skills/speech-to-text-with-speakers/SKILL.md
Speech to Text With Speakers: Transcribe audio from file_id or public_url with three tiered actions for recordings up to 15, 30, or 60 minutes. Use when an agent needs speech to text with speakers, transcribe meeting recordings, generate subtitles and captions for videos, convert voice memos to searchable text, transcribe podcast episodes, get task, task id, list tasks through AgentPMT-hosted remote tool calls. Discovery terms: speech to text with speakers, transcribe meeting recordings.
npx skillsauth add AgentPMT/agent-skills speech-to-text-with-speakersInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last updated: 2026-07-22.
If the current date is more than 7 days after the last updated date, reinstall this skill from skills.sh or ClawHub before relying on endpoints, schemas, setup steps, or examples.
Turn any audio recording into clean, searchable text in seconds. Transcribe voice memos, meetings, interviews, podcasts, and webinars with accurate speech recognition that handles accents and background noise. Get plain text for quick reference, SRT or WebVTT subtitles for video captioning, or rich JSON output with word-level timestamps and speaker identification. Choose from three tiers based on recording length — up to 15, 30, or 60 minutes — and optionally enable speaker diarization to label who said what, profanity filtering, and alternative transcripts for maximum accuracy.
Transcribe audio with one tool. Transcribe actions run as background tasks: the submit response returns a task_id immediately, and short clips usually complete inline in that same response. Poll get_task for anything still processing.
{
"action": "get_instructions"
}
{
"action": "transcribe_quick",
"file_id": "FILE_ID",
"language_code": "en-US",
"output_format": "text"
}
{
"action": "transcribe_standard",
"public_url": "https://example.com/meeting.m4a",
"output_format": "vtt",
"enable_word_timestamps": true,
"enable_diarization": true
}
{
"action": "transcribe_extended",
"public_url": "https://example.com/interview.webm",
"output_format": "json",
"max_alternatives": 2
}
{
"action": "get_task",
"task_id": "TASK_ID"
}
{
"action": "list_tasks",
"limit": 20
}
transcribe_quick: audio up to 15 minutes. Price: 100 credits.transcribe_standard: audio up to 30 minutes. Price: 150 credits.transcribe_extended: audio up to 60 minutes. Price: 200 credits.get_task: check a transcription task's progress and retrieve its result. Price: 1 credit.list_tasks: list recent transcription tasks. Price: 1 credit.{action, task_id, status, ...}. ALWAYS check status before polling — short clips finish within the submit request and return status: "completed" with outputs inline, costing zero polls.status is "processing", poll get_task with the returned task_id every 10-15 seconds. progress advances as the job moves through download, validation, and recognition.outputs[0]: the selected content inline (text, srt_content, vtt_content, or json_data), plus speakers, word_count, confidence_score, audio_metadata, and the File Manager artifact fields result_file_id/result_signed_url.error. Tier-limit failures also carry error_details with recommended_actions — resubmit with the suggested larger tier.processing forever.error_details.recommended_actions before resubmitting.file_id or public_url.public_url must be an HTTPS URL and cannot point to private or internal network addresses.language_code is omitted, the tool defaults to en-US.text, srt, vtt, json.enable_diarization, enable_word_timestamps, remove_filler_words, enable_profanity_filter, max_alternatives.remove_filler_words defaults to true, which uses Google STT V2's cleaned transcript path.remove_filler_words to false to preserve disfluencies through Vercel AI Gateway using the openai/whisper-1 gateway model slug. This path always requests word-level timestamps from the gateway for clipping workflows.remove_filler_words=false does not support enable_diarization=true or max_alternatives greater than 1; use the default cleaned path for those features.enable_diarization=true supports audio up to 20 minutes (a provider limit). Longer recordings fail with guidance: disable diarization, or split the audio into 20-minute segments and transcribe them individually.enable_diarization=true, text output is speaker-labelled one line per turn ([0:04] Speaker 0: ...), inline and in the stored transcription.txt, so it is readable without reformatting. Without diarization it is the flat transcript. Speaker numbers match the speaker_tag values in speakers/json_data.text and json results are stored for every successful transcription; srt and vtt results are stored when the generated subtitle content is non-empty. The corresponding filenames are transcription.txt, transcription.json, transcription.srt, and transcription.vtt.result_file_id and result_signed_url fields in outputs[0]. Agents should use result_file_id for later File Manager operations.result_file_error explains that the File Manager file could not be created.Speech to Text With Speakers on AgentPMT.get_task, list_tasks, transcribe_extended, transcribe_quick, transcribe_standard.file-management, page: https://clawhub.ai/agentpmt/file-management; skills.sh: npx skills add AgentPMT/agent-skills --skill file-management)No categories or industry tags are published for this tool.
Complete generated action schema: ./schema.md.
