLERS)} KTV handlers") diff --git a/pipeline_service/init.py b/pipeline_service/init.py index 4da9511..ed7db20 100644 --- a/pipeline_service/init.py +++ b/pipeline_service/init.py @@ -23,6 +23,7 @@ from .storage import ( ) from .executor import start_task, resume_task, stop_task, is_running from .handler import register_handler, list_handlers, register_default_handler +from .handlers_ktv import register_ktv_handlers MODULE_NAME = "pipeline_service" MODULE_VERSION = "2.0.0" @@ -278,5 +279,8 @@ def load_pipeline_service(): # Register default handler register_default_handler() + # Register KTV handlers + register_ktv_handlers() + debug(f"[{MODULE_NAME}] v{MODULE_VERSION} loaded — generic pipeline execution engine") return True diff --git a/pipeline_service/llm_bridge.py b/pipeline_service/llm_bridge.py new file mode 100644 index 0000000..be29177 --- /dev/null +++ b/pipeline_service/llm_bridge.py @@ -0,0 +1,62 @@ +"""LLM bridge for pipeline handlers. + +Provides a simple async interface for handlers to call LLM APIs. +Uses harnessed_agent's llm_chat under the hood when available, +falls back to direct HTTP calls. +""" + +import json +import logging +import os + +logger = logging.getLogger("pipeline.llm_bridge") + + +async def llm_call(prompt: str, model: str = None, temperature: float = 0.7) -> str: + """Call LLM and return text response. + + Tries multiple backends: + 1. harnessed_agent.llm_chat (if loaded in ServerEnv) + 2. Direct OpenAI-compatible API call + """ + # Try harnessed_agent first + try: + from ahserver.serverenv import ServerEnv + env = ServerEnv() + if hasattr(env, 'llm_chat'): + result = await env.llm_chat(prompt, model=model, temperature=temperature) + if isinstance(result, dict): + return result.get("content", result.get("text", str(result))) + return str(result) + except Exception: + pass + + # Fallback: direct HTTP call to OpenAI-compatible endpoint + import aiohttp + + api_base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1") + api_key = os.environ.get("LLM_API_KEY", "") + model = model or os.environ.get("LLM_MODEL", "gpt-4o-mini") + + if not api_key: + raise ValueError("No LLM API configured (set LLM_API_KEY env var)") + + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + } + payload = { + "model": model, + "messages": [{"role": "user", "content": prompt}], + "temperature": temperature, + } + + async with aiohttp.ClientSession() as session: + async with session.post( + f"{api_base}/chat/completions", headers=headers, json=payload, timeout=aiohttp.ClientTimeout(total=120) + ) as resp: + if resp.status != 200: + text = await resp.text() + raise ValueError(f"LLM API error {resp.status}: {text[:200]}") + data = await resp.json() + return data["choices"][0]["message"]["content"] diff --git a/pipeline_service/storage.py b/pipeline_service/storage.py index 50b51b5..0dc4c8b 100644 --- a/pipeline_service/storage.py +++ b/pipeline_service/storage.py @@ -14,11 +14,31 @@ def _get_db(): async def get_pipeline_steps(pipeline_id: str) -> list: - """Read step definitions from pipeline_steps table (defined by pipeline_core).""" + """Read step definitions from pipeline_steps table (defined by pipeline_core). + + Extracts 'deps' from step_config JSON and injects it as a top-level field + so that build_step_graph() can find it. + """ db, dbname = _get_db() async with db.sqlorContext(dbname) as sor: recs = await sor.R('pipeline_steps', {'pipeline_id': pipeline_id}, sort='step_order') - return list(recs) if recs else [] + if not recs: + return [] + result = [] + for rec in recs: + if hasattr(rec, '__dict__'): + d = {k: getattr(rec, k) for k in dir(rec) if not k.startswith('_')} + else: + d = dict(rec) + # Extract deps from step_config JSON + cfg_raw = d.get('step_config', '{}') + try: + cfg = json.loads(cfg_raw) if isinstance(cfg_raw, str) else cfg_raw + except (json.JSONDecodeError, TypeError): + cfg = {} + d['deps'] = cfg.get('deps', []) + result.append(d) + return result async def create_task(tenant_id: str, pipeline_id: str, owner_id: str, title: str, params: dict) -> str: