update readme

README.md

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     * > faster-whisper is a reimplementation of OpenAI's Whisper model using [CTranslate2](https://github.com/OpenNMT/CTranslate2/), which is a fast inference engine for Transformer models.
       >
       > This implementation is up to 4 times faster than [openai/whisper](https://github.com/openai/whisper) for the same accuracy while using less memory. The efficiency can be further improved with 8-bit quantization on both CPU and GPU.
-* [x] [m-bain/whisperX](https://github.com/m-bain/whisperX)
-  * >fast automatic speech recognition (70x realtime with large-v2) with word-level timestamps and speaker diarization.
-      >- ⚡️ Batched inference for 70x realtime transcription using whisper large-v2
-      >- 🪶 [faster-whisper](https://github.com/guillaumekln/faster-whisper) backend, requires <8GB gpu memory for large-v2 with beam_size=5
-      >- 🎯 Accurate word-level timestamps using wav2vec2 alignment
-      >- 👯‍♂️ Multispeaker ASR using speaker diarization from [pyannote-audio](https://github.com/pyannote/pyannote-audio) (speaker ID labels) 
-      >- 🗣️ VAD preprocessing, reduces hallucination & batching with no WER degradation.
-* [x] [jianfch/stable-ts](https://github.com/jianfch/stable-ts)
-  * >**Stabilizing Timestamps for Whisper**: This library modifies [Whisper](https://github.com/openai/whisper) to produce more reliable timestamps and extends its functionality.
-* [x] [Hugging Face Transformers](https://huggingface.co/tasks/automatic-speech-recognition)
-  * > Hugging Face implementation of Whisper.  Any speech recognition pretrained model from the Hugging Face hub can be used as well.
-* [x] [API/openai/whisper](https://platform.openai.com/docs/guides/speech-to-text)
-  * > OpenAI Whisper via their API
+  * [x] [m-bain/whisperX](https://github.com/m-bain/whisperX)
+    * >fast automatic speech recognition (70x realtime with large-v2) with word-level timestamps and speaker diarization.
+      >  - ⚡️ Batched inference for 70x realtime transcription using whisper large-v2
+      >  - 🪶 [faster-whisper](https://github.com/guillaumekln/faster-whisper) backend, requires <8GB gpu memory for large-v2 with beam_size=5
+      >  - 🎯 Accurate word-level timestamps using wav2vec2 alignment
+      >  - 👯‍♂️ Multispeaker ASR using speaker diarization from [pyannote-audio](https://github.com/pyannote/pyannote-audio) (speaker ID labels) 
+      >  - 🗣️ VAD preprocessing, reduces hallucination & batching with no WER degradation.
+  * [x] [jianfch/stable-ts](https://github.com/jianfch/stable-ts)
+    * >**Stabilizing Timestamps for Whisper**: This library modifies [Whisper](https://github.com/openai/whisper) to produce more reliable timestamps and extends its functionality.
+  * [x] [Hugging Face Transformers](https://huggingface.co/tasks/automatic-speech-recognition)
+    * > Hugging Face implementation of Whisper.  Any speech recognition pretrained model from the Hugging Face hub can be used as well.
+  * [x] [API/openai/whisper](https://platform.openai.com/docs/guides/speech-to-text)
+    * > OpenAI Whisper via their API
 
 * Web UI
   * Fully offline, no third party services