Skip to main content

Python SDK for RedenLab ML inference service

Project description

RedenLab Extract SDK

Python SDK for RedenLab's ML inference service. Run machine learning models on audio files using Redenlab's proprietary endpoints.

Features

  • Simple, Pythonic API for ML inference
  • Async job management with polling
  • Built-in retry logic and error handling
  • Support for multiple ML models

Installation

pip install redenlab-extract

Quick Start

from redenlab_extract 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_extract 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: als-intelligibility
from redenlab_extract 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:

  • als-intelligibility
  • 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'

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)
  • FLAC (.flac)

Requirements

  • Python 3.10+
  • requests
  • tenacity
  • pyyaml

Examples

See the examples/ directory for more usage examples:

Documentation

Support

License

MIT License - see LICENSE file for details

Changelog

See CHANGELOG.md for version history and release notes.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

redenlab_extract-1.1.0.tar.gz (120.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

redenlab_extract-1.1.0-py3-none-any.whl (39.6 kB view details)

Uploaded Python 3

File details

Details for the file redenlab_extract-1.1.0.tar.gz.

File metadata

  • Download URL: redenlab_extract-1.1.0.tar.gz
  • Upload date:
  • Size: 120.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.19

File hashes

Hashes for redenlab_extract-1.1.0.tar.gz
Algorithm Hash digest
SHA256 b563648f2ff39f5da80a326b2e1d2eb0e7807bce14ad14631361566378d93df1
MD5 e345b623e591562cb4cf1c2a28f1c342
BLAKE2b-256 9ba9c64b6557146831e490e9fc9eeef9ed84832ad4e2124bc9e56a64810c8c02

See more details on using hashes here.

File details

Details for the file redenlab_extract-1.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for redenlab_extract-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 4350e78825a09615c36283b9d38cb45663d1bedd69195db49d3b7be7a1756a0e
MD5 d76f5aa49f9805a46852151e99a25919
BLAKE2b-256 ef0a42a1e0b95598133a075c25f0caf7f8eaf1a77021b1e8c53f45ea8e769780

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page