SenseAudio Meeting Summarizer
Transform meeting recordings into structured transcripts with speaker identification, timestamps, and local meeting summaries.
What This Skill Does
- Transcribe recorded meetings with official SenseAudio ASR endpoints
- Separate speakers with diarization on supported models
- Generate word and segment timestamps for navigation
- Produce local summaries and action-item candidates from transcripts
- Support optional realtime transcription for live meetings
- Export transcript and notes in text-friendly formats
Credential and Dependency Rules
- Read the API key from
SENSEAUDIO_API_KEY.
- Send auth only as
Authorization: Bearer <API_KEY>.
- Do not place API keys in query parameters, logs, or saved examples.
- If Python helpers are used, this skill expects
python3, requests, and websockets.
- This skill should work without any external LLM credentials.
- If a user explicitly asks for LLM-based summarization, require a separately declared credential for that provider instead of assuming one exists.
Official ASR Constraints
Use the official SenseAudio ASR rules summarized below:
- HTTP endpoint:
POST https://api.senseaudio.cn/v1/audio/transcriptions
- WebSocket endpoint:
wss://api.senseaudio.cn/ws/v1/audio/transcriptions
- File upload limit:
<=10MB per request
- Meeting-oriented HTTP model:
sense-asr-pro
- Realtime WebSocket model:
sense-asr-deepthink
enable_speaker_diarization is supported only on sense-asr / sense-asr-promax_speakers is documented only for sense-asr-proenable_sentiment and timestamp_granularities[] are supported only on sense-asr / sense-asr-pro- WebSocket audio must be
pcm, 16000Hz, mono
Recommended Workflow
- Validate the meeting asset:
- Prefer clear audio with limited background noise.
- Split files larger than
10MB before upload.
- Transcribe with the right mode:
- Use HTTP
sense-asr-pro for recorded meetings needing diarization and timestamps.
- Use WebSocket
sense-asr-deepthink only for live streaming scenarios.
- Request only needed features:
- For meeting notes, use
response_format=verbose_json.
- Enable diarization, timestamps, and sentiment only when the user needs them.
- Provide
max_speakers only when known and using sense-asr-pro.
- Summarize locally first:
- Build summaries, decisions, and action-item candidates from the transcript itself.
- Keep the no-extra-credentials path as the default behavior.
- Handle sensitive output carefully:
- Treat returned
session_id, trace_id, and transcript contents as potentially sensitive.
- Do not expose provider identifiers unless needed for debugging.
Minimal HTTP Transcription Helper
import os
import requests
API_KEY = os.environ["SENSEAUDIO_API_KEY"]
API_URL = "https://api.senseaudio.cn/v1/audio/transcriptions"
def transcribe_meeting(audio_file, max_speakers=None, language=None, target_language=None):
with open(audio_file, "rb") as handle:
response = requests.post(
API_URL,
headers={"Authorization": f"Bearer {API_KEY}"},
files={"file": handle},
data={
"model": "sense-asr-pro",
"response_format": "verbose_json",
"enable_speaker_diarization": "true",
"enable_sentiment": "true",
"enable_punctuation": "true",
"timestamp_granularities[]": ["word", "segment"],
**({"max_speakers": max_speakers} if max_speakers else {}),
**({"language": language} if language else {}),
**({"target_language": target_language} if target_language else {}),
},
timeout=300,
)
response.raise_for_status()
return response.json()
Transcript Processing Pattern
- Read
text for the full transcript
- Read
segments for speaker-separated timeline entries
- Use
speaker, start, end, text, and optional sentiment fields when present
- Use
words only when word timestamps were requested
Local Summary Pattern
Generate notes from transcript structure without external services:
summary: 3-6 bullets capturing the meeting arcdecisions: statements containing agreements or final choicesaction_items: statements with owners, deadlines, or explicit follow-upsparticipants: derived from speaker labelstimeline: ordered segments with timestamps
Heuristics that work without an LLM:
- Detect action items from patterns like
will, need to, follow up, by Friday
- Detect decisions from patterns like
decided, agreed, we will, final choice
- Aggregate speaker time by summing
end - start
Realtime Meeting Pattern
For live meetings, use WebSocket only when streaming audio is actually available.
import asyncio
import json
import os
import websockets
API_KEY = os.environ["SENSEAUDIO_API_KEY"]
WS_URL = "wss://api.senseaudio.cn/ws/v1/audio/transcriptions"
async def transcribe_live_meeting(audio_stream):
async with websockets.connect(
WS_URL,
additional_headers={"Authorization": f"Bearer {API_KEY}"},
) as ws:
await ws.recv()
await ws.send(json.dumps({
"event": "task_start",
"model": "sense-asr-deepthink",
"audio_setting": {
"sample_rate": 16000,
"format": "pcm",
"channel": 1,
},
}))
async for audio_chunk in audio_stream:
await ws.send(audio_chunk)
await ws.send(json.dumps({"event": "task_finish"}))
Output Options
- Full transcript with timestamps in
txt or json
- Meeting notes in
md
- Action-item list in
json or csv
- Speaker statistics in
json
- Optional sentiment timeline when requested
Error Handling
- Poor audio quality: run an audio-quality check first or request a cleaner recording
- Large files: split recordings into chunks under
10MB
- Wrong language detection: set
language explicitly on HTTP transcription
- Too many speakers: provide
max_speakers only with sense-asr-pro
- Realtime failures: inspect WebSocket
task_failed and base_resp.status_msg
Safety Notes
- Do not assume any external summarization provider credential exists.
- Do not add an LLM call unless the user asks for it and the skill metadata is updated to declare that credential.
- Prefer local transcript-based summarization for the default path.
