requirements.txt updated @torch1.4

speed augmentation updated
paramters updated

README.md

@@ -9,9 +9,10 @@ Clone it to find a collection of:
  - Utility Function for Model I/O
  - DL Modules
  - A Plotter Object
- - Audio Related Tools and Funtion
+ - Audio related Tools and Funtion
    - Librosa
    - Scipy Signal
+ - PointCloud related Tools and Functions
  
  ###Notes:
  - Use directory links to link from your main project folder to the ml_lib folder. Pycharm will automatically use 
@@ -19,11 +20,10 @@ Clone it to find a collection of:
     \
     \
     For Windows Users:
-    ```
+    ``` bash
      mklink /d "ml_lib" "..\ml_lib""
     ```
     For Unix User:
+    ``` bash
+    ln -s ../ml_lib ml_lib
     ```
-    TBA
-    ```
-  - Cheers

audio_toolset/audio_augmentation.py

@@ -9,18 +9,19 @@ import numpy as np
 
 class Speed(object):
 
-    def __init__(self, max_ratio=0.3, speed_factor=1):
+    def __init__(self, max_amount=0.3, speed_min=1, speed_max=1):
-        self.speed_factor = speed_factor
+        self.speed_max = speed_max
-        self.max_ratio = max_ratio
+        self.speed_min = speed_min
+        self.max_amount = max_amount
 
     def __call__(self, x):
-        if not all([self.speed_factor, self.max_ratio]):
+        assert all([self.speed_min, self.speed_max, self.max_amount])
-            return x
+        start = int(np.random.randint(low=0, high=x.shape[-1], size=1))
-        start = int(np.random.randint(0, x.shape[-1],1))
+        width = np.random.uniform(low=0, high=self.max_amount, size=1) * x.shape[-1]
-        end = int((np.random.uniform(0, self.max_ratio, 1) * x.shape[-1]) + start)
+        end = int(width + start)
         end = min(end, x.shape[-1])
         try:
-            speed_factor = float(np.random.uniform(min(self.speed_factor, 1), max(self.speed_factor, 1), 1))
+            speed_factor = float(np.random.uniform(low=self.speed_min, high=self.speed_max, size=1))
             aug_data = librosa.effects.time_stretch(x[start:end], speed_factor)
             return np.concatenate((x[:start], aug_data, x[end:]), axis=0)[:x.shape[-1]]
         except ValueError:

requirements.txt

@@ -1,62 +1,8 @@
-absl-py==0.9.0
-attrs==19.3.0
-bravado==10.6.0
-bravado-core==5.17.0
-cachetools==4.1.0
-certifi==2020.4.5.1
-chardet==3.0.4
-click==7.1.2
-future==0.18.2
-gitdb==4.0.5
-GitPython==3.1.2
-google-auth==1.15.0
-google-auth-oauthlib==0.4.1
-grpcio==1.29.0
-idna==2.9
-imageio==2.8.0
-jsonpointer==2.0
-jsonref==0.2
-jsonschema==3.2.0
-Markdown==3.2.2
-monotonic==1.5
-msgpack==1.0.0
-msgpack-python==0.5.6
 natsort==7.0.1
-neptune-client==0.4.113
+neptune-client==0.4.109
-numpy==1.18.4
-oauthlib==3.1.0
-packaging==20.4
-pandas==1.0.3
-Pillow==7.1.2
-protobuf==3.12.0
-py3nvml==0.2.6
-pyasn1==0.4.8
-pyasn1-modules==0.2.8
-PyJWT==1.7.1
-pyparsing==2.4.7
-pyrsistent==0.16.0
-python-dateutil==2.8.1
 pytorch-lightning==0.7.6
-pytz==2020.1
-PyYAML==5.3.1
-requests==2.23.0
-requests-oauthlib==1.3.0
-rfc3987==1.3.8
-rsa==4.0
-simplejson==3.17.0
-six==1.14.0
-smmap==3.0.4
-strict-rfc3339==0.7
-swagger-spec-validator==2.5.0
-tensorboard==2.2.1
-tensorboard-plugin-wit==1.6.0.post3
 test-tube==0.7.5
-torch==1.5.0+cu101
+torch==1.4.0
-torchvision==0.6.0+cu101
+torchcontrib==0.0.2
-tqdm==4.46.0
+torchvision==0.5.0
-typing-extensions==3.7.4.2
+tqdm==4.45.0
-urllib3==1.25.9
-webcolors==1.11.1
-websocket-client==0.57.0
-Werkzeug==1.0.1
-xmltodict==0.12.0