merge with libpython-clj-update

Libpython clj update to 1.32

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Changelog
 
+## 0.1.3-ALPHA
+### Changed
+- changes due to update of libpython-clj to version 1.32
+- updated README.md
+
 ## 0.1.2-ALPHA
 ### Changed
 - changed arguments order in separate and write-estimates funcs

README.md

@@ -19,34 +19,37 @@ pip3 install -r requirements.txt
 
 * add dependency to the project.clj
 ```clj
-[dragoon000320/open-unmix-pytorch-clj "0.1.2-SNAPSHOT"]
+[dragoon000320/open-unmix-pytorch-clj "0.1.3-ALPHA"]
 ```
 
 ## Usage
 
 A little demo how to use it
 ```clj
 ;; require namespaces
-(require '[open-unmix-pytorch-clj.core :as core])
+(require '[open-unmix-pytorch-clj.core :refer :all])
 
-(require '[open-unmix-pytorch-clj.io :as oup.io])
+(require '[open-unmix-pytorch-clj.io :refer :all])
 
-(require '[open-unmix-pytorch-clj.convert :as conv])
+(require '[open-unmix-pytorch-clj.convert :refer :all])
 
 (-> "your-audio-file.wav"
     ;; reads audio file
-    oup.io/soundfile-read
+    soundfile-read
     ;; converts audio data to 2 channel one
-    conv/->2-channels
+    ->2-channels
     ;; separates audio data into required audio sources
-    (core/separate ["vocals" "drums" "other" "bass"])
+    (separate ["vocals" "drums" "other" "bass"]
+        :device "cpu"
+        ;; or if you have cuda enabled uncomment line below
+        ;; :device "cuda")
     ;; writes estimates for each audio source to the output directory
-    (oup.io/writes-estimates "out-dir"))
+    (writes-estimates "out-dir"))
 ```
 
 ## Disclaimer
 
-This is __early alpha__, current state of library is described below:
+The library is in __alpha__ at the moment, current state of library is described below:
 * for now only __separation__ of audio source implemented
 * so there is no implementation of __model training__
 * performance must be further improved

project.clj

@@ -1,10 +1,10 @@
-(defproject dragoon/open-unmix-pytorch-clj "0.1.2-ALPHA"
+(defproject dragoon/open-unmix-pytorch-clj "0.1.3-ALPHA"
   :description "Clojure bindings for PyTorch implementation of Open-Unmix"
   :url "https://github.com/dragoon000320/open-unmix-pytorch-clj"
   :license {:name "Eclipse Public License"
             :url  "http://www.eclipse.org/legal/epl-v10.html"}
   :dependencies [[org.clojure/clojure "1.10.1"]
-                 [cnuernber/libpython-clj "1.30"]]
+                 [cnuernber/libpython-clj "1.32"]]
   :plugins [[lein-cloverage "1.0.13"]
             [lein-shell "0.5.0"]
             [lein-ancient "0.6.15"]

src/open_unmix_pytorch_clj/convert.clj

@@ -3,10 +3,11 @@
   Encapsulates functionality for conversions
   "
   (:require [libpython-clj.require :refer [require-python]]
-            [libpython-clj.python :refer [$a get-item]]
+            [libpython-clj.python :refer [py.. get-item]]
             [open-unmix-pytorch-clj.validator :refer [is-ndarray?]]
             [open-unmix-pytorch-clj.model :refer [AudioMap?]]))
 
+(require-python '[numpy :as np])
 (require-python '[scipy.signal :as sig])
 (require-python '[builtins :refer [slice]])
 
@@ -36,7 +37,7 @@
         :or {sample-rate 44100.0, window "hann"
              n-fft 4096, n-hopsize 1024}}]
   {:pre [(is-ndarray? X)]}
-  (let [Zxx ($a X "__truediv__" (/ n-fft 2))
+  (let [Zxx (py.. X (__truediv__ (/ n-fft 2)))
         n-overlap (- n-fft n-hopsize)]
     (sig/istft Zxx
                sample-rate

src/open_unmix_pytorch_clj/core.clj

@@ -5,9 +5,10 @@
   (:require [open-unmix-pytorch-clj.model :refer :all]
             [open-unmix-pytorch-clj.validator :refer :all]
             [libpython-clj.require :refer [require-python]]
-            [libpython-clj.python :refer [$. $.. $a get-item]]
+            [libpython-clj.python :refer [py. py.. get-item]]
             [open-unmix-pytorch-clj.convert :refer [istft]]))
 
