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import subprocess
import sys
from pathlib import Path
from typing import Any
import cv2
import numpy as np
from transformers import AutoProcessor
from vllm import LLM, SamplingParams
CAPTION_LENGTH_LABELS = ("very small", "small", "medium", "large", "very large")
CAPTION_SETTING_FIELD_CHOICES = {
"vulgarity": ("none", "low", "medium", "high"),
"uncertainty": ("none", "low", "medium", "high"),
"character_names": ("none", "ambiguous", "single", "multiple"),
"fluff": ("none", "low", "medium", "high"),
"speculation": ("none", "low", "medium", "high"),
"temporal_detail": ("static", "low", "medium", "high"),
"visual_specificity": ("generic", "moderate", "detailed", "excessive"),
"camera_detail": ("none", "low", "medium", "high"),
"caption_style": ("plain", "verbose", "ornate", "robotic"),
}
def bool_from_value(value: Any, field_name: str) -> bool:
if isinstance(value, bool):
return value
if isinstance(value, str):
lowered = value.strip().lower()
if lowered in {"1", "true", "yes", "y", "on"}:
return True
if lowered in {"0", "false", "no", "n", "off"}:
return False
raise ValueError(f"{field_name} must be boolean-like, got {value!r}")
def format_caption_settings_prompt(settings: dict[str, Any]) -> str:
caption_length = str(settings["caption_length"]).strip().lower()
if caption_length not in CAPTION_LENGTH_LABELS:
raise ValueError(f"caption_length must be one of {CAPTION_LENGTH_LABELS}, got {caption_length!r}")
include_watermark_info = bool_from_value(
settings["include_watermark_info"], "include_watermark_info"
)
has_repetition = bool_from_value(settings["has_repetition"], "has_repetition")
has_thinking = bool_from_value(settings["has_thinking"], "has_thinking")
normalized: dict[str, str] = {}
for field_name, choices in CAPTION_SETTING_FIELD_CHOICES.items():
value = str(settings[field_name]).strip().lower()
if value not in choices:
raise ValueError(f"{field_name} must be one of {choices}, got {value!r}")
normalized[field_name] = value
watermark_instruction = (
"Include watermark info." if include_watermark_info else "Do not include watermark info."
)
thinking_instruction = (
"Output thought JSON before the final caption."
if has_thinking
else "Do not output thought JSON; output only the caption."
)
setting_text = (
f"vulgarity={normalized['vulgarity']}; "
f"uncertainty={normalized['uncertainty']}; "
f"character_names={normalized['character_names']}; "
f"fluff={normalized['fluff']}; "
f"has_repetition={str(has_repetition).lower()}; "
f"has_thinking={str(has_thinking).lower()}; "
f"speculation={normalized['speculation']}; "
f"temporal_detail={normalized['temporal_detail']}; "
f"visual_specificity={normalized['visual_specificity']}; "
f"camera_detail={normalized['camera_detail']}; "
f"caption_style={normalized['caption_style']}"
)
return (
f"Write a {caption_length} caption for this clip using both the visuals and the audio. "
f"{watermark_instruction} {thinking_instruction} Match these caption settings: {setting_text}."
