Results for “real-time-transcription”

Transcribe audio files to text with optional speaker diarization and known-speaker hints using OpenAI models.

Ingest, index, search, and edit video and live streams with timestamps, subtitles, overlays, and real-time alerts.

Transcribe audio to text using ElevenLabs Scribe v2, supporting 90+ languages, speaker diarization, and word-level timestamps.

Transcribe audio with high accuracy using ElevenLabs Scribe models, supporting speaker diarization, audio event tagging, forced alignment, and subtitle generation via the inference.sh CLI.

Transcribe and translate speech across 99 languages using OpenAI's Whisper model, with support for multiple model sizes, batch processing, and subtitle generation.

Transcribe audio to text using ElevenLabs Scribe and Whisper models via the inference.sh CLI, supporting timestamps, speaker diarization, translation, and multi-language transcription.

Train, evaluate, export, and run inference on TAO action-recognition models for classifying temporal actions in video clips using RGB, optical flow, or joint input.

Train, evaluate, distill, quantize, export, and run inference for RT-DETR object detection models using NVIDIA TAO.

Trains, evaluates, exports, prunes, quantizes, retrains, and runs inference for TAO OCRNet models for scene text recognition from cropped text-region images, supporting CTC and attention-based decoders.

Trains, evaluates, exports, quantizes, and runs inference for Sparse4D multi-camera temporal 3D object detection and tracking models using TAO.

Run state-of-the-art machine learning models directly in JavaScript/TypeScript across browsers and server-side runtimes using Transformers.js.

Transform the voice in an audio recording into a different target voice while preserving emotion, timing, and delivery using the ElevenLabs Voice Changer API.

Train or fine-tune sentence-transformers models for retrieval, similarity, clustering, classification, and reranking, with support for bi-encoders, cross-encoders, and sparse encoders.

Transcribes audio from URLs or local files to text using the Speech is Cheap API, with options for speaker diarization, timestamps, and multiple output formats.

Use RWKV, a linear-time RNN-Transformer hybrid, for efficient long-context inference and training with constant memory usage.

RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.

Benchmarks automatic speech translation and recognition on English-French and English-Romanian datasets, reporting BLEU and WER on tokenized outputs.

Transcribes audio from URLs or local files into text with speaker diarization, word timestamps, and multiple output formats via a command-line tool.

Transcribes audio from URLs or local files into text using a low-cost speech-to-text API, with options for speaker diarization, word timestamps, and multiple output formats.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

Ingest, index, search, and edit video and audio from files, URLs, live streams, or desktop sessions with timestamps, subtitles, overlays, and real-time alerts.