Setting Up Audio Transcription Review

Transcription review is essential for quality ASR training data. This tutorial shows you how to build an interface where annotators can listen to audio, view waveforms, and correct machine-generated transcriptions.

What We're Building

An interface with:

Waveform visualization
Playback controls (play, pause, speed adjustment)
Editable transcript text
Quality rating for audio
Confidence marking for uncertain segments

Basic Configuration

annotation_task_name: "Transcription Review"
 
data_files:
  - "data/transcripts.json"
 
item_properties:
  id_key: id
  text_key: asr_transcript
 
annotation_schemes:
  # Audio playback
  - annotation_type: audio_annotation
    name: audio_player
    audio_key: audio_path
 
  # Corrected transcript
  - annotation_type: text
    name: corrected_transcript
    description: "Edit the transcript to match what you hear"
    textarea: true
    placeholder: "Type the corrected transcript..."
    required: true
 
  # Quality rating
  - annotation_type: radio
    name: audio_quality
    description: "Rate the audio quality"
    labels:
      - Clear
      - Slightly noisy
      - Very noisy
      - Unintelligible

Sample Data Format

Create data/transcripts.json:

{"id": "audio_001", "audio_path": "/audio/recording_001.wav", "asr_transcript": "Hello how are you doing today"}
{"id": "audio_002", "audio_path": "/audio/recording_002.wav", "asr_transcript": "The weather is nice outside"}
{"id": "audio_003", "audio_path": "/audio/recording_003.wav", "asr_transcript": "Please call me back when your free"}

Audio Annotation Setup

Audio annotation in Potato uses the audio_annotation type within annotation schemes. The audio player provides waveform visualization and playback controls automatically.

annotation_schemes:
  - annotation_type: audio_annotation
    name: audio_player
    audio_key: audio_path
    description: "Listen to the audio recording"

The audio player includes built-in controls for play/pause, seeking, and speed adjustment.

Comprehensive Transcription Interface

annotation_task_name: "ASR Correction and Annotation"
 
data_files:
  - "data/asr_output.json"
 
item_properties:
  id_key: id
  text_key: hypothesis
 
annotation_schemes:
  # Audio player
  - annotation_type: audio_annotation
    name: audio_player
    audio_key: audio_url
 
  # Main transcript correction
  - annotation_type: text
    name: transcript
    description: "Correct the transcript below"
    textarea: true
    rows: 4
    required: true
 
  # Speaker identification
  - annotation_type: radio
    name: num_speakers
    description: "How many speakers are in this recording?"
    labels:
      - "1 speaker"
      - "2 speakers"
      - "3+ speakers"
      - "Cannot determine"
 
  # Audio quality
  - annotation_type: radio
    name: quality
    description: "Overall audio quality"
    labels:
      - name: Excellent
        description: "Crystal clear, studio quality"
      - name: Good
        description: "Clear speech, minor background noise"
      - name: Fair
        description: "Understandable but noisy"
      - name: Poor
        description: "Very difficult to understand"
      - name: Unusable
        description: "Cannot transcribe accurately"
 
  # Issues checklist
  - annotation_type: multiselect
    name: issues
    description: "Select all issues present (if any)"
    labels:
      - Background noise
      - Overlapping speech
      - Accented speech
      - Fast speech
      - Mumbling/unclear
      - Technical audio issues
      - Non-English words
      - Profanity present
      - None
 
  # Confidence
  - annotation_type: likert
    name: confidence
    description: "How confident are you in your transcription?"
    size: 5
    min_label: "Guessing"
    max_label: "Certain"
 
annotation_guidelines:
  title: "Transcription Guidelines"
  content: |
    ## Your Task
    Listen to the audio and correct the ASR transcript.
 
    ## Transcription Rules
    - Transcribe exactly what is said
    - Include filler words (um, uh, like)
    - Use proper punctuation and capitalization
    - Mark unintelligible sections with [unintelligible]
    - Mark uncertain words with [word?]
 
    ## Special Notations
    - [unintelligible] - Cannot understand
    - [word?] - Uncertain about word
    - [crosstalk] - Overlapping speech
    - [noise] - Non-speech sound
    - [pause] - Significant silence

Word-Level Annotation

For detailed word-level corrections, you can use span annotation alongside text fields:

annotation_schemes:
  - annotation_type: audio_annotation
    name: audio_player
    audio_key: audio_path
 
  - annotation_type: text
    name: transcript
    textarea: true
 
  - annotation_type: span
    name: word_corrections
    description: "Mark words that needed correction"
    source_field: transcript
    labels:
      - name: corrected
        color: "#FCD34D"
        description: "Word was changed"
      - name: inserted
        color: "#4ADE80"
        description: "Word was added"
      - name: uncertain
        color: "#F87171"
        description: "Still not sure"

Segment-Based Transcription

For long audio files, you can prepare your data as segments with timing information:

data_files:
  - "data/segments.json"
 
item_properties:
  id_key: id
  text_key: asr_text
 
annotation_schemes:
  - annotation_type: audio_annotation
    name: audio_player
    audio_key: audio_path
 
  - annotation_type: text
    name: transcript
    textarea: true
    description: "Correct the transcript for this segment"

Data format with segment timing:

{
  "id": "seg_001",
  "audio_path": "/audio/long_recording.wav",
  "start_time": 0.0,
  "end_time": 5.5,
  "asr_text": "Welcome to today's presentation"
}

Output Format

{
  "id": "audio_001",
  "audio_path": "/audio/recording_001.wav",
  "original_transcript": "Hello how are you doing today",
  "annotations": {
    "transcript": "Hello, how are you doing today?",
    "num_speakers": "1 speaker",
    "quality": "Good",
    "issues": ["None"],
    "confidence": 5
  },
  "annotator": "transcriber_01",
  "time_spent_seconds": 45
}

Quality Control

Potato tracks annotation time automatically. For quality control, consider including attention check items in your data file - items with known correct answers that you can use to verify annotator accuracy.

You can configure output settings to track annotations:

output_annotation_dir: "annotation_output"
output_annotation_format: "json"

Tips for Transcription Tasks

Good headphones: Essential for accuracy
Quiet environment: Reduces fatigue
Speed adjustment: Slow down for difficult sections
Multiple passes: Listen once, transcribe, then verify
Regular breaks: Transcription is mentally demanding

Next Steps

Add speaker diarization for multi-speaker audio
Set up emotion classification alongside transcription
Configure crowdsourcing for large-scale transcription

Full audio documentation at /docs/features/audio-annotation.