diff --git a/README.md b/README.md
index 178fe97..01f2d65 100644
--- a/README.md
+++ b/README.md
@@ -37,7 +37,7 @@ Fun-CineForge relies on Conda and Python environments. Execute **setup.py** to a
```shell
# Conda
-git clone git@github.com:FunAudioLLM/FunCineForge.git
+git clone https://github.com/FunAudioLLM/FunCineForge.git
conda create -n FunCineForge python=3.10 -y && conda activate FunCineForge
sudo apt-get install ffmpeg
# Initial settings
@@ -111,6 +111,15 @@ cd exps
bash infer.sh
```
+### Interactive Demo
+Want to try it out interactively? Launch the Gradio demo for a web-based interface:
+
+```shell
+python gradio_demo.py
+```
+
+The demo will start at `http://localhost:7860` where you can upload your own videos and audio references, customize voice prompts, and generate dubbed videos on the fly.
+
The API for multi-speaker dubbing from raw videos and SRT scripts is under development ...
diff --git a/README_zh.md b/README_zh.md
index 074275e..3653302 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -36,7 +36,7 @@ Fun-CineForge 依赖 Conda 和 Python 环境。执行 **setup.py** 自动安装
```shell
# Conda
-git clone git@github.com:FunAudioLLM/FunCineForge.git
+git clone https://github.com/FunAudioLLM/FunCineForge.git
conda create -n FunCineForge python=3.10 -y && conda activate FunCineForge
sudo apt-get install ffmpeg
# 初始化设置
@@ -109,6 +109,15 @@ cd exps
bash infer.sh
```
+### 交互式演示
+想要交互式体验吗？启动 Gradio 演示程序，使用基于网页的界面：
+
+```shell
+python gradio_demo.py
+```
+
+演示程序将在 `http://localhost:7860` 启动，您可以上传自己的视频和音频参考，自定义语音提示，即时生成配音视频。
+
从原始视频和 SRT 脚本进行多人配音的 API 调用接口在开发中 ...
diff --git a/funcineforge/models/causal_hifigan.py b/funcineforge/models/causal_hifigan.py
index 28ac80e..4c64283 100644
--- a/funcineforge/models/causal_hifigan.py
+++ b/funcineforge/models/causal_hifigan.py
@@ -16,6 +16,7 @@
from torch.nn.utils.parametrize import remove_parametrizations
from torch.nn.utils.parametrizations import weight_norm
import logging
+import soundfile as sf
from funcineforge.register import tables
from funcineforge.utils.device_funcs import to_device
import os
@@ -828,9 +829,11 @@ def inference(
new_freq=output_sr
)
wav_sr = output_sr
- torchaudio.save(
- os.path.join(wav_out_dir, f"{key[0]}.wav"), recon_speech.cpu(),
- sample_rate=wav_sr, encoding='PCM_S', bits_per_sample=16
+ sf.write(
+ os.path.join(wav_out_dir, f"{key[0]}.wav"),
+ recon_speech.cpu().squeeze(0).numpy(),
+ samplerate=wav_sr,
+ subtype='PCM_16'
)
return recon_speech
\ No newline at end of file
diff --git a/funcineforge/models/inference_model.py b/funcineforge/models/inference_model.py
index 2149b43..b79c90c 100644
--- a/funcineforge/models/inference_model.py
+++ b/funcineforge/models/inference_model.py
@@ -4,6 +4,7 @@
import numpy as np
import os
import torchaudio
+import soundfile as sf
import time
import shutil
from funcineforge.register import tables
@@ -70,9 +71,11 @@ def inference(
wav_out_dir = os.path.join(output_dir, "wav")
os.makedirs(wav_out_dir, exist_ok=True)
output_wav_path = os.path.join(wav_out_dir, f"{key[0]}.wav")
- torchaudio.save(
- output_wav_path, wav.cpu(),
- sample_rate=self.sample_rate, encoding='PCM_S', bits_per_sample=16
+ sf.write(
+ output_wav_path,
+ wav.cpu().squeeze(0).numpy(),
+ samplerate=self.sample_rate,
+ subtype='PCM_16'
)
silent_video_path = data_in[0]["video"]
diff --git a/gradio_demo.py b/gradio_demo.py
new file mode 100644
index 0000000..281c7db
--- /dev/null
+++ b/gradio_demo.py
@@ -0,0 +1,496 @@
+#!/usr/bin/env python3
+"""
+FunCineForge Gradio Demo
+Run from the repo root: python gradio_demo.py
+"""
+
+import os, sys, json, pickle, tempfile, uuid, shutil, logging
