Update model file extension (#1422)

* Update model file ext to ```.pth```

* Update docs

* Rename more

* Find model files

docs/source/finetuning.md

@@ -93,13 +93,13 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
 
     ```bash
     CUDA_VISIBLE_DEVICES="0" python recipes/ljspeech/glow_tts/train_glowtts.py \
-        --restore_path  /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
+        --restore_path  /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth
     ```
 
     ```bash
     CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py \
         --config_path  /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/config.json \
-        --restore_path  /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
+        --restore_path  /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth
     ```
 
     As stated above, you can also use command-line arguments to change the model configuration.
@@ -107,7 +107,7 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
 
     ```bash
     CUDA_VISIBLE_DEVICES="0" python recipes/ljspeech/glow_tts/train_glowtts.py \
-        --restore_path  /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
+        --restore_path  /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth
         --coqpit.run_name "glow-tts-finetune" \
         --coqpit.lr 0.00001
     ```

docs/source/inference.md

@@ -44,7 +44,7 @@ Run your own TTS model (Using Griffin-Lim Vocoder)
 
 ```bash
 tts --text "Text for TTS" \
-    --model_path path/to/model.pth.tar \
+    --model_path path/to/model.pth \
     --config_path path/to/config.json \
     --out_path folder/to/save/output.wav
 ```
@@ -54,9 +54,9 @@ Run your own TTS and Vocoder models
 ```bash
 tts --text "Text for TTS" \
     --config_path path/to/config.json \
-    --model_path path/to/model.pth.tar \
+    --model_path path/to/model.pth \
     --out_path folder/to/save/output.wav \
-    --vocoder_path path/to/vocoder.pth.tar \
+    --vocoder_path path/to/vocoder.pth \
     --vocoder_config_path path/to/vocoder_config.json
 ```
 

docs/source/training_a_model.md

@@ -33,7 +33,7 @@
     If you like to run a multi-gpu training using DDP back-end,
 
     ```bash
-    $ CUDA_VISIBLE_DEVICES="0, 1, 2" python TTS/bin/distribute.py --script <path_to_your_script>/train_glowtts.py
+    $ CUDA_VISIBLE_DEVICES="0, 1, 2" python -m trainer.distribute --script <path_to_your_script>/train_glowtts.py
     ```
 
     The example above runs a multi-gpu training using GPUs `0, 1, 2`.
@@ -122,7 +122,7 @@
 
     ```bash
     $ tts --text "Text for TTS" \
-          --model_path path/to/checkpoint_x.pth.tar \
+          --model_path path/to/checkpoint_x.pth \
           --config_path path/to/config.json \
           --out_path folder/to/save/output.wav
     ```

docs/source/tutorial_for_nervous_beginners.md

@@ -50,13 +50,13 @@ A breakdown of a simple script that trains a GlowTTS model on the LJspeech datas
     - Fine-tune a model.
 
         ```bash
-        CUDA_VISIBLE_DEVICES=0 python train.py --restore_path path/to/model/checkpoint.pth.tar
+        CUDA_VISIBLE_DEVICES=0 python train.py --restore_path path/to/model/checkpoint.pth
         ```
 
     - Run multi-gpu training.
 
         ```bash
-        CUDA_VISIBLE_DEVICES=0,1,2 python TTS/bin/distribute.py --script train.py
+        CUDA_VISIBLE_DEVICES=0,1,2 python -m trainer.distribute --script train.py
         ```
 
 ### CLI Way