Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Speech to Text Transcription
Transcribe audio and video files to text with speaker detection, timestamps, and format conversion.
skill openclawclawhub Free
Transcribe audio and video files to text with speaker detection, timestamps, and format conversion.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, memory-template.md, setup.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Setup
On first use, read setup.md and start helping with transcription needs.
When to Use
User has audio or video files that need transcription. Agent handles local files, URLs, voice memos, podcasts, interviews, meetings, and lectures.
Architecture
Memory lives in ~/speech-to-text-transcription/. See memory-template.md for structure. ~/speech-to-text-transcription/ βββ memory.md # Provider preferences, defaults βββ transcripts/ # Saved transcriptions βββ temp/ # Processing workspace
Quick Reference
TopicFileSetup processsetup.mdMemory templatememory-template.md
1. Detect File Type First
Before transcription, identify the input: Local file path β verify exists, check format URL β download to temp, then process Meeting recording β likely needs speaker diarization Voice memo β usually single speaker, shorter
2. Choose Provider Based on Context
ScenarioBest ProviderWhyQuick local transcriptionWhisper (local)No API key, free, privateHigh accuracy neededOpenAI Whisper APIBest qualitySpeaker identificationAssemblyAINative diarizationReal-time/streamingDeepgramLow latencyLong content (>2 hours)Split + batchAvoid timeouts
3. Handle Long Audio
Files over 25MB or 2 hours: Split into chunks (use ffmpeg) Process each chunk Merge transcripts with proper timestamps Never attempt single upload for large files
4. Preserve Context
After transcription: Ask if user wants the transcript saved Suggest filename based on content Offer to extract action items or summary
5. Output Formats
Default to plain text. Offer alternatives: .txt β clean text, no timestamps .srt / .vtt β subtitles with timing .json β structured with word-level timing .md β formatted with speaker labels
Common Traps
Assuming one provider works for all β Whisper fails on diarization, AssemblyAI needs API key Uploading huge files directly β Timeouts, memory errors. Split first. Ignoring audio quality β Noisy audio needs preprocessing (ffmpeg noise reduction) Not checking language β Whisper auto-detects but can fail on mixed-language content Losing speaker context β Multi-speaker content without diarization becomes unusable
Requirements
Required: ffmpeg (for audio processing) Optional API keys (only if using cloud providers): OPENAI_API_KEY β for OpenAI Whisper API ASSEMBLYAI_API_KEY β for AssemblyAI (speaker diarization) DEEPGRAM_API_KEY β for Deepgram (real-time) Local Whisper works without any API keys.
Local Whisper (No API Key)
# Install pip install openai-whisper # Basic transcription whisper audio.mp3 --model base --output_format txt # With timestamps whisper audio.mp3 --model medium --output_format srt Models: tiny (fast) β base β small β medium β large (accurate)
OpenAI Whisper API
curl -X POST https://api.openai.com/v1/audio/transcriptions \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: multipart/form-data" \ -F file="@audio.mp3" \ -F model="whisper-1"
AssemblyAI (Speaker Diarization)
# Upload curl -X POST https://api.assemblyai.com/v2/upload \ -H "Authorization: $ASSEMBLYAI_API_KEY" \ --data-binary @audio.mp3 # Transcribe with speakers curl -X POST https://api.assemblyai.com/v2/transcript \ -H "Authorization: $ASSEMBLYAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{"audio_url": "URL", "speaker_labels": true}'
Extract Audio from Video
ffmpeg -i video.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 audio.wav
Reduce Noise
ffmpeg -i noisy.wav -af "afftdn=nf=-25" clean.wav
Split Long Audio
# Split into 10-minute chunks ffmpeg -i long.mp3 -f segment -segment_time 600 -c copy chunk_%03d.mp3
Security & Privacy
Data that stays local: Transcripts in ~/speech-to-text-transcription/transcripts/ Local Whisper processes entirely on-device Data that leaves your machine (if using APIs): Audio file sent to chosen provider (OpenAI, AssemblyAI, Deepgram) Transcript returned and stored locally This skill does NOT: Store API keys in plain text (use environment variables) Auto-upload without confirmation Retain files on external servers after processing
External Endpoints
EndpointData SentPurposeapi.openai.com/v1/audioAudio fileWhisper API transcriptionapi.assemblyai.com/v2Audio fileAssemblyAI transcriptionapi.deepgram.com/v1Audio streamDeepgram transcription Only called when user explicitly chooses cloud provider. Local Whisper sends nothing.
Trust
By using cloud transcription providers, audio data is sent to OpenAI, AssemblyAI, or Deepgram. Only install if you trust these services with your audio. For sensitive content, use local Whisper.
Related Skills
Install with clawhub install <slug> if user confirms: audio β General audio processing ffmpeg β Video and audio conversion podcast β Podcast creation and editing
Feedback
If useful: clawhub star speech-to-text-transcription Stay updated: clawhub sync
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs
- SKILL.md
- memory-template.md
- setup.md