mirror of
https://github.com/aigc3d/LAM_Audio2Expression.git
synced 2026-02-04 09:29:24 +08:00
bcc6c2eea7
1.spleeter==2.4.2需要的依赖，有一个没有版本。spleeter==2.4.0需要的依赖会与gradio需要的依赖产生冲突。所以先安装spleeter==2.4.0，再安装requirements。 2.zip命令无法直接在Windows中使用，所以改为调用patoolib进行压缩。并添加依赖patool。
295 lines
10 KiB
Python
295 lines
10 KiB
Python
"""
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Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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https://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import os
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import base64
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import gradio as gr
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import argparse
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from omegaconf import OmegaConf
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from gradio_gaussian_render import gaussian_render
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from engines.defaults import (
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default_argument_parser,
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default_config_parser,
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default_setup,
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)
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from engines.infer import INFER
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from pathlib import Path
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try:
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import spaces
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except:
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pass
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import patoolib
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h5_rendering = True
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def assert_input_image(input_image):
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if input_image is None:
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raise gr.Error('No image selected or uploaded!')
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def prepare_working_dir():
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import tempfile
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working_dir = tempfile.TemporaryDirectory()
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return working_dir
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def get_image_base64(path):
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with open(path, 'rb') as image_file:
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encoded_string = base64.b64encode(image_file.read()).decode()
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return f'data:image/png;base64,{encoded_string}'
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def do_render():
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print('WebGL rendering ....')
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return
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def audio_loading():
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print("Audio loading ....")
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return "None"
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def parse_configs():
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", type=str)
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parser.add_argument("--infer", type=str)
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args, unknown = parser.parse_known_args()
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cfg = OmegaConf.create()
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cli_cfg = OmegaConf.from_cli(unknown)
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# parse from ENV
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if os.environ.get("APP_INFER") is not None:
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args.infer = os.environ.get("APP_INFER")
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if os.environ.get("APP_MODEL_NAME") is not None:
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cli_cfg.model_name = os.environ.get("APP_MODEL_NAME")
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args.config = args.infer if args.config is None else args.config
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if args.config is not None:
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cfg_train = OmegaConf.load(args.config)
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cfg.source_size = cfg_train.dataset.source_image_res
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try:
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cfg.src_head_size = cfg_train.dataset.src_head_size
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except:
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cfg.src_head_size = 112
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cfg.render_size = cfg_train.dataset.render_image.high
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_relative_path = os.path.join(
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cfg_train.experiment.parent,
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cfg_train.experiment.child,
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os.path.basename(cli_cfg.model_name).split("_")[-1],
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)
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cfg.save_tmp_dump = os.path.join("exps", "save_tmp", _relative_path)
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cfg.image_dump = os.path.join("exps", "images", _relative_path)
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cfg.video_dump = os.path.join("exps", "videos", _relative_path) # output path
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if args.infer is not None:
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cfg_infer = OmegaConf.load(args.infer)
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cfg.merge_with(cfg_infer)
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cfg.setdefault(
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"save_tmp_dump", os.path.join("exps", cli_cfg.model_name, "save_tmp")
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)
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cfg.setdefault("image_dump", os.path.join("exps", cli_cfg.model_name, "images"))
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cfg.setdefault(
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"video_dump", os.path.join("dumps", cli_cfg.model_name, "videos")
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)
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cfg.setdefault("mesh_dump", os.path.join("dumps", cli_cfg.model_name, "meshes"))
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cfg.motion_video_read_fps = 30
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cfg.merge_with(cli_cfg)
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cfg.setdefault("logger", "INFO")
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assert cfg.model_name is not None, "model_name is required"
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return cfg, cfg_train
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def create_zip_archive(output_zip='assets/arkitWithBSData.zip', base_dir=""):
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if os.path.exists(output_zip):
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os.remove(output_zip)
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print(f"Remove previous file: {output_zip}")
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try:
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# 创建压缩包
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patoolib.create_archive(
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archive=output_zip,
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filenames=[base_dir], # 要压缩的目录
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verbosity=-1, # 静默模式
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program='zip' # 指定使用zip格式
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)
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print(f"Archive created successfully: {output_zip}")
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except Exception as e:
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raise ValueError(f"Archive creation failed: {str(e)}")
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def demo_lam_audio2exp(infer, cfg):
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def core_fn(image_path: str, audio_params, working_dir):
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base_id = os.path.basename(image_path).split(".")[0]
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# set input audio
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cfg.audio_input = audio_params
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cfg.save_json_path = os.path.join("./assets/sample_lam", base_id, 'arkitWithBSData', 'bsData.json')
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infer.infer()
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output_file_name = base_id+'_'+os.path.basename(audio_params).split(".")[0]+'.zip'
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assetPrefix = 'gradio_api/file=assets/'
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output_file_path = os.path.join('./assets',output_file_name)
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create_zip_archive(output_zip=output_file_path, base_dir=os.path.join("./assets/sample_lam", base_id))
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return 'gradio_api/file='+audio_params, assetPrefix+output_file_name
