# How to Handle Videos Larger Than 25MB for Whisper API Processing in Claude Video > Claude Video effortlessly processes videos over 25MB by automatically chunking audio for Whisper API. Get accurate transcripts and timestamps for large files. - Repository: [bradautomates/claude-video](https://github.com/bradautomates/claude-video) - Tags: how-to-guide - Published: 2026-07-11 --- **Claude Video automatically chunks oversized audio files to stay within the 25 MB Whisper API limit, then stitches the transcripts back together with accurate timestamps.** The `bradautomates/claude-video` repository provides a `watch` skill that transcribes videos using Groq or OpenAI Whisper APIs. Both services enforce a strict upload ceiling of approximately 25 MB, so the codebase implements an intelligent chunking pipeline that splits audio, transcribes each segment, and reassembles the results without user intervention. ## The 25 MB Limit and Safety Threshold In [`skills/watch/scripts/whisper.py`](https://github.com/bradautomates/claude-video/blob/main/skills/watch/scripts/whisper.py), the skill defines a conservative safety margin: ```python MAX_UPLOAD_BYTES = 24 * 1024 * 1024 # 24 MiB to stay under 25 MB limit ``` This constant (lines 35–38) ensures that even with metadata overhead, uploads stay within the Groq and OpenAI Whisper API limits. When `extract_audio` produces a file larger than this threshold, the `transcribe_video` function automatically triggers the chunking workflow rather than attempting a single upload. ## Automatic Chunking Pipeline The chunking system operates through a six-step pipeline orchestrated in [`skills/watch/scripts/whisper.py`](https://github.com/bradautomates/claude-video/blob/main/skills/watch/scripts/whisper.py): ### Step 1: Audio Extraction The `extract_audio` function (lines 15–33) uses **ffmpeg** to generate a mono 16 kHz MP3 at approximately 480 kB per minute. This constant-bitrate encoding (64 kbps) makes size calculations predictable. ### Step 2: Size Check The `transcribe_video` entry point (lines 44–51) compares `audio_path.stat().st_size` against `MAX_UPLOAD_BYTES`. If the file is under the limit, it uploads directly; otherwise, it proceeds to chunking. ### Step 3: Chunk Planning The `plan_chunks` function (lines 40–62) calculates: - Number of chunks required - Byte offset for each chunk - Duration per chunk to stay under the byte limit Because the MP3 uses constant bitrate, the planning math remains linear and reliable. ### Step 4: Audio Splitting The `split_audio` function (lines 64–80) invokes ffmpeg with `-c copy` to slice the original MP3 into the planned segments. This preserves audio quality without re-encoding. ### Step 5: Transcription and Timestamp Shifting The `transcribe_chunks` function (lines 71–80) processes each chunk through `_transcribe_file` and `_post_whisper`. The `shift_segments` helper (lines 32–40) adjusts each segment's start and end times by the chunk's offset, ensuring the final transcript reflects the original video timeline. ### Step 6: Final Assembly The function returns a single ordered list of segments (lines 64–66), concatenating results from all chunks as if the entire audio had been processed at once. ## Practical Usage Examples ### CLI with Automatic Chunking ```bash # Automatically handles any video size watch watch "https://www.youtube.com/watch?v=example" ``` ### Force Specific Backend ```bash # Force Groq Whisper (falls back to OpenAI if unavailable) watch watch "my-video.mp4" --backend groq ``` ### Programmatic Python Usage ```python from pathlib import Path from skills.watch.scripts.whisper import transcribe_video video_path = "big-lecture.mp4" audio_out = Path("audio.mp3") # Automatically chunks if audio exceeds 24 MiB segments, backend = transcribe_video(video_path, audio_out) print(f"Transcribed with {backend}:") for seg in segments: print(f"{seg['start']:.2f}-{seg['end']:.2f}: {seg['text']}") ``` ### Environment Configuration Create a private configuration file for API keys (referenced in [`setup.py`](https://github.com/bradautomates/claude-video/blob/main/setup.py) lines 40–50): ```bash mkdir -p ~/.config/watch cat > ~/.config/watch/.env <