Spaces:
Running on Zero
Running on Zero
Latent audio lipsync
#1
by Monarch-1 - opened
- README.md +1 -2
- app.py +2 -53
- latentsync/models/attention.py +2 -2
- latentsync/models/motion_module.py +2 -2
- latentsync/models/unet.py +1 -1
- latentsync/pipelines/lipsync_pipeline.py +1 -1
- requirements.txt +11 -14
README.md
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@@ -4,10 +4,9 @@ emoji: 👄
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colorFrom: blue
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colorTo: blue
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned: false
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disable_embedding: true
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short_description: Audio Conditioned LipSync with Latent Diffusion Models
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---
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colorFrom: blue
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colorTo: blue
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sdk: gradio
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sdk_version: 5.12.0
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app_file: app.py
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pinned: false
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short_description: Audio Conditioned LipSync with Latent Diffusion Models
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---
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app.py
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@@ -1,33 +1,4 @@
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def _patch_asyncio_event_loop_del():
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"""
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Patch a noisy asyncio teardown issue sometimes seen in Spaces environments.
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In some runtime/container combinations, Python may try to close an already
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invalid file descriptor when the event loop is garbage-collected. We silence
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only that specific harmless case.
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"""
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try:
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import asyncio.base_events as base_events
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original_del = getattr(base_events.BaseEventLoop, "__del__", None)
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if original_del is None:
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return
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def patched_del(self):
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try:
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original_del(self)
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except ValueError as e:
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if "Invalid file descriptor" not in str(e):
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raise
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base_events.BaseEventLoop.__del__ = patched_del
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except Exception:
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pass
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_patch_asyncio_event_loop_del()
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import gradio as gr
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import spaces
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import os
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import sys
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import shutil
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os.makedirs("checkpoints", exist_ok=True)
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snapshot_download(
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repo_id = "
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local_dir = "./checkpoints"
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)
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@@ -108,26 +79,7 @@ from accelerate.utils import set_seed
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from latentsync.whisper.audio2feature import Audio2Feature
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@spaces.GPU(duration=180)
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def main(video_path, audio_path, progress=gr.Progress(track_tqdm=True)):
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"""
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Perform lip-sync video generation using an input video and a separate audio track.
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This function takes an input video (usually a person speaking) and an audio file,
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and synchronizes the video frames so that the lips of the speaker match the audio content.
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It uses a latent diffusion model-based pipeline (LatentSync) for audio-conditioned lip synchronization.
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Args:
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video_path (str): File path to the input video in MP4 format.
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audio_path (str): File path to the input audio file (e.g., WAV or MP3).
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progress (gr.Progress, optional): Gradio progress tracker for UI feedback (auto-injected).
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Returns:
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str: File path to the generated output video with lip synchronization applied.
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"""
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gr.Info("180 seconds will be used from your daily ZeroGPU time credits.")
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inference_ckpt_path = "checkpoints/latentsync_unet.pt"
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unet_config_path = "configs/unet/second_stage.yaml"
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config = OmegaConf.load(unet_config_path)
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unet = unet.to(dtype=torch.float16)
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"""
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# set xformers
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if is_xformers_available():
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unet.enable_xformers_memory_efficient_attention()
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"""
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pipeline = LipsyncPipeline(
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vae=vae,
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@@ -267,4 +216,4 @@ with gr.Blocks(css=css) as demo:
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outputs = [video_result]
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)
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demo.queue().launch(
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import gradio as gr
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import os
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import sys
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import shutil
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os.makedirs("checkpoints", exist_ok=True)
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snapshot_download(
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repo_id = "chunyu-li/LatentSync",
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local_dir = "./checkpoints"
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)
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from latentsync.whisper.audio2feature import Audio2Feature
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def main(video_path, audio_path, progress=gr.Progress(track_tqdm=True)):
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inference_ckpt_path = "checkpoints/latentsync_unet.pt"
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unet_config_path = "configs/unet/second_stage.yaml"
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config = OmegaConf.load(unet_config_path)
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unet = unet.to(dtype=torch.float16)
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# set xformers
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if is_xformers_available():
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unet.enable_xformers_memory_efficient_attention()
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pipeline = LipsyncPipeline(
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vae=vae,
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outputs = [video_result]
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)
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demo.queue().launch(show_api=False, show_error=True)
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latentsync/models/attention.py
CHANGED
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@@ -9,10 +9,10 @@ import torch.nn.functional as F
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from torch import nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.
