396a27f43a
Adapter pattern: pipeline-service is the stable engine, pipeline-ktv registers its step_types and handlers independently. - 17 KTV step handlers (audio/video/demucs/lyrics/music/subtitle/video) - Step type metadata with categories (media/llm) - load_ktv_adapter() one-call integration - Depends on pipeline_service>=3.1.0
936 lines
33 KiB
Python
936 lines
33 KiB
Python
"""KTV pipeline step handlers.
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Implements the 17 step types for KTV production pipelines.
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Each handler: async def handler(tenant_id, task_id, step_name, input_data, config) -> dict
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Architecture:
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- Heavy compute (demucs, video gen) runs on GPU server via SSH
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- ffmpeg/audio processing runs locally
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- LLM calls via harnessed_agent (llm_chat)
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- ASR via SenseVoice
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- External APIs: Suno/MiniMax for music, wan2.7 for images/video
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"""
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import asyncio
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import json
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import os
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import logging
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import tempfile
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import time
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logger = logging.getLogger("pipeline.handlers.ktv")
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# GPU server config (from memory: ymq@opencomputing.net, 8x4090)
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GPU_HOST = "ymq@opencomputing.net"
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GPU_DEMUCS_VENV = "/data/ymq/demucs_venv"
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GPU_WAN22_DIR = "/data/ymq/wan22-service"
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GPU_PIPELINE_DIR = "/data/pipeline/ktv"
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# Local work directory
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LOCAL_WORK_DIR = "/data/pipeline/ktv"
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def _task_dir(task_id: str) -> str:
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"""Get working directory for a task."""
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d = os.path.join(LOCAL_WORK_DIR, task_id)
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os.makedirs(d, exist_ok=True)
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return d
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def _gpu_task_dir(task_id: str) -> str:
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"""Get GPU server working directory for a task."""
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return f"{GPU_PIPELINE_DIR}/{task_id}"
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async def _run_local(cmd: str, timeout: int = 300) -> tuple:
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"""Run a local command, return (stdout, stderr, returncode)."""
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proc = await asyncio.create_subprocess_shell(
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cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
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)
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try:
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stdout, stderr = await asyncio.wait_for(proc.communicate(), timeout=timeout)
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return stdout.decode("utf-8", errors="replace"), stderr.decode("utf-8", errors="replace"), proc.returncode
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except asyncio.TimeoutError:
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proc.kill()
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return "", "timeout", -1
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async def _run_gpu(cmd: str, timeout: int = 600) -> tuple:
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"""Run a command on GPU server via SSH."""
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ssh_cmd = f"ssh -o StrictHostKeyChecking=no {GPU_HOST} '{cmd}'"
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return await _run_local(ssh_cmd, timeout=timeout)
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async def _copy_to_gpu(local_path: str, remote_path: str):
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"""SCP file to GPU server."""
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await _run_local(f"scp -o StrictHostKeyChecking=no '{local_path}' {GPU_HOST}:{remote_path}")
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async def _copy_from_gpu(remote_path: str, local_path: str):
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"""SCP file from GPU server."""
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await _run_local(f"scp -o StrictHostKeyChecking=no {GPU_HOST}:{remote_path} '{local_path}'")
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# ─── Media Preparation ────────────────────────────────────────────────
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async def handle_audio_preparing(tenant_id, task_id, step_name, input_data, config):
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"""Download/copy audio file, extract duration with ffprobe."""
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work_dir = _task_dir(task_id)
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params = input_data.get("task_params", {})
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audio_url = params.get("audio_url", params.get("audio_path", ""))
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if not audio_url:
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raise ValueError("缺少 audio_url 或 audio_path 参数")
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# Download or copy audio
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audio_path = os.path.join(work_dir, "original_audio.mp3")
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if audio_url.startswith("http"):
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stdout, stderr, rc = await _run_local(f"curl -sL -o '{audio_path}' '{audio_url}'")
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if rc != 0:
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raise ValueError(f"下载音频失败: {stderr}")
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else:
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await _run_local(f"cp '{audio_url}' '{audio_path}'")
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# Extract duration
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stdout, stderr, rc = await _run_local(
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f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{audio_path}'"
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)
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duration = float(stdout.strip()) if rc == 0 else 0
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return {
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"audio_path": audio_path,
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"duration": duration,
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"format": os.path.splitext(audio_path)[1].lstrip("."),
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}
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async def handle_video_preparing(tenant_id, task_id, step_name, input_data, config):
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"""Download/copy video, extract audio track with ffmpeg."""
