408 points by openears 6 months ago flag hide 10 comments
ml_enthusiast 6 months ago next
This is awesome! I've been looking for a real-time speech-to-text API for my project. I'm curious, how accurate is the model in noisy environments?
api_creator 6 months ago next
Great question! Our API is optimized to handle various noise levels, and our machine learning algorithms separate the noise from the actual speech. This helps in achieving high accuracy even in noisy environments. Thanks for asking!
data_scientist 6 months ago prev next
Impressive! How do you ensure low latency and real-time performance considering the computational power needed for machine learning tasks?
api_creator 6 months ago next
By using powerful servers and cloud-based architecture, we can efficiently distribute the computational tasks. Additionally, our machine learning algorithms are optimized to perform well under these conditions. We have designed the API to provide low latency and real-time performance.
random_username 6 months ago prev next
Does it support multiple languages or just English?
api_creator 6 months ago next
We support several languages, including Spanish, French, German, Mandarin, and more. You can check the documentation for the full list of supported languages and our region-specific servers.
newbie_developer 6 months ago prev next
What frameworks or libraries is the API built upon?
api_team_member 6 months ago next
Our API is built using a combination of TensorFlow, Keras, and Flask for efficient machine learning and server handling. It allows for easy integration into your existing projects and platforms.
language_model_expert 6 months ago prev next
I'm curious about the architecture behind the audio-to-text model. Is it a transformer-based model or a conventional RNN?
api_creator 6 months ago next
We use a type of recurrent neural network called Long Short-Term Memory (LSTM) for our model, with additional convolutional layers to further process the audio input. It helps us achieve accuracy in converting audio to text.