+import numpy as np
+import cv2
+import gradio as gr
+
+# ── make sure the repo root is on the path ─────────────────────────────────
+ROOT = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, ROOT)
+EXPS_DIR = os.path.join(ROOT, "exps")
+
+# ── checkpoint paths (relative to exps/) ───────────────────────────────────
+LM_CKPT = "funcineforge_zh_en/llm/ds-model.pt.best/mp_rank_00_model_states.pt"
+FM_CKPT = "funcineforge_zh_en/flow/ds-model.pt.best/mp_rank_00_model_states.pt"
+VOC_CKPT = "funcineforge_zh_en/vocoder/ds-model.pt.best/avg_5_removewn.pt"
+FACE_ONNX = os.path.join(ROOT, "speaker_diarization/pretrained_models/face_recog_ir101.onnx")
+DECODE_CONF = os.path.join(EXPS_DIR, "decode_conf")
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("FunCineForge-Demo")
+
+# ── lazy model loading ──────────────────────────────────────────────────────
+_pipeline = None
+
+def get_pipeline():
+ global _pipeline
+ if _pipeline is not None:
+ return _pipeline
+
+ import torch
+ from funcineforge import AutoModel
+ from funcineforge.models.utils import dtype_map
+
+ os.chdir(EXPS_DIR)
+
+ def _load(exp_dir, model_name, ckpt_path, device="cuda:0"):
+ return AutoModel(
+ model=os.path.join(exp_dir, model_name),
+ init_param=ckpt_path,
+ output_dir=None,
+ device=device,
+ )
+
+ log.info("Loading LM model …")
+ lm_exp_dir, lm_model_name, _, _ = LM_CKPT.rsplit("/", 3)
+ lm_model = _load(lm_exp_dir, lm_model_name, LM_CKPT)
+ lm_model.model.to(dtype_map["fp32"])
+
+ log.info("Loading FM model …")
+ fm_exp_dir, fm_model_name, _, _ = FM_CKPT.rsplit("/", 3)
+ fm_model = _load(fm_exp_dir, fm_model_name, FM_CKPT)
+ fm_model.model.to(dtype_map["fp32"])
+
+ log.info("Loading Vocoder …")
+ voc_exp_dir, voc_model_name, _, _ = VOC_CKPT.rsplit("/", 3)
+ voc_model = _load(voc_exp_dir, voc_model_name, VOC_CKPT)
+ voc_model.model.to(dtype_map["fp32"])
+
+ log.info("Building inference model …")
+ from funcineforge.register import tables
+
+ infer_model = AutoModel(
+ model="FunCineForgeInferModel",
+ model_conf={},
+ output_dir=None,
+ device="cuda:0",
+ lm_model=lm_model,
+ fm_model=fm_model,
+ voc_model=voc_model,
+ tokenizer=None,
+ # decode params
+ sampling="ras",
+ lm_use_prompt=True,
+ fm_use_prompt=True,
+ use_llm_cache=True,
+ max_length=1500,
+ min_length=50,
+ llm_dtype="fp32",
+ fm_dtype="fp32",
+ voc_dtype="fp32",
+ batch_size=1,
+ xvec_model="funcineforge_zh_en/camplus.onnx",
+ dataset_conf={
+ "load_meta_data_key": "text,clue,face,dialogue,vocal,video",
+ "sos": 6561, "eos": 6562,
+ "turn_of_speech": 6563, "fill_token": 6564,
+ "ignore_id": -100,
+ "startofclue_token": 151646, "endofclue_token": 151647,
+ "frame_shift": 25, "timebook_size": 1500,
+ "pangbai": 1500, "dubai": 1501, "duihua": 1502, "duoren": 1503,
+ "male": 1504, "female": 1505,
+ "child": 1506, "youth": 1507, "adult": 1508,
+ "middle": 1509, "elderly": 1510,
+ "speaker_id_start": 1511,
+ },
+ index_ds="FunCineForgeDS",
+ disable_pbar=True,
+ random_seed=0,
+ )
+
+ _pipeline = infer_model
+ log.info("Pipeline ready ✓")
+ return _pipeline
+
+
+# ── face embedding extraction using the ONNX model ─────────────────────────
+_face_ort = None
+
+def get_face_ort():
+ global _face_ort
+ if _face_ort is None:
+ import onnxruntime
+ opts = onnxruntime.SessionOptions()
+ opts.intra_op_num_threads = 4
+ opts.inter_op_num_threads = 4
+ _face_ort = onnxruntime.InferenceSession(
+ FACE_ONNX, sess_options=opts, providers=["CPUExecutionProvider"]
+ )
+ return _face_ort
+
+
+def preprocess_face(img_bgr: np.ndarray) -> np.ndarray:
+ """Resize & normalise a face crop for the IR101 face-rec model (112×112)."""
