feat: deepgram text to speech

.github/workflows/tests.yml

@@ -113,6 +113,7 @@ jobs:
         if: ${{ matrix.skip_pytests != 'true' }}
         env:
           ML_BACKEND: ${{ matrix.backend_dir_name }}
+          TEST_ENV: "true"
         run: |
           docker compose -f label_studio_ml/examples/${{ matrix.backend_dir_name }}/docker-compose.yml exec -T ${{ matrix.backend_dir_name }} pytest -vvv --cov --cov-report=xml:/tmp/coverage.xml
 

label_studio_ml/examples/deepgram/Dockerfile

@@ -0,0 +1,48 @@
+# syntax=docker/dockerfile:1
+ARG PYTHON_VERSION=3.13
+
+FROM python:${PYTHON_VERSION}-slim AS python-base
+ARG TEST_ENV
+
+WORKDIR /app
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PORT=${PORT:-9090} \
+    PIP_CACHE_DIR=/.cache \
+    WORKERS=1 \
+    THREADS=8
+
+# Update the base OS
+RUN --mount=type=cache,target="/var/cache/apt",sharing=locked \
+    --mount=type=cache,target="/var/lib/apt/lists",sharing=locked \
+    set -eux; \
+    apt-get update; \
+    apt-get upgrade -y; \
+    apt install --no-install-recommends -y  \
+        git; \
+    apt-get autoremove -y
+
+# install base requirements
+COPY requirements-base.txt .
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
+    pip install -r requirements-base.txt
+
+# install custom requirements
+COPY requirements.txt .
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
+    pip install -r requirements.txt
+
+# install test requirements if needed
+COPY requirements-test.txt .
+# build only when TEST_ENV="true"
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
+    if [ "$TEST_ENV" = "true" ]; then \
+      pip install -r requirements-test.txt; \
+    fi
+
+COPY . .
+
+EXPOSE 9090
+CMD gunicorn --preload --bind :$PORT --workers $WORKERS --threads $THREADS --timeout 0 _wsgi:app
+

label_studio_ml/examples/deepgram/README.md

@@ -0,0 +1,35 @@
+
+# Using Deepgram with Label Studio for Text to Speech annotation
+
+This backend uses the Deepgram API to take the input text from the user, do text to speech, and return the output audio for annotation in Label Studio.
+
+https://github.com/user-attachments/assets/9569a955-0baf-4a95-9e8a-d08250a0a298
+
+
+IMPORTANT NOTE: YOU MUST REFRESH THE PAGE AFTER SUBMITTING THE TEXT TO SEE THE AUDIO APPEAR.
+
+## Prerequistes 
+1. [Deepgram API Key](https://deepgram.com/) -- create an account and follow the instructions to get an api key with default permissions. Store this key as `DEEPGRAM_API_KEY` in `docker_compose.yml`
+2. AWS Storage -- make sure you configure the following parameters in `docker_compose.yml`: 
+      - `AWS_ACCESS_KEY_ID` -- your AWS access key id
+      - `AWS_SECRET_ACCESS_KEY` -- your AWS secret access key
+      - `AWS_SESSION_TOKEN` -- your AWS session token
+      - `AWS_DEFAULT_REGION` - the region you want to use for S3
+      - `S3_BUCKET` -- the name of the bucket where you'd like to store the created audio files
+      - `S3_FOLDER` -- the name of the folder within the specified bucket where you'd like to store the audio files. 
+3. Label Studio -- make sure you set your `LABEL_STUDIO_URL` and your `LABEL_STUDIO_API_KEY` in `docker_compose.yml`. As of 11/12/25, you must use the LEGACY TOKEN. 
+
+## Labeling Config 
+This is the base labeling config to be used with this backend. Note that you may add additional annotations to the document after the audio without breaking anything!
+```
+<View>
+  <Header value="What would you like to TTS?"/>
+  <TextArea name="text" toName="audio" placeholder="What do you want to tts?" value="$text" valrows="4" maxSubmissions="1"/>
+  <Audio name="audio" value="$audio" zoom="true" hotkey="ctrl+enter"/>
+</View>
+```
+## A Data Note 
+Note that in order for this to work, you need to upload dummy data (i.e. empty text and audio) so that the tasks populate. You can use `dummy_data.json` as this data. 
+
+## Configuring the backend 
+When you attach the model to Label Studio in your model settings, make sure to toggle ON interactive preannotations! 

