Update Replicate to use multiple outputs

.gitignore

@@ -0,0 +1,2 @@
+checkpoints/waveunet/
+.cog

README.md

@@ -1,5 +1,5 @@
 # Wave-U-Net (Pytorch)
-<a href="https://replicate.ai/f90/wave-u-net-pytorch"><img src="https://img.shields.io/static/v1?label=Replicate&message=Demo and Docker Image&color=darkgreen" height=20></a>
+<a href="https://replicate.com/f90/wave-u-net-pytorch"><img src="https://replicate.com/f90/wave-u-net-pytorch/badge"></a>
 
 Improved version of the [Wave-U-Net](https://arxiv.org/abs/1806.03185) for audio source separation, implemented in Pytorch.
 
@@ -97,6 +97,10 @@ We provide the default model in a pre-trained form as download so you can separa
 Download our pretrained model [here](https://www.dropbox.com/s/r374hce896g4xlj/models.7z?dl=1).
 Extract the archive into the ``checkpoints`` subfolder in this repository, so that you have one subfolder for each model (e.g. ``REPO/checkpoints/waveunet``)
 
+If you have Docker installed, you can run this script to download the weights from [Replicate](https://replicate.com/f90/wave-u-net-pytorch):
+
+    $ script/download-weights
+
 ## Run pretrained model
 
 To apply our pretrained model to any of your own songs, simply point to its audio file path using the ``input_path`` parameter:

cog.yaml

@@ -1,5 +1,5 @@
 build:
-  python_version: "3.6"
+  python_version: "3.7"
   gpu: false
   python_packages:
     - future==0.18.2
@@ -17,4 +17,4 @@ build:
   system_packages:
     - libsndfile-dev
     - ffmpeg
-predict: "cog_predict.py:waveunetPredictor"
+predict: "cog_predict.py:Predictor"

cog_predict.py

@@ -1,16 +1,24 @@
+import argparse
 import os
-import cog
 import tempfile
 import zipfile
-from pathlib import Path
-import argparse
+
+from cog import BasePredictor, Input, Path, BaseModel
+
 import data.utils
 import model.utils as model_utils
-from test import predict_song
 from model.waveunet import Waveunet
+from test import predict_song
+
+
+class Output(BaseModel):
+    bass: Path
+    drums: Path
+    other: Path
+    vocals: Path
 
 
-class waveunetPredictor(cog.Predictor):
+class Predictor(BasePredictor):
     def setup(self):
         """Init wave u net model"""
         parser = argparse.ArgumentParser()
@@ -112,33 +120,24 @@ def setup(self):
         )
 
         if args.cuda:
-            self.model = model_utils.DataParallel(model)
+            self.model = model_utils.DataParallel(self.model)
             print("move model to gpu")
             self.model.cuda()
 
         print("Loading model from checkpoint " + str(args.load_model))
         state = model_utils.load_model(self.model, None, args.load_model, args.cuda)
         print("Step", state["step"])
 
-    @cog.input("input", type=Path, help="audio mixture path")
-    def predict(self, input):
+    def predict(self, mix: Path = Input(description="audio mixture path")) -> Output:
         """Separate tracks from input mixture audio"""
 
-        out_path = Path(tempfile.mkdtemp())
-        zip_path = Path(tempfile.mkdtemp()) / "output.zip"
+        tmpdir = Path(tempfile.mkdtemp())
 
-        preds = predict_song(self.args, input, self.model)
-
-        out_names = []
+        preds = predict_song(self.args, mix, self.model)
+        output = {}
         for inst in preds.keys():
-            temp_n = os.path.join(
-                str(out_path), os.path.basename(str(input)) + "_" + inst + ".wav"
-            )
-            data.utils.write_wav(temp_n, preds[inst], self.args.sr)
-            out_names.append(temp_n)
-
-        with zipfile.ZipFile(str(zip_path), "w") as zf:
-            for i in out_names:
-                zf.write(str(i))
+            path = tmpdir / (inst + ".wav")
+            data.utils.write_wav(path, preds[inst], self.args.sr)
+            output[inst] = path
 
-        return zip_path
+        return Output(**output)

script/download-weights

@@ -0,0 +1,3 @@
+#!/bin/bash
+id=$(docker create r8.im/f90/wave-u-net-pytorch)
+docker cp $id:/src/checkpoints ./