+ face = cv2.resize(img_bgr, (112, 112))
+ face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB).astype(np.float32)
+ face = (face - 127.5) / 127.5
+ face = face.transpose(2, 0, 1)[np.newaxis] # (1, 3, 112, 112)
+ return face
+
+
+def extract_face_embeddings_from_video(video_path: str, every_n_frames: int = 5):
+ """
+ Sample frames from a video every `every_n_frames` frames,
+ detect the dominant face bounding box (using simple haar or centre-crop),
+ and compute 512-d embeddings with the ONNX face-rec model.
+
+ Returns a dict with keys: embeddings, faceI, frameI (matching the .pkl format).
+ """
+ ort = get_face_ort()
+ cap = cv2.VideoCapture(video_path)
+ if not cap.isOpened():
+ raise RuntimeError(f"Cannot open video: {video_path}")
+
+ face_cascade = cv2.CascadeClassifier(
+ cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+ )
+
+ embeddings, faceI, frameI = [], [], []
+ speech_token_idx = 0 # maps to speech-token index (25 tokens/sec → one token per 40 ms)
+ fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
+
+ frame_idx = 0
+ while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+ if frame_idx % every_n_frames == 0:
+ # Map video-frame idx → speech-token idx
+ token_idx = int(frame_idx / fps * 25) # 25 tokens/s
+
+ # Try face detection; fall back to centre crop
+ gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ faces = face_cascade.detectMultiScale(gray, 1.1, 5, minSize=(40, 40))
+ if len(faces) > 0:
+ x, y, w, h = sorted(faces, key=lambda f: f[2] * f[3], reverse=True)[0]
+ crop = frame[y: y + h, x: x + w]
+ else:
+ # centre-crop fallback
+ h, w = frame.shape[:2]
+ side = min(h, w)
+ y0, x0 = (h - side) // 2, (w - side) // 2
+ crop = frame[y0: y0 + side, x0: x0 + side]
+
+ inp = preprocess_face(crop)
+ emb = ort.run(None, {ort.get_inputs()[0].name: inp})[0][0] # (512,)
+ embeddings.append(emb.astype(np.float32))
+ faceI.append(token_idx)
+ frameI.append(token_idx)
+ frame_idx += 1
+
+ cap.release()
+ return {"embeddings": embeddings, "faceI": np.array(faceI), "frameI": np.array(frameI),
+ "face": [], "face_bbox": [], "lip": [], "lip_bbox": []}
+
+
+# ── inference helper ────────────────────────────────────────────────────────
+def run_inference(
+ text: str,
+ clue: str,
+ vocal_path: str,
+ video_path: str,
+ gender: str,
+ age: str,
+ speech_type: str,
+ duration: float,
+ output_dir: str,
+ utt_id: str,
+):
+ """Build the data dict the pipeline expects and run inference."""
