clipflow 0.3.1

Trim, compress, and highlight video clips — auto-managed ffmpeg, zero setup

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Trim · Compress · Highlight

Typed Python API and CLI for video clipping — auto-managed ffmpeg, zero setup. Zero runtime dependencies. Now with Docker support for instant development.

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Why clipflow?

Most Python video libraries convert frames to NumPy arrays — slow, memory-heavy, and unnecessary for trimming. clipflow builds and runs ffmpeg commands directly as subprocess calls:

• Lossless stream-copy — trimming a 10 GB file takes seconds with zero quality loss
• Frame-accurate re-encode when you need compression
• Highlight routing — a first-class concept missing from every other package
• Clean typed API — all inputs and outputs are typed dataclasses, no dicts
• Zero runtime dependencies — stdlib + system ffmpeg, nothing else

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Installation

Option 1: pip (Traditional)

pip install clipflow

Requires: Python ≥ 3.9

Option 2: Docker (Zero Setup)

# Pull pre-built image from Docker Hub
docker pull ronaldgosso/clipflow:latest

# Run tests instantly
docker compose run --rm test

# Use CLI
docker run --rm -v /path/to/videos:/data ronaldgosso/clipflow:latest trim video.mp4 00:00-01:00

No Python installation required! See DOCKER.md for complete Docker setup.

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FFmpeg: Automatically downloaded and managed on first use — no manual installation required! 🎉

How FFmpeg Management Works

On first use, clipflow automatically:

1. Detects your platform (Windows, macOS, or Linux)
2. Downloads the appropriate FFmpeg binaries from trusted sources
3. Caches them locally for future use
4. Falls back to system PATH if you already have FFmpeg installed

You can also pre-download FFmpeg explicitly:

import clipflow

# Optional: Pre-download FFmpeg (happens automatically on first use anyway)
clipflow.setup_ffmpeg()

# Check where FFmpeg is located 
print(clipflow.get_ffmpeg_path())
print(clipflow.get_ffprobe_path())

Cache location:

• Windows: %LOCALAPPDATA%\clipflow\ffmpeg\
• macOS: ~/Library/Caches/clipflow/ffmpeg/
• Linux: ~/.cache/clipflow/ffmpeg/

If you prefer to use your system FFmpeg, ensure it's on your PATH — clipflow will use it automatically.

Manual FFmpeg Installation (Optional)

If you want to install FFmpeg manually or use a specific version:

# Windows — via Chocolatey
choco install ffmpeg

# Windows — via winget
winget install Gyan.FFmpeg

# macOS — via Homebrew
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

# Fedora
sudo dnf install ffmpeg

---

Quick start

import clipflow

results = clipflow.trim(
    "documentary.mp4",
    clipflow.ClipSpec(clipflow.parse_range("01:00", "02:30")),
)
print(results[0])
# ClipResult(✓ 'clip_01' → output/clip_01.mp4 [0.43s])

clipflow trim documentary.mp4 01:00-02:30

---

Python API

clipflow.trim()

import clipflow
from clipflow import ClipSpec, parse_range, COMPRESS_HIGH, AR_9_16

# Lossless stream-copy
results = clipflow.trim(
    "input.mp4",
    ClipSpec(parse_range("00:30", "02:15")),
    output_dir="clips",
)

# Multiple ranges
clips = [
    ClipSpec(parse_range("00:00", "01:00"), label="intro"),
    ClipSpec(parse_range("10:30", "12:00"), label="climax"),
    ClipSpec(parse_range("58:00", "60:00"), label="outro"),
]
results = clipflow.trim("lecture.mp4", clips, output_dir="out")

# Compress + aspect ratio + highlight
results = clipflow.trim(
    "raw_footage.mp4",
    ClipSpec(
        parse_range("05:00", "06:30"),
        highlight=True,          # copied to out/highlights/ automatically
        compress=COMPRESS_HIGH,  # CRF 18, slow preset
        aspect_ratio=AR_9_16,    # crop/pad to 9:16 for Reels/Shorts
        label="hero_moment",
    ),
    output_dir="out",
)
print(results[0].highlight_path)  # out/highlights/hero_moment.mp4

# Progress callback
def on_progress(idx, total, result):
    print(f"[{idx}/{total}] {'✓' if result.ok else '✗'} {result.spec.effective_label()}")

clipflow.trim("video.mp4", clips, output_dir="out", on_progress=on_progress)

ClipResult fields:

Field	Type	Description
ok	bool	True if clip was produced without error
output_path	Path | None	Absolute path to the output file
highlight_path	Path | None	Path to highlight copy, or None
duration_s	float	Wall-clock seconds the ffmpeg call took
error	str | None	Error message if ok is False

---

clipflow.inspect()

info = clipflow.inspect("documentary.mp4")

print(info.resolution)    # '1920×1080'
print(info.duration_fmt)  # '01:23:45'
print(info.fps)           # 29.97
print(info.video_codec)   # 'h264'
print(info.audio_codec)   # 'aac'
print(info.size_mb)       # 842.3

