Spaces:
Running
Running
File size: 4,699 Bytes
f5cd164 e15eab8 f5cd164 8e81ebe f5cd164 e15eab8 f5cd164 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | import os
import sys
import threading
from typing import Dict, Any, List
# Add backend directory to path
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
if CURRENT_DIR not in sys.path:
sys.path.append(CURRENT_DIR)
try:
from .sign_bridge_inference_hq import SignBridgeInferenceHQ
except (ImportError, ValueError):
from sign_bridge_inference_hq import SignBridgeInferenceHQ
# HQ Weights are stored in a dedicated directory
MODEL_ROOT_HQ = os.path.join(os.path.dirname(CURRENT_DIR), "weights_hq")
CONFIG_PATH_HQ = os.path.join(os.path.dirname(CURRENT_DIR), "model_configs", "Sign-IDD-HQ.yaml")
class SignModelHQ:
"""
Singleton wrapper for the High Fidelity SignBridgeInference engine.
Uses the uncompressed 1.1GB weights and optimized motion sampling.
"""
_instance = None
_lock = threading.Lock()
def __new__(cls):
with cls._lock:
if cls._instance is None:
cls._instance = super(SignModelHQ, cls).__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
print("Initializing SignBridge HQ Model (High Fidelity Path)...")
self.engine = None
self.is_loaded = False
self._load_error = None
self._initialized = True
# Load in background
threading.Thread(target=self._load_model_async, daemon=True).start()
def _load_model_async(self):
try:
# 1. Ensure weight directory exists
os.makedirs(MODEL_ROOT_HQ, exist_ok=True)
weight_path = os.path.join(MODEL_ROOT_HQ, "best.ckpt")
# 2. Check if weights need to be downloaded (Runtime bypass for 1GB repo limit)
if not os.path.exists(weight_path):
print(f"HQ Weights not found at {weight_path}. Attempting download from Hub...")
from huggingface_hub import hf_hub_download
repo_id = os.environ.get("HF_REPO_ID_HQ", "ExploWebsite/SignBridge-Weights")
token = os.environ.get("HF_TOKEN") # Optional: needed if repo is private
print(f"Downloading HQ Weights from {repo_id}...")
downloaded_file = hf_hub_download(
repo_id=repo_id,
filename="best.ckpt",
local_dir=MODEL_ROOT_HQ,
token=token
)
print(f"✅ Download complete: {downloaded_file}")
# 3. Initialize the HQ-specific inference engine
self.engine = SignBridgeInferenceHQ(MODEL_ROOT_HQ)
self.is_loaded = True
print("✅ SignBridge HQ Model loaded and ready for high-fidelity inference.")
except Exception as e:
self._load_error = str(e)
print(f"❌ Failed to load SignBridge HQ Model: {e}")
import traceback
traceback.print_exc()
def inference(self, text: str) -> Dict[str, Any]:
if not self.is_loaded:
if self._load_error:
raise RuntimeError(f"HQ Model failed to load: {self._load_error}")
raise RuntimeError("HQ Model is still loading. Please try again in 30 seconds.")
print(f"HQ Inference Request: '{text}'")
try:
# HIGH FIDELITY PARAMS:
# We use 90 steps (matching original training) and potentially different guidance or length heuristics
skeletons = self.engine.translate(text, sampling_steps=90)
import uuid
from video_renderer import render_skeleton_to_video
filename = f"hq_gen_{uuid.uuid4().hex[:8]}.mp4"
output_dir = os.path.join(CURRENT_DIR, "output")
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, filename)
# Use standard renderer (stable)
render_skeleton_to_video(skeletons, output_path, mode="hq")
# URL resolution (assumes same static mount)
video_url = f"https://explowebsite-sign-idd-inference.hf.space/static/{filename}"
if os.environ.get("LOCAL_DEV"):
video_url = f"http://127.0.0.1:8001/static/{filename}"
return {
"skeletons": None,
"video_url": video_url,
"glosses": self.engine.text_to_glosses(text)
}
except Exception as e:
print(f"HQ Inference error: {e}")
raise e
# Global singleton instance for HQ
sign_model_hq = SignModelHQ()
|