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from diffusers.utils import BaseOutput
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from diffusers.utils.import_utils import is_xformers_available
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from diffusers.models.attention import
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from einops import rearrange, repeat
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from .utils import zero_module
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from torch import nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.modeling_utils import ModelMixin
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from diffusers.utils import BaseOutput
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from diffusers.utils.import_utils import is_xformers_available
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from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm
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from einops import rearrange, repeat
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from .utils import zero_module
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latentsync/models/motion_module.py
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@@ -11,10 +11,10 @@ import torch.nn.functional as F
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from torch import nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.
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from diffusers.utils import BaseOutput
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from diffusers.utils.import_utils import is_xformers_available
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from diffusers.models.attention import
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from einops import rearrange, repeat
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import math
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from torch import nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.modeling_utils import ModelMixin
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from diffusers.utils import BaseOutput
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from diffusers.utils.import_utils import is_xformers_available
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from diffusers.models.attention import CrossAttention, FeedForward
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from einops import rearrange, repeat
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import math
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latentsync/models/unet.py
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import torch.utils.checkpoint
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.
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from diffusers import UNet2DConditionModel
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from diffusers.utils import BaseOutput, logging
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from diffusers.models.embeddings import TimestepEmbedding, Timesteps
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import torch.utils.checkpoint
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.modeling_utils import ModelMixin
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from diffusers import UNet2DConditionModel
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from diffusers.utils import BaseOutput, logging
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from diffusers.models.embeddings import TimestepEmbedding, Timesteps
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latentsync/pipelines/lipsync_pipeline.py
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from diffusers.configuration_utils import FrozenDict
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from diffusers.models import AutoencoderKL
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from diffusers.
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from diffusers.schedulers import (
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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from diffusers.configuration_utils import FrozenDict
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from diffusers.models import AutoencoderKL
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import (
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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requirements.txt
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torch==2.
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torchvision==0.
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--extra-index-url https://download.pytorch.org/whl/cu121
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xformers==0.0.
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triton==
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diffusers==0.
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transformers==4.
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huggingface-hub
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imageio==2.27.0
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decord==0.6.0
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accelerate==0.26.1
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einops==0.7.0
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omegaconf==2.3.0
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safetensors
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opencv-python==4.9.0.80
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mediapipe==0.10.11
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av
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torch-fidelity==0.3.0
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torchmetrics==1.3.1
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python_speech_features==0.6
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ninja==1.11.1.1
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pandas==2.0.3
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numpy==1.24.4
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pydub
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moviepy==1.0.3
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hf-xet==1.1.8
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spaces
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gradio[mcp]
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torch==2.2.2
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torchvision==0.17.2
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--extra-index-url https://download.pytorch.org/whl/cu121
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xformers==0.0.26
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triton==2.2.0
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diffusers==0.11.1
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transformers==4.38.0
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huggingface-hub==0.25.2
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imageio==2.27.0
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decord==0.6.0
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accelerate==0.26.1
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einops==0.7.0
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omegaconf==2.3.0
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safetensors==0.4.2
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opencv-python==4.9.0.80
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mediapipe==0.10.11
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av==11.0.0
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torch-fidelity==0.3.0
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torchmetrics==1.3.1
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python_speech_features==0.6
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ninja==1.11.1.1
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pandas==2.0.3
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numpy==1.24.4
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pydub
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moviepy==1.0.3
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