An End-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 makes audio recognition algos identity voiceprints better
Speech Segmentation
Identity different sounds, background noises and train your ML algos 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, for annotating data at scale
Isn't it time you stopped ruining your AI with low quality training data ?