Artifact Types

Artifact types define what your model will learn to detect. Each type represents a specific kind of audio issue.

What are artifact types?

Artifact types are categories that:

Label annotations during training
Categorize detections during inference
Help organize and filter results

{
  "name": "glitch",
  "description": "Audio pop, click, or distortion",
  "color": "#FF4444"
}

Artifact type structure

Field	Type	Required	Description
`name`	string	Yes	Unique identifier (1-100 characters)
`description`	string	No	Human-readable explanation
`color`	string	No	Hex color for visualization (default: #FF0000)

Defining artifact types

Define types when creating a dataset:

Python

response = requests.post(
    f"{BASE_URL}/api/v1/datasets",
    headers={"X-API-Key": API_KEY},
    json={
        "name": "TTS Quality Detection",
        "artifact_types": [
            {
                "name": "glitch",
                "description": "Audio pop, click, or digital distortion",
                "color": "#FF4444"
            },
            {
                "name": "long_pause",
                "description": "Unnatural silence > 500ms",
                "color": "#4444FF"
            },
            {
                "name": "hallucination",
                "description": "Words or sounds not in the input text",
                "color": "#44FF44"
            },
            {
                "name": "echo",
                "description": "Reverb or repeated audio",
                "color": "#FF8844"
            }
        ]
    }
)

Common artifact types

Here are common types for different Voice AI applications:

TTS (Text-to-Speech)

Type	Description
`glitch`	Pops, clicks, digital artifacts
`long_pause`	Unnatural silence between words
`hallucination`	Extra words not in input
`mispronunciation`	Incorrect word pronunciation
`clipping`	Audio amplitude exceeding limits
`distortion`	Waveform distortion

Voice agents

Type	Description
`crosstalk`	Overlapping speech from multiple sources
`echo`	Audio reflection or reverb
`dropout`	Missing audio segments
`static`	Background noise or interference
`latency_gap`	Delays in response

Speech recognition

Type	Description
`filler_word`	”Um”, “uh”, “like”
`hesitation`	Unnatural pauses mid-sentence
`repetition`	Repeated words or phrases
`false_start`	Sentence restarts

Naming conventions

Use lowercase with underscores

# Good
"long_pause"
"tts_hallucination"
"background_noise"

# Avoid
"Long Pause"
"TTS-Hallucination"
"backgroundNoise"

Keep names short but descriptive

# Good
"glitch"
"echo"
"dropout"

# Too long
"audio_glitch_or_pop_or_click"
"speaker_echo_or_reverb_artifact"

Be specific

# Good - specific types
"long_pause"    # Pause > 500ms
"short_pause"   # Pause 100-500ms

# Too vague
"pause"         # Which kind?
"issue"         # What issue?

Choosing artifact types

Start focused

Begin with 2-3 well-defined types:

Python

"artifact_types": [
    {"name": "glitch", "description": "Audio pop or click"},
    {"name": "long_pause", "description": "Silence > 500ms"}
]

Expand as needed

Add more types after your initial model is working:

Python

# Get current types
dataset = requests.get(...).json()
current_types = dataset["artifact_types"]

# Add new type
current_types.append({
    "name": "echo",
    "description": "Reverb or repeated audio"
})

# Update dataset
requests.patch(
    f"{BASE_URL}/api/v1/datasets/{dataset_id}",
    headers={"X-API-Key": API_KEY},
    json={"artifact_types": current_types}
)

Avoid overlap

Each artifact should fit into exactly one type:

# Good - distinct types
"glitch"        → Pops and clicks
"distortion"    → Waveform distortion
"clipping"      → Amplitude clipping

# Problematic - overlapping definitions
"audio_issue"   → Too broad, overlaps with everything
"sound_problem" → What does this mean?

Using artifact types

In annotations

Specify the artifact type when creating annotations:

Python

{
    "audio_file_id": "...",
    "artifact_type": "glitch",  # Must match dataset's defined types
    "start_ms": 1200,
    "end_ms": 1450
}

In training

Choose which types to train on:

Python

{
    "dataset_id": "...",
    "annotation_set_id": "...",
    "config": {
        "artifact_types": ["glitch", "long_pause"]  # Train on subset
    }
}

In inference results

Detections include the artifact type:

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

Colors for visualization

Colors help distinguish types in UIs:

Python

"artifact_types": [
    {"name": "glitch", "color": "#FF4444"},      # Red
    {"name": "long_pause", "color": "#4444FF"},  # Blue
    {"name": "hallucination", "color": "#44FF44"},# Green
    {"name": "echo", "color": "#FF8844"}         # Orange
]

Use contrasting colors for easy differentiation.

Best practices

Document definitions

Include clear descriptions:

Python

{
    "name": "long_pause",
    "description": "Silence > 500ms that breaks natural speech flow. Don't label intentional pauses at sentence boundaries."
}

Create labeling guidelines

Document criteria for each type:

## Glitch
- Include: Pops, clicks, digital artifacts > 10ms
- Exclude: Background noise, recording quality issues
- Boundary: Start 10ms before audible start, end 10ms after

## Long Pause
- Include: Silence > 500ms mid-sentence
- Exclude: Natural pauses at sentence boundaries
- Boundary: From last sound to first sound

Review and iterate

After initial training, review detections to refine definitions:

Are there false positives that suggest type overlap?
Are there missed artifacts that need a new type?
Are definitions clear enough for consistent labeling?

Getting Started

Guides

Concepts

SDKs

Artifact Types

What are artifact types?

Artifact type structure

Defining artifact types

Common artifact types

TTS (Text-to-Speech)

Voice agents

Speech recognition

Naming conventions

Use lowercase with underscores

Keep names short but descriptive

Be specific

Choosing artifact types

Start focused

Expand as needed

Avoid overlap

Using artifact types

In annotations

In training

In inference results

Colors for visualization

Best practices

Document definitions

Create labeling guidelines

Review and iterate

​What are artifact types?

​Artifact type structure

​Defining artifact types

​Common artifact types

​TTS (Text-to-Speech)

​Voice agents

​Speech recognition

​Naming conventions

​Use lowercase with underscores

​Keep names short but descriptive

​Be specific

​Choosing artifact types

​Start focused

​Expand as needed

​Avoid overlap

​Using artifact types

​In annotations

​In training

​In inference results

​Colors for visualization

​Best practices

​Document definitions

​Create labeling guidelines

​Review and iterate

What are artifact types?

Artifact type structure

Defining artifact types

Common artifact types

TTS (Text-to-Speech)

Voice agents

Speech recognition

Naming conventions

Use lowercase with underscores

Keep names short but descriptive

Be specific

Choosing artifact types

Start focused

Expand as needed

Avoid overlap

Using artifact types

In annotations

In training

In inference results

Colors for visualization

Best practices

Document definitions

Create labeling guidelines

Review and iterate