docker dependencies

Dockerfile

@@ -4,7 +4,8 @@ WORKDIR /code
 
 # Install system dependencies
 RUN apt-get update && apt-get install -y \
-    python3.9 \
+    python3.10 \
+    python3.10-distutils \
     python3-pip \
     git \
     ffmpeg \
@@ -12,19 +13,25 @@ RUN apt-get update && apt-get install -y \
     libxext6 \
     && rm -rf /var/lib/apt/lists/*
 
-# Clone MMAudio and install dependencies
-RUN git clone https://github.com/hkchengrex/MMAudio.git && \
-    cd MMAudio && \
-    # Install PyTorch first as specified in README
-    pip3 install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 && \
-    # Install additional dependencies
-    pip3 install -r requirements.txt && \
-    # Install MMAudio
-    pip3 install -e .
+# Ensure we're using Python 3.10
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1
+
+# Install pip for Python 3.10
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
+
+# Clone MMAudio
+RUN git clone https://github.com/hkchengrex/MMAudio.git
 
 # Set working directory to MMAudio
 WORKDIR /code/MMAudio
 
+# Install dependencies
+RUN pip3 install --no-cache-dir numpy && \
+    pip3 install --no-cache-dir torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 && \
+    pip3 install --no-cache-dir colorlog && \
+    pip3 install --no-cache-dir -r requirements.txt && \
+    pip3 install -e .
+
 # Create output directory
 RUN mkdir -p output/gradio && chmod 777 output/gradio
 
@@ -32,6 +39,7 @@ RUN mkdir -p output/gradio && chmod 777 output/gradio
 ENV PYTHONUNBUFFERED=1
 ENV GRADIO_SERVER_NAME=0.0.0.0
 ENV GRADIO_SERVER_PORT=7860
+ENV PYTHONPATH=/code/MMAudio
 
 # Expose Gradio port
 EXPOSE 7860

app.py

@@ -1,128 +0,0 @@
-import gc
-import logging
-from datetime import datetime
-from fractions import Fraction
-from pathlib import Path
-
-import gradio as gr
-import torch
-import torchaudio
-
-from mmaudio.eval_utils import (ModelConfig, VideoInfo, all_model_cfg, generate, load_image,
-                               load_video, make_video, setup_eval_logging)
-from mmaudio.model.flow_matching import FlowMatching
-from mmaudio.model.networks import MMAudio, get_my_mmaudio
-from mmaudio.model.sequence_config import SequenceConfig
-from mmaudio.model.utils.features_utils import FeaturesUtils
-
-# Setup logging
-setup_eval_logging()
-log = logging.getLogger()
-
-# Configure device and dtype
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-if device == 'cpu':
-    log.warning('CUDA is not available, running on CPU')
-dtype = torch.bfloat16
-
-# Configure model and paths
-model: ModelConfig = all_model_cfg['large_44k_v2']
-model.download_if_needed()
-output_dir = Path('./output/gradio')
-output_dir.mkdir(exist_ok=True, parents=True)
-
-def get_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]:
-    seq_cfg = model.seq_cfg
-    
-    net: MMAudio = get_my_mmaudio(model.model_name).to(device, dtype).eval()
-    net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True))
-    log.info(f'Loaded weights from {model.model_path}')
-    
-    feature_utils = FeaturesUtils(tod_vae_ckpt=model.vae_path,
-                                synchformer_ckpt=model.synchformer_ckpt,
-                                enable_conditions=True,
-                                mode=model.mode,
-                                bigvgan_vocoder_ckpt=model.bigvgan_16k_path,
-                                need_vae_encoder=False)
-    feature_utils = feature_utils.to(device, dtype).eval()
-    
-    return net, feature_utils, seq_cfg
-
-# Load model once at startup
-net, feature_utils, seq_cfg = get_model()
-
-@torch.inference_mode()
-def video_to_audio(video: gr.Video, prompt: str, negative_prompt: str, seed: int, num_steps: int,
-                   cfg_strength: float, duration: float):
-    try:
-        rng = torch.Generator(device=device)
-        if seed >= 0:
-            rng.manual_seed(seed)
-        else:
-            rng.seed()
-        fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps)
-
-        video_info = load_video(video, duration)
-        clip_frames = video_info.clip_frames.unsqueeze(0)
-        sync_frames = video_info.sync_frames.unsqueeze(0)
-        duration = video_info.duration_sec
-        
-        seq_cfg.duration = duration
-        net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len)
-
-        audios = generate(clip_frames, sync_frames, [prompt],
-                        negative_text=[negative_prompt],
-                        feature_utils=feature_utils,
-                        net=net,
-                        fm=fm,
-                        rng=rng,
-                        cfg_strength=cfg_strength)
-        audio = audios.float().cpu()[0]
-
-        current_time_string = datetime.now().strftime('%Y%m%d_%H%M%S')
-        video_save_path = output_dir / f'{current_time_string}.mp4'
-        make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate)
-        
-        gc.collect()
-        torch.cuda.empty_cache()
-        
-        return video_save_path
-    except Exception as e:
-        log.error(f"Error in video_to_audio: {str(e)}")
-        raise gr.Error(f"An error occurred: {str(e)}")
-
-# Create the Gradio interface
-demo = gr.Interface(
-    fn=video_to_audio,
-    title="MMAudio — Video-to-Audio Synthesis",
-    description="""
-    Generate realistic audio for your videos using MMAudio!
-    
-    Project page: [MMAudio](https://hkchengrex.com/MMAudio/)
-    Code: [GitHub](https://github.com/hkchengrex/MMAudio)
-    
-    Note: Processing high-resolution videos (>384px on shorter side) takes longer and doesn't improve results.
-    """,
-    inputs=[
-        gr.Video(label="Upload Video"),
-        gr.Text(label="Prompt", placeholder="Describe the audio you want to generate..."),
-        gr.Text(label="Negative prompt", value="music", placeholder="What you don't want in the audio..."),
-        gr.Number(label="Seed (-1: random)", value=-1, precision=0, minimum=-1),
-        gr.Number(label="Number of steps", value=25, precision=0, minimum=1),
-        gr.Slider(label="Guidance Strength", value=4.5, minimum=1, maximum=10, step=0.5),
-        gr.Slider(label="Duration (seconds)", value=8, minimum=1, maximum=30, step=1),
-    ],
-    outputs=gr.Video(label="Generated Result"),
-    examples=[
-        ["https://huggingface.co/hkchengrex/MMAudio/resolve/main/examples/sora_beach.mp4",
-         "waves, seagulls", "", 0, 25, 4.5, 10],
-        ["https://huggingface.co/hkchengrex/MMAudio/resolve/main/examples/sora_serpent.mp4",
-         "", "music", 0, 25, 4.5, 10],
-    ],
-    cache_examples=True,
-)
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-