diff --git a/README.md b/README.md old mode 100644 new mode 100755 index ff32b14..a7c68b4 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Tacotron 2 (without wavenet) -Tacotron 2 PyTorch implementation of [Natural TTS Synthesis By Conditioning +PyTorch implementation of [Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions](https://arxiv.org/pdf/1712.05884.pdf). This implementation includes **distributed** and **fp16** support @@ -11,9 +11,7 @@ Distributed and FP16 support relies on work by Christian Sarofeen and NVIDIA's ![Alignment, Predicted Mel Spectrogram, Target Mel Spectrogram](tensorboard.png) -[Download demo audio](https://github.com/NVIDIA/tacotron2/blob/master/demo.wav) trained on LJS and using Ryuchi Yamamoto's [pre-trained Mixture of Logistics -wavenet](https://github.com/r9y9/wavenet_vocoder/) -"Scientists at the CERN laboratory say they have discovered a new particle." +Visit our [website] for audio samples. ## Pre-requisites 1. NVIDIA GPU + CUDA cuDNN @@ -24,11 +22,9 @@ wavenet](https://github.com/r9y9/wavenet_vocoder/) 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) +5. Install [PyTorch 1.0] 6. Install python requirements or build docker image - Install python requirements: `pip install -r requirements.txt` - - **OR** - - Build docker image: `docker build --tag tacotron2 .` ## Training 1. `python train.py --output_directory=outdir --log_directory=logdir` @@ -37,17 +33,22 @@ wavenet](https://github.com/r9y9/wavenet_vocoder/) ## Multi-GPU (distributed) and FP16 Training 1. `python -m multiproc train.py --output_directory=outdir --log_directory=logdir --hparams=distributed_run=True,fp16_run=True` -## Inference -When performing Mel-Spectrogram to Audio synthesis with a WaveNet model, make sure Tacotron 2 and WaveNet were trained on the same mel-spectrogram representation. Follow these steps to use a a simple inference pipeline using griffin-lim: - -1. `jupyter notebook --ip=127.0.0.1 --port=31337` -2. load inference.ipynb +## Inference demo +1. Download our published [Tacotron 2] model +2. Download our published [WaveGlow] model +3. `jupyter notebook --ip=127.0.0.1 --port=31337` +4. Load inference.ipynb +N.b. When performing Mel-Spectrogram to Audio synthesis, make sure Tacotron 2 +and the Mel decoder were trained on the same mel-spectrogram representation. ## Related repos -[nv-wavenet](https://github.com/NVIDIA/nv-wavenet/): Faster than real-time -wavenet inference +[WaveGlow](https://github.com/NVIDIA/WaveGlow) Faster than real time Flow-based +Generative Network for Speech Synthesis + +[nv-wavenet](https://github.com/NVIDIA/nv-wavenet/) Faster than real time +WaveNet. ## Acknowledgements This implementation uses code from the following repos: [Keith @@ -61,3 +62,7 @@ We are thankful to the Tacotron 2 paper authors, specially Jonathan Shen, Yuxuan Wang and Zongheng Yang. +[WaveGlow]: https://drive.google.com/file/d/1cjKPHbtAMh_4HTHmuIGNkbOkPBD9qwhj/view?usp=sharing +[Tacotron 2]: https://drive.google.com/file/d/1c5ZTuT7J08wLUoVZ2KkUs_VdZuJ86ZqA/view?usp=sharing +[pytorch 1.0]: https://github.com/pytorch/pytorch#installation +[website]: https://nv-adlr.github.io/WaveGlow diff --git a/data_utils.py b/data_utils.py index 09f42ac..fdfd287 100644 --- a/data_utils.py +++ b/data_utils.py @@ -14,9 +14,8 @@ class TextMelLoader(torch.utils.data.Dataset): 2) normalizes text and converts them to sequences of one-hot vectors 3) computes mel-spectrograms from audio files. """ - def __init__(self, audiopaths_and_text, hparams, shuffle=True): - self.audiopaths_and_text = load_filepaths_and_text( - audiopaths_and_text, hparams.sort_by_length) + def __init__(self, audiopaths_and_text, hparams): + self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) self.text_cleaners = hparams.text_cleaners self.max_wav_value = hparams.max_wav_value self.sampling_rate = hparams.sampling_rate @@ -26,8 +25,7 @@ class