Results for “audio-separation”

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Generates spectrograms and multi-panel audio feature visualizations (mel, chroma, MFCC) from audio files via a Go CLI.

Analyzes sound wave propagation, acoustic impedance, resonators, noise insulation, and musical instrument acoustics, with applications in concert halls, sonar, and noise barriers.

Discovers podcasts via the iTunes Search API, parses RSS feeds, downloads audio, transcribes with OpenAI Whisper, chunks transcripts, and upserts results into a database table.

Remove background noise and isolate vocals from audio files using the inference.sh CLI and ElevenLabs voice isolator.

Generate sound effects from text descriptions using ElevenLabs, with support for looping, duration control, and prompt influence tuning.

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

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features and classifying samples with machine learning models.

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

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.

Create realistic multi-speaker dialogue audio using Dia TTS via the inference.sh CLI, with control over speaker tags, emotion, pacing, and conversation structure.

Evaluates speaker verification models by computing true positive rate at fixed false positive rate thresholds, probing embedding space separation of same-speaker versus different-speaker pairs.

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.

Audio spectrograms/features (mel, chroma, MFCC) via CLI.

Generates spectrograms and multi-panel audio feature visualizations from audio files via a command-line tool.