A fast-growing generative audio AI startup needed to train models to better understand speech patterns, emotions, and vocal nuances. But labeling large volumes of audio data with that level of detail is subjective and complex. They lacked the expert support needed to handle these nuances accurately.
Databrewery provided a powerful combination of advanced audio labeling tools and a team of specialists from voice acting, theater, and performing arts. Using the platform’s custom audio editor, they labeled complex audio down to the millisecond capturing emotional tones, pacing, and vocal details.
The startup received high quality annotated datasets that captured expressive and unique sound patterns. This gave their models the precision needed to generate lifelike, emotionally intelligent audio helping them push their technology forward faster.
Audio tasks are becoming essential to how AI evolves, as voice interfaces and audio insights change how people interact with technology. From improving text-to-speech systems to detecting speaker intent and enabling audio translation, training models with high quality labeled audio is key to building the next generation of AI.
A fast-growing generative AI startup focused on building advanced audio models wanted to improve their voice, text-to-speech, and sound capabilities. To do that, they needed to evaluate and label large volumes of subjective, time-based audio data. Their goal was to structure the data as commands to train models to detect sentiment and emotion in human speech.
But labeling audio like this isn’t simple. Emotional cues and speech patterns are interpreted differently across situations, mixed emotions create ambiguity, and human bias adds another layer of complexity.
This challenge combined with the need for precise audio segmentation created a major roadblock. Without the right tools or skilled resources to label complex audio data, they turned to Databrewery and used Brewforce to get the job done.
The company shared a large set of audio files that needed to be reviewed and annotated to identify segments with high emotional content such as anger, happiness, or disgust or unique speech styles like sarcasm, slurring, or whining. After spotting these segments, experts were required to describe how they were spoken. These descriptions were written as commands to help train models to replicate the tone, emotion, and style in speech.
Using Databrewery’s labeling services powered by the Brewforce network of skilled human talent, a team was quickly assembled and onboarded. These experts came from theater, performing arts, and voice acting backgrounds. Their experience made them especially effective at detecting emotional changes and writing detailed descriptions of how those emotions were expressed in the audio.
“Working in voice acting taught me to listen beyond words to catch the rhythm, stress, and emotion that shape how something is said. That experience helps me annotate audio with the kind of precision AI needs to truly understand human speech.” – Maya L., Professional Voice Actor and Audio Dialogue Coach
Databrewery’s audio editor provided all the features needed for precise work—waveform visualization, custom ontologies with temporal tagging, and millisecond-level timestamps. Experts also used the auto-transcription tool, powered by the Whisper model, to generate accurate transcripts in just a few clicks.
Faced with multiple data provider options, the company chose Databrewery for its ability to quickly bring in a trained team of experts to analyze audio samples and deliver high quality labeled data. With the support of the Brewforce network and Databrewery’s advanced platform, trainers were able to annotate the data in detail using custom ontologies, waveform tools, and precise timestamps.
Today, this audio-focused AI company continues to rely on Databrewery to support its work at the forefront of generative audio technology.