TextMelLoader(torch.utils.data.Dataset): hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin, hparams.mel_fmax) random.seed(1234) - if shuffle: - random.shuffle(self.audiopaths_and_text) + random.shuffle(self.audiopaths_and_text) def get_mel_text_pair(self, audiopath_and_text): # separate filename and text @@ -38,7 +36,10 @@ class TextMelLoader(torch.utils.data.Dataset): def get_mel(self, filename): if not self.load_mel_from_disk: - audio = load_wav_to_torch(filename, self.sampling_rate) + audio, sampling_rate = load_wav_to_torch(filename) + if sampling_rate != self.stft.sampling_rate: + raise ValueError("{} {} SR doesn't match target {} SR".format( + sampling_rate, self.stft.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) @@ -87,9 +88,9 @@ class TextMelCollate(): text = batch[ids_sorted_decreasing[i]][0] text_padded[i, :text.size(0)] = text - # Right zero-pad mel-spec with extra single zero vector to mark the end + # Right zero-pad mel-spec num_mels = batch[0][1].size(0) - max_target_len = max([x[1].size(1) for x in batch]) + 1 + max_target_len = max([x[1].size(1) for x in batch]) if max_target_len % self.n_frames_per_step != 0: max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step assert max_target_len % self.n_frames_per_step == 0 @@ -103,7 +104,7 @@ class TextMelCollate(): for i in range(len(ids_sorted_decreasing)): mel = batch[ids_sorted_decreasing[i]][1] mel_padded[i, :, :mel.size(1)] = mel - gate_padded[i, mel.size(1):] = 1 + gate_padded[i, mel.size(1)-1:] = 1 output_lengths[i] = mel.size(1) return text_padded, input_lengths, mel_padded, gate_padded, \ diff --git a/distributed.py b/distributed.py index ebe3b5b..1dd5910 100644 --- a/distributed.py +++ b/distributed.py @@ -118,3 +118,55 @@ class DistributedDataParallel(Module): super(DistributedDataParallel, self).train(mode) self.module.train(mode) ''' +''' +Modifies existing model to do gradient allreduce, but doesn't change class +so you don't need "module" +''' +def apply_gradient_allreduce(module): + if not hasattr(dist, '_backend'): + module.warn_on_half = True + else: + module.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False + + for p in module.state_dict().values(): + if not torch.is_tensor(p): + continue + dist.broadcast(p, 0) + + def allreduce_params(): + if(module.needs_reduction): + module.needs_reduction = False + buckets = {} + for param in module.parameters(): + if param.requires_grad and param.grad is not None: + tp = type(param.data) + if tp not in buckets: + buckets[tp] = [] + buckets[tp].append(param) + if module.warn_on_half: + if torch.cuda.HalfTensor in buckets: + print("WARNING: gloo dist backend for half parameters may be extremely slow." + + " It is recommended to use the NCCL backend in this case. This currently requires" + + "PyTorch built from top of tree master.") + module.warn_on_half = False + + for tp in buckets: + bucket = buckets[tp] + grads = [param.grad.data for param in bucket] + coalesced = _flatten_dense_tensors(grads) + dist.all_reduce(coalesced) + coalesced /= dist.get_world_size() + for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)): + buf.copy_(synced) + + for param in list(module.parameters()): + def allreduce_hook(*unused): + param._execution_engine.queue_callback(allreduce_params) + if param.requires_grad: + param.register_hook(allreduce_hook) + + def set_needs_reduction(self, input, output): + self.needs_reduction = True + + module.register_forward_hook(set_needs_reduction) + return module diff --git a/hparams.py b/hparams.py index a3203e2..0f5e90c 100644 --- a/hparams.py +++ b/hparams.py @@ -10,7 +10,7 @@ def create_hparams(hparams_string=None, verbose=False): # Experiment Parameters # ################################ epochs=500, - iters_per_checkpoint=500, + iters_per_checkpoint=1000, seed=1234, dynamic_loss_scaling=True, fp16_run=False, @@ -24,10 +24,9 @@ 