4 ms·

Agreed. Excited to try it but I want to know a teensy bit more about why I should switch to this over an in-house DeepSpeech based solution. The custom models p

• dylanbfox 8y ago
  Dylan from AssemblyAI here. We're surprised to see ourselves on HN tonight -- but if you do try out the API I would love to see what you think! Thanks for your interest. DeepSpeech is an awesome open source project, and we absolutely support open source speech-to-text. We're going to be open sourcing parts of our stack in the future as well. We're a lot more accurate on real-world data (like phone calls, podcasts, accents, noise, etc.) than the current DeepSpeech model. We're actually less accurate on LibriSpeech Clean than what DeepSpeech reports, but we've found Libri Clean isn't very representative to real-world data and as a result isn't a great benchmark. We're planning to put out more thorough benchmarks comparing our API to other services (including DeepSpeech, Kaldi, and CMU Sphinx) in about 1-2 weeks. If you want to email me at dylan[at]assemblyai.com I can send you the benchmarks once we have them. Another thing that's hard to do is host RNN based models like DeepSpeech in production at scale. It gets expensive fast, since you need to do inference on GPUs to keep your latency time down. We spend a lot of time optimizing our infrastructure to keep costs down, so there's a good chance we can host RNN based models cheaper than if you were to do it on your own since we specialize in this.