+(require-python '[numpy :as np])
 (require-python '[norbert])
 (require-python '[torch])
 (require-python '[torch.hub])
@@ -57,11 +58,11 @@
         use-softmask (not= alpha 1.0)
         ;; convert numpy audio data to torch
         audio-torch (-> audio-data
-                        ($. T)
+                        (py.. -T)
                         (get-item [nil Ellipsis])
                         torch/tensor
-                        ($a float)
-                        ($a "to" device))
+                        (py.. (float))
+                        (py.. (to device)))
         ;; for each target load an open-unmix-pytorch model
         unmix-targets (mapv #(torch.hub/load
                               "sigsep/open-unmix-pytorch"
@@ -74,29 +75,29 @@
                        (= (count targets) 1) (conj "accompaniment")
                        residual-model (conj "residual"))
         X (-> first-unmix
-              ($a stft audio-torch)
-              ($a detach)
-              ($a cpu)
-              ($a numpy)
+              (py.. (stft audio-torch))
+              (py.. (detach))
+              (py.. (cpu))
+              (py.. (numpy))
               ;; converts to complex numpy type
               (as-> x
                   ; X = X[..., 0] + X[..., 1]*1j
-                  ($a (get-item x [Ellipsis 0])
-                      "__add__"
-                      ($a (get-item x [Ellipsis 1])
-                          "__mul__"
-                          (complex 0 1))))
+                  (py.. (get-item x [Ellipsis 0])
+                        (__add__
+                         (py.. (get-item x [Ellipsis 1])
+                               (__mul__
+                                (complex 0 1))))))
               (get-item [0])
-              ($a transpose [2 1 0]))
+              (py.. (transpose [2 1 0])))
         V (cond->
               (-> (mapv #(-> audio-torch
                              %
-                             ($a cpu)
-                             ($a detach)
-                             ($a numpy)
+                             (py.. (cpu))
+                             (py.. (detach))
+                             (py.. (numpy))
                              (cond->
                                  ;; exponentiate the model if use a softmask V**alpha
-                                 use-softmask ($a "__pow__" alpha))
+                                 use-softmask (py.. (__pow__ alpha)))
                              ;; remove sample dim V[:, 0, ...]
                              (get-item [(slice nil) 0 Ellipsis]))
                         unmix-targets)
@@ -107,7 +108,7 @@
                 (= (count targets) 1))
             (norbert/residual_model X :alpha alpha))
         Y (norbert/wiener V
-                          ($a X astype np/complex128)
+                          (py.. X (astype np/complex128))
                           niter
                           use-softmask)]
     (->SeparationResult
@@ -117,11 +118,11 @@
              [(keyword target-name)
               (-> Y
                   (get-item [Ellipsis idx])
-                  ($. T)
+                  (py.. -T)
                   (istft :sample-rate sample-rate
-                         :n-fft ($.. first-unmix stft n_fft)
-                         :n-hopsize ($.. first-unmix stft n_hop))
+                         :n-fft (py.. first-unmix -stft -n_fft)
+                         :n-hopsize (py.. first-unmix -stft -n_hop))
                   last
-                  ($. T))]))
+                  (py.. -T))]))
           (into {}))
      sample-rate)))

src/open_unmix_pytorch_clj/io.clj

@@ -5,7 +5,7 @@
   (:require [open-unmix-pytorch-clj.model :refer :all]
             [open-unmix-pytorch-clj.validator :refer :all]
             [libpython-clj.require :refer [require-python]]
-            [libpython-clj.python :refer [$.]]
+            [libpython-clj.python :refer [py..]]
             [clojure.zip :as zip]))
 
 (require-python '[soundfile :as sf])
@@ -75,5 +75,5 @@
   "
   [file-path]
   (let [[audio sample-rate] (sf/read file-path :always_2d true)
-        shape ($. audio shape)]
+        shape (py.. audio -shape)]
     (->AudioMap audio sample-rate (first shape) (last shape))))