+ from funcineforge.register import tables
+
+ # ── face embeddings ─────────────────────────────────────────────────────
+ log.info("Extracting face embeddings …")
+ face_pkl_path = os.path.join(output_dir, f"{utt_id}.pkl")
+ face_data = extract_face_embeddings_from_video(video_path)
+ with open(face_pkl_path, "wb") as f:
+ pickle.dump(face_data, f)
+
+ # ── dialogue metadata ───────────────────────────────────────────────────
+ dialogue = [{"start": 0.0, "duration": duration, "spk": "1",
+ "gender": gender.lower(), "age": age.lower()}]
+
+ # ── dataset conf (must match decode.yaml) ──────────────────────────────
+ TIMEBOOK_SIZE = 1500
+ TYPE_MAP = {"monologue": TIMEBOOK_SIZE + 1, "dialogue": TIMEBOOK_SIZE + 2,
+ "narration": TIMEBOOK_SIZE, "multi-speaker": TIMEBOOK_SIZE + 3}
+ type_id = TYPE_MAP.get(speech_type, TIMEBOOK_SIZE + 1)
+
+ GENDER_MAP = {"male": TIMEBOOK_SIZE + 4, "female": TIMEBOOK_SIZE + 5}
+ AGE_MAP = {"child": TIMEBOOK_SIZE + 6, "teenager": TIMEBOOK_SIZE + 7,
+ "adult": TIMEBOOK_SIZE + 8, "middle-aged": TIMEBOOK_SIZE + 9,
+ "elderly": TIMEBOOK_SIZE + 10}
+ SPEAKER_ID_START = TIMEBOOK_SIZE + 11
+ FRAME_SHIFT = 25
+
+ starts = np.array([d["start"] for d in dialogue])
+ durations= np.array([d["duration"] for d in dialogue])
+ speakers = np.array([int(d["spk"]) for d in dialogue])
+ start_idxs = (starts * FRAME_SHIFT + 1).astype(np.int64)
+ end_idxs = ((starts + durations) * FRAME_SHIFT + 1).astype(np.int64)
+ spk_ids = (SPEAKER_ID_START + speakers - 1).astype(np.int64)
+ gender_ids = [GENDER_MAP.get(d["gender"], -100) for d in dialogue]
+ age_ids = [AGE_MAP.get(d["age"], -100) for d in dialogue]
+
+ n = len(dialogue)
+ timespk_ids = np.full(n * 5, -100, dtype=np.int64)
+ timespk_ids[0::5] = start_idxs
+ timespk_ids[1::5] = spk_ids
+ timespk_ids[2::5] = gender_ids
+ timespk_ids[3::5] = age_ids
+ timespk_ids[4::5] = end_idxs
+
+ # ── build data dict ─────────────────────────────────────────────────────
+ data = {
+ "utt": utt_id,
+ "text": text,
+ "clue": clue,
+ "vocal": vocal_path,
+ "video": video_path,
+ "face": face_pkl_path,
+ "type_id": type_id,
+ "timespk_ids": timespk_ids,
+ "speech_len": int(duration * FRAME_SHIFT),
+ "source_len": int(duration * FRAME_SHIFT) * 2 + 200,
+ }
+
+ # ── run the pipeline ────────────────────────────────────────────────────
+ pipeline = get_pipeline()
+
+ class _SingleDS:
+ def __len__(self): return 1
+ def __getitem__(self, _): return data
+
+ pipeline.kwargs["output_dir"] = output_dir
+ pipeline.inference(input=_SingleDS(), input_len=1)
+
+ wav_path = os.path.join(output_dir, "wav", f"{utt_id}.wav")
+ mp4_path = os.path.join(output_dir, "mp4", f"{utt_id}.mp4")
+ return (wav_path if os.path.exists(wav_path) else None,
+ mp4_path if os.path.exists(mp4_path) else None)
+
+
+# ── Gradio callback ─────────────────────────────────────────────────────────
+def infer_gradio(
+ text, clue, vocal_file, video_file,
+ gender, age, speech_type,
+ duration, progress=gr.Progress(track_tqdm=True)
+):
+ if not text.strip():
+ raise gr.Error("Please enter the script text.")
+ if vocal_file is None:
+ raise gr.Error("Please upload a reference audio file.")
+ if video_file is None:
+ raise gr.Error("Please upload a video file.")
