Dockerfile: adding dockerfile

Dockerfile

@@ -1,4 +1,3 @@
 FROM pytorch/pytorch:0.4_cuda9_cudnn7
 
-RUN pip install numpy scipy matplotlib librosa==0.6.0 tensorflow tensorboardX
-inflect==0.2.5 Unidecode==1.0.22
+RUN pip install numpy scipy matplotlib librosa==0.6.0 tensorflow tensorboardX inflect==0.2.5 Unidecode==1.0.22

README.md

@@ -20,12 +20,11 @@ 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 -r requirements.txt`
+6. Install python requirements or use docker container (tbd)
+    - Install python requirements: `pip install requirements.txt`
     - **OR**
-    - Build docker image: `docker build --tag tacotron2 .` 
+    - Docker container `(tbd)` 
 
 ## Training
 1. `python train.py --output_directory=outdir --log_directory=logdir`

data_utils.py

@@ -1,5 +1,4 @@
 import random
-import numpy as np
 import torch
 import torch.utils.data
 
@@ -20,7 +19,6 @@ 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,
@@ -37,19 +35,12 @@ class TextMelLoader(torch.utils.data.Dataset):
         return (text, mel)
 
     def get_mel(self, filename):
-        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))
-
+        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)
         return melspec
 
     def get_text(self, text):

hparams.py

@@ -23,7 +23,6 @@ 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

@@ -98,11 +98,8 @@
    "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",
-    "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.load_state_dict(torch.load(checkpoint_path)['state_dict'])\n",
+    "model = model.module\n",
     "_ = model.eval()"
    ]
   },

loss_scaler.py

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

model.py

@@ -221,7 +221,7 @@ class Decoder(nn.Module):
             [hparams.prenet_dim, hparams.prenet_dim])
 
         self.attention_rnn = nn.LSTMCell(
-            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
+            hparams.prenet_dim + hparams.encoder_embedding_dim,
             hparams.attention_rnn_dim)
 
         self.attention_layer = Attention(
@@ -230,7 +230,7 @@ class Decoder(nn.Module):
             hparams.attention_location_kernel_size)
 
         self.decoder_rnn = nn.LSTMCell(
-            hparams.prenet_dim + hparams.encoder_embedding_dim,
+            hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
             hparams.decoder_rnn_dim, 1)
 
         self.linear_projection = LinearNorm(
@@ -351,7 +351,8 @@ class Decoder(nn.Module):
         attention_weights:
         """
 
-        cell_input = torch.cat((self.decoder_hidden, self.attention_context), -1)
+        decoder_input = self.prenet(decoder_input)
+        cell_input = torch.cat((decoder_input, self.attention_context), -1)
         self.attention_hidden, self.attention_cell = self.attention_rnn(
             cell_input, (self.attention_hidden, self.attention_cell))
 
@@ -363,8 +364,8 @@ class Decoder(nn.Module):
             attention_weights_cat, self.mask)
 
         self.attention_weights_cum += self.attention_weights
-        prenet_output = self.prenet(decoder_input)
-        decoder_input = torch.cat((prenet_output, self.attention_context), -1)
+        decoder_input = torch.cat(
+            (self.attention_hidden, self.attention_context), -1)
         self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
             decoder_input, (self.decoder_hidden, self.decoder_cell))
 
@@ -401,8 +402,9 @@ 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]
-            gate_outputs += [gate_output.squeeze(1)]
+
+            mel_outputs += [mel_output.squeeze(1)]
+            gate_outputs += [gate_output.squeeze()]
             alignments += [attention_weights]
 
             decoder_input = decoder_inputs[len(mel_outputs) - 1]
@@ -429,11 +431,12 @@ 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]
-            gate_outputs += [gate_output.squeeze(1)]
+            mel_outputs += [mel_output.squeeze(1)]
+            gate_outputs += [gate_output.squeeze()]
             alignments += [alignment]
 
             if F.sigmoid(gate_output.data) > self.gate_threshold:
@@ -467,8 +470,8 @@ class Tacotron2(nn.Module):
         text_padded, input_lengths, mel_padded, gate_padded, \
             output_lengths = batch
         text_padded = to_gpu(text_padded).long()
-        max_len = int(torch.max(input_lengths.data).numpy())
         input_lengths = to_gpu(input_lengths).long()
+        max_len = torch.max(input_lengths.data)
         mel_padded = to_gpu(mel_padded).float()
         gate_padded = to_gpu(gate_padded).float()
         output_lengths = to_gpu(output_lengths).long()

requirements.txt

@@ -1,6 +1,6 @@
 torch==0.2.0.post3
 matplotlib==2.1.0
-tensorflow
+tensorflow==1.5.0
 numpy==1.13.3
 inflect==0.2.5
 librosa==0.6.0

train.py

@@ -2,7 +2,6 @@ import os
 import time
 import argparse
 import math
-from numpy import finfo
 
 import torch
 from distributed import DistributedDataParallel
@@ -78,9 +77,7 @@ def prepare_directories_and_logger(output_directory, log_directory, rank):
 
 def load_model(hparams):
     model = Tacotron2(hparams).cuda()
-    if hparams.fp16_run:
-        model = batchnorm_to_float(model.half())
-        model.decoder.attention_layer.score_mask_value = float(finfo('float16').min)
+    model = batchnorm_to_float(model.half()) if hparams.fp16_run else model
 
     if hparams.distributed_run:
         model = DistributedDataParallel(model)
@@ -279,7 +276,7 @@ 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)

utils.py

@@ -5,7 +5,7 @@ import torch
 
 def get_mask_from_lengths(lengths):
     max_len = torch.max(lengths)
-    ids = torch.arange(0, max_len).long().cuda()
+    ids = torch.arange(0, max_len, out=torch.LongTensor(max_len)).cuda()
     mask = (ids < lengths.unsqueeze(1)).byte()
     return mask