268건 · 관련도순
Build or repair NVIDIA voice agents with Pipecat or LiveKit for real-time spoken conversations. Set up speech recognition, language models, and speech synthesis, customize pronunciation or multilingual routing, and deploy locally or in the cloud.
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Deploy, run, and test NVIDIA speech recognition, speech synthesis, and translation services in the cloud or on your own infrastructure. Guidance covers model selection, environment checks, custom models, and adjustments to speech processing and pronunciation.
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Prepare an environment for evaluating speech recognition on clinical terms. Checks the API key and Python dependencies, then tests NVIDIA's hosted services by generating speech from one synthetic sentence and transcribing it before the next stage.
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Build English clinical speech-recognition benchmarks by selecting medical terms, checking pronunciations, and generating synthetic audio. Outputs include audio files and a NeMo manifest, a structured file listing recordings and metadata, ready for a separate scoring stage.
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Evaluate speech recognition on synthetic clinical audio listed in a NeMo manifest. Produce four error metrics and a five-section report highlighting missed medical terms, then use pronunciation and noise breakdowns to guide dataset improvements or fine-tuning decisions.
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Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK. Triggers: "speech to text REST", "short audio transcription", "speech recognition REST API", "STT REST", "recognize speech REST". DO NOT USE FOR: Long audio (>60 seconds), real-time streaming, batch transcription, custom speech models, speech translation. Use Speech SDK or Batch Transcription API instead.
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Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text. Triggers: "text analytics", "sentiment analysis", "entity recognition", "key phrase", "PII detection", "TextAnalyticsClient".
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Determines when and how revenue is recognized — performance obligations, contract terms that change the answer, and the deal structures that create accounting problems. Use this to work out how a contract should be recognized, review a non-standard deal before it is signed, understand deferred revenue, or spot terms that would delay or reverse recognition.
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PaddleOCR text recognition skill using PP-OCRv5 lightweight model. Supports natural scene and complex document text detection and recognition. Use when user needs OCR text extraction from images.
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Extract a license plate number from an image URL using the CarsXE Plate Recognition API. Use this when a user shares a photo of a vehicle or license plate and wants to identify the plate number.
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Operate ASRT SpeechRecognition Chinese ASR data, acoustic models, pinyin language model, and serving clients.
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Automates invoice generation, billing workflows, payment tracking, and revenue recognition for SaaS and service businesses. Use when building billing pipelines, generating invoice PDFs, usage-based invoicing, subscription management, payment reminders, or financial reporting. Trigger words: invoice, billing, payment, subscription, usage billing, revenue recognition, accounts receivable, dunning, proration, Stripe billing, generate invoice, create bill.
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Recognize and extract text from scanned PDFs and images with ComPDF OCR workflows. Use for OCR, searchable PDF generation, text recognition, table recognition, and scanned-document extraction requests.
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Aural skills development covering interval recognition, chord quality identification, rhythmic and melodic dictation, sight-singing, and progressive pedagogy. Covers the Kodaly method (hand signs, relative solmization, singing-first approach), interval identification strategies, chord quality and inversion recognition, harmonic dictation, solfege systems (fixed-do vs. movable-do), Gordon's Music Learning Theory, Suzuki method principles, and structured difficulty sequencing. Use when developing listening skills, practicing dictation, preparing sight-singing, or designing ear training curricula.
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Recognize and locate instances of an object in an image with a local vision LLM. Wraps kuzco's tool_llm_image_recognition() ellmer tool. Use when the task needs finding, counting, or locating specific objects (faces, cars, animals) in an image.
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Generate MaaFramework Pipeline nodes and recognition snippets from screenshots or observed UI state. Use for OCR node generation, ROI sweep, choosing TemplateMatch/OCR/ColorMatch/CustomRecognition, preserving target-file schema style, and designing `next`/`[JumpBack]` links before merging generated nodes into pipeline JSON.
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Guide a person in systematic observation of systems, patterns, or phenomena. AI coaches neutral attention, field notes methodology, pattern recognition, hypothesis formation, and structured reporting for debugging, research, and system understanding. Use when a person wants to understand a system's behavior before intervening, when someone keeps jumping to conclusions and needs the discipline of observation first, when preparing an evidence-based report, or when studying team dynamics or process effectiveness through direct observation.
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Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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Naturalistic decision-making research showing how experts make decisions through recognition-primed processes
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Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text. Triggers: "text analytics", "sentiment analysis", "entity recognition", "key phrase", "PII detection", "TextAnalyticsClien...
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Spot patterns appearing in 3+ domains to find universal principles
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Transcribe speech to text using the Speech framework. Use when implementing live microphone transcription with AVAudioEngine, recognizing pre-recorded audio files, configuring on-device vs server-based recognition, handling authorization flows, or adopting the new SpeechAnalyzer API (iOS 26+) for modern async/await speech-to-text.
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OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
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OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
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