pin audiotools version!

scripts/utils/process_folder-c2f.py

@@ -1,124 +0,0 @@
-
-from audiotools import AudioSignal
-import torch
-from pathlib import Path
-import argbind
-from tqdm import tqdm
-import random
-
-from typing import List
-
-from collections import defaultdict
-
-def coarse2fine_infer(
-        signal, 
-        model, 
-        vqvae, 
-        device,
-    ):
-        output = {}
-        w = signal
-        w = w.to(device)
-        z = vqvae.encode(w.audio_data, w.sample_rate)["codes"]
-
-        model.to(device)
-        output["reconstructed"] = model.to_signal(z, vqvae).cpu()
-
-        # make a full mask
-        mask = torch.ones_like(z)
-        mask[:, :model.n_conditioning_codebooks, :] = 0
-
-        output["sampled"] = model.sample(
-            codec=vqvae, 
-            time_steps=z.shape[-1], 
-            sampling_steps=12, 
-            start_tokens=z, 
-            mask=mask, 
-            temperature=0.85, 
-            top_k=None, 
-            sample="gumbel", 
-            typical_filtering=True, 
-            return_signal=True
-        ).cpu()
-
-        output["argmax"] = model.sample(
-            codec=vqvae, 
-            time_steps=z.shape[-1], 
-            sampling_steps=1, 
-            start_tokens=z, 
-            mask=mask, 
-            temperature=1.0, 
-            top_k=None, 
-            sample="argmax", 
-            typical_filtering=True, 
-            return_signal=True
-        ).cpu()
-
-        return output
-
-
-
-@argbind.bind(without_prefix=True)
-def main(
-        sources=[
-            "/data/spotdl/audio/val", "/data/spotdl/audio/test"
-        ], 
-        exp_name="noise_mode",
-        model_paths=[
-            "runs/c2f-exp-03.22.23/ckpt/mask/epoch=400/vampnet/weights.pth",
-            "runs/c2f-exp-03.22.23/ckpt/random/epoch=400/vampnet/weights.pth",
-        ],
-        model_keys=[
-            "mask",
-            "random",
-        ],
-        vqvae_path: str = "runs/codec-ckpt/codec.pth",
-        device: str = "cuda",
-        output_dir: str = ".",
-        max_excerpts: int = 5000,
-        duration: float = 3.0,
-    ):
-    from vampnet.modules.transformer import VampNet
-    from lac.model.lac import LAC
-
-    models = {
-        k: VampNet.load(p) for k, p in zip(model_keys, model_paths)
-    }
-    for model in models.values(): 
-        model.eval()
-    print(f"Loaded {len(models)} models.")
-
-    vqvae = LAC.load(vqvae_path)
-    vqvae.to(device)
-    vqvae.eval()
-    print("Loaded VQVAE.")
-
-    output_dir = Path(output_dir) / f"{exp_name}-samples"
-
-    from audiotools.data.datasets import AudioLoader, AudioDataset
-
-    loader = AudioLoader(sources=sources)
-    dataset = AudioDataset(loader, 
-        sample_rate=vqvae.sample_rate, 
-        duration=duration, 
-        n_examples=max_excerpts, 
-        without_replacement=True,
-    )
-    for i in tqdm(range(max_excerpts)):
-        sig = dataset[i]["signal"]
-        sig.resample(vqvae.sample_rate).normalize(-24).ensure_max_of_audio(1.0)
-
-        for model_key, model in models.items():
-            out = coarse2fine_infer(sig, model, vqvae, device)
-            out_dir = output_dir / model_key / Path(sig.path_to_file).stem
-            out_dir.mkdir(parents=True, exist_ok=True)
-            for k, s in out.items():
-                s.write(out_dir / f"{k}.wav")
-        
-
-if __name__ == "__main__":
-    args = argbind.parse_args()
-
-    with argbind.scope(args):
-        main()
-

setup.py

@@ -28,7 +28,7 @@ setup(
     install_requires=[
         "torch",
         "argbind>=0.3.2",
-        "audiotools @ git+https://github.com/descriptinc/audiotools.git",
+        "audiotools @ git+https://github.com/descriptinc/audiotools.git@f35914b5b3c6f1bf589cd09481478d741538828e",
         "dac @ git+https://github.com/descriptinc/descript-audio-codec.git",
         "gradio", 
         "tensorboardX",