tiktools 0.1.0

A Python toolkit for TikTok data extraction and analysis using TikAPI

tiktools

A Python toolkit for TikTok data extraction and analysis using TikAPI.

Extract post metadata, transcribe videos using TikTok's built-in subtitles and analyze content at scale. Perfect for researchers, journalists and data analysts.

Features

• Fetch post metadata - Download complete post data for any TikTok user
• Extract transcripts - Get speech-to-text from videos using TikTok's ASR subtitles
• Incremental updates - Only fetch new content to save API costs
• Audio detection - Flag videos with non-original audio (songs vs. speech)
• Pip-installable - Easy to install and use in your projects
• Extensible - Build custom analysis tools on top of the core toolkit

Installation

pip install tiktools

Quick start

1. Set up API keys

TikTools requires a TikAPI key for core functionality:

export TIKAPI_KEY="your_tikapi_key_here"

For testing, you can use the sandbox key: DemoAPIKeyTokenSeHYGXDfd4SFD320Sc39Asd0Sc39Asd4s

2. Fetch posts and extract transcripts

from tiktools import fetch_user_posts, extract_transcripts
from pathlib import Path

# Fetch posts
posts_data = fetch_user_posts(
    username="davis_big_dawg",
    output_file=Path("data/davis_big_dawg/posts.json")
)

# Extract transcripts
results = extract_transcripts(
    posts_file=Path("data/davis_big_dawg/posts.json"),
    language="eng"
)

print(f"Extracted {results['transcripts_downloaded']} transcripts")

3. Use the CLI scripts

# Fetch all posts
python scripts/fetch_posts.py davis_big_dawg

# Extract transcripts
python scripts/extract_transcripts.py data/davis_big_dawg/davis_big_dawg_posts.json

# See generic analysis template
python scripts/analyze.py data/davis_big_dawg/transcripts/davis_big_dawg_transcripts.json

Incremental updates

Save API costs by only fetching new content:

# Only fetch NEW posts
python scripts/fetch_posts.py davis_big_dawg --update

# Only transcribe NEW posts
python scripts/extract_transcripts.py data/davis_big_dawg/davis_big_dawg_posts.json --update

Output structure

data/
└── davis_big_dawg/
    ├── davis_big_dawg_posts.json       # Post metadata
    └── transcripts/
        ├── 7575304937580547342.txt     # Individual transcripts
        └── davis_big_dawg_transcripts.json  # All transcripts

Example: Food reviews analysis

See examples/food_reviews/ for a complete example that:

• Extracts structured review data using OpenAI
• Calculates statistics by category and day
• Handles scoring and categorization

cd examples/food_reviews
python extract_reviews.py ../../data/davis_big_dawg/transcripts/davis_big_dawg_transcripts.json
python calculate_stats.py ../../data/davis_big_dawg/davis_big_dawg_reviews.json

API Reference

Core functions

fetch_user_posts()

Fetch TikTok post metadata for a user.

from tiktools import fetch_user_posts
from pathlib import Path

data = fetch_user_posts(
    username="davis_big_dawg",
    api_key=None,  # Uses TIKAPI_KEY env var
    max_posts=100,  # Limit number of posts
    output_file=Path("output.json"),
    sandbox=False,
    update_mode=False  # Only fetch new posts
)

extract_transcripts()

Extract transcripts from TikTok videos using subtitle files.

from tiktools import extract_transcripts
from pathlib import Path

results = extract_transcripts(
    posts_file=Path("posts.json"),
    output_dir=None,  # Defaults to posts_file.parent/transcripts
    output_format="individual",  # or "combined" or "both"
    language="eng",
    update_mode=False  # Only process new posts
)

get_best_subtitle()

Get the best available subtitle for a post (prioritizes ASR over MT).

from tiktools import get_best_subtitle

subtitle = get_best_subtitle(post, preferred_language="eng")
if subtitle:
    print(f"Found {subtitle['LanguageCodeName']} ({subtitle['Source']})")

API Client

from tiktools import TikAPIClient

client = TikAPIClient()  # Uses TIKAPI_KEY env var

# Get profile
profile = client.get_profile("davis_big_dawg")
print(profile['nickname'], profile['videoCount'])

# Iterate through posts
for post in client.get_posts(profile['secUid'], max_count=10):
    print(post['desc'])

Transcript limitations

TikTok's automatic speech recognition (ASR) has some limitations:

1. Speech recognition errors: May misinterpret words (e.g., "Baha Blast" → "Brawha Blast")
2. Non-speech audio: Videos using TikTok sounds may contain song lyrics instead of speech

Recommendations:

• Filter by is_original_audio: true for speech-only content
• Manually verify proper nouns and brand names for journalistic work
• Check the needs_review flag if using AI extraction

Requirements

• Python 3.8+
• TikAPI key (get one at tikapi.io)
• Optional: OpenAI API key (for AI-powered analysis examples)

Dependencies

• tikapi - TikTok API client
• requests - HTTP requests
• pathlib - File path handling

Development

# Clone the repository
git clone https://github.com/stiles/tiktools.git
cd tiktools

# Install in development mode
pip install -e .

# Run tests
pytest tests/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details.

Acknowledgments

• Built on top of TikAPI
• Inspired by the need for more TikTok research tools

Support

• Email: [e-mail]
• Issues: GitHub Issues
• Discussions: GitHub Discussions

Citation

If you use this toolkit in your research, please cite:

@software{tiktools2025,
  author = {Matt Stiles},
  title = {tiktools: A Python toolkit for TikTok data extraction and analysis},
  year = {2025},
  url = {https://github.com/stiles/tiktools}
}