Update documentation for multi-gpu training

docs/source/faq.md

@@ -59,8 +59,7 @@ We tried to collect common issues and questions we receive about 🐸TTS. It is
 
 6. Train your model.
     - SingleGPU training: ```CUDA_VISIBLE_DEVICES="0" python train_tts.py --config_path config.json```
-    - MultiGPU training: ```CUDA_VISIBLE_DEVICES="0,1,2" python distribute.py --script train_tts.py --config_path config.json```
-        - This command uses all the GPUs given in ```CUDA_VISIBLE_DEVICES```. If you don't specify, it uses all the GPUs available.
+    - MultiGPU training: ```python3 -m trainer.distribute --gpus "0,1" --script TTS/bin/train_tts.py --config_path config.json```
 
 **Note:** You can also train your model using pure 🐍 python. Check ```{eval-rst} :ref: 'tutorial_for_nervous_beginners'```.
 
@@ -111,4 +110,4 @@ The best approach is to pick a set of promising models and run a Mean-Opinion-Sc
 ## My Tacotron model does not stop - I see "Decoder stopped with 'max_decoder_steps" - Stopnet does not work.
 - In general, all of the above relates to the `stopnet`. It is the part of the model telling the `decoder` when to stop.
 - In general, a poor `stopnet` relates to something else that is broken in your model or dataset. Especially the attention module.
-- One common reason is the silent parts in the audio clips at the beginning and the ending. Check ```trim_db``` value in the config. You can find a better value for your dataset by using ```CheckSpectrogram``` notebook. If this value is too small, too much of the audio will be trimmed. If too big, then too much silence will remain. Both will curtail the `stopnet` performance.
+- One common reason is the silent parts in the audio clips at the beginning and the ending. Check ```trim_db``` value in the config. You can find a better value for your dataset by using ```CheckSpectrogram``` notebook. If this value is too small, too much of the audio will be trimmed. If too big, then too much silence will remain. Both will curtail the `stopnet` performance.