Update README to fix references

Author: ZDisket

examples/multiband_pwgan/README.md

@@ -1,22 +1,22 @@
 # Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech (With ParallelWaveGAN discriminator)
-Based on the script [`train_multiband_pwgan.py`](https://github.com/dathudeptrai/TensorflowTTS/tree/master/examples/multiband_pwgan/train_multiband_pwgan.py).
+Based on the script [`train_multiband_pwgan.py`](https://github.com/tensorspeech/TensorflowTTS/tree/master/examples/multiband_pwgan/train_multiband_pwgan.py).
 
-## Training Multi-band MelGAN from scratch with LJSpeech dataset.
+## Training Multi-band MelGAN with PWGAN generator from scratch with LJSpeech dataset.
 This example code show you how to train MelGAN from scratch with Tensorflow 2 based on custom training loop and tf.function. The data used for this example is LJSpeech, you can download the dataset at  [link](https://keithito.com/LJ-Speech-Dataset/).
 
 ### Step 1: Create Tensorflow based Dataloader (tf.dataset)
-Please see detail at [examples/melgan/](https://github.com/dathudeptrai/TensorflowTTS/tree/master/examples/melgan#step-1-create-tensorflow-based-dataloader-tfdataset)
+Please see detail at [examples/melgan/](https://github.com/tensorspeech/TensorflowTTS/tree/master/examples/melgan#step-1-create-tensorflow-based-dataloader-tfdataset)
 
 ### Step 2: Training from scratch
-After you re-define your dataloader, pls modify an input arguments, train_dataset and valid_dataset from [`train_multiband_pwgan.py`](https://github.com/dathudeptrai/TensorflowTTS/tree/master/examples/multiband_pwgan/train_multiband_pwgan.py). Here is an example command line to training melgan-stft from scratch:
+After you re-define your dataloader, pls modify an input arguments, train_dataset and valid_dataset from [`train_multiband_pwgan.py`](https://github.com/tensorspeech/TensorflowTTS/tree/master/examples/multiband_pwgan/train_multiband_pwgan.py). Here is an example command line to training melgan-stft from scratch:
 
 First, you need training generator with only stft loss: 
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python examples/multiband_pwgan/train_multiband_pwgan.py \
   --train-dir ./dump/train/ \
   --dev-dir ./dump/valid/ \
-  --outdir ./examples/multiband_pwgan/exp/train.multiband_melgan.v1/ \
+  --outdir ./examples/multiband_pwgan/exp/train.multiband_pwgan.v1/ \
   --config ./examples/multiband_pwgan/conf/multiband_pwgan.v1.yaml \
   --use-norm 1 \
   --generator_mixed_precision 1 \
@@ -29,18 +29,18 @@ Then resume and start training generator + discriminator:
 CUDA_VISIBLE_DEVICES=0 python examples/multiband_pwgan/train_multiband_pwgan.py \
   --train-dir ./dump/train/ \
   --dev-dir ./dump/valid/ \
-  --outdir ./examples/multiband_pwgan/exp/train.multiband_melgan.v1/ \
+  --outdir ./examples/multiband_pwgan/exp/train.multiband_pwgan.v1/ \
   --config ./examples/multiband_pwgan/conf/multiband_pwgan.v1.yaml \
   --use-norm 1 \
-  --resume ./examples/multiband_pwgan/exp/train.multiband_melgan.v1/checkpoints/ckpt-200000
+  --resume ./examples/multiband_pwgan/exp/train.multiband_pwgan.v1/checkpoints/ckpt-200000
 ```
 
 IF you want to use MultiGPU to training you can replace `CUDA_VISIBLE_DEVICES=0` by `CUDA_VISIBLE_DEVICES=0,1,2,3` for example. You also need to tune the `batch_size` for each GPU (in config file) by yourself to maximize the performance. Note that MultiGPU now support for Training but not yet support for Decode. 
 
 In case you want to resume the training progress, please following below example command line:
 
