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A repository for generating stylized talking 3D and 3D face

Python 93.68% Starlark 0.20% C++ 6.01% Shell 0.09% Ruby 0.02%

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Thank you for your great work. Is it convenient to publish the code for the statistical study section？

the ground truth

Hi,sorry to interrupt! how did you get the ground truth to train the second part?I mean that the style code is extracted from other videos,there shouldn't have the ground truth about the output video which identity from the static photo while the style from another video, so I am a little confused about the ground truth Y in the second loss.Can you explain it?Thank you!

error

Downloading: "https://www.adrianbulat.com/downloads/python-fan/2DFAN4-11f355bf06.pth.tar" to /root/.cache/torch/hub/checkpoints/2DFAN4-11f355bf06.pth.tar
100% 91.2M/91.2M [00:05<00:00, 19.1MB/s]
WARNING: Logging before flag parsing goes to stderr.
W1124 05:18:17.878163 140281471424384 module_wrapper.py:139] From /content/style_avatar/deep_3drecon/utils.py:68: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

W1124 05:18:17.879213 140281471424384 module_wrapper.py:139] From /content/style_avatar/deep_3drecon/utils.py:14: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

W1124 05:18:17.879425 140281471424384 module_wrapper.py:139] From /content/style_avatar/deep_3drecon/utils.py:15: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.

W1124 05:18:18.932533 140281471424384 module_wrapper.py:139] From /content/style_avatar/deep_3drecon/face_decoder.py:129: The name tf.cross is deprecated. Please use tf.linalg.cross instead.

W1124 05:18:18.933541 140281471424384 deprecation.py:506] From /content/style_avatar/deep_3drecon/face_decoder.py:131: calling l2_normalize (from tensorflow.python.ops.nn_impl) with dim is deprecated and will be removed in a future version.
Instructions for updating:
dim is deprecated, use axis instead
W1124 05:18:19.503320 140281471424384 deprecation.py:323] From /content/style_avatar/deep_3drecon/mesh_renderer/mesh_renderer.py:165: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
W1124 05:18:19.969749 140281471424384 module_wrapper.py:139] From /content/style_avatar/deep_3drecon/utils.py:85: The name tf.GPUOptions is deprecated. Please use tf.compat.v1.GPUOptions instead.

W1124 05:18:19.970036 140281471424384 module_wrapper.py:139] From /content/style_avatar/deep_3drecon/utils.py:86: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

W1124 05:18:19.970237 140281471424384 module_wrapper.py:139] From /content/style_avatar/deep_3drecon/utils.py:86: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

2021-11-24 05:18:19.983109: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299995000 Hz
2021-11-24 05:18:19.983801: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5565daed5d40 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-11-24 05:18:19.983842: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2021-11-24 05:18:19.987771: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2021-11-24 05:18:19.992980: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:19.993832: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5565daed5b80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-11-24 05:18:19.993868: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7
2021-11-24 05:18:19.994966: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:19.995591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: 
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:04.0
2021-11-24 05:18:20.022505: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-11-24 05:18:20.211194: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-11-24 05:18:20.237375: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2021-11-24 05:18:20.260865: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2021-11-24 05:18:20.509169: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2021-11-24 05:18:20.528118: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2021-11-24 05:18:20.896984: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-11-24 05:18:20.897234: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:20.898117: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:20.898852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2021-11-24 05:18:20.902326: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-11-24 05:18:20.903938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-11-24 05:18:20.903995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0 
2021-11-24 05:18:20.904026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N 
2021-11-24 05:18:20.905518: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:20.906512: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:20.907194: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10199 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
2021-11-24 05:18:24.483044: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.483631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: 
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:04.0
2021-11-24 05:18:24.483758: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-11-24 05:18:24.483833: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-11-24 05:18:24.483898: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2021-11-24 05:18:24.483972: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2021-11-24 05:18:24.484039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2021-11-24 05:18:24.484101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2021-11-24 05:18:24.484165: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-11-24 05:18:24.484282: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.484860: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.485335: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2021-11-24 05:18:24.486420: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.486925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: 
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:04.0
2021-11-24 05:18:24.487000: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-11-24 05:18:24.487065: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-11-24 05:18:24.487127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2021-11-24 05:18:24.487192: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2021-11-24 05:18:24.487254: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2021-11-24 05:18:24.487315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2021-11-24 05:18:24.487376: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-11-24 05:18:24.487489: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.488060: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.488592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2021-11-24 05:18:24.488651: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-11-24 05:18:24.488687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0 
2021-11-24 05:18:24.488714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N 
2021-11-24 05:18:24.488897: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.489464: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:24.489974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10199 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
/content/style_avatar/align_img.py:21: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.
To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.
  k,_,_,_ = np.linalg.lstsq(A,b)
/content/style_avatar/align_img.py:97: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  trans_params = np.array([w0,h0,102.0/s,t[0],t[1]])
2021-11-24 05:18:27.965252: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-11-24 05:18:30.431787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-11-24 05:18:32.221454: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2021-11-24 05:18:32.233011: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2021-11-24 05:18:32.244708: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2021-11-24 05:18:32.255462: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2021-11-24 05:18:32.268217: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2021-11-24 05:18:32.280419: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2021-11-24 05:18:32.280486: W tensorflow/core/common_runtime/bfc_allocator.cc:305] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.
2021-11-24 05:18:34.034494: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 75497472 exceeds 10% of system memory.
rm: cannot remove '/content/outt/*.png': No such file or directory
2021-11-24 05:18:35.471827: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:35.472222: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: 
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:04.0
2021-11-24 05:18:35.472343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-11-24 05:18:35.472443: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-11-24 05:18:35.472528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2021-11-24 05:18:35.472621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2021-11-24 05:18:35.472738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2021-11-24 05:18:35.472816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2021-11-24 05:18:35.472911: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-11-24 05:18:35.473096: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:35.476636: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:35.477987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2021-11-24 05:18:35.479073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-11-24 05:18:35.479127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0 
2021-11-24 05:18:35.479157: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N 
2021-11-24 05:18:35.479720: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:35.480132: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-24 05:18:35.480518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10199 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
2021-11-24 05:18:36.163937: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 57802752 exceeds 10% of system memory.
FATAL Flags parsing error: Unknown command line flag 'in_img'
Pass --helpshort or --helpfull to see help on flags.

