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 <source-stem>_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:

powershell

& '<YOUR_CONDA_ENV_PYTHON_PATH>' 'C:\path\to\your\meeting-to-text\scripts\meeting_to_text.py' --input '<SOURCE_PATH>' --output '<OUTPUT_TARGET>'

If you need a stable temp location, add:

powershell

--work-dir '<YOUR_WORKSPACE_TEMP_PATH>'

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