A top-tier text-to-speech AI company was facing two major hurdles: scaling up the volume of high-quality, human-generated data to speed up development, and acquiring that data quickly enough to prevent slowdowns after model training. To keep their projects on track, they needed reliable transcription data and a dedicated workflow along with expert teams to meet tight development deadlines.
To overcome these challenges, the company partnered with Databrewery, gaining full control over their data pipeline and real-time insight into labeling quality. This allowed their internal teams to fine-tune quality assurance (QA) efforts as the data was being labeled. They also used Databrewery’s Labeling Services, which connect companies with skilled annotators capable of capturing complex speech elements like tone, accent, pacing, and pronunciation.
With help from Databrewery’s human-in-the-loop transcription services, the company saw a 3x boost in data accuracy over their previous datasets. Their model development cycles also became significantly faster, shrinking from months to just a few weeks thanks to on-demand, high-quality labeling customized for their text-to-speech needs.
As a pioneer in cutting-edge text-to-speech technology, the company set out to make audio content like videos and podcasts seamlessly accessible in multiple languages. Their vision wasn’t just to translate speech, but to preserve the speaker’s original voice, tone, and emotional nuance. This breakthrough approach enables creators and businesses to localize their content at scale without compromising the authenticity of the original audio. Driving this innovation is a combination of advanced generative AI and sophisticated post-training techniques.
To support this mission, the team needed a scalable way to gather high-quality, human-generated transcription data to train and refine their AI models. The demand for fast turnaround times meant their data pipeline had to keep pace with rapid product development cycles. However, managing all transcription and annotation work in-house quickly became overwhelming. Coordinating internal and external labeling resources added layers of complexity that made maintaining data quality a constant challenge.
The nature of audio transcription especially when capturing subtleties like emotion, pronunciation, and pacing requires deep expertise and agile workflows. To meet aggressive deadlines, the company had to build out dedicated tools and teams while ensuring precise annotations were delivered quickly, even when working with partially pre-transcribed audio from their existing models.
To solve these issues, they turned to Databrewery, a data labeling platform that gave them complete oversight and control of their labeling operations. With real-time transparency and robust QA features, they were able to monitor specific areas for improvement and fine-tune their processes on the fly. They also leveraged Databrewery Labeling Services, which specialize in surfacing nuanced speech features like pitch, accent, tempo, and inflection.
Databrewery’s global network of expert annotators, backed by the Brewforce community, offered support across a wide variety of languages and domains. This diverse pool of skilled labelers allowed the company to tailor their data annotation workflows through a mix of in-house teams, outsourced professionals, or hybrid models depending on the task at hand. This flexibility ensured they could meet quality standards while optimizing for speed and scalability.
Thanks to this approach, the company saw a more than 3x increase in transcription accuracy compared to other solutions. Their model development timelines shrank dramatically from several months to just weeks thanks to the efficiency and quality of Databrewery’s on-demand labeling services. Looking ahead, they’re scaling their post-training workflows with the help of Databrewery and its extensive network of human data experts to push the boundaries of what’s possible in generative AI.