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', + training_files='filelists/ljs_audio22khz_text_train_filelist.txt', + validation_files='filelists/ljs_audio22khz_text_val_filelist.txt', text_cleaners=['english_cleaners'], - sort_by_length=False, ################################ # Audio Parameters # @@ -39,7 +38,7 @@ def create_hparams(hparams_string=None, verbose=False): win_length=1024, n_mel_channels=80, mel_fmin=0.0, - mel_fmax=None, # if None, half the sampling rate + mel_fmax=8000.0, ################################ # Model Parameters # @@ -57,7 +56,9 @@ def create_hparams(hparams_string=None, verbose=False): decoder_rnn_dim=1024, prenet_dim=256, max_decoder_steps=1000, - gate_threshold=0.6, + gate_threshold=0.5, + p_attention_dropout=0.1, + p_decoder_dropout=0.1, # Attention parameters attention_rnn_dim=1024, @@ -78,9 +79,9 @@ def create_hparams(hparams_string=None, verbose=False): use_saved_learning_rate=False, learning_rate=1e-3, weight_decay=1e-6, - grad_clip_thresh=1, - batch_size=48, - mask_padding=False # set model's padded outputs to padded values + grad_clip_thresh=1.0, + batch_size=64, + mask_padding=True # set model's padded outputs to padded values ) if hparams_string: diff --git a/inference.ipynb b/inference.ipynb index 2aa9b50..904ab73 100644 --- a/inference.ipynb +++ b/inference.ipynb @@ -17,18 +17,27 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dcg-adlr-rafaelvalle-source.cosmos597/repos/nvidia/tacotron2/plotting_utils.py:2: UserWarning: matplotlib.pyplot as already been imported, this call will have no effect.\n", + " matplotlib.use(\"Agg\")\n" + ] + } ], "source": [ "import matplotlib\n", "matplotlib.use(\"Agg\")\n", "import matplotlib.pylab as plt\n", + "%matplotlib inline\n", "import IPython.display as ipd\n", "\n", + "import sys\n", + "sys.path.append('waveglow/')\n", "import numpy as np\n", "import torch\n", "\n", @@ -37,14 +46,12 @@ "from layers import TacotronSTFT\n", "from audio_processing import griffin_lim\n", "from train import load_model\n", - "from text import text_to_sequence\n", - "\n", - "%matplotlib inline" + "from text import text_to_sequence\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -92,11 +99,12 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "checkpoint_path = \"/home/scratch.adlr-gcf/audio_denoising/runs/TTS-Tacotron2-LJS-MSE-DRC-NoMaskPadding-Unsorted-Distributed-22khz/checkpoint_15500\"\n", + "checkpoint_path = \"tacotron2_statedict\"\n", + "\n", "model = load_model(hparams)\n", "try:\n", " model = model.module\n", @@ -106,6 +114,34 @@ "_ = model.eval()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load WaveGlow for mel2audio synthesis" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/torch/serialization.py:425: SourceChangeWarning: source code of class 'glow_old.WaveGlow' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n", + " warnings.warn(msg, SourceChangeWarning)\n", + "/opt/conda/lib/python3.6/site-packages/torch/serialization.py:425: SourceChangeWarning: source code of class 'glow_old.WN' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n", + " warnings.warn(msg, SourceChangeWarning)\n" + ] + } + ], + "source": [ + "waveglow_path = 'waveglow_old.pt'\n", + "waveglow = torch.load(waveglow_path)['model']" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -115,11 +151,11 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "text = \"This is an example of text to speech synthesis after 14 hours training.\"\n", + "text = \"Waveglow is really awesome!