An End-to-End Workflow for everything Audio
Feed your audio ML models with quality training data, every time
Intent Classification
Gauge the tone of the audio recordings and segregate them with characteristics like sound quality, tone or language spoken.
Transcription & Translation (including local languages)
Train models to transcribe spoken language into text or interpret verbal commands accurately.
Speaker Diarization
Label different speakers in audio recordings to make audio recognition algorithms to identify voiceprints better.
Speech Segmentation
Identify different sounds, background noises and train your ML algorithms to identify different sounds.
Pre-label datasets from our arsenal of trained models to blaze through raw data.
Leverage our QC methods, Maker Checker, Editor and Majority Vote to generate quality training data.
Access our vetted & highly experienced team of annotators, preferred by the biggest F500s.
Isn't it time you stopped ruining your AI with low quality training data?