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work_dir = _task_dir(task_id)
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params = input_data.get("task_params", {})
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video_url = params.get("video_url", params.get("video_path", ""))
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if not video_url:
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raise ValueError("缺少 video_url 或 video_path 参数")
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video_path = os.path.join(work_dir, "original_video.mp4")
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audio_path = os.path.join(work_dir, "original_audio.mp3")
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if video_url.startswith("http"):
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await _run_local(f"curl -sL -o '{video_path}' '{video_url}'")
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else:
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await _run_local(f"cp '{video_url}' '{video_path}'")
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# Extract audio
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await _run_local(
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f"ffmpeg -y -i '{video_path}' -vn -acodec libmp3lame -q:a 2 '{audio_path}'"
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)
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# Get duration
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stdout, _, rc = await _run_local(
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f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{video_path}'"
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)
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duration = float(stdout.strip()) if rc == 0 else 0
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return {
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"video_path": video_path,
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"audio_path": audio_path,
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"duration": duration,
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}
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# ─── Demucs Separation ───────────────────────────────────────────────
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async def handle_demucs_separating(tenant_id, task_id, step_name, input_data, config):
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"""Run Demucs on GPU server to separate vocals and accompaniment."""
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work_dir = _task_dir(task_id)
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gpu_dir = _gpu_task_dir(task_id)
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# Find audio path from deps
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audio_path = None
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for dep_name, dep_output in input_data.items():
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if isinstance(dep_output, dict):
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audio_path = dep_output.get("audio_path")
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if audio_path:
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break
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if not audio_path:
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raise ValueError("上游步骤未提供 audio_path")
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# Prepare GPU directory
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await _run_gpu(f"mkdir -p {gpu_dir}")
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# Copy audio to GPU
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remote_audio = f"{gpu_dir}/audio.mp3"
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await _copy_to_gpu(audio_path, remote_audio)
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# Run Demucs on GPU
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demucs_cmd = (
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f"cd {gpu_dir} && "
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f"source {GPU_DEMUCS_VENV}/bin/activate && "
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f"python -m demucs --two-stems vocals -n htdemucs --mp3 '{remote_audio}' && "
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f"deactivate"
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)
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stdout, stderr, rc = await _run_gpu(demucs_cmd, timeout=600)
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if rc != 0:
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raise ValueError(f"Demucs 分离失败: {stderr}")
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# Copy results back
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vocals_local = os.path.join(work_dir, "vocals.wav")
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no_vocals_local = os.path.join(work_dir, "no_vocals.wav")
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base = os.path.splitext(os.path.basename(remote_audio))[0]
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await _copy_from_gpu(f"{gpu_dir}/separated/htdemucs/{base}/vocals.wav", vocals_local)
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await _copy_from_gpu(f"{gpu_dir}/separated/htdemuds/{base}/no_vocals.wav", no_vocals_local)
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return {
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"vocals_path": vocals_local,
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"no_vocals_path": no_vocals_local,
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}
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# ─── Lyric Calibration ───────────────────────────────────────────────
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async def handle_lyric_calibrating(tenant_id, task_id, step_name, input_data, config):
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"""ASR timing recognition + LLM calibration against original lyrics."""