\"\n", "sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]\n", "sequence = torch.autograd.Variable(\n", " torch.from_numpy(sequence)).cuda().long()" @@ -134,16 +170,16 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -161,47 +197,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Load TacotronSTFT and convert mel-spectrogram to spectrogram" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "taco_stft = TacotronSTFT(\n", - " hparams.filter_length, hparams.hop_length, hparams.win_length, \n", - " sampling_rate=hparams.sampling_rate)\n", - "mel_decompress = taco_stft.spectral_de_normalize(mel_outputs_postnet)\n", - "mel_decompress = mel_decompress.transpose(1, 2).data.cpu()\n", - "spec_from_mel_scaling = 1000\n", - "spec_from_mel = torch.mm(mel_decompress[0], taco_stft.mel_basis)\n", - "spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)\n", - "spec_from_mel = spec_from_mel * spec_from_mel_scaling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Synthesize audio from spectrogram using the Griffin-Lim algorithm" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - ], - "source": [ - "waveform = griffin_lim(torch.autograd.Variable(spec_from_mel[:, :, :-1]), \n", - " taco_stft.stft_fn, 60)" + "#### Synthesize audio from spectrogram using WaveGlow" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -209,7 +210,7 @@ "text/html": [ "\n", " \n", " " @@ -218,13 +219,15 @@ "" ] }, - "execution_count": 56, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ipd.Audio(waveform[0].data.cpu().numpy(), rate=hparams.sampling_rate) " + "with torch.no_grad():\n", + " audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)\n", + "ipd.Audio(audio[0].data.cpu().numpy(), rate=hparams.sampling_rate)" ] } ], @@ -244,7 +247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/layers.py b/layers.py index f4935d5..615a64a 100644 --- a/layers.py +++ b/layers.py @@ -10,7 +10,7 @@ class LinearNorm(torch.nn.Module): super(LinearNorm, self).__init__() self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) - torch.nn.init.xavier_uniform( + torch.nn.init.xavier_uniform_( self.linear_layer.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) @@ -31,7 +31,7 @@ class ConvNorm(torch.nn.Module): padding=padding, dilation=dilation, bias=bias) - torch.nn.init.xavier_uniform( + torch.nn.init.xavier_uniform_( self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) def forward(self, signal): @@ -42,7 +42,7 @@ class ConvNorm(torch.nn.Module): class TacotronSTFT(torch.nn.Module): def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0, - mel_fmax=None): + mel_fmax=8000.0): super(TacotronSTFT, self).__init__() self.n_mel_channels = n_mel_channels self.sampling_rate = sampling_rate diff --git a/model.py b/model.py index 263faa6..6673b7c 100644 --- a/model.py +++ b/model.py @@ -1,3 +1,4 @@ +from math import sqrt import torch from torch.autograd import Variable from torch import nn @@ -56,7 +57,7 @@ class Attention(nn.Module): processed_query = self.query_layer(query.unsqueeze(1)) processed_attention_weights = self.location_layer(attention_weights_cat) - energies = self.v(F.tanh( + energies = self.v(torch.tanh( processed_query + processed_attention_weights + processed_memory)) energies = energies.squeeze(-1) @@ -107,7 +108,6 @@ class Postnet(nn.Module): def __init__(self, hparams): super(Postnet, self).__init__() - self.dropout = nn.Dropout(0.5) self.convolutions = nn.ModuleList() self.convolutions.append( @@ -141,9 +141,8 @@ class Postnet(nn.Module): def forward(self, x): for i in range(len(self.convolutions) - 1): - x = self.dropout(F.tanh(self.convolutions[i](x))) - - x = self.dropout(self.convolutions[-1](x)) + x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training) + x = F.dropout(self.convolutions[-1](x), 0.5, self.training) return x @@ -155,7 +154,6 @@ class Encoder(nn.Module): """ def __init__(self, hparams): super(Encoder, self).