My team at Bandwidth Labs has put together a speech-to-text model from the ground up, focused on ultra low latency streaming use cases. We started with a focus on voice agents, but have quickly started to see various use cases for embedding the model directly into webpages to provide transcriptions. I wanted to share some of our teams work, in hopes we could provide a new completely free resource to the webdev community here.
Our API supports websocket connections natively, and you can see a browser demo at https://labs.bandwidth.com/experiments/speech-to-text that doesn't require an account if you want to try it. If you sign up for a labs account you can get free access to the model while it’s on labs via API.
Since we focused from the beginning on building this for streaming use cases, we have a heavy focus on reducing latency, while trying to not give up on accuracy. In testing at scale, we found the model to achieve a 4.225% WER on the OpenASR Leaderboards (were awaiting them to run the official tests). Additionally, we only emit final transcripts, not partials, and were able to acheive on average 60ms latency from end of micro batched audio to time when the transcript is outputted.
The API's themselves handle all of the chunking and batching logic, all your website needs to do is make the connection via the SDK's.
One important disclosure: this is a research/beta service. We monitor usage and may retain and review audio, transcripts and related data submitted to the experiment so we can evaluate the model, investigate failures and improve it. Full details are in the Labs terms.
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