meeting-to-text

---

name: meeting-to-text description: Create a fully local speaker-separated .txt transcript from a meeting recording, meeting screen recording, speech audio, or local video/audio file. Use this whenever the user wants to transcribe a local recording into plain text, generate a meeting transcript, convert audio or video to txt, or explicitly asks to distinguish speakers with default labels like 说话人1, 说话人2, etc. Trigger even if the user only provides an input file path and an output path and says things like "转文字", "做逐字稿", "会议录音转 txt", or "区分发言人".

---

Meeting To Text

Use this skill when the job is a local file-to-transcript workflow.

Do not use this skill if the user only wants audio extraction, a meeting summary, environment setup, or an explanation of the models.

Inputs To Collect

Always collect:

one local source file path

one output target path

Output target rules:

If the target ends with .txt, write exactly to that file.

Otherwise treat it as a directory and write _transcript.txt inside it.

Supported source types:

Video: .mp4, .mkv, .mov, .avi, .webm

Audio: .wav, .mp3, .m4a, .aac, .flac, .ogg

Runtime

Read references/runtime_paths.md before running the script.

Run the bundled entrypoint with the local ASR environment:

& '' 'C:\path\to\your\meeting-to-text\scripts\meeting_to_text.py' --input '' --output ''

If you need a stable temp location, add:

--work-dir ''

Result Handling

The script may print library noise before the final machine-readable result.

Always treat the last non-empty stdout line as the JSON result object.

Interpret results this way:

Exit code 0 with status: success: transcript file was created with no warnings.

Exit code 0 with status: warning: transcript file was created, but you must report the warnings and any skipped segments.

Non-zero exit code or status: error: do not claim success; surface the warning list and the intended output path.

Important fields in the final JSON:

output_path: final transcript file path

speaker_count: number of detected 说话人N labels in the written transcript

segment_count: normalized diarization segments sent into transcription

transcribed_segment_count: segments that produced text

skipped_segment_count: dropped or failed segments

failed_segments: segment-level failures with start, end, and reason

warnings: run-level warnings such as only one speaker detected

Behavior Guarantees

The entrypoint already enforces the workflow. Do not rewrite the pipeline ad hoc in the conversation.

The script will:

normalize audio with FFmpeg instead of renaming extensions

use local SenseVoiceSmall for ASR

use local 3D-Speaker embeddings plus clustering for diarization

write a plain text transcript with timestamps and 说话人N

stop on diarization failure instead of silently emitting a non-speaker-separated transcript

Report Back To The User

On success, report:

the final transcript path

whether the source was audio or video

the detected speaker count

any warnings that matter for review

On failure, report:

the exit code category

the warning message from the JSON result

whether the failure happened during validation, media normalization, diarization, transcription, or output writing

References

Read these only when needed:

references/runtime_paths.md: fixed local paths and command template

references/troubleshooting.md: common runtime issues and how to interpret them