 ```bash
---resume ./examples/multiband_pwgan/exp/train.multiband_melgan.v1/checkpoints/ckpt-100000
+--resume ./examples/multiband_pwgan/exp/train.multiband_pwgan.v1/checkpoints/ckpt-100000
 ```
 
 **IMPORTANT NOTES**:
@@ -55,37 +55,26 @@ To running inference on folder mel-spectrogram (eg valid folder), run below comm
 CUDA_VISIBLE_DEVICES=0 python examples/multiband_pwgan/decode_mb_melgan.py \
   --rootdir ./dump/valid/ \
   --outdir ./prediction/multiband_melgan.v1/ \
-  --checkpoint ./examples/multiband_pwgan/exp/train.multiband_melgan.v1/checkpoints/generator-940000.h5 \
+  --checkpoint ./examples/multiband_pwgan/exp/train.multiband_pwgan.v1/checkpoints/generator-940000.h5 \
   --config ./examples/multiband_pwgan/conf/multiband_pwgan.v1.yaml \
   --batch-size 32 \
   --use-norm 1
 ```
 
 ## Finetune Multi-Band MelGAN + PWGAN Disc with ljspeech pretrained on other languages
-Download generator weights of Multi-Band MelGAN model, pass to `--pretrained` argument
+Download generator weights of (any) Multi-Band MelGAN model, pass to `--pretrained` argument.
+It's recommended to use (and tune if necessary), the dedicated finetuning config `train.multiband_pwgan.v1ft.yaml`
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python examples/multiband_pwgan/train_multiband_pwgan.py \
   --train-dir ./dump/train/ \
   --dev-dir ./dump/valid/ \
-  --outdir ./examples/multiband_pwgan/exp/train.multiband_melgan.v1ft/ \
-  --config ./examples/multiband_pwgan/conf/multiband_pwgan.v1.yaml \
+  --outdir ./examples/multiband_pwgan/exp/train.multiband_pwgan.v1/ \
+  --config ./examples/multiband_pwgan/conf/train.multiband_pwgan.v1ft.yaml \
   --use-norm 1 \
   --generator_mixed_precision 1 \
   --pretrained "ptgen.h5"
 ```
-## Learning Curves
-Here is a learning curves of melgan based on this config [`multiband_melgan.v1.yaml`](https://github.com/dathudeptrai/TensorflowTTS/tree/master/examples/multiband_pwgan/conf/multiband_pwgan.v1.yaml)
-
-<img src="fig/eval.png" height="300" width="850">
-
-<img src="fig/train.png" height="300" width="850">
-
-## Pretrained Models and Audio samples
-| Model                                                                                                          | Conf                                                                                                                        | Lang  | Fs [Hz] | Mel range [Hz] | FFT / Hop / Win [pt] | # iters |
-| :------                                                                                                        | :---:                                                                                                                       | :---: | :----:  | :--------:     | :---------------:    | :-----: |
-| [multiband_melgan.v1](https://drive.google.com/drive/folders/1Hg82YnPbX6dfF7DxVs4c96RBaiFbh-cT?usp=sharing)             | [link](https://github.com/tensorspeech/TensorFlowTTS/tree/master/examples/multiband_pwgan/conf/multiband_pwgan.v1.yaml)          | EN    | 22.05k  | 80-7600        | 1024 / 256 / None    | 940K    |
-| [multiband_melgan.v1](https://drive.google.com/drive/folders/199XCXER51PWf_VzUpOwxfY_8XDfeXuZl?usp=sharing)             | [link](https://github.com/dathudeptrai/TensorflowTTS/tree/master/examples/multiband_pwgan/conf/multiband_pwgan.v1.yaml)          | KO    | 22.05k  | 80-7600        | 1024 / 256 / None    | 1000K    |
 
 ## Notes
 1. Using RAdam for discriminator