docs(readme): add EspNet for Speech Processing Tasks

README.md

@@ -131,6 +131,7 @@ Further resources:
       - [Federated Learning](#python-federated-learning)
       - [Kaggle Competition Source Code](#python-kaggle-competition-source-code)
       - [Reinforcement Learning](#python-reinforcement-learning)
+      - [Speech Recognition](#python-speech-recognition)
   - [Ruby](#ruby)
       - [Natural Language Processing](#ruby-natural-language-processing)
       - [General-Purpose Machine Learning](#ruby-general-purpose-machine-learning)
@@ -1474,6 +1475,10 @@ be
 * [RLlib](https://github.com/ray-project/ray) - RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
 * [DI-engine](https://github.com/opendilab/DI-engine) - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
 
+<a name="python-speech-recognition"></a>
+#### Speech Recognition
+* [EspNet](https://github.com/espnet/espnet) - ESPnet is an end-to-end speech processing toolkit covering end-to-end speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, and so on. ESPnet uses pytorch as a deep learning engine and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for various speech processing experiments.
+
 <a name="ruby"></a>
 ## Ruby