question

This is amazing work. I was wondering, how would I add eye blinking to the generated output video? Would I need to train a new talking style?

thank you

unknown mat file type, version 49, 50

when i load "BFM_model_front.mat", the error "unknown mat file type, version 49, 50" happened? can you share what version scipy you install @wuhaozhe

BFM_model_front.mat not exists

Hi, the download link of BFM_model_front.mat is not working, could you provide a new one? Thanks.

render 部分的noisy dots 有什么解决思路吗

看到您提到的将要解决的问题，包含render 部分的noisy dots ，我也有这种情况，有什么思路可以尝试吗？

如何调整成对中文输入也支持？

大佬您好，感谢您的分享！
如果想尝试调整成对中文输入也适用的方案，不知道要调整哪些内容？
这边也看到其他的有类似的疑问：
#1 (comment)
“The deepspeech is trained on English, you can test it in Chinese, but the result wouldn't be satisfactory.”
还有一个疑问，输入的音频是训练集中不存在的，那么音画同步效果如何？

Thank you for your great work. Is it convenient to publish the code for the statistical study section？

- Run `git lfs checkout ./deep_3drecon/BFM/BFM_model_front.mat`

hi:
It do not work,can u give me some ad?

运行demo.py，生成了很奇怪的结果

您好，非常感谢这么有意思的一个项目。我完全按照你的步骤执行下来，直接运行demo.py

[然后生成了很奇怪的结果。请看下面的图片。

bad result

use your model, demo code, example.png and example.wav, but the generated result is bad

what is the problem？ @wuhaozhe

demo.py生成的10种风格没有head pose

你好，我运行python demo.py生成了10个result，发现他们均缺少pose部分，请问这是正常的吗？

请问可以分享一些相关的生成结果的视频吗？

您好，感谢大佬开源的代码！
不知道是否可以分享一些生成结果的视频？谢谢啦

已创建中文的讨论组想加入的请添加微信xaaheng

Reading the LMDB data issue

I have download the xa* data and followed the commands to create "data.mdb", but I run this code:

env = lmdb.open(lmdb_path, map_size=1099511627776, max_dbs = 64)
train_video = env.open_db("train_video".encode())

I get this error: "lmdb.PageNotFoundError: mdb_dbi_open: MDB_PAGE_NOTFOUND: Requested page not found"

Well, it seems strange because the data.mdb is almost 17 gigabytes

When I print the env.stat() I get this:
{'psize': 4096, 'depth': 1, 'branch_pages': 0, 'leaf_pages': 1, 'overflow_pages': 0, 'entries': 35}

Getting the UV mapping from UVAtlas

@wuhaozhe Thank you very much for sharing your code!
I have a question on how you generate the UV mappings
In the paper, you have mentioned that you use UVAtlas, but as I am going through your demo.py code, it seems that the UV maps are created by passing the 3DMM face model to the google Mesh_UV renderer.
Just to be exact: In this method of the Face3D, the mesh_uv from mesh_renderer is called.
Could you elaborate more on this, on how the UV maps of shape (H, W, 2) are generated.

mean_std question

Hi, how to get exp_mean_std.pkl and pose_mean_std.pkl?

rasterize_triangles_kernel.so not found

python demo.py
Traceback (most recent call last):
File "demo.py", line 1, in
import deep_3drecon
File "E:\AI\style_avatar\deep_3drecon_init_.py", line 6, in
from .utils import Reconstructor
File "E:\AI\style_avatar\deep_3drecon\utils.py", line 11, in
from .face_decoder import Face3D
File "E:\AI\style_avatar\deep_3drecon\face_decoder.py", line 5, in
from .mesh_renderer import mesh_renderer
File "E:\AI\style_avatar\deep_3drecon\mesh_renderer\mesh_renderer.py", line 24, in
from . import rasterize_triangles
File "E:\AI\style_avatar\deep_3drecon\mesh_renderer\rasterize_triangles.py", line 30, in
rasterize_triangles_module = tf.load_op_library(
File "C:\Users\anaconda3\envs\python38\lib\site-packages\tensorflow\python\framework\load_library.py", line 54, in load_op_library
lib_handle = py_tf.TF_LoadLibrary(library_filename)
tensorflow.python.framework.errors_impl.NotFoundError: E:\AI\style_avatar\deep_3drecon\mesh_renderer\kernels\rasterize_triangles_kernel.so not found

RuntimeError: CUDA error: out of memory CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

Hi! Thanks for your great work!
Hovewer, I faced problem when I run the python demo.py --in_img ./example/example.png --in_audio ./example/example.wav --output_path ./output
Can, you please, give me some tips to overcome this issue?

The code implementation is inconsistent with the paper

Thank you share awesome work!
In the code, you extract the speech features and energy features from the audio clips. However, in your paper, you only mentioned leveraging the DeepSpeech model to extract speech features.
Could you give me some advice for the above situation？

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