---

clipflow.batch()

from pathlib import Path
from clipflow import BatchSpec, ClipSpec, parse_range
import clipflow

specs = [
    BatchSpec(
        input_path=Path("ep01.mp4"),
        output_dir=Path("ep01_clips"),
        clips=[
            ClipSpec(parse_range("00:30", "01:30"), label="cold_open"),
            ClipSpec(parse_range("20:00", "21:00"), label="twist", highlight=True),
        ],
    ),
]
all_results = clipflow.batch(specs)

---

CLI

# Lossless trim
clipflow trim lecture.mp4 01:00-02:30

# Multiple ranges
clipflow trim lecture.mp4 00:00-01:00 10:30-12:00 --output clips/

# Compress + crop + highlight
clipflow trim concert.mp4 05:00-06:30 --compress high --aspect 9:16 --highlight

# Custom CRF + H.265
clipflow trim raw.mp4 00:00-30:00 --crf 20 --codec libx265

# Inspect metadata
clipflow inspect documentary.mp4
clipflow inspect documentary.mp4 --json | jq .fps

# Batch from JSON spec
clipflow batch spec.json

spec.json format:

[
  {
    "input": "lecture.mp4",
    "output_dir": "clips",
    "clips": [
      { "start": "00:30", "end": "02:15", "label": "intro", "compress": "medium" },
      { "start": "10:00", "end": "11:30", "highlight": true, "aspect": "16:9" }
    ]
  }
]

---

Time formats

All time inputs accept:

Format	Example	Seconds
"MM:SS"	"01:30"	90.0
"HH:MM:SS"	"01:02:03"	3723.0
"SS"	"90"	90.0
int / float	90 / 90.5	90.0 / 90.5

---

Compression presets

Constant	CRF	Speed	Use case
COMPRESS_LOW	28	fast	Smallest file
COMPRESS_MEDIUM	23	medium	Balanced (ffmpeg default)
COMPRESS_HIGH	18	slow	Best quality

Custom:

from clipflow import CompressOptions

custom = CompressOptions(crf=20, preset="slower", codec="libx265", audio_bitrate="192k")

---

Aspect ratio shortcuts

Constant	Ratio	Use case
AR_16_9	16:9	YouTube, landscape
AR_9_16	9:16	Reels, Shorts, TikTok
AR_1_1	1:1	Instagram square
AR_4_3	4:3	Classic TV

from clipflow import AspectRatio
ultra_wide = AspectRatio(21, 9)

---

How it works

clipflow builds ffmpeg commands as list[str] and runs them with subprocess.run.

FFmpeg Management: On first use, clipflow automatically downloads and caches FFmpeg binaries for your platform. The binaries are stored in a local cache directory and reused on subsequent runs. If you have FFmpeg on your PATH, clipflow will use your system installation instead.

Stream-copy (default — lossless, fast):

ffmpeg -y -ss <start> -t <duration> -i input.mp4 -c copy output.mp4

-ss before -i = keyframe seek. Bytes are copied directly, no re-encoding.

Re-encode (with compress=):

ffmpeg -y -i input.mp4 -ss <start> -t <duration> -c:v libx264 -crf 23 -preset medium -c:a copy output.mp4

-ss after -i = frame-accurate cut. Required when the encoder needs full frame data.

Highlights: shutil.copy2 of the finished clip to output/highlights/. No second ffmpeg call.

The entire subprocess layer is isolated in clipflow/_ffmpeg.py — no other module touches subprocess.

---

Development

Local Setup

git clone https://github.com/ronaldgosso/clipflow.git
cd clipflow
pip install -e ".[dev]"

pytest                                           # 82 tests
pytest --cov=clipflow --cov-report=term          # coverage (>90%)
ruff check clipflow/ tests/ && black clipflow/   # lint + format
mypy clipflow/ --ignore-missing-imports          # strict types

Docker Setup

No Python installation required! Use Docker for instant development:

# Run full test suite in Docker
docker compose run --rm test

# Development shell with hot-reload
docker compose run --rm dev

# Use CLI via Docker
docker run --rm -v /path/to/videos:/data ronaldgosso/clipflow:latest trim video.mp4 00:00-01:00

# Pull pre-built image from Docker Hub
docker pull ronaldgosso/clipflow:latest

See DOCKER.md for complete Docker documentation including CI/CD integration and production deployment.

Note: FFmpeg binaries are automatically managed. Tests mock the FFmpeg manager to avoid actual downloads.

Release:

# Bump version in pyproject.toml + clipflow/__init__.py, then:
git tag v0.3.1 && git push origin v0.3.1
# publish.yml triggers → PyPI via OIDC + Docker image to Docker Hub

See CONTRIBUTING.md for detailed guidelines.

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License

MIT © Ronald Isack Gosso

_{Built with Python · auto-managed ffmpeg · subprocess · zero magic}