label_studio_ml/examples/deepgram/_wsgi.py

@@ -0,0 +1,122 @@
+import os
+import argparse
+import json
+import logging
+import logging.config
+
+logging.config.dictConfig({
+  "version": 1,
+  "disable_existing_loggers": False,
+  "formatters": {
+    "standard": {
+      "format": "[%(asctime)s] [%(levelname)s] [%(name)s::%(funcName)s::%(lineno)d] %(message)s"
+    }
+  },
+  "handlers": {
+    "console": {
+      "class": "logging.StreamHandler",
+      "level": os.getenv('LOG_LEVEL'),
+      "stream": "ext://sys.stdout",
+      "formatter": "standard"
+    }
+  },
+  "root": {
+    "level": os.getenv('LOG_LEVEL'),
+    "handlers": [
+      "console"
+    ],
+    "propagate": True
+  }
+})
+
+from label_studio_ml.api import init_app
+from model import DeepgramModel
+
+
+_DEFAULT_CONFIG_PATH = os.path.join(os.path.dirname(__file__), 'config.json')
+
+
+def get_kwargs_from_config(config_path=_DEFAULT_CONFIG_PATH):
+    if not os.path.exists(config_path):
+        return dict()
+    with open(config_path) as f:
+        config = json.load(f)
+    assert isinstance(config, dict)
+    return config
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Label studio')
+    parser.add_argument(
+        '-p', '--port', dest='port', type=int, default=9090,
+        help='Server port')
+    parser.add_argument(
+        '--host', dest='host', type=str, default='0.0.0.0',
+        help='Server host')
+    parser.add_argument(
+        '--kwargs', '--with', dest='kwargs', metavar='KEY=VAL', nargs='+', type=lambda kv: kv.split('='),
+        help='Additional LabelStudioMLBase model initialization kwargs')
+    parser.add_argument(
+        '-d', '--debug', dest='debug', action='store_true',
+        help='Switch debug mode')
+    parser.add_argument(
+        '--log-level', dest='log_level', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'], default=None,
+        help='Logging level')
+    parser.add_argument(
+        '--model-dir', dest='model_dir', default=os.path.dirname(__file__),
+        help='Directory where models are stored (relative to the project directory)')
+    parser.add_argument(
+        '--check', dest='check', action='store_true',
+        help='Validate model instance before launching server')
+    parser.add_argument('--basic-auth-user',
+                        default=os.environ.get('ML_SERVER_BASIC_AUTH_USER', None),
+                        help='Basic auth user')
+
+    parser.add_argument('--basic-auth-pass',
+                        default=os.environ.get('ML_SERVER_BASIC_AUTH_PASS', None),
+                        help='Basic auth pass')    
+
+    args = parser.parse_args()
+
+    # setup logging level
+    if args.log_level:
+        logging.root.setLevel(args.log_level)
+
+    def isfloat(value):
+        try:
+            float(value)
+            return True
+        except ValueError:
+            return False
+
+    def parse_kwargs():
+        param = dict()
+        for k, v in args.kwargs:
+            if v.isdigit():
+                param[k] = int(v)
+            elif v == 'True' or v == 'true':
+                param[k] = True
+            elif v == 'False' or v == 'false':
+                param[k] = False
+            elif isfloat(v):
+                param[k] = float(v)
+            else:
+                param[k] = v
+        return param
+
+    kwargs = get_kwargs_from_config()
+
+    if args.kwargs:
+        kwargs.update(parse_kwargs())
+
+    if args.check:
+        print('Check "' + DeepgramModel.__name__ + '" instance creation..')
+        model = DeepgramModel(**kwargs)
+
+    app = init_app(model_class=DeepgramModel, basic_auth_user=args.basic_auth_user, basic_auth_pass=args.basic_auth_pass)
+
+    app.run(host=args.host, port=args.port, debug=args.debug)
+
+else:
+    # for uWSGI use
+    app = init_app(model_class=DeepgramModel)

label_studio_ml/examples/deepgram/docker-compose.yml

@@ -0,0 +1,53 @@
+services:
+  deepgram:
+    container_name: ml-backend
+    image: humansignal/ml-backend:v0
+    build:
+      context: .
+      args:
+        TEST_ENV: ${TEST_ENV}
+
+#    deploy:
+#      resources:
+#        reservations:
+#          devices:
+#            - driver: nvidia
+#              count: 1
+#              capabilities: [ gpu ]
+
+
+    environment:
+      # specify these parameters if you want to use basic auth for the model server
+      - BASIC_AUTH_USER=
+      - BASIC_AUTH_PASS=
+      # set the log level for the model server
+      - LOG_LEVEL=DEBUG
+      # any other parameters that you want to pass to the model server
+      - ANY=PARAMETER
+      # specify the number of workers and threads for the model server
+      - WORKERS=1
+      - THREADS=8
+      # specify the model directory (likely you don't need to change this)
+      - MODEL_DIR=/data/models
+      # specify device
+      - DEEPGRAM_API_KEY=
+
+      # For AWS upload 
+      - AWS_ACCESS_KEY_ID=
+      - AWS_SECRET_ACCESS_KEY=
+      - AWS_SESSION_TOKEN=
+      - AWS_DEFAULT_REGION=us-east-1
+      - S3_BUCKET=
+      - S3_FOLDER=
+
+      # Specify the Label Studio URL and API key to access
+      # uploaded, local storage and cloud storage files.
+      # Do not use 'localhost' as it does not work within Docker containers.
+      # Use prefix 'http://' or 'https://' for the URL always.
+      # Determine the actual IP using 'ifconfig' (Linux/Mac) or 'ipconfig' (Windows).
+      - LABEL_STUDIO_URL=
+      - LABEL_STUDIO_API_KEY=
+    ports:
+      - "9090:9090"
+    volumes:
+      - "./data/server:/data"

label_studio_ml/examples/deepgram/dummy_data.json

@@ -0,0 +1,3 @@
+{
+    "data": {"audio": "", "text": ""}
+}