__init__() - self.dropout = nn.Dropout(0.5) convolutions = [] for _ in range(hparams.encoder_n_convolutions): @@ -175,7 +173,7 @@ class Encoder(nn.Module): def forward(self, x, input_lengths): for conv in self.convolutions: - x = self.dropout(F.relu(conv(x))) + x = F.dropout(F.relu(conv(x)), 0.5, self.training) x = x.transpose(1, 2) @@ -194,7 +192,7 @@ class Encoder(nn.Module): def inference(self, x): for conv in self.convolutions: - x = self.dropout(F.relu(conv(x))) + x = F.dropout(F.relu(conv(x)), 0.5, self.training) x = x.transpose(1, 2) @@ -215,13 +213,15 @@ class Decoder(nn.Module): self.prenet_dim = hparams.prenet_dim self.max_decoder_steps = hparams.max_decoder_steps self.gate_threshold = hparams.gate_threshold + self.p_attention_dropout = hparams.p_attention_dropout + self.p_decoder_dropout = hparams.p_decoder_dropout self.prenet = Prenet( hparams.n_mel_channels * hparams.n_frames_per_step, [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( @@ -230,12 +230,12 @@ 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( hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, - hparams.n_mel_channels*hparams.n_frames_per_step) + hparams.n_mel_channels * hparams.n_frames_per_step) self.gate_layer = LinearNorm( hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1, @@ -350,10 +350,13 @@ class Decoder(nn.Module): gate_output: gate output energies attention_weights: """ - - cell_input = torch.cat((self.decoder_hidden, self.attention_context), -1) + 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)) + self.attention_hidden = F.dropout( + self.attention_hidden, self.p_attention_dropout, self.training) + self.attention_cell = F.dropout( + self.attention_cell, self.p_attention_dropout, self.training) attention_weights_cat = torch.cat( (self.attention_weights.unsqueeze(1), @@ -363,10 +366,14 @@ 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)) + self.decoder_hidden = F.dropout( + self.decoder_hidden, self.p_decoder_dropout, self.training) + self.decoder_cell = F.dropout( + self.decoder_cell, self.p_decoder_dropout, self.training) decoder_hidden_attention_context = torch.cat( (self.decoder_hidden, self.attention_context), dim=1) @@ -391,22 +398,23 @@ class Decoder(nn.Module): alignments: sequence of attention weights from the decoder """ - decoder_input = self.get_go_frame(memory) + decoder_input = self.get_go_frame(memory).unsqueeze(0) decoder_inputs = self.parse_decoder_inputs(decoder_inputs) + decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0) + decoder_inputs = self.prenet(decoder_inputs) + self.initialize_decoder_states( memory, mask=~get_mask_from_lengths(memory_lengths)) mel_outputs, gate_outputs, alignments = [], [], [] - - while len(mel_outputs) < decoder_inputs.size(0): + while len(mel_outputs) < decoder_inputs.size(0) - 1: + decoder_input = decoder_inputs[len(mel_outputs)] 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] - mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs( mel_outputs, gate_outputs, alignments) @@ -430,13 +438,14 @@ class Decoder(nn.Module): mel_outputs, gate_outputs, alignments = [], [], [] while True: + decoder_input = self.prenet(decoder_input) 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] alignments += [alignment] - if F.sigmoid(gate_output.data) > self.gate_threshold: + if torch.sigmoid(gate_output.data) > self.gate_threshold: break elif len(mel_outputs) == self.max_decoder_steps: print("Warning! Reached max decoder steps") @@ -459,8 +468,9 @@ class Tacotron2(nn.Module): self.n_frames_per_step = hparams.n_frames_per_step self.embedding = nn.Embedding( hparams.n_symbols, hparams.symbols_embedding_dim) - torch.nn.init.xavier_uniform_(self.embedding.weight.data) - + std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim)) + val = sqrt(3.0) * std # uniform bounds for std + self.embedding.weight.data.uniform_(-val, val) self.encoder = Encoder(hparams) self.decoder = Decoder(hparams) self.postnet = Postnet(hparams) @@ -469,8 +479,8 @@ 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).item() mel_padded = to_gpu(mel_padded).float() gate_padded = to_gpu(gate_padded).float() output_lengths = to_gpu(output_lengths).long() @@ -485,7 +495,7 @@ class Tacotron2(nn.Module): def parse_output(self, outputs, output_lengths=None): if self.mask_padding and output_lengths is not None: - mask = ~get_mask_from_lengths(output_lengths+1) # +1 token + mask = ~get_mask_from_lengths(output_lengths) mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1)) mask = mask.permute(1, 0, 2) @@ -494,7 +504,6 @@ class Tacotron2(nn.Module): outputs[2].data.masked_fill_(mask[:, 0, :], 1e3) # gate energies outputs = fp16_to_fp32(outputs) if self.fp16_run else outputs - return outputs def forward(self, inputs): @@ -512,14 +521,6 @@ class Tacotron2(nn.Module): mel_outputs_postnet = self.postnet(mel_outputs) mel_outputs_postnet = mel_outputs + mel_outputs_postnet - # DataParallel expects equal sized inputs/outputs, hence padding - if input_lengths is not None: - alignments = alignments.unsqueeze(0) - alignments = nn.functional.pad( - alignments, - (0, max_len - alignments.size(3), 0, 0), - "constant", 0) - alignments = alignments.squeeze() return self.parse_output( [mel_outputs, mel_outputs_postnet, gate_outputs, alignments], output_lengths) diff --git a/requirements.txt b/requirements.txt index a4dfc43..11eccea 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ -torch==0.4.0 matplotlib==2.1.0 tensorflow numpy==1.13.3 diff --git a/stft.py b/stft.py index 8e137d3..03cd82f 100644 --- a/stft.py +++ b/stft.py @@ -61,7 +61,7 @@ class STFT(torch.nn.Module): np.linalg.pinv(scale * fourier_basis).T[:, None, :]) if window is not None: - assert(win_length >= filter_length) + assert(filter_length >= win_length) # get window and zero center pad it to filter_length fft_window = get_window(window, win_length, fftbins=True) fft_window = pad_center(fft_window, filter_length) diff --git a/text/__init__.py b/text/__init__.py index 2720c55..02ecf0e 100644 --- a/text/__init__.py +++ b/text/__init__.py @@ -37,8 +37,6 @@ def text_to_sequence(text, cleaner_names): sequence += _arpabet_to_sequence(m.group(2)) text = m.group(3) - # Append EOS token - sequence.append(_symbol_to_id['~']) return sequence diff --git a/text/symbols.py b/text/symbols.py index 7212f92..1be47bf 100644 --- a/text/symbols.py +++ b/text/symbols.py @@ -7,11 +7,12 @@ The default is a set of ASCII characters that works well for English or text tha from text import cmudict _pad = '_' -_eos = '~' -_characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!\'(),-.:;? ' +_punctuation = '!\'(),.:;? ' +_special = '-' +_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' # Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters): _arpabet = ['@' + s for s in cmudict.valid_symbols] # Export all symbols: -symbols = [_pad, _eos] + list(_characters) + _arpabet +symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet diff --git a/train.py b/train.py index 4413549..7600ceb 100644 --- a/train.py +++ b/train.py @@ -5,9 +5,9 @@ import math from numpy import finfo import torch -from distributed import DistributedDataParallel +from distributed import apply_gradient_allreduce +import torch.distributed as dist from torch.utils.data.distributed import DistributedSampler -from torch.nn import DataParallel from torch.utils.data import DataLoader from fp16_optimizer import FP16_Optimizer @@ -30,19 +30,20 @@ def batchnorm_to_float(module): def reduce_tensor(tensor, num_gpus): rt = tensor.clone() - torch.distributed.all_reduce(rt, op=torch.distributed.reduce_op.SUM) + dist.all_reduce(rt, op=dist.reduce_op.SUM) rt /= num_gpus return rt def init_distributed(hparams, n_gpus, rank, group_name): assert torch.cuda.is_available(), "Distributed mode requires CUDA." - print("Initializing distributed") + print("Initializing Distributed") + # Set cuda device so everything is done on the right GPU. torch.cuda.set_device(rank % torch.cuda.device_count()) # Initialize distributed communication - torch.distributed.init_process_group( + dist.init_process_group( backend=hparams.dist_backend, init_method=hparams.dist_url, world_size=n_gpus, rank=rank, group_name=group_name) @@ -131,22 +132,20 @@ def validate(model, criterion, valset, iteration, batch_size, n_gpus, pin_memory=False, collate_fn=collate_fn) val_loss = 