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work_dir = _task_dir(task_id)
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params = input_data.get("task_params", {})
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lyrics_text = params.get("lyrics", params.get("lyrics_text", ""))
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# Find vocals path from deps
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vocals_path = None
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for dep_name, dep_output in input_data.items():
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if isinstance(dep_output, dict):
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vocals_path = dep_output.get("vocals_path")
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if vocals_path:
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break
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if not vocals_path:
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raise ValueError("上游步骤未提供 vocals_path")
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if not lyrics_text:
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raise ValueError("缺少 lyrics 参数")
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# Step 1: Run SenseVoice ASR on vocals to get timing
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asr_result_path = os.path.join(work_dir, "asr_timings.json")
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# Copy vocals to GPU for ASR
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gpu_dir = _gpu_task_dir(task_id)
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await _run_gpu(f"mkdir -p {gpu_dir}")
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remote_vocals = f"{gpu_dir}/vocals.wav"
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await _copy_to_gpu(vocals_path, remote_vocals)
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# Run SenseVoice ASR
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asr_script = f"""
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cd {gpu_dir}
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source {GPU_DEMUCS_VENV}/bin/activate
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python -c "
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import json
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from funasr import AutoModel
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model = AutoModel(model='iic/SenseVoiceSmall', trust_remote_code=True)
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res = model.generate(input='{remote_vocals}', batch_size_s=300)
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segments = []
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for item in res:
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for ts in item.get('timestamp', []):
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segments.append({{'text': item.get('text', ''), 'start': ts[0]/1000, 'end': ts[1]/1000}})
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with open('asr_timings.json', 'w') as f:
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json.dump(segments, f, ensure_ascii=False)
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print('ASR done:', len(segments), 'segments')
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"
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"""
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stdout, stderr, rc = await _run_gpu(asr_script, timeout=300)
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await _copy_from_gpu(f"{gpu_dir}/asr_timings.json", asr_result_path)
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with open(asr_result_path, "r") as f:
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asr_timings = json.load(f)
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# Step 2: LLM calibration — align ASR timings with original lyrics
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from pipeline_service.handler import get_handler
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calibrated = await _llm_calibrate(lyrics_text, asr_timings)
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# Save calibrated lyrics
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calibrated_path = os.path.join(work_dir, "calibrated_lyrics.json")
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with open(calibrated_path, "w", encoding="utf-8") as f:
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json.dump(calibrated, f, ensure_ascii=False, indent=2)
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return {
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"calibrated_lyrics_path": calibrated_path,
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"calibrated_lyrics": calibrated,
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"segment_count": len(calibrated),
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}
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async def _llm_calibrate(lyrics_text: str, asr_timings: list) -> list:
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"""Use LLM to align raw lyrics text with ASR timings."""
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prompt = f"""你是一个歌词时间轴校准专家。
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原始歌词文本:
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{lyrics_text}
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ASR识别的时间戳（秒）:
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{json.dumps(asr_timings, ensure_ascii=False)}
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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:
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- text: 歌词文本
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- start: 开始时间（秒，浮点数）
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- end: 结束时间（秒，浮点数）
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要求:
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1. 保持原始歌词的文本和顺序
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2. 时间戳以ASR结果为基础进行微调
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3. 确保时间不重叠，每句之间留适当间隔
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4. 只输出JSON，不要其他内容"""
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try:
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from pipeline_service.llm_bridge import llm_call
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result = await llm_call(prompt)
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# Parse JSON from LLM response
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result = result.strip()
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if result.startswith("```"):
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result = result.split("\n", 1)[1].rsplit("```", 1)[0]
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return json.loads(result)
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except Exception as e:
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logger.warning(f"LLM calibration failed, using ASR timings directly: {e}")
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return asr_timings
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# ─── Subtitle Rendering ──────────────────────────────────────────────
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async def handle_subtitle_rendering(tenant_id, task_id, step_name, input_data, config):
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"""Generate ASS karaoke subtitle file from calibrated lyrics."""
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work_dir = _task_dir(task_id)
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# Find calibrated lyrics
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calibrated = None
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for dep_name, dep_output in input_data.items():
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if isinstance(dep_output, dict):
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calibrated = dep_output.get("calibrated_lyrics")
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if not calibrated and dep_output.get("calibrated_lyrics_path"):
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with open(dep_output["calibrated_lyrics_path"], "r") as f:
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calibrated = json.load(f)
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if calibrated:
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break
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if not calibrated:
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raise ValueError("上游步骤未提供 calibrated_lyrics")
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# Generate ASS file
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ass_path = os.path.join(work_dir, "karaoke.ass")
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_write_ass_file(ass_path, calibrated)
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return {"ass_path": ass_path}
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def _write_ass_file(path: str, segments: list):
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"""Write segments to ASS subtitle file with karaoke effect."""
