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Found 90 Skills
Transcribe local or remote audio into durable text and timestamp artifacts using hosted Whisper models. Use this when the job is speech-to-text from audio files and you need request/response persistence, optional timestamps, and subtitle-ready outputs.
Use when deploying ANY machine learning model on-device, converting models to CoreML, compressing models, or implementing speech-to-text. Covers CoreML conversion, MLTensor, model compression (quantization/palettization/pruning), stateful models, KV-cache, multi-function models, async prediction, SpeechAnalyzer, SpeechTranscriber.
ElevenLabs speech-to-text with Scribe models and forced alignment via inference.sh CLI. Models: Scribe v1/v2 (98%+ accuracy, 90+ languages). Capabilities: transcription, speaker diarization, audio event tagging, word-level timestamps, forced alignment, subtitle generation. Use for: meeting transcription, subtitles, podcast transcripts, lip-sync timing, karaoke. Triggers: elevenlabs stt, elevenlabs transcription, scribe, elevenlabs speech to text, forced alignment, word alignment, subtitle timing, diarization, speaker identification, audio event detection, eleven labs transcribe
Find the right Deepgram documentation for any task. Use whenever someone needs help locating docs, understanding which API to use, or wants to ask questions about Deepgram. Covers all product areas: speech-to-text, text-to-speech, voice agents, audio intelligence, and self-hosted deployments.
Speech-to-text transcription using Whisper with word-level timestamps. Use when users ask to transcribe audio or video to text, generate subtitles, or recognize speech.
Clone a ready-to-run Deepgram demo app and start building on top of it. Use whenever someone wants a quick working demo, needs to prototype with Deepgram, or is starting a new project that uses speech-to-text, text-to-speech, voice agents, audio intelligence, or live streaming. Match the user's language, framework, and desired Deepgram feature to the right starter.
Deepgram API reference for speech-to-text, text-to-speech, voice agents, audio intelligence, and account management. Use whenever building with Deepgram APIs — REST or WebSocket. Covers authentication, all endpoints, query parameters, request/response schemas, and WebSocket message formats. Reference files are organized by domain: listen (STT), speak (TTS), agent (voice agents), read (text/audio intelligence), models, projects, auth, and self-hosted.
Terminal transcription of audio files and URLs with the Gladia CLI (gladia speech-to-text). Use when the user has gladia-cli installed, wants shell-based transcription, or asks an agent to transcribe audio then answer questions about the content. For audio intelligence features not available as CLI flags, use the SDK skills instead.
Transcribe audio to text using local whisper.cpp. Use when user wants to convert audio/video to text, get transcription, or speech-to-text.
Chunked sliding-window streaming speech-to-text via OpenAI Whisper HTTP API — compatible with local Faster-Whisper, Groq, and OpenRouter endpoints.
Transcribe audio and video files to text using OpenAI Whisper or compatible speech-to-text APIs.
Unified media processing center for audio and video transcription, format conversion, frame extraction, and content understanding. Handles batch processing, subtitle generation, and speech-to-text with multi-language support. Use when: "媒体处理", "media processing", "音视频转录", "video transcription", "格式转换", "audio to text", "视频转文字", "subtitle generation", "extract frames", "内容理解". Cross-references: pitch-deck-creator, amap-navigator. Built by UniqueClub 🌐 https://uniqueclub.ai