+
+ # Get the actual file paths (Gradio passes temp file paths)
+ vocal_path = vocal_file if isinstance(vocal_file, str) else vocal_file.name
+ video_path = video_file if isinstance(video_file, str) else video_file.name
+
+ # Create a unique temp output dir
+ run_id = str(uuid.uuid4())[:8]
+ out_dir = os.path.join(tempfile.gettempdir(), f"fcf_{run_id}")
+ os.makedirs(out_dir, exist_ok=True)
+
+ try:
+ progress(0.1, desc="Loading models …")
+ get_pipeline() # ensure models are loaded
+
+ progress(0.3, desc="Extracting face embeddings …")
+ wav_out, mp4_out = run_inference(
+ text=text.strip(),
+ clue=clue.strip() if clue.strip() else "A speaker delivers the dialogue naturally.",
+ vocal_path=vocal_path,
+ video_path=video_path,
+ gender=gender,
+ age=age,
+ speech_type=speech_type,
+ duration=float(duration),
+ output_dir=out_dir,
+ utt_id=f"demo_{run_id}",
+ )
+ progress(1.0, desc="Done!")
+
+ if wav_out is None:
+ raise gr.Error("Inference completed but no output audio was found.")
+
+ # Copy outputs to stable temp paths so Gradio can serve them
+ final_wav = os.path.join(tempfile.gettempdir(), f"fcf_{run_id}_out.wav")
+ shutil.copy2(wav_out, final_wav)
+
+ final_mp4 = None
+ if mp4_out and os.path.exists(mp4_out):
+ final_mp4 = os.path.join(tempfile.gettempdir(), f"fcf_{run_id}_out.mp4")
+ shutil.copy2(mp4_out, final_mp4)
+
+ return final_wav, final_mp4, "✅ Generation complete!"
+
+ except Exception as e:
+ log.exception("Inference failed")
+ shutil.rmtree(out_dir, ignore_errors=True)
+ raise gr.Error(f"Inference failed: {e}")
+
+
+# ── UI ──────────────────────────────────────────────────────────────────────
+TITLE = "Video Dubbing Demo"
+DESCRIPTION = """
+This demo showcases **FunCineForge**, a cinematic speech synthesis system.
+
+It uses an a pipeline with LLM for prosody modeling, Flow-Matching for acoustic modeling, and a high-fidelity Vocoder. By leveraging both text and visual cues (from the reference video's face embeddings), it generates character-accurate, emotionally charged speech perfectly suited for film and video dubbing.
+"""
+
+CUSTOM_CSS = """
+ #generate-btn { background: linear-gradient(135deg, #7c3aed, #4f46e5); color: white; font-size: 1.1rem; }
+ #status-box { font-size: 0.9rem; color: #6b7280; }
+ .gr-panel { border-radius: 12px !important; }
+ footer { display: none !important; }
+"""
+DEMO_THEME = gr.themes.Ocean()
+
+with gr.Blocks(title="FunCineForge Demo") as demo:
+ gr.Markdown(f"# {TITLE}\n{DESCRIPTION}")
+
+ with gr.Row():
+ # ── LEFT: inputs ────────────────────────────────────────────────────
+ with gr.Column(scale=3):
+ gr.Markdown("### Script & Prompt")
+ text_input = gr.Textbox(
+ label="Script Text",
+ placeholder="Enter the dialogue or narration text here …",
+ lines=3,
+ )
+ clue_input = gr.Textbox(
+ label="Voice / Emotion Clue",
+ placeholder=(
+ "E.g., 'A calm, 40-year-old male speaker with a deep voice, "
+ "delivering a professional and thoughtful statement.'"