)
def load_video_frames(path: str | Path, num_frames: int) -> tuple[np.ndarray, dict[str, Any]]:
video_path = str(path)
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise RuntimeError(f"Could not open video: {video_path}")
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0)
fps = float(cap.get(cv2.CAP_PROP_FPS) or 24.0)
if total_frames <= 0:
cap.release()
raise RuntimeError(f"Could not determine frame count: {video_path}")
indices = np.linspace(0, max(0, total_frames - 1), num_frames).round().astype(int)
frames = []
for idx in indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
ok, frame_bgr = cap.read()
if ok:
frames.append(cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB))
cap.release()
if not frames:
raise RuntimeError(f"Could not read frames: {video_path}")
metadata = {
"fps": fps,
"duration": total_frames / fps if fps > 0 else 0.0,
"total_num_frames": total_frames,
"frames_indices": [int(x) for x in indices[: len(frames)]],
"video_backend": "opencv",
"do_sample_frames": False,
}
return np.stack(frames, axis=0), metadata
def load_audio(path: str | Path, sampling_rate: int) -> tuple[np.ndarray, int]:
video_path = str(path)
cmd = [
"ffmpeg",
"-hide_banner",
"-loglevel",
"error",
"-i",
video_path,
"-vn",
"-ac",
"1",
"-ar",
str(sampling_rate),
"-f",
"f32le",
"-",
]
result = subprocess.run(cmd, check=False, capture_output=True)
if result.returncode == 0 and result.stdout:
return np.frombuffer(result.stdout, dtype=np.float32), sampling_rate
frames, metadata = load_video_frames(video_path, 2)
del frames
duration = max(1.0, float(metadata.get("duration") or 1.0))
return np.zeros(max(1, int(duration * sampling_rate)), dtype=np.float32), sampling_rate
def build_prompt(
*,
processor: AutoProcessor,
model_dir: str | Path,
video_path: str | Path,
prompt_override: str,
prompt_settings: dict[str, Any],
) -> str:
del model_dir
prompt_text = prompt_override.strip() or format_caption_settings_prompt(prompt_settings)
messages = [
{
"role": "user",
"content": [
{"type": "video", "path": str(video_path)},
{"type": "text", "text": prompt_text},
{"type": "audio", "path": "/tmp/polished_model_v3_audio.wav"},
],
},
{"role": "assistant", "content": [{"type": "text", "text": ""}]},
]
prompt = processor.apply_chat_template(messages, tokenize=False)
assistant_tail = "<turn|>\n"
if prompt.endswith(assistant_tail):
prompt = prompt[: -len(assistant_tail)]
return prompt
def build_vllm_request(
*,
processor: AutoProcessor,
model_dir: str | Path,
video_path: str | Path,
num_frames: int,
sampling_rate: int,
prompt_override: str,
prompt_settings: dict[str, Any],
) -> dict[str, Any]:
video, video_metadata = load_video_frames(video_path, num_frames)
audio, sr = load_audio(video_path, sampling_rate)
prompt = build_prompt(
processor=processor,
model_dir=model_dir,
video_path=video_path,
prompt_override=prompt_override,
prompt_settings=prompt_settings,
)
return {
"prompt": prompt,
"multi_modal_data": {
"video": [(video, video_metadata)],
"audio": (audio, sr),
},
}
def load_processor(model_dir: str | Path) -> AutoProcessor:
return AutoProcessor.from_pretrained(str(model_dir), local_files_only=True)
def load_llm(
*,
model_dir: str | Path,
max_model_len: int,
max_num_seqs: int,
gpu_memory_utilization: float,
dtype: str,
quantization: str | None,
enforce_eager: bool,
enable_prefix_caching: bool,
trust_remote_code: bool,
) -> LLM:
kwargs: dict[str, Any] = {
"model": str(model_dir),
"tokenizer": str(model_dir),
"max_model_len": max_model_len,
"max_num_seqs": max_num_seqs,
"gpu_memory_utilization": gpu_memory_utilization,
"limit_mm_per_prompt": {"video": 1, "audio": 1, "image": 0},
"enforce_eager": enforce_eager,
"enable_prefix_caching": enable_prefix_caching,
"trust_remote_code": trust_remote_code,
"dtype": dtype,
}
if quantization:
kwargs["quantization"] = quantization
return LLM(**kwargs)
def sampling_params(
*,
temperature: float,
max_tokens: int,
top_p: float,
repetition_penalty: float,
) -> SamplingParams:
kwargs: dict[str, Any] = {
"temperature": temperature,
"max_tokens": max_tokens,
"repetition_penalty": repetition_penalty,
}
if temperature > 0:
kwargs["top_p"] = top_p
return SamplingParams(**kwargs)
def ensure_local_vllm_source(script_dir: Path) -> None:
local_vllm = script_dir / "vllm"
if local_vllm.is_dir():
sys.path.insert(0, str(local_vllm))
|