redenlab-extract 0.3.0

Python SDK for RedenLab ML inference service

RedenLab Extract SDK

Python SDK for RedenLab's ML inference service. Run machine learning models on audio files using AWS SageMaker async inference endpoints.

Features

• Simple, Pythonic API for ML inference
• Automatic file upload to S3
• Async job management with polling
• Built-in retry logic and error handling
• Support for multiple ML models (intelligibility, speaker diarization, etc.)

Installation

pip install redenlab-extract

Quick Start

from redenlab_ml import InferenceClient

# Initialize client with API key
client = InferenceClient(api_key="sk_live_your_api_key_here")

# Run inference on an audio file
result = client.predict(file_path="audio.wav")

print(result)
# {
#   'job_id': 'uuid',
#   'status': 'completed',
#   'result': {'intelligibility_score': 0.85},
#   'created_at': '2025-01-15T10:30:00Z',
#   'completed_at': '2025-01-15T10:35:00Z'
# }

Authentication

The SDK looks for your API key in the following order:

1. Constructor parameter: InferenceClient(api_key="sk_live_...")
2. Environment variable: REDENLAB_ML_API_KEY
3. Config file: ~/.redenlab-ml/config.yaml

Using Environment Variables

export REDENLAB_ML_API_KEY=sk_live_your_api_key_here

from redenlab_ml import InferenceClient

# API key is loaded from environment
client = InferenceClient()
result = client.predict(file_path="audio.wav")

Using Config File

Create ~/.redenlab-ml/config.yaml:

api_key: sk_live_your_api_key_here
base_url: https://your-api-gateway-url.amazonaws.com/prod  # optional
model_name: intelligibility  # optional

from redenlab_ml import InferenceClient

# API key is loaded from config file
client = InferenceClient()
result = client.predict(file_path="audio.wav")

Advanced Usage

Specify Model

client = InferenceClient(
    api_key="sk_live_...",
    model_name="speaker_diarisation_workflow"
)

Available models:

• intelligibility (default)
• speaker_diarisation_workflow
• ataxia-naturalness
• ataxia-intelligibility

Custom Timeout

client = InferenceClient(
    api_key="sk_live_...",
    timeout=7200  # 2 hours
)

Batch Processing (Submit + Poll Pattern)

For processing multiple files efficiently, use submit() and poll() separately:

# Submit all jobs first (fast - no waiting)
job_ids = []
for audio_file in audio_files:
    job_id = client.submit(file_path=audio_file)
    job_ids.append(job_id)
    print(f"Submitted: {job_id}")

# Poll for results (efficient - single loop checking all jobs)
results = {}
pending = set(job_ids)

while pending:
    for job_id in list(pending):
        status = client.get_status(job_id)

        if status['status'] == 'completed':
            results[job_id] = status['result']
            pending.remove(job_id)
        elif status['status'] == 'failed':
            results[job_id] = {'error': status.get('error')}
            pending.remove(job_id)

    if pending:
        time.sleep(10)  # Check all jobs every 10 seconds

print(f"Processed {len(results)} files!")

Submit and Poll Separately

# Submit job and get job_id immediately
job_id = client.submit(file_path="audio.wav")
print(f"Job submitted: {job_id}")

# ... do other work, or even exit and resume later ...

# Poll when ready
result = client.poll(job_id)
print(result['result'])

Check Job Status (Non-blocking)

# Get status of a running job without blocking
status = client.get_status(job_id="existing-job-uuid")
print(status['status'])  # 'upload_pending', 'processing', 'completed', 'failed'

Error Handling

from redenlab_ml import InferenceClient, InferenceError, TimeoutError, AuthenticationError

try:
    client = InferenceClient(api_key="sk_live_...")
    result = client.predict(file_path="audio.wav")
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except TimeoutError as e:
    print(f"Inference timed out: {e}")
except InferenceError as e:
    print(f"Inference failed: {e}")

Configuration Options

Parameter	Environment Variable	Config File	Default
api_key	REDENLAB_ML_API_KEY	api_key	Required
base_url	REDENLAB_ML_BASE_URL	base_url	Production endpoint
model_name	REDENLAB_ML_MODEL	model_name	intelligibility
timeout	REDENLAB_ML_TIMEOUT	timeout	3600 (1 hour)

Supported File Types

• WAV (.wav)
• MP3 (.mp3)
• M4A (.m4a)
• FLAC (.flac)

Requirements

• Python 3.8+
• requests
• tenacity
• pyyaml

Development

Install Development Dependencies

# Clone from CodeCommit (requires AWS credentials)
git clone ssh://git-codecommit.us-west-2.amazonaws.com/v1/repos/redenlab-extract
cd redenlab-extract
pip install -e ".[dev]"

Run Tests

pytest tests/

Code Formatting

black src/ tests/
ruff check src/ tests/

Type Checking

mypy src/

Examples

See the examples/ directory for more usage examples:

• quickstart.py - Basic usage
• batch_inference.py - Process multiple files
• error_handling.py - Robust error handling

Documentation

• API Reference
• Configuration Guide
• Troubleshooting

Support

• Email: [e-mail]
• Website: https://redenlab.com

License

MIT License - see LICENSE file for details

Changelog

See CHANGELOG.md for version history and release notes.