🗣7 speech recognition tools
To develop your own ML speech recognition system, you can use the following frameworks and libraries:
• wav2letter - an open-course open source toolkit from Facebook AI Research merged with a larger library called Flashlight https://github.com/flashlight/wav2letter
• DeepSpeech powered by Baidu DeepSpeech, which will help you decode an audio file using pre-trained models or set up/train a custom dataset https://deepspeech.readthedocs.io/en/r0.9/?badge=latest
• TensorFlowASR - Open source package from Tensorflow implements some reference models trained using RNN with CTC https://github.com/TensorSpeech/TensorFlowASR
• OpenSeq2Seq - research project by NVIDIA on the problems of converting sequences to sequences https://github.com/NVIDIA/OpenSeq2Seq/blob/master/Streaming-ASR.ipynb
• SpeechRecognition - the project provides access to several automatic speech recognition models, including speech API wrappers from Google, Microsoft Azure and IBM https://github.com/Uberi/speech_recognition

We also note 2 ready-made services that provide an API for accessing the capabilities of services, from speech recognition to generating "natural" voice data:
• SmartSpeech by SberDevices https://sberdevices.ru/smartspeech/
• Yandex SpeechKit by Yandex https://cloud.yandex.ru/services/speechkit