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header = """[Script Info]
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Title: KTV Karaoke Subtitles
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ScriptType: v4.00+
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PlayResX: 1920
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PlayResY: 1080
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WrapStyle: 0
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[V4+ Styles]
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Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
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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
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[Events]
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Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
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"""
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with open(path, "w", encoding="utf-8") as f:
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f.write(header)
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for seg in segments:
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start = _seconds_to_ass_time(seg["start"])
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end = _seconds_to_ass_time(seg["end"])
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text = seg["text"].replace("\n", "\\N")
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# Karaoke highlight effect
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duration_ms = int((seg["end"] - seg["start"]) * 100)
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f.write(f"Dialogue: 0,{start},{end},KTV,,0,0,0,,{{\\k{duration_ms}}}{text}\n")
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def _seconds_to_ass_time(seconds: float) -> str:
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"""Convert seconds to ASS time format H:MM:SS.CC"""
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h = int(seconds // 3600)
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m = int((seconds % 3600) // 60)
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s = int(seconds % 60)
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cs = int((seconds % 1) * 100)
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return f"{h}:{m:02d}:{s:02d}.{cs:02d}"
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# ─── Subtitle Exporting ──────────────────────────────────────────────
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async def handle_subtitle_exporting(tenant_id, task_id, step_name, input_data, config):
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"""Export ASS subtitle as standalone file (already done by rendering)."""
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ass_path = None
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for dep_name, dep_output in input_data.items():
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if isinstance(dep_output, dict):
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ass_path = dep_output.get("ass_path")
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if ass_path:
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break
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if not ass_path or not os.path.exists(ass_path):
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raise ValueError("上游步骤未提供有效的 ass_path")
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return {"subtitle_path": ass_path, "format": "ass"}
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# ─── Lyric Generation & Evaluation (Mode C) ──────────────────────────
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async def handle_lyric_generating(tenant_id, task_id, step_name, input_data, config):
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"""LLM generates lyrics from topic/outline."""
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params = input_data.get("task_params", {})
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topic = params.get("topic", params.get("outline", ""))
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style = params.get("style", "流行")
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language = params.get("language", "zh")
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if not topic:
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raise ValueError("缺少 topic/outline参数")
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prompt = f"""请创作一首{style}风格的{language}歌词。
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主题/大纲: {topic}
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要求:
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1. 包含完整结构: 主歌(Verse)、副歌(Chorus)、桥段(Bridge)
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2. 每句歌词节奏感强，适合演唱
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3. 押韵自然，情感真实
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4. 总长度适合3-5分钟歌曲
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直接输出歌词文本，标注段落结构。"""
|
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|
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try:
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from pipeline_service.llm_bridge import llm_call
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lyrics = await llm_call(prompt)
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return {"lyrics": lyrics.strip(), "topic": topic, "style": style}
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||
except Exception as e:
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raise ValueError(f"歌词生成失败: {e}")
|
||
|
||
|
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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):
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lyrics = dep_output.get("lyrics")
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||
if lyrics:
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||
break
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||
|
||
if not lyrics:
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raise ValueError("上游步骤未提供歌词")
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# Evaluate via LLM
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prompt = f"""请从以下维度评估这首歌词的质量(1-10分):
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||
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1. 韵律节奏: 押韵、节奏感、可唱性
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2. 情感表达: 情感真实度、共鸣力
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3. 文学性: 用词、意象、修辞
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4. 结构完整: 段落编排、层次感
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5. 商业潜力: 流行度、记忆点
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||
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歌词:
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{lyrics}
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||
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||
输出JSON: {{"score": 8.5, "dimensions": {{...}}, "suggestions": "..."}}
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||
只输出JSON。"""
|
||
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||
try:
|
||
from pipeline_service.llm_bridge import llm_call
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||
result = await llm_call(prompt)
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||
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,
|
||
}
|