Running Inference

After training a model, use it to detect artifacts in new audio files. Inference returns timestamped detections with confidence scores.

Creating an inference job

Each inference job uses one model and can process multiple audio files:

Python

import requests
import os

API_KEY = os.environ["RELAY_API_KEY"]
BASE_URL = "https://api.relayai.dev"

response = requests.post(
    f"{BASE_URL}/api/v1/inference-jobs",
    headers={"X-API-Key": API_KEY},
    json={
        "model_id": "your-model-id",
        "config": {
            "threshold": 0.5,
            "merge_window_ms": 200,
            "min_duration_ms": 50
        }
    }
)
job = response.json()
inference_job_id = job["id"]

Inference configuration

Parameter	Default	Range	Description
`threshold`	0.5	0.0-1.0	Minimum confidence score to report
`merge_window_ms`	200	0-5000	Merge detections within this time window
`min_duration_ms`	50	0-1000	Ignore detections shorter than this

Threshold

Controls the sensitivity of detection:

Lower threshold (0.3-0.5): More detections, including uncertain ones
Higher threshold (0.7-0.9): Fewer detections, higher confidence

Python

# Sensitive detection (catch more, but more false positives)
"config": {"threshold": 0.3}

# Conservative detection (miss some, but fewer false positives)
"config": {"threshold": 0.8}

Merge window

Adjacent detections of the same type are merged if they’re within this window:

Python

# Without merging, you might get:
# glitch: 1000-1100ms
# glitch: 1150-1250ms
# glitch: 1280-1380ms

# With merge_window_ms: 200, these become:
# glitch: 1000-1380ms

Minimum duration

Filters out very short detections that may be noise:

Python

# Ignore detections shorter than 100ms
"config": {"min_duration_ms": 100}

Uploading audio for inference

Upload audio files using presigned URLs (same pattern as dataset uploads):

Python

import os

file_path = "test_audio.wav"
file_size = os.path.getsize(file_path)

# Step 1: Get upload URL
response = requests.post(
    f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}/files/upload-url",
    headers={"X-API-Key": API_KEY},
    json={
        "filename": os.path.basename(file_path),
        "content_type": "audio/wav",
        "file_size_bytes": file_size
    }
)
upload_info = response.json()

# Step 2: Upload to presigned URL
with open(file_path, "rb") as f:
    requests.post(
        upload_info["upload_url"],
        data=upload_info["upload_fields"],
        files={"file": f}
    )

# Step 3: Confirm upload (starts processing)
response = requests.post(
    f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}/files/confirm",
    headers={"X-API-Key": API_KEY},
    json={"file_id": upload_info["file_id"]}
)
print(f"File queued for inference: {response.json()['status']}")

Batch inference

Upload multiple files for parallel processing:

Python

import concurrent.futures

def upload_for_inference(file_path):
    file_size = os.path.getsize(file_path)

    # Get upload URL
    response = requests.post(
        f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}/files/upload-url",
        headers={"X-API-Key": API_KEY},
        json={
            "filename": os.path.basename(file_path),
            "content_type": "audio/wav",
            "file_size_bytes": file_size
        }
    )
    upload_info = response.json()

    # Upload file
    with open(file_path, "rb") as f:
        requests.post(
            upload_info["upload_url"],
            data=upload_info["upload_fields"],
            files={"file": f}
        )

    # Confirm
    requests.post(
        f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}/files/confirm",
        headers={"X-API-Key": API_KEY},
        json={"file_id": upload_info["file_id"]}
    )

    return upload_info["file_id"]

# Upload multiple files in parallel
audio_files = ["audio1.wav", "audio2.wav", "audio3.wav"]
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    file_ids = list(executor.map(upload_for_inference, audio_files))

print(f"Uploaded {len(file_ids)} files for inference")

Getting results

Poll job status

Python

import time

while True:
    response = requests.get(
        f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}",
        headers={"X-API-Key": API_KEY}
    )
    job = response.json()

    print(f"Processed: {job['processed_files']}/{job['total_files']}")

    if job["processed_files"] == job["total_files"] and job["total_files"] > 0:
        print("All files processed!")
        break

    if job["status"] == "failed":
        print(f"Job failed: {job['error_message']}")
        break

    time.sleep(2)

Get job details with files

Python

response = requests.get(
    f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}",
    headers={"X-API-Key": API_KEY}
)
job = response.json()

for file in job["files"]:
    print(f"\nFile: {file['original_filename']}")
    print(f"Status: {file['status']}")
    print(f"Detections: {file['detection_count']}")

    for detection in file["detections"]:
        print(f"  {detection['artifact_type']}: "
              f"{detection['start_ms']}-{detection['end_ms']}ms "
              f"(confidence: {detection['confidence']:.2f})")

