aws-deepgram-sagemaker

Test Deepgram Transcription on SageMaker

JavaScript

Python

Follow these steps if you'd like to test the Deepgram Speech-to-Text (STT) Amazon SageMaker

• Ensure you've deployed an Amazon SageMaker endpoint
• Install the uv package manager
• Install dependencies: uv pip install -r requirements.txt

Python Microphone Stress Test Examples

The stt_microphone_stress.py script streams live microphone audio to Deepgram on SageMaker for real-time transcription. It supports multiple simultaneous connections for load testing.

Basic usage (single connection):

uv run python-stt/stt_microphone_stress.py your-endpoint-name

With specific AWS region:

uv run python-stt/stt_microphone_stress.py your-endpoint-name --region us-west-2

Multiple simultaneous connections (load testing):

uv run python-stt/stt_microphone_stress.py your-endpoint-name --connections 5

With speaker diarization:

uv run python-stt/stt_microphone_stress.py your-endpoint-name --diarize true

With different model and language:

The default Speech-to-Text (STT) transcription model is nova-3.

uv run python-stt/stt_microphone_stress.py your-endpoint-name --model nova-2 --language es

With keywords boosting:

Keywords are only conmpatible with nova-2. For nova-3 use keyterms instead.

uv run python-stt/stt_microphone_stress.py your-endpoint-name --keywords "Deepgram:5,SageMaker:10,transcription:3"

Run for a fixed duration (useful for automated tests):

uv run python-stt/stt_microphone_stress.py your-endpoint-name --duration 30

Timed load test with multiple connections:

uv run python-stt/stt_microphone_stress.py your-endpoint-name --connections 5 --duration 120

Full example with all options:

uv run python-stt/stt_microphone_stress.py your-endpoint-name \
  --connections 3 \
  --model nova-2 \
  --language en \
  --diarize true \
  --punctuate true \
  --keywords "hello:5,world:10" \
  --duration 60 \
  --region us-east-1 \
  --log-level DEBUG

Available options:

• --connections N - Number of simultaneous streaming connections (default: 1)
• --model MODEL - Deepgram model to use (default: nova-3)
• --language LANG - Language code (default: en)
• --diarize true|false - Enable speaker diarization (default: false)
• --punctuate true|false - Enable punctuation (default: true)
• --keywords KEYWORDS - Comma-delimited keywords with intensity (format: "word:intensity,word:intensity")
• --duration SECONDS - Stop automatically after this many seconds (default: run until Ctrl+C)
• --region REGION - AWS region (default: us-east-1)
• --log-level LEVEL - Logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL (default: INFO)

Python WAV File Stress Test Examples

The stt_wav_stress.py script supports two sub-commands: stream and batch.

Requirements: The WAV file must be 16-bit PCM (linear16). To convert any audio file, use ffmpeg:

ffmpeg -i input.mp3 -ar 16000 -ac 1 -sample_fmt s16 output.wav

---

stream — Real-time bidirectional streaming

Streams the WAV file to Deepgram on SageMaker in real-time, paced to match the file's actual sample rate. Behaves like a live microphone source, enabling repeatable and automated load testing without requiring a physical microphone.

Basic usage (single connection, play file once):

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav

With a specific AWS region:

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav --region us-west-2

Multiple simultaneous connections (load testing):

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav --connections 5

Loop the file continuously until Ctrl+C:

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav --loop

Loop for a fixed duration (useful for automated tests):

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav --loop --duration 120

Timed load test with multiple connections:

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav \
  --connections 10 --loop --duration 300

With speaker diarization:

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav --diarize true

With a different model and language:

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav \
  --model nova-2 --language es

With keywords boosting (nova-2 only) or keyterms (nova-3):

Keywords are only compatible with nova-2. For nova-3 use --keyterms instead.

