Manage episode 297437206 series 2466203
With the rising availability of computation in everyday devices, there has been a corresponding increase in the appetite for voice as the primary interface. To accomodate this desire it is necessary for us to have high quality libraries for being able to process and generate audio data that can make sense of human speech. To facilitate research and industry applications for speech data Mirco Ravanelli and Peter Plantinga are building SpeechBrain. In this episode they explain how it works under the hood, the projects that they are using it for, and how you can get started with it today.
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- Your host as usual is Tobias Macey and today I’m interviewing Mirco Ravanelli and Peter Plantinga about SpeechBrain, an open-source and all-in-one speech toolkit powered by PyTorch
- How did you get introduced to Python?
- Can you describe what SpeechBrain is and the story behind it?
- What are the goals and target use cases of the SpeechBrain project?
- What are some of the ways that processing audio with a focus on speech differs from more general audio processing?
- What are some of the other libraries/frameworks/services that are available to work with speech data and what are the unique capabilities that SpeechBrain offers?
- How is SpeechBrain implemented?
- What was your decision process for determining which framework to build on top of?
- What are some of the original ideas and assumptions that you had for SpeechBrain which have been changed or invalidated as you worked through implementing it?
- Can you talk through the workflow of using SpeechBrain?
- What would be involved in developing a system to automate transcription with speaker recognition and diarization?
- In the documentation it mentions that SpeechBrain is built to be used for research purposes. What are some of the kinds of research that it is being used for?
- What are some of the features or capabilities of SpeechBrain which might be non-obvious that you would like to highlight?
- What are the most interesting, innovative, or unexpected ways that you have seen SpeechBrain used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on SpeechBrain?
- When is SpeechBrain the wrong choice?
- What do you have planned for the future of SpeechBrain?
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- Speech Processing
- Speech Enhancement
- Speech Recognition
- Sequence to Sequence (Seq2Seq)
- PyTorch Lightning
- Generative Adversarial Network