Setting Up

Before You Begin

If you aren’t certain which products your contract includes or if you are interested in adding Hotpepper to your on-premises deployment, please consult a Deepgram Account Representative. To reach one, contact us!

You will also need an account on Docker Hub, so you can conveniently get the latest Deepgram software and updates.

Installing

Deepgram makes all of its products available through Docker Hub.

1. Log in to your Docker Hub account from one of your servers.

2. Run the following command:

   docker pull deepgram/onprem-dashscript

Deploying

Before you can run your on-prem deployment with Hotpepper, you must configure Hotpepper. To do this, you will need to update your Docker Compose and container configuration files, then restart Docker.

Update Your Docker Compose File

Hotpepper requires paths to four resources:

• Directory that stores the Hotpepper database
• Directory that stores input datasets (collections of audio files to transcribe)
• Directory to which packaged datasets should be output
• Configuration file, which is written in TOML to promote easy human editing

1. To house your Hotpepper database, in your root directory, create a directory named db:

   mkdir db

   You can name this directory whatever you like, as long as you update the docker-compose.yml file accordingly.

2. To house your input datasets, in your root directory, create a directory named datasets:

   mkdir datasets

   You can name this directory whatever you like, as long as you update the docker-compose.yml file accordingly.

3. To house your output datasets, in your root directory, create a directory named packaged:

   mkdir packaged

   You can name this directory whatever you like, as long as you update the docker-compose.yml file accordingly.

4. Add the following to the end of your default docker-compose.yml file in your root directory and modify the file to include the appropriate paths:

   # Hotpepper, the on-premises human transcription/labeling tool for creating training
   # sets. (Note: Naming conventions may mention Dashscript, a previous version
   # of Hotpepper; they are the same tool.)
   dashscript:
     image: deepgram/onprem-dashscript:latest
   
   # Here we expose the service port to the host machine. The container port
   # (right-hand side) must match the port that the service is listening
   # on (from its configuration file).
   ports:
     - "8081:80"
   
   # An admin "super-user" must be present in the database to execute user and dataset
   # management actions through the app.  Setting these environment variables will
   # ensure that the app starts with at least one.
   environment:
     DASHSCRIPT_ADMIN_USER: "admin"
     DASHSCRIPT_ADMIN_PASSWORD: "admin_pass"
   
   volumes:
     # The container paths (right side) should align with those used in the Hotpepper
     # config file.
   
     # Path to the Hotpepper database directory.  The name of the database will be
     # pulled from the Hotpepper config file.
     - "/path/to/database:/db"
   
     # Path to the directory containing input datasets. New datasets are
     # created by adding subdirectories to this folder and placing audio data
     # there.  This directory should be structured like so:
     # /path/to/input/data/
     #    |_ dataset1/
     #      |_ audio1.mp3
     #      |_ audio2.mp3
     #   ...
     - "/path/to/input/data:/datasets"
   
     # Path to where Hotpepper
     # will save its finalized, packaged datasets.
     - "/path/to/output/data:/packaged"
   
     # Path to the Hotpepper config file.
     - "/path/to/config/file.toml:/config.toml:ro"
   
     # Invoke Hotpepper, giving it the path to the config file (in the
     # container).
     command: /config.toml serve

5. Add the first Hotpepper administrator by configuring the following environment variables in your docker-compose.yml file:

   • DASHSCRIPT_ADMIN_USER
   • DASHSCRIPT_ADMIN_PASSWORD

Update Your Container Configuration File

1. In your config subdirectory in your root directory, create the config.toml file:

   cd config
   touch config.toml

2. Add the following code to the config.toml file:

# The container path to the Hotpepper database. (Note: Naming conventions
# may mention Dashscript, a previous version of Hotpepper; they are the same tool.)
db = "/db/dashscript.db"

# A directory for storing input datasets (collections of audio files to
# transcribe).
# Path to the directory containing input datasets. New datasets are
# created by adding subdirectories to this folder and placing audio data
# there. This directory should be structured like so:
# /datasets/
#   |_ dataset1/
#      |_ audio1.mp3
#      |_ audio2.mp3
#   ...
datasets = "/datasets"

# A directory for outputting packaged datasets.
packaged_dataset_location = "/packaged"

[server]
  port = 80

# For customers who have ASR enabled, the following configuration will place
# a button "Get ASR" on the L1 transcription page to pre-populate the
# transcript field with ASR.
# Ensure that the endpoint points to your on-premise API instance!
[asr]
   endpoint = "http://api:8080/v1/listen?punctuate=true"  # Docker Compose

Allowing Automatic Transcription

Hotpepper can be configured to allow users labeling at level L1 to submit assigned files to an on-premise Deepgram Speech Engine for automatic speech recognition (ASR) and transcription. When ASR is used, the Hotpepper server sends the assigned audio file to the configured Speech API endpoint, parses a transcript from the results, and automatically populates the Transcript textarea of the labeling view with the returned transcript. In our experience, users value this feature highly when labeling.

In Hotpepper’s config.toml file, notice the following lines, which enable ASR:

# Ensure that the endpoint points to your Speech API instance

[asr]
  # docker compose
  # endpoint = "http://api:8080/v1/listen?punctuate=true"

When Hotpepper is properly configured for ASR, the File Details area of the Hotpepper labeling view will include a Get ASR button.

Backing up Data

To avoid losing all transcripts produced by Hotpepper, back up both your dataset directories and the Hotpepper database. Hotpepper uses a SQLite database, so you only need to copy the configured database file to a secure location.