Fix all indentation errors - consistent 4-space indentation throughout

app.py

@@ -3,14 +3,14 @@ import subprocess
 import sys
 
 try:
-        import mmcv
-        print("mmcv already installed")
+    import mmcv
+    print("mmcv already installed")
 except ImportError:
-        print("Installing mmcv with --no-build-isolation...")
-        subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-build-isolation", "mmcv>=2.0.0"])
-        print("mmcv installed successfully")
+    print("Installing mmcv with --no-build-isolation...")
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-build-isolation", "mmcv>=2.0.0"])
+    print("mmcv installed successfully")
 
-    import torch
+import torch
 import numpy as np
 import random
 import soundfile as sf
@@ -30,28 +30,31 @@ onset_ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="onset_model.ckpt",
 taro_ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="taro_ckpt.pt", cache_dir=CACHE_DIR)
 print("Checkpoints downloaded.")
 
+
 def set_global_seed(seed):
-        np.random.seed(seed % (2**32))
-        random.seed(seed)
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
-        torch.backends.cudnn.deterministic = True
-
-    def strip_audio_from_video(video_path, output_path):
-            """Strip any existing audio from a video file, outputting a silent video."""""
-            (
-                        ffmpeg
-                        .input(video_path)
-                        .output(output_path, vcodec="libx264", an=None)
-                        .run(overwrite_output=True, quiet=True)
-            )
-
-        @spaces.GPU(duration=300)
+    np.random.seed(seed % (2**32))
+    random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+def strip_audio_from_video(video_path, output_path):
+    """Strip any existing audio from a video file, outputting a silent video."""
+    (
+        ffmpeg
+        .input(video_path)
+        .output(output_path, vcodec="libx264", an=None)
+        .run(overwrite_output=True, quiet=True)
+    )
+
+
+@spaces.GPU(duration=300)
 def generate_audio(video_file, seed_val, cfg_scale, num_steps, mode):
-        seed_val = int(seed_val)
-        if seed_val < 0:
-                    seed_val = random.randint(0, 2**32 - 1)
-                set_global_seed(seed_val)
+    seed_val = int(seed_val)
+    if seed_val < 0:
+        seed_val = random.randint(0, 2**32 - 1)
+    set_global_seed(seed_val)
     torch.set_grad_enabled(False)
     device = "cuda" if torch.cuda.is_available() else "cpu"
     weight_dtype = torch.bfloat16
@@ -62,18 +65,20 @@ def generate_audio(video_file, seed_val, cfg_scale, num_steps, mode):
     from samplers import euler_sampler, euler_maruyama_sampler
     from diffusers import AudioLDM2Pipeline
 
-    extract_cavp = Extract_CAVP_Features(device=device, config_path="./cavp/cavp.yaml", ckpt_path=cavp_ckpt_path)
+    extract_cavp = Extract_CAVP_Features(
+        device=device, config_path="./cavp/cavp.yaml", ckpt_path=cavp_ckpt_path
+    )
 
     state_dict = torch.load(onset_ckpt_path, map_location=device, weights_only=False)["state_dict"]
     new_state_dict = {}
     for key, value in state_dict.items():
-                if "model.net.model" in key:
-                                new_key = key.replace("model.net.model", "net.model")
-                elif "model.fc." in key:
-                                new_key = key.replace("model.fc", "fc")
-                else:
-                                new_key = key
-                            new_state_dict[new_key] = value
+        if "model.net.model" in key:
+            new_key = key.replace("model.net.model", "net.model")
+        elif "model.fc." in key:
+            new_key = key.replace("model.fc", "fc")
+        else:
+            new_key = key
+        new_state_dict[new_key] = value
     onset_model = VideoOnsetNet(False).to(device)
     onset_model.load_state_dict(new_state_dict)
     onset_model.eval()
@@ -110,37 +115,37 @@ def generate_audio(video_file, seed_val, cfg_scale, num_steps, mode):
     onset_feats_sliced = onset_feats[:truncate_onset]
     actual_onset_len = onset_feats_sliced.shape[0]
     if actual_onset_len < truncate_onset:
-                pad_len = truncate_onset - actual_onset_len
-                onset_feats_sliced = np.pad(
-                                onset_feats_sliced,
-                                ((0, pad_len),),
-                                mode="constant",
-                                constant_values=0,
-                )
-            onset_feats_t = torch.from_numpy(onset_feats_sliced).unsqueeze(0).to(device).to(weight_dtype)
+        pad_len = truncate_onset - actual_onset_len
+        onset_feats_sliced = np.pad(
+            onset_feats_sliced,
+            ((0, pad_len),),
+            mode="constant",
+            constant_values=0,
+        )
+    onset_feats_t = torch.from_numpy(onset_feats_sliced).unsqueeze(0).to(device).to(weight_dtype)
 
     z = torch.randn(len(video_feats), model.in_channels, 204, 16, device=device).to(weight_dtype)
 
     sampling_kwargs = dict(
-                model=model,
-                latents=z,
-                y=onset_feats_t,
-                context=video_feats,
-                num_steps=int(num_steps),
-                heun=False,
-                cfg_scale=float(cfg_scale),
-                guidance_low=0.0,
-                guidance_high=0.7,
-                path_type="linear",
+        model=model,
+        latents=z,
+        y=onset_feats_t,
+        context=video_feats,
+        num_steps=int(num_steps),
+        heun=False,
+        cfg_scale=float(cfg_scale),
+        guidance_low=0.0,
+        guidance_high=0.7,
+        path_type="linear",
     )
 
     with torch.no_grad():
-                if mode == "sde":
-                                samples = euler_maruyama_sampler(**sampling_kwargs)
-                else:
-                                samples = euler_sampler(**sampling_kwargs)
+        if mode == "sde":
+            samples = euler_maruyama_sampler(**sampling_kwargs)
+        else:
+            samples = euler_sampler(**sampling_kwargs)
 
-                        samples = vae.decode(samples / latents_scale).sample
+    samples = vae.decode(samples / latents_scale).sample
 
     # Cast to float32 before vocoder (HiFi-GAN requires float32)
     wav_samples = vocoder(samples.squeeze().float()).detach().cpu().numpy()
@@ -153,39 +158,41 @@ def generate_audio(video_file, seed_val, cfg_scale, num_steps, mode):
     output_video = os.path.join(tmp_dir, "output.mp4")
 
     (
-                ffmpeg
-                .input(silent_video, ss=0, t=duration)
-                .output(trimmed_video, vcodec="libx264", an=None)
-                .run(overwrite_output=True, quiet=True)
+        ffmpeg
+        .input(silent_video, ss=0, t=duration)
+        .output(trimmed_video, vcodec="libx264", an=None)
+        .run(overwrite_output=True, quiet=True)
     )
 
     input_v = ffmpeg.input(trimmed_video)
     input_a = ffmpeg.input(audio_path)
     (
-                ffmpeg
-                .output(input_v, input_a, output_video, vcodec="libx264", acodec="aac", strict="experimental")
-                .run(overwrite_output=True, quiet=True)
+        ffmpeg
+        .output(input_v, input_a, output_video, vcodec="libx264", acodec="aac", strict="experimental")
+        .run(overwrite_output=True, quiet=True)
     )
 
     return output_video, audio_path
 
+
 def get_random_seed():
-        return random.randint(0, 2**32 - 1)
-
-    demo = gr.Interface(
-            fn=generate_audio,
-            inputs=[
-                        gr.Video(label="Input Video"),
-                        gr.Number(label="Seed", value=get_random_seed, precision=0),
-                        gr.Slider(label="CFG Scale", minimum=1, maximum=15, value=8, step=0.5),
-                        gr.Slider(label="Sampling Steps", minimum=10, maximum=50, value=25, step=1),
-                        gr.Radio(label="Sampling Mode", choices=["sde", "ode"], value="sde"),
-            ],
-            outputs=[
-                        gr.Video(label="Output Video with Audio"),
-                        gr.Audio(label="Generated Audio"),
-            ],
-            title="TARO: Video-to-Audio Synthesis (ICCV 2025)",
-            description="Upload a video and generate synchronized audio using TARO. Optimal duration is 8.2s.",
-    )
+    return random.randint(0, 2**32 - 1)
+
+
+demo = gr.Interface(
+    fn=generate_audio,
+    inputs=[
+        gr.Video(label="Input Video"),
+        gr.Number(label="Seed", value=get_random_seed, precision=0),
+        gr.Slider(label="CFG Scale", minimum=1, maximum=15, value=8, step=0.5),
+        gr.Slider(label="Sampling Steps", minimum=10, maximum=50, value=25, step=1),
+        gr.Radio(label="Sampling Mode", choices=["sde", "ode"], value="sde"),
+    ],
+    outputs=[
+        gr.Video(label="Output Video with Audio"),
+        gr.Audio(label="Generated Audio"),
+    ],
+    title="TARO: Video-to-Audio Synthesis (ICCV 2025)",
+    description="Upload a video and generate synchronized audio using TARO. Optimal duration is 8.2s.",
+)
 demo.queue().launch()