minor revision

.gitignore

@@ -113,7 +113,7 @@ montreal-forced-aligner/
 # data, checkpoint, and models
 preprocessed/
 ckpt/
-result/
+results/
 synth/
 log/
 eval/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Chung-Ming Chien
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -6,7 +6,7 @@ This is a Pytorch implementation of Microsoft's text-to-speech system [**FastSpe
 
 # Audio Samples
 Audio samples generated by this implementation can be found [here](https://ming024.github.io/FastSpeech2/).  
-- The model used to generate these samples is trained for 30k steps on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) dataset.
+- The model used to generate these samples is trained for 300k steps on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) dataset.
 - Audio samples are converted from mel-spectrogram to raw waveform via [NVIDIA's pretrained WaveGlow](https://github.com/NVIDIA/waveglow).
 
 # Quickstart
@@ -89,7 +89,7 @@ Train your model with
 python3 train.py
 ```
 
-The model takes less than 10000 steps (less than 1 hour on my GTX1080 GPU) of training to generate audio samples with acceptable quality, which is much more efficient than the autoregressive models such as Tacotron2.
+The model takes less than 10k steps (less than 1 hour on my GTX1080 GPU) of training to generate audio samples with acceptable quality, which is much more efficient than the autoregressive models such as Tacotron2.
 
 There might be some room for improvement for this repository. For example, I just simply add up the duration loss, f0 loss, energy loss and mel loss without any weighting. Please inform me if you find any useful tip for training the FastSpeech2 model.
 

train.py

@@ -101,8 +101,7 @@ def main(args):
 
                 # Cal Loss
                 mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss = Loss(
-                        duration_output, D, f0_output, f0, energy_output, energy, mel_output, mel_postnet_output, mel_target, mel_len)
-
+                        duration_output, D, f0_output, f0, energy_output, energy, mel_output, mel_postnet_output, mel_target)
                 total_loss = mel_loss + mel_postnet_loss + d_loss + f_loss + e_loss
 
                 # Logger