train.py: fixing typo

Load mel from disk

README.md

@@ -20,9 +20,10 @@ Distributed and FP16 support relies on work by Christian Sarofeen and NVIDIA's
 2. Clone this repo: `git clone https://github.com/NVIDIA/tacotron2.git`
 3. CD into this repo: `cd tacotron2`
 4. Update .wav paths: `sed -i -- 's,DUMMY,ljs_dataset_folder/wavs,g' filelists/*.txt`
+    - Alternatively, set `load_mel_from_disk=True` in `hparams.py` and update mel-spectrogram paths 
 5. Install [pytorch 0.4](https://github.com/pytorch/pytorch)
 6. Install python requirements or build docker image 
-    - Install python requirements: `pip install requirements.txt`
+    - Install python requirements: `pip install -r requirements.txt`
     - **OR**
     - Build docker image: `docker build --tag tacotron2 .` 
 

data_utils.py

@@ -1,4 +1,5 @@
 import random
+import numpy as np
 import torch
 import torch.utils.data
 
@@ -19,6 +20,7 @@ class TextMelLoader(torch.utils.data.Dataset):
         self.text_cleaners = hparams.text_cleaners
         self.max_wav_value = hparams.max_wav_value
         self.sampling_rate = hparams.sampling_rate
+        self.load_mel_from_disk = hparams.load_mel_from_disk
         self.stft = layers.TacotronSTFT(
             hparams.filter_length, hparams.hop_length, hparams.win_length,
             hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
@@ -35,12 +37,19 @@ class TextMelLoader(torch.utils.data.Dataset):
         return (text, mel)
 
     def get_mel(self, filename):
-        audio = load_wav_to_torch(filename, self.sampling_rate)
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
-        melspec = self.stft.mel_spectrogram(audio_norm)
-        melspec = torch.squeeze(melspec, 0)
+        if not self.load_mel_from_disk:
+            audio = load_wav_to_torch(filename, self.sampling_rate)
+            audio_norm = audio / self.max_wav_value
+            audio_norm = audio_norm.unsqueeze(0)
+            audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
+            melspec = self.stft.mel_spectrogram(audio_norm)
+            melspec = torch.squeeze(melspec, 0)
+        else:
+            melspec = torch.from_numpy(np.load(filename))
+            assert melspec.size(0) == self.stft.n_mel_channels, (
+                'Mel dimension mismatch: given {}, expected {}'.format(
+                    melspec.size(0), self.stft.n_mel_channels))
+
         return melspec
 
     def get_text(self, text):

hparams.py

@@ -23,6 +23,7 @@ def create_hparams(hparams_string=None, verbose=False):
         ################################
         # Data Parameters             #
         ################################
+        load_mel_from_disk=False,
         training_files='filelists/ljs_audio_text_train_filelist.txt',
         validation_files='filelists/ljs_audio_text_val_filelist.txt',
         text_cleaners=['english_cleaners'],

inference.ipynb

@@ -98,8 +98,11 @@
    "source": [
     "checkpoint_path = \"/home/scratch.adlr-gcf/audio_denoising/runs/TTS-Tacotron2-LJS-MSE-DRC-NoMaskPadding-Unsorted-Distributed-22khz/checkpoint_15500\"\n",
     "model = load_model(hparams)\n",
-    "model.load_state_dict(torch.load(checkpoint_path)['state_dict'])\n",
-    "model = model.module\n",
+    "try:\n",
+    "    model = model.module\n",
+    "except:\n",
+    "    pass\n",
+    "model.load_state_dict({k.replace('module.',''):v for k,v in torch.load(checkpoint_path)['state_dict'].items()})\n",
     "_ = model.eval()"
    ]
   },

loss_scaler.py

@@ -51,11 +51,10 @@ class DynamicLossScaler:
 
     # `x` is a torch.Tensor
     def _has_inf_or_nan(x):
-        inf_count = torch.sum(x.abs() == float('inf'))
-        if inf_count > 0:
+        cpu_sum = float(x.float().sum())
+        if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
             return True
-        nan_count = torch.sum(x != x)
-        return nan_count > 0
+        return False
 
     # `overflow` is boolean indicating whether we overflowed in gradient
     def update_scale(self, overflow):

model.py

@@ -402,9 +402,8 @@ class Decoder(nn.Module):
         while len(mel_outputs) < decoder_inputs.size(0):
             mel_output, gate_output, attention_weights = self.decode(
                 decoder_input)
-
-            mel_outputs += [mel_output.squeeze(1)]
-            gate_outputs += [gate_output.squeeze()]
+            mel_outputs += [mel_output]
+            gate_outputs += [gate_output.squeeze(1)]
             alignments += [attention_weights]
 
             decoder_input = decoder_inputs[len(mel_outputs) - 1]
@@ -431,12 +430,11 @@ class Decoder(nn.Module):
         self.initialize_decoder_states(memory, mask=None)
 
         mel_outputs, gate_outputs, alignments = [], [], []
-
         while True:
             mel_output, gate_output, alignment = self.decode(decoder_input)
 
-            mel_outputs += [mel_output.squeeze(1)]
-            gate_outputs += [gate_output.squeeze()]
+            mel_outputs += [mel_output]
+            gate_outputs += [gate_output.squeeze(1)]
             alignments += [alignment]
 
             if F.sigmoid(gate_output.data) > self.gate_threshold:

train.py

@@ -2,6 +2,7 @@ import os
 import time
 import argparse
 import math
+from numpy import finfo
 
 import torch
 from distributed import DistributedDataParallel
@@ -77,7 +78,9 @@ def prepare_directories_and_logger(output_directory, log_directory, rank):
 
 def load_model(hparams):
     model = Tacotron2(hparams).cuda()
-    model = batchnorm_to_float(model.half()) if hparams.fp16_run else model
+    if hparams.fp16_run:
+        model = batchnorm_to_float(model.half())
+        model.decoder.attention_layer.score_mask_value = float(finfo('float16').min)
 
     if hparams.distributed_run:
         model = DistributedDataParallel(model)
@@ -276,7 +279,7 @@ if __name__ == '__main__':
     torch.backends.cudnn.benchmark = hparams.cudnn_benchmark
 
     print("FP16 Run:", hparams.fp16_run)
-    print("Dynamic Loss Scaling", hparams.dynamic_loss_scaling)
+    print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)
     print("Distributed Run:", hparams.distributed_run)
     print("cuDNN Enabled:", hparams.cudnn_enabled)
     print("cuDNN Benchmark:", hparams.cudnn_benchmark)