Supported action count: 5.
x402 availability: not enabled for this product.
get_task (action slug: get-task): Check a transcription task's progress and retrieve its result. Completed tasks carry the full transcription payload in outputs[0]. Price: 1 credits. Parameters: task_id.list_tasks (action slug: list-tasks): List recent transcription tasks with status and progress. Price: 1 credits. Parameters: limit.transcribe_extended (action slug: transcribe-extended): Start an asynchronous transcription of audio up to 60 minutes. Returns a task_id immediately (short clips complete inline in the same response); poll get_task for the completed transcript and File Manager artifact. Price: 200 credits. Parameters: enable_diarization, enable_profanity_filter, enable_word_timestamps, file_id, language_code, max_alternatives, output_format, public_url, plus 1 more.transcribe_quick (action slug: transcribe-quick): Start an asynchronous transcription of audio up to 15 minutes. Returns a task_id immediately (short clips complete inline in the same response); poll get_task for the completed transcript and File Manager artifact. Price: 100 credits. Parameters: enable_diarization, enable_profanity_filter, enable_word_timestamps, file_id, language_code, max_alternatives, output_format, public_url, plus 1 more.transcribe_standard (action slug: transcribe-standard): Start an asynchronous transcription of audio up to 30 minutes. Returns a task_id immediately (short clips complete inline in the same response); poll get_task for the completed transcript and File Manager artifact. Price: 150 credits. Parameters: enable_diarization, enable_profanity_filter, enable_word_timestamps, file_id, language_code, max_alternatives, output_format, public_url, plus 1 more.Use the compact schema above for ordinary calls. Before a new production integration, or whenever parameters, enum values, nested objects, outputs, or examples are unclear, fetch live details first.
agentpmt-tool-search-and-execution with action: "get_schema", and tool_id: "speech-to-text-with-speakers".agentpmt-tool-search-and-execution with action: "get_instructions" and tool_id: "speech-to-text-with-speakers", or call this product with action: "get_instructions" when the product tool is already selected.MCP schema lookup through the main AgentPMT MCP server:
{
"method": "tools/call",
"params": {
"name": "AgentPMT-Tool-Search-and-Execution",
"arguments": {
"action": "get_schema",
"tool_id": "speech-to-text-with-speakers"
}
}
}
For live examples, keep the same MCP tool and use these arguments:
{
"action": "get_instructions",
"tool_id": "speech-to-text-with-speakers"
}
Authenticated AgentPMT REST schema lookup body:
{
"name": "agentpmt-tool-search-and-execution",
"parameters": {
"action": "get_schema",
"tool_id": "speech-to-text-with-speakers"
}
}
Authenticated AgentPMT REST live examples body:
{
"name": "agentpmt-tool-search-and-execution",
"parameters": {
"action": "get_instructions",
"tool_id": "speech-to-text-with-speakers"
}
}
Product slug: speech-to-text-with-speakers
Marketplace page: https://www.agentpmt.com/marketplace/speech-to-text-with-speakers
../agentpmt-account-mcp-rest-api-setup to connect the main MCP server or REST API for an Agent Group where this tool is enabled.../what-is-agentpmt for marketplace, Agent Group, workflow, MCP, REST, and payment concepts.If those setup skills are not installed beside this product skill, use the downloads below.
Core AgentPMT setup skills:
openclaw skills install what-is-agentpmtnpx skills add AgentPMT/agent-skills --skill what-is-agentpmtopenclaw skills install agentpmt-account-mcp-rest-api-setupnpx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setupskills.sh install script:
npx skills add AgentPMT/agent-skills --skill what-is-agentpmt
npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup
MCP call shape after the main AgentPMT MCP server is connected:
{
"method": "tools/call",
"params": {
"name": "Speech-to-Text-With-Speakers",
"arguments": {
"action": "get_task",
"task_id": "example task id"
}
}
}
Use the exact tool name returned by tools/list; the name above is the expected readable form.
Authenticated AgentPMT REST call body:
{
"name": "speech-to-text-with-speakers",
"parameters": {
"action": "get_task",
"task_id": "example task id"
}
}
Use the setup skill for the account connection details before making REST calls.
passed or success-style boolean, use it as the workflow gate.get_schema or get_instructions before retrying.get_task fails, preserve the request parameters and retry only after fixing schema, auth, or payment errors.what-is-agentpmt, page: https://clawhub.ai/agentpmt/what-is-agentpmt; skills.sh: npx skills add AgentPMT/agent-skills --skill what-is-agentpmt)agentpmt-account-mcp-rest-api-setup, page: https://clawhub.ai/agentpmt/agentpmt-account-mcp-rest-api-setup; skills.sh: npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup)tools
Plaud Transcripts Corrected With Your Own Terminology Glossary: Fixes the words your transcription keeps getting wrong, by giving the pipeline your vocabulary instead of hoping a bigger model guesses right. Every speech model mangles terms it has never seen: cell line and reagent names, drug and device names, case and matter numbers, part numbers, local spelling and number conventions, team and client names. Swapping to a different model does not fix this, because none of them have your terms e.
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Plaud Spoken Field Notes to a Structured Sheet: Turns a spoken site visit into a filled-in spreadsheet row, so measurements and specs never get typed up twice. Built for anyone who dictates structured details on the job rather than writing them down: window and flooring measurements, equipment specs, inspection findings, punch lists, service call notes. Say the details out loud in the same order each visit (client, room, width, drop, colour, notes) and the workflow reads each new Plaud recordin.
development
Plaud Recordings to Google Calendar Events: Puts the meetings you agree to out loud straight onto your Google Calendar, without Zapier in the middle. Plaud's own app has no Calendar integration, so this closes that gap directly: each new recording is scanned, the transcript pulled, and any genuine scheduling commitment spoken in it ("let's do Tuesday at 3", "I'll come back out Thursday morning") is extracted with the relative date resolved against the recording's own date and your timezone. Eac.
development
One Plaud Recording, Several Differently Formatted Summaries: Gets you past the one-template-per-recording ceiling. The Plaud app applies a single AutoFlow template to a recording, so if you want a short recap for yourself, a decisions-only version for the people who missed it, and a clean action list for your task manager, you are re-running or rewriting by hand. This workflow reads the transcript once and produces every format you have defined in a single pass: you list the output formats you.