Get single file results

Python

file_id = "file-uuid"
response = requests.get(
    f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}/files/{file_id}",
    headers={"X-API-Key": API_KEY}
)
file_result = response.json()

Detection output format

Each detection includes:

{
  "artifact_type": "glitch",
  "start_ms": 1200,
  "end_ms": 1450,
  "confidence": 0.87
}

Field	Type	Description
`artifact_type`	string	Type of detected artifact
`start_ms`	integer	Start time in milliseconds
`end_ms`	integer	End time in milliseconds
`confidence`	float	Model confidence (0.0-1.0)

File statuses

Status	Description
`pending`	Upload not yet confirmed
`queued`	Waiting for processing
`processing`	Running inference
`completed`	Finished, detections available
`failed`	Processing failed (check `error_message`)

Listing inference jobs

Python

# List all jobs
response = requests.get(
    f"{BASE_URL}/api/v1/inference-jobs",
    headers={"X-API-Key": API_KEY}
)

# Filter by model
response = requests.get(
    f"{BASE_URL}/api/v1/inference-jobs",
    headers={"X-API-Key": API_KEY},
    params={"model_id": model_id}
)

# Filter by status
response = requests.get(
    f"{BASE_URL}/api/v1/inference-jobs",
    headers={"X-API-Key": API_KEY},
    params={"status": "completed"}
)

Cancelling inference

Cancel a pending or processing job:

Python

requests.delete(
    f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}",
    headers={"X-API-Key": API_KEY}
)

Downloading processed audio

Download the original audio file:

Python

file_id = "file-uuid"
response = requests.get(
    f"{BASE_URL}/api/v1/inference-jobs/{inference_job_id}/files/{file_id}/download-url",
    headers={"X-API-Key": API_KEY}
)
download_info = response.json()

# Download the file
audio_response = requests.get(download_info["url"])
with open("downloaded.wav", "wb") as f:
    f.write(audio_response.content)

Production patterns

Continuous inference pipeline

For production use, create a worker that processes audio as it arrives:

Python

import time
from queue import Queue
from threading import Thread

def inference_worker(audio_queue, model_id):
    """Worker that processes audio from a queue."""

    # Create a new job for each batch
    response = requests.post(
        f"{BASE_URL}/api/v1/inference-jobs",
        headers={"X-API-Key": API_KEY},
        json={"model_id": model_id}
    )
    job_id = response.json()["id"]

    while True:
        file_path = audio_queue.get()
        if file_path is None:
            break

        # Upload and process
        upload_for_inference(file_path)

        # Wait for result
        # ...

# Usage
audio_queue = Queue()
worker = Thread(target=inference_worker, args=(audio_queue, model_id))
worker.start()

# Add files to process
audio_queue.put("audio1.wav")
audio_queue.put("audio2.wav")

# Signal done
audio_queue.put(None)
worker.join()

Webhook-style results

Poll efficiently with exponential backoff:

Python

import time

def wait_for_results(job_id, max_wait=300):
    """Wait for results with exponential backoff."""
    start = time.time()
    wait_time = 1

    while time.time() - start < max_wait:
        response = requests.get(
            f"{BASE_URL}/api/v1/inference-jobs/{job_id}",
            headers={"X-API-Key": API_KEY}
        )
        job = response.json()

        if job["processed_files"] == job["total_files"]:
            return job

        time.sleep(wait_time)
        wait_time = min(wait_time * 2, 30)  # Max 30 second wait

    raise TimeoutError("Inference timed out")

Getting Started

Guides

Concepts

SDKs

Running Inference

Creating an inference job

Inference configuration

Threshold

Merge window

Minimum duration

Uploading audio for inference

Batch inference

Getting results

Poll job status

Get job details with files

Get single file results

Detection output format

File statuses

Listing inference jobs

Cancelling inference

Downloading processed audio

Production patterns

Continuous inference pipeline

Webhook-style results

Getting Started

Guides

Concepts

SDKs

​Creating an inference job

​Inference configuration

​Threshold

​Merge window

​Minimum duration

​Uploading audio for inference

​Batch inference

​Getting results

​Poll job status

​Get job details with files

​Get single file results

​Detection output format

​File statuses

​Listing inference jobs

​Cancelling inference

​Downloading processed audio

​Production patterns

​Continuous inference pipeline

​Webhook-style results

Creating an inference job

Inference configuration

Threshold

Merge window

Minimum duration

Uploading audio for inference

Batch inference

Getting results

Poll job status

Get job details with files

Get single file results

Detection output format

File statuses

Listing inference jobs

Cancelling inference

Downloading processed audio

Production patterns

Continuous inference pipeline

Webhook-style results