document on readme

README.md

@@ -2,17 +2,67 @@
 
 Welcome to the Mimic Recording Studio.
 
+![demo](demo.gif)
+
 ## Quick Start
 
 ### Dependencies
-* [Docker](https://docs.docker.com/) (community edition is fine)
-* [Docker-compose](https://docs.docker.com/compose/install/)
 
-### Build and Rum
+* [Docker](https://docs.docker.com/) (community edition is fine)
-* `git clone `
+* [Docker Compose](https://docs.docker.com/compose/install/)
+
+Why docker? To make this super easy to set up and run cross platforms.
+
+### Build and Run
+
+* `git clone https://github.com/MycroftAI/mimic-recording-studio.git`
 * `cd mimic-recording-studio`
-* `docker-compose up`
+* `docker-compose up` to build and run
-* In browser go to `http://localhost:3000`
+  * Alternatively, you can build and run seperately. `docker-compose build`, `docker-compose up`
+* In browser, go to `http://localhost:3000`
 
 **Note**
-First run will require 
+First `docker-compose up` will take a while as this command will also build the docker containers. Subsequent `docker-compose up` should be quicker to boot.
+
+## Data
+
+### Saved Audios
+
+Audios can be found in the `backend/audio_file/{uuid}` directory. The backend automatically trims the beginning and ending silence for all wav files using [ffmpeg](https://www.ffmpeg.org/).
+
+### Corpus
+
+Right now, we have an english corpus, `english_corpus.csv` made available which can be found in `backend/prompt/`. To use your own corpus follow these steps.
+
+1. create a csv file in the same format as `english_corpus.csv` using tabs (`\t`) as the delimitter.
+2. change the `CORPUS` environment variable in `docker-compose.yml` to your corpus name.
+
+## Technologies
+
+### Frontend
+
+The web UI is built using javascript and [React](https://reactjs.org/) and [create-react-app](https://github.com/facebook/create-react-app) as a scaffolding tool.
+
+#### Functions
+
+* Record and play audio
+* Display audio visualization
+* Calculate and display metrics
+
+### Backend
+
+The web service is built using python, [Flask](http://flask.pocoo.org/) as the backend framework, [gunicorn](https://gunicorn.org/) as a http webserver, and [sqlite](https://www.sqlite.org/index.html) as the database.
+
+#### Functions
+
+* Process audio
+* Serves corpus and metrics data
+* Record info in database
+
+### Docker
+
+Docker is used to containerized both applications. By default, frontend uses network port `3000` while the backend uses networking port `5000`. You can configure these in the `docker-compose.yml` file.
+
+## Contributions
+
+PR's are accepted!

backend/app/file_system.py

@@ -4,9 +4,12 @@ import os
 import csv
 import hashlib
 import subprocess
+import os
 from subprocess import DEVNULL
 from .protocol import response
 
+corpus_name = os.environ["CORPUS"]
+
 prompts_dir = prompts_path = os.path.join(
     os.path.dirname(os.path.abspath(__file__)),
     "../prompts/"
@@ -14,7 +17,8 @@ prompts_dir = prompts_path = os.path.join(
 os.makedirs(prompts_dir, exist_ok=True)
 prompts_path = os.path.join(
     prompts_dir,
-    "../prompts/english_prompts_v2.csv"
+    "../prompts",
+    corpus_name
 )
 
 audio_dir = os.path.join(

backend/start_prod.sh

@@ -1 +1 @@
-gunicorn -w 4 -b 0.0.0.0:5000 app:app -c gunicorn_conf.py --capture-output
+gunicorn -w $WEBWORKERS -b 0.0.0.0:$APIPORT app:app -c gunicorn_conf.py --capture-output

docker-compose.yml

@@ -8,6 +8,10 @@ services:
      - "5000:5000"
     volumes: 
      - ./backend/:/src
+    environment:
+    - CORPUS=english_corpus.csv
+    - APIPORT=5000
+    - WEBWORKERS=1
   frontend:
     container_name: mrs-frontend
     build: