好像卡在了FFMPEG， 但是我路径设置了呀，echo %FFMPEG_PATH%也返回正确

[windwolfx23]

(echomimic_v2) Z:\AI\echomimic_v2-main>python app.py
A matching Triton is not available, some optimizations will not be enabled
Traceback (most recent call last):
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\xformers_init_.py", line 57, in _is_triton_available
import triton # noqa
ModuleNotFoundError: No module named 'triton'
CUDA版本：12.4
Pytorch版本：2.5.1+cu124
显卡型号：NVIDIA GeForce RTX 3090
显存大小：24.00GB
精度：float16
add ffmpeg to path

• Running on local URL: http://127.0.0.1:7860

To create a public link, set share=True in launch().
WARNING:py.warnings:Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\diffusers\models\lora.py:306: FutureWarning: LoRACompatibleConv is deprecated and will be removed in version 1.0.0. Use of LoRACompatibleConv is deprecated. Please switch to PEFT backend by installing PEFT: pip install peft.
deprecate("LoRACompatibleConv", "1.0.0", deprecation_message)

Some weights of the model checkpoint were not used when initializing UNet2DConditionModel:
['down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_q.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.0.attentions.0.transformer_blocks.0.norm2.weight, down_blocks.0.attentions.0.transformer_blocks.0.norm2.bias, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_q.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_k.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_v.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.0.attentions.1.transformer_blocks.0.norm2.weight, down_blocks.0.attentions.1.transformer_blocks.0.norm2.bias, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.1.attentions.0.transformer_blocks.0.norm2.weight, down_blocks.1.attentions.0.transformer_blocks.0.norm2.bias, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.1.attentions.1.transformer_blocks.0.norm2.weight, down_blocks.1.attentions.1.transformer_blocks.0.norm2.bias, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_q.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.2.attentions.0.transformer_blocks.0.norm2.weight, down_blocks.2.attentions.0.transformer_blocks.0.norm2.bias, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_q.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, down_blocks.2.attentions.1.transformer_blocks.0.norm2.weight, down_blocks.2.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.1.attentions.0.transformer_blocks.0.norm2.weight, up_blocks.1.attentions.0.transformer_blocks.0.norm2.bias, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.1.attentions.1.transformer_blocks.0.norm2.weight, up_blocks.1.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.1.attentions.2.transformer_blocks.0.norm2.weight, up_blocks.1.attentions.2.transformer_blocks.0.norm2.bias, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_q.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.2.attentions.0.transformer_blocks.0.norm2.weight, up_blocks.2.attentions.0.transformer_blocks.0.norm2.bias, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_q.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.2.attentions.1.transformer_blocks.0.norm2.weight, up_blocks.2.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_q.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_k.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_v.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.2.attentions.2.transformer_blocks.0.norm2.weight, up_blocks.2.attentions.2.transformer_blocks.0.norm2.bias, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_q.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_k.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_v.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.3.attentions.0.transformer_blocks.0.norm2.weight, up_blocks.3.attentions.0.transformer_blocks.0.norm2.bias, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_q.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_k.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_v.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.3.attentions.1.transformer_blocks.0.norm2.weight, up_blocks.3.attentions.1.transformer_blocks.0.norm2.bias, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_q.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.weight, up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.bias, up_blocks.3.attentions.2.transformer_blocks.0.norm2.weight, up_blocks.3.attentions.2.transformer_blocks.0.norm2.bias, mid_block.attentions.0.transformer_blocks.0.attn2.to_q.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_v.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_out.0.weight, mid_block.attentions.0.transformer_blocks.0.attn2.to_out.0.bias, mid_block.attentions.0.transformer_blocks.0.norm2.weight, mid_block.attentions.0.transformer_blocks.0.norm2.bias, conv_norm_out.weight, conv_norm_out.bias, conv_out.weight, conv_out.bias']
using motion module
WARNING:py.warnings:Z:\AI\echomimic_v2-main\src\models\whisper\whisper_init_.py:109: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint = torch.load(fp, map_location=device)

Pose: assets/halfbody_demo/pose/01
Reference: C:\Users\Administrator\AppData\Local\Temp\gradio\4531ae6d40056fec68a1d0e6fe971d26bc56ae017eb5907ac906236fc0ce4080\halfbody.png
Audio: C:\Users\Administrator\AppData\Local\Temp\gradio\f4ce3a48ce2f7608c61da4baeb11498185b7f5209c5835ae8b1aa703ab06405f\audio.wav
Traceback (most recent call last):
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\gradio\queueing.py", line 624, in process_events
response = await route_utils.call_process_api(
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\gradio\route_utils.py", line 323, in call_process_api
output = await app.get_blocks().process_api(
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\gradio\blocks.py", line 2015, in process_api
result = await self.call_function(
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\gradio\blocks.py", line 1562, in call_function
prediction = await anyio.to_thread.run_sync( # type: ignore
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\anyio\to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\anyio_backends_asyncio.py", line 2441, in run_sync_in_worker_thread
return await future
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\anyio_backends_asyncio.py", line 943, in run
result = context.run(func, *args)
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\gradio\utils.py", line 865, in wrapper
response = f(*args, **kwargs)
File "Z:\AI\echomimic_v2-main\app.py", line 177, in generate
video = pipe(
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\torch\utils_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "Z:\AI\echomimic_v2-main\src\pipelines\pipeline_echomimicv2.py", line 467, in call
whisper_feature = self.audio_guider.audio2feat(audio_path)
File "Z:\AI\echomimic_v2-main\src\models\whisper\audio2feature.py", line 100, in audio2feat
result = self.model.transcribe(audio_path)
File "Z:\AI\echomimic_v2-main\src\models\whisper\whisper\transcribe.py", line 85, in transcribe
mel = log_mel_spectrogram(audio)
File "Z:\AI\echomimic_v2-main\src\models\whisper\whisper\audio.py", line 111, in log_mel_spectrogram
audio = load_audio(audio)
File "Z:\AI\echomimic_v2-main\src\models\whisper\whisper\audio.py", line 42, in load_audio
ffmpeg.input(file, threads=0)
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\ffmpeg_run.py", line 313, in run
process = run_async(
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\site-packages\ffmpeg_run.py", line 284, in run_async
return subprocess.Popen(
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\subprocess.py", line 971, in init
self._execute_child(args, executable, preexec_fn, close_fds,
File "Z:\Users\Administrator\miniconda3\envs\echomimic_v2\lib\subprocess.py", line 1456, in _execute_child
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
FileNotFoundError: [WinError 2] 系统找不到指定的文件。

[mengrang]

参考 #38

[TryingToDoBetter25]

写个启动脚本设置一下ffmpeg的路径就行了，参考Windows系统下部署EchoMimic_v2：
视频：https://www.bilibili.com/video/BV1mhz5YxEf3/?vd_source=669a41b5549159436dfac89400bacf7e
文档：https://cqldg89mjy4.feishu.cn/docx/Ayp7dZehNo1M42x4hkNcX3IAn4e?from=from_copylink