# nova-2 keywords
uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav \
  --keywords "Deepgram:5,SageMaker:10"

# nova-3 keyterms
uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav \
  --keyterms "Deepgram,SageMaker"

With PII redaction:

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav \
  --redact "pii,ssn,email_address"

Full example with all stream options:

uv run python-stt/stt_wav_stress.py stream your-endpoint-name --file audio.wav \
  --connections 3 \
  --model nova-3 \
  --language en \
  --diarize true \
  --punctuate true \
  --keyterms "Deepgram,SageMaker" \
  --redact "pii,ssn" \
  --interim-results true \
  --loop \
  --duration 60 \
  --region us-east-2 \
  --log-level DEBUG

Available stream options:

Option	Description	Default
--file WAV_FILE	Path to a 16-bit PCM WAV file (required)	—
--connections N	Number of simultaneous streaming connections	1
--model MODEL	Deepgram model	nova-3
--language LANG	Language code	en
--diarize true|false	Enable speaker diarization	false
--punctuate true|false	Enable punctuation	true
--keywords WORD:N,...	Keyword boosting with intensity, e.g. hello:5,world:10 (nova-2 only)	—
--keyterms TERM,...	Comma-separated keyterms to boost recognition (nova-3)	—
--redact ENTITY,...	Comma-separated entity types to redact, e.g. pii,ssn,email_address	—
--interim-results true|false	Emit interim (partial) transcripts	true
--loop	Loop the WAV file until --duration is reached or Ctrl+C	off
--duration SECONDS	Stop after this many seconds	play file once
--region REGION	AWS region	us-east-2
--log-level LEVEL	DEBUG, INFO, WARNING, ERROR, CRITICAL	INFO

---

batch — Pre-recorded HTTP transcription

Posts the entire WAV file in a single HTTP request using the SageMaker InvokeEndpoint API. Supports configurable parallelism via --concurrency for throughput and latency stress testing. Each concurrent request runs on its own Python thread with its own boto3 client. After all requests complete, a summary table shows min/avg/p95/max latency, throughput, and success/failure counts.

Note: SageMaker InvokeEndpoint has a 6 MB request body limit. For larger files, use stream mode or split the file:

ffmpeg -i input.wav -f segment -segment_time 60 segment_%03d.wav

Basic usage (single request):

uv run python-stt/stt_wav_stress.py batch your-endpoint-name --file audio.wav

Send 10 concurrent requests (load testing):

uv run python-stt/stt_wav_stress.py batch your-endpoint-name --file audio.wav --concurrency 10

Send 100 total requests, 10 at a time:

uv run python-stt/stt_wav_stress.py batch your-endpoint-name --file audio.wav \
  --concurrency 10 --requests 100

With a different model and language:

uv run python-stt/stt_wav_stress.py batch your-endpoint-name --file audio.wav \
  --model nova-2 --language es

With keyterms (nova-3):

uv run python-stt/stt_wav_stress.py batch your-endpoint-name --file audio.wav \
  --keyterms "Deepgram,SageMaker"

With speaker diarization and PII redaction:

uv run python-stt/stt_wav_stress.py batch your-endpoint-name --file audio.wav \
  --diarize true --redact "pii,ssn,email_address"

Full example with all batch options:

uv run python-stt/stt_wav_stress.py batch your-endpoint-name --file audio.wav \
  --concurrency 5 \
  --requests 50 \
  --model nova-3 \
  --language en \
  --diarize true \
  --punctuate true \
  --keyterms "Deepgram,SageMaker" \
  --redact "pii,ssn" \
  --region us-east-2 \
  --log-level DEBUG

Available batch options:

Option	Description	Default
--file WAV_FILE	Path to a 16-bit PCM WAV file, max 6 MB (required)	—
--concurrency N	Number of requests to run in parallel	1
--requests N	Total number of requests to send	same as --concurrency
--model MODEL	Deepgram model	nova-3
--language LANG	Language code	en
--diarize true|false	Enable speaker diarization	false
--punctuate true|false	Enable punctuation	true
--keyterms TERM,...	Comma-separated keyterms to boost recognition (nova-3)	—
--redact ENTITY,...	Comma-separated entity types to redact, e.g. pii,ssn,email_address	—
--region REGION	AWS region	us-east-2
--log-level LEVEL	DEBUG, INFO, WARNING, ERROR, CRITICAL	INFO

Test Deepgram Text-to-Speech (TTS) on SageMaker

JavaScript

TBD

Python

TBD

Transcription Load Test

To run transcription load test:

1. Set your AWS credentials (eg. AWS_SHARED_CREDENTIALS_FILE and AWS_PROFILE variables)
2. Ensure Node.js is installed
3. Set the AWS region, SageMaker Endpoint name, input audio file name, and query string parameters, in stt.file.ts
4. Run npx tsx stress-stt.ts