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with gr.Blocks(analytics_enabled=False) as demo:
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logo_url = './assets/images/logo.jpeg'
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logo_base64 = get_image_base64(logo_url)
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gr.HTML(f"""
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<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
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<div>
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<h1> LAM-A2E: Audio to Expression</h1>
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</div>
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</div>
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""")
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gr.HTML(
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"""<p><h4 style="color: blue;"> Notes: This project leverages audio input to generate ARKit blendshapes-driven facial expressions in ⚡real-time⚡, powering ultra-realistic 3D avatars generated by <a href="https://github.com/aigc3d/LAM">LAM</a>.</h4></p>"""
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)
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# DISPLAY
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with gr.Row():
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with gr.Column(variant='panel', scale=1):
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with gr.Tabs(elem_id='lam_input_image'):
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with gr.TabItem('Input Image'):
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with gr.Row():
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input_image = gr.Image(label='Input Image',
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image_mode='RGB',
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height=480,
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width=270,
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sources='upload',
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type='filepath', # 'numpy',
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elem_id='content_image')
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# EXAMPLES
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with gr.Row():
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examples = [
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['assets/sample_input/barbara.jpg'],
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['assets/sample_input/status.png'],
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['assets/sample_input/james.png'],
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['assets/sample_input/vfhq_case1.png'],
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]
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gr.Examples(
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examples=examples,
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inputs=[input_image],
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examples_per_page=20,
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)
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with gr.Column():
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with gr.Tabs(elem_id='lam_input_audio'):
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with gr.TabItem('Input Audio'):
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with gr.Row():
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audio_input = gr.Audio(label='Input Audio',
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type='filepath',
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waveform_options={
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'sample_rate': 16000,
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'waveform_progress_color': '#4682b4'
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},
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elem_id='content_audio')
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examples = [
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['assets/sample_audio/Nangyanwen_chinese.wav'],
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['assets/sample_audio/LiBai_TTS_chinese.wav'],
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['assets/sample_audio/LinJing_TTS_chinese.wav'],
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['assets/sample_audio/BarackObama_english.wav'],
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['assets/sample_audio/HillaryClinton_english.wav'],
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['assets/sample_audio/XitongShi_japanese.wav'],
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['assets/sample_audio/FangXiao_japanese.wav'],
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]
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gr.Examples(
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examples=examples,
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inputs=[audio_input],
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examples_per_page=10,
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)
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# SETTING
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with gr.Row():
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with gr.Column(variant='panel', scale=1):
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submit = gr.Button('Generate',
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elem_id='lam_generate',
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variant='primary')
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if h5_rendering:
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gr.set_static_paths(Path.cwd().absolute() / "assets/")
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with gr.Row():
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gs = gaussian_render(width=380, height=680)
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working_dir = gr.State()
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selected_audio = gr.Textbox(visible=False)
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selected_render_file = gr.Textbox(visible=False)
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submit.click(
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fn=assert_input_image,
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inputs=[input_image],
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queue=False,
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).success(
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fn=prepare_working_dir,
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outputs=[working_dir],
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queue=False,
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).success(
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fn=core_fn,
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inputs=[input_image, audio_input,
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working_dir],
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outputs=[selected_audio, selected_render_file],
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queue=False,
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).success(
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fn=audio_loading,
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outputs=[selected_audio],
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js='''(output_component) => window.loadAudio(output_component)'''
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).success(
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fn=do_render(),
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outputs=[selected_render_file],
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js='''(selected_render_file) => window.start(selected_render_file)'''
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)
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demo.queue()
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demo.launch(inbrowser=True)
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def launch_gradio_app():
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os.environ.update({
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'APP_ENABLED': '1',
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'APP_MODEL_NAME':'',
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'APP_INFER': 'configs/lam_audio2exp_streaming_config.py',
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'APP_TYPE': 'infer.audio2exp',
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'NUMBA_THREADING_LAYER': 'omp',
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})
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args = default_argument_parser().parse_args()
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args.config_file = 'configs/lam_audio2exp_config_streaming.py'
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cfg = default_config_parser(args.config_file, args.options)
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cfg = default_setup(cfg)
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cfg.ex_vol = True
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infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg))
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demo_lam_audio2exp(infer, cfg)
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if __name__ == '__main__':
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launch_gradio_app()
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