+ ),
+ lines=3,
+ )
+
+ gr.Markdown("### Reference Files")
+ with gr.Row():
+ vocal_input = gr.Audio(
+ label="Reference Audio (WAV — same character)",
+ type="filepath",
+ )
+ video_input = gr.Video(
+ label="Reference Video (MP4 — scene to dub)",
+ )
+
+ gr.Markdown("### Speaker Metadata")
+ with gr.Row():
+ gender_input = gr.Radio(
+ ["male", "female"],
+ value="male",
+ label="Gender",
+ )
+ age_input = gr.Dropdown(
+ choices=["child", "teenager", "adult", "middle-aged", "elderly"],
+ value="adult",
+ label="Age Group",
+ )
+ type_input = gr.Dropdown(
+ choices=["monologue", "dialogue", "narration", "multi-speaker"],
+ value="monologue",
+ label="Scene Type",
+ )
+ duration_input = gr.Slider(
+ minimum=1.0, maximum=30.0, value=5.0, step=0.5,
+ label="Approximate Speech Duration (seconds)",
+ info="Estimate of the expected audio length",
+ )
+
+ generate_btn = gr.Button("Generate Speech", elem_id="generate-btn", variant="primary")
+
+ # ── RIGHT: outputs ───────────────────────────────────────────────────
+ with gr.Column(scale=2):
+ gr.Markdown("### Generated Output")
+ audio_output = gr.Audio(
+ label="Generated Speech",
+ type="filepath",
+ interactive=False,
+ )
+ video_output = gr.Video(
+ label="Dubbed Video (if video was provided)",
+ interactive=False,
+ )
+ status_output = gr.Textbox(
+ label="Status",
+ elem_id="status-box",
+ interactive=False,
+ )
+
+ # ── examples ─────────────────────────────────────────────────────────────
+ def get_example_path(rel_path):
+ p = os.path.join(EXPS_DIR, rel_path)
+ return p if os.path.exists(p) else None
+
+ examples = [
+ [
+ "Every closet on a Carnival cruise ship. To make the numbers work, I needed a lot of cedar, fast and cheap.",
+ "A single middle-aged male speaker describes a business or construction requirement with a practical and matter-of-fact tone. His voice is deep and slightly gravelly, maintaining a professional and informative demeanor.",
+ get_example_path("data/clipped/en_monologue_1.wav"),
+ get_example_path("data/clipped/en_monologue_1.mp4"),
+ "male",
+ "middle-aged",
+ "monologue",
+ 5.74,
+ ],
+ [
+ "Oh my God. Do you remember that bottle of wine we put aside the night Haley was born?",
+ "An adult female speaker expresses a sense of sudden realization and excitement. Her tone is bright and nostalgic as she recalls a significant event from the past. The overall emotion is one of pleasant surprise and anticipation.",
+ get_example_path("data/clipped/en_monologue_2.wav"),
+ get_example_path("data/clipped/en_monologue_2.mp4"),
+ "female",
+ "adult",
+ "monologue",
+ 4.06,
+ ],
+ [
+ "I was just letting you know that if you were having any problems, you could come to me with them.",
+ "A single adult female speaker delivers a supportive and reassuring message. Her tone is friendly and caring, with a hint of advice, and her emotions fluctuate greatly. She speaks clearly and at a moderate pace, offering assistance to the listener.",
+ get_example_path("data/clipped/en_monologue_3.wav"),
+ get_example_path("data/clipped/en_monologue_3.mp4"),
+ "female",
+ "adult",
+ "monologue",
+ 4.94,
+ ],
+ ]
+
+ # Filter out examples where files are missing
+ valid_examples = [ex for ex in examples if ex[2] and ex[3]]
+
+ if valid_examples:
+ gr.Examples(
+ examples=valid_examples,
+ inputs=[text_input, clue_input, vocal_input, video_input,
+ gender_input, age_input, type_input, duration_input],
+ label="📂 Try an example from the demo dataset",
+ )
+
+ generate_btn.click(
+ fn=infer_gradio,
+ inputs=[
+ text_input, clue_input, vocal_input, video_input,
+ gender_input, age_input, type_input, duration_input,
+ ],
+ outputs=[audio_output, video_output, status_output],
+ )
+
+
+if __name__ == "__main__":
+ demo.launch(
+ server_name="0.0.0.0",
+ server_port=7860,
+ share=True,
+ inbrowser=False,
+ theme=DEMO_THEME,
+ css=CUSTOM_CSS,
+ )
diff --git a/requirements.txt b/requirements.txt
index 1567847..f1831e6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -55,9 +55,9 @@ simplejson
soundfile==0.13.1
starlette==0.47.2
tensorboardX==2.6.4
-torch==2.4.1
-torchaudio==2.4.1
-torchvision==0.19.1
+torch==2.9.1
+torchaudio==2.9.1
+torchvision==0.24.1
tqdm==4.67.1
transformers==4.57.0
x_transformers==2.16.2