A Guide to DeepSpeech Speech to Text

This article provides a comprehensive guide on using the Python library DeepSpeech for speech-to-text conversion. It begins with an overview of DeepSpeech, which is an open-source software inspired by Baidu's 2014 paper and currently maintained by Mozilla. The author then delves into how to set up DeepSpeech locally for transcription purposes. The setup involves installing necessary libraries such as deepspeech, numpy, and webrtcvad using pip. Three files are required for the asynchronous transcription: one each for handling WAV data, transcribing speech to text on a WAV file, and using these two in the command line. The first file, wav_handler.py, is responsible for reading and writing audio data from/to a WAV file, creating frames of audio data, and detecting voice-activated frames for speech recognition with DeepSpeech. The second file, wav_transcriber.py, transcribes speech to text for a WAV file using DeepSpeech models. It also includes functions to load the pre-trained model into memory and generate VAD segments. The final part of the guide focuses on creating a command line interface (CLI) for real-time and asynchronous speech recognition with DeepSpeech. The CLI allows users to pass in options to choose whether they want to do real-time speech recognition or run speech recognition on an existing WAV audio file. The article concludes by summarizing the steps involved in setting up DeepSpeech for local transcription and using it through a command line interface.