Update README and update paths.

Psc2/modes/mlsplainer.py

@@ -25,7 +25,7 @@ class MLSplainer(mode.Mode):
 
   def __init__(self,
                client,
-               base_models_path='/home/psc/pyosc/psc2/magenta_models',
+               base_models_path='~/Psc2/magenta_models',
                model_name='attention_rnn.mag',
                min_primer_length=20,
                max_robot_length=20,

README.md

@@ -9,9 +9,38 @@ the arrangements take well-known songs from pop, rock and jazz and re-build them
 psc is a señor swesearcher in google brain, and is experimenting with using machine learning models (including generative models for music) as part of the live performance. most of the code used will be available in this repo.
 
 ## Installation
+1.  Install [SuperCollider](https://supercollider.github.io/)
 
-From the root directory, run:
+2.  Create a virtualenv and activate it. We need Python2 (and _not_ Python3)
+    because `pyosc` is not compatible with Python3. This step is optional but
+    recommended:
+
+    ```
+    virtualenv --system-site-packages -p python2 venv
+    source venv/bin/activate
+    ```
+
+3.  `pip install pyosc`.
+
+4.  Install Tensorflow (instructions
+    [here](https://www.tensorflow.org/install/)).
+
+5.  Install Magenta (instructions [here](https://github.com/tensorflow/magenta)).
+
+4.  Clone this repo.
+
+5.  Open SuperCollider, open `Psc2/server.sc` and run the main group (enclosed
+    in parentheses).
+
+6.  From the root directory, run:
+
+    ```
+    python setup.py install
+    ```
+
+7.  Start the python server:
+
+    ```
+    python Psc2/server.py
+    ```
 
-```
-python setup.py install
-```

talks/canarie2018/server.py

@@ -45,7 +45,7 @@
 improv_status = 'psc'  # One of 'psc' or 'robot'.
 
 # Read in the PerformanceRNN model.
-MODEL_PATH = '/home/psc/pyosc/psc2/magenta_models/attention_rnn.mag'
+MODEL_PATH = '~/Psc2/magenta_models/attention_rnn.mag'
 bundle = magenta.music.read_bundle_file(MODEL_PATH)