package v1.0.0 (#9)

* model downloader

* packaging script. add classifiers to setup.py

* minor correction

Author: tachi-hi

MANIFEST.in

@@ -1,2 +1,4 @@
+include README.md
+include LICENSE
 include tdmelodic/nn/lang/mecab/my_mecabrc
 include tdmelodic/nn/resource/net_it_2500000

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-<img src="imgs/logo/logo_tdmelodic.svg" width="200" />
+<img src="https://github.com/PKSHATechnology-Research/tdmelodic/raw/master/imgs/logo/logo_tdmelodic.svg" width="200" />
 </p>
 
 

packege.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+
+# remove binary and create dummy file
+echo -----------------------------------------------------------
+files_to_exclude="
+tdmelodic/nn/resource/net_it_2500000
+"
+
+for f in $files_to_exclude;
+do
+    echo replace $f with an empty file
+    if [ -e $f ]; then
+	mv $f ${f}_bak
+    fi
+    touch $f
+done
+
+# remove old files
+echo -----------------------------------------------------------
+echo removing obosolete files
+rm -rf dist/*
+rm -rf tdmelodic.egg-info*
+
+# sdist
+echo -----------------------------------------------------------
+python3 setup.py sdist bdist_wheel
+
+# back
+echo -----------------------------------------------------------
+for f in $files_to_exclude;
+do
+    echo moving ${f}_bak back to $f
+    mv ${f}_bak ${f}
+done
+
+# done
+echo -----------------------------------------------------------
+echo Done.

setup.py

@@ -24,10 +24,12 @@ def _get_version():
     author="Hideyuki Tachibana",
     author_email='[email protected]',
     python_requires='>=3.7',
+    url="https://github.com/PKSHATechnology-Research/tdmelodic",
 
     description="tdmelodic: Tokyo Japanese Accent Estimator",
     long_description=_readme(),
-
+    long_description_content_type="text/markdown",
+ 
     install_requires=_requirements(),
     tests_requires=_requirements(),
     setup_requires=[],
@@ -44,5 +46,19 @@ def _get_version():
             'tdmelodic-neologd-patch = tdmelodic.script.neologd_patch:main',
             'tdmelodic-modify-unigram-cost = tdmelodic.script.postprocess_modify_unigram_cost:main',
         ]
-    }
+    },
+
+    classifiers=[
+        'Development Status :: 5 - Production/Stable',
+        'Environment :: Console',
+        'Intended Audience :: Science/Research',
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: BSD License',
+        'Operating System :: POSIX',
+        'Programming Language :: Python :: 3.7',
+        'Topic :: Text Processing :: Linguistic',
+        'Topic :: Multimedia :: Sound/Audio :: Speech',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Natural Language :: Japanese',
+    ]
 )

tdmelodic/nn/inference.py

@@ -11,6 +11,7 @@
 
 import numpy as np
 from tqdm import tqdm
+import urllib.request
 
 import chainer
 import chainer.functions as F
@@ -27,13 +28,42 @@
 gpu_id = -1 # cpu
 bs = 1
 embed_dim = 64
-_default_path= os.path.dirname(os.path.abspath(__file__)) + "/resource/net_it_2500000"
+
+_github_url = "https://github.com/PKSHATechnology-Research/tdmelodic"
+model_location={
+    "path" : os.path.dirname(os.path.abspath(__file__)) + "/resource/net_it_2500000",
+    "url" : _github_url + "/raw/master/tdmelodic/nn/resource/net_it_2500000"
+}
+
+# ------------------------------------------------------------------------------
+class model_downloader(object):
+    def __init__(self, path, url=model_location["url"]):
+        self.path = path
+        self.url = url
+        if self.__check_if_file_empty(self.path):
+            self.__download()
+        else:
+            self.__already_downloaded()
+
+    def __check_if_file_empty(self, path_):
+        return not os.path.exists(path_) or os.path.getsize(path_) == 0
+
+    def __download(self):
+        print("[ tdmelodic Model Downloader ] Downloading the pretrained model.")
+        print("[ tdmelodic Model Downloader ] From {}".format(self.url))
+        print("[ tdmelodic Model Downloader ] To   {}".format(self.path))
+        urllib.request.urlretrieve(self.url, self.path)
+        print("[ tdmelodic Model Downloader ] Done")
+
+    def __already_downloaded(self):
+        print("[ tdmelodic Model Downloader ] The tdmelodic pretrained model already on your system.")
 
 # ------------------------------------------------------------------------------
 class InferAccent(object):
     def __init__(self,
-            model_path=_default_path,
+            model_path=model_location["path"],
             model_dim=embed_dim):
+        model_downloader(model_path)
         self.net = self.__load_model(model_path, model_dim)
 
     def __load_model(self, model_path, model_dim):