0.0 - if distributed_run or torch.cuda.device_count() > 1: - batch_parser = model.module.parse_batch - else: - batch_parser = model.parse_batch - for i, batch in enumerate(val_loader): - x, y = batch_parser(batch) + x, y = model.parse_batch(batch) y_pred = model(x) loss = criterion(y_pred, y) - reduced_val_loss = reduce_tensor(loss.data, n_gpus)[0] \ - if distributed_run else loss.data[0] + if distributed_run: + reduced_val_loss = reduce_tensor(loss.data, num_gpus).item() + else: + reduced_val_loss = loss.item() val_loss += reduced_val_loss val_loss = val_loss / (i + 1) model.train() - return val_loss + print("Validation loss {}: {:9f} ".format(iteration, reduced_val_loss)) + logger.log_validation(reduced_val_loss, model, y, y_pred, iteration) def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus, @@ -176,6 +175,9 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus, optimizer = FP16_Optimizer( optimizer, dynamic_loss_scale=hparams.dynamic_loss_scaling) + if hparams.distributed_run: + model = apply_gradient_allreduce(model) + criterion = Tacotron2Loss() logger = prepare_directories_and_logger( @@ -194,15 +196,10 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus, checkpoint_path, model, optimizer) if hparams.use_saved_learning_rate: learning_rate = _learning_rate - iteration += 1 # next iteration is iteration + 1 epoch_offset = max(0, int(iteration / len(train_loader))) model.train() - if hparams.distributed_run or torch.cuda.device_count() > 1: - batch_parser = model.module.parse_batch - else: - batch_parser = model.parse_batch # ================ MAIN TRAINNIG LOOP! =================== for epoch in range(epoch_offset, hparams.epochs): print("Epoch: {}".format(epoch)) @@ -212,18 +209,21 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus, param_group['lr'] = learning_rate model.zero_grad() - x, y = batch_parser(batch) + x, y = model.parse_batch(batch) y_pred = model(x) + loss = criterion(y_pred, y) - reduced_loss = reduce_tensor(loss.data, n_gpus)[0] \ - if hparams.distributed_run else loss.data[0] + if hparams.distributed_run: + reduced_loss = reduce_tensor(loss.data, num_gpus).item() + else: + reduced_loss = loss.item() if hparams.fp16_run: optimizer.backward(loss) grad_norm = optimizer.clip_fp32_grads(hparams.grad_clip_thresh) else: loss.backward() - grad_norm = torch.nn.utils.clip_grad_norm( + grad_norm = torch.nn.utils.clip_grad_norm_( model.parameters(), hparams.grad_clip_thresh) optimizer.step() @@ -234,20 +234,14 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus, duration = time.perf_counter() - start print("Train loss {} {:.6f} Grad Norm {:.6f} {:.2f}s/it".format( iteration, reduced_loss, grad_norm, duration)) - logger.log_training( reduced_loss, grad_norm, learning_rate, duration, iteration) if not overflow and (iteration % hparams.iters_per_checkpoint == 0): - reduced_val_loss = validate( - model, criterion, valset, iteration, hparams.batch_size, - n_gpus, collate_fn, logger, hparams.distributed_run, rank) + validate(model, criterion, valset, iteration, hparams.batch_size, + n_gpus, collate_fn, logger, hparams.distributed_run, rank) if rank == 0: - print("Validation loss {}: {:9f} ".format( - iteration, reduced_val_loss)) - logger.log_validation( - reduced_val_loss, model, y, y_pred, iteration) checkpoint_path = os.path.join( output_directory, "checkpoint_{}".format(iteration)) save_checkpoint(model, optimizer, learning_rate, iteration, diff --git a/utils.py b/utils.py index 633ecff..c843d95 100644 --- a/utils.py +++ b/utils.py @@ -4,29 +4,26 @@ import torch def get_mask_from_lengths(lengths): - max_len = torch.max(lengths) - ids = torch.arange(0, max_len).long().cuda() + max_len = torch.max(lengths).item() + ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len)) mask = (ids < lengths.unsqueeze(1)).byte() return mask -def load_wav_to_torch(full_path, sr): +def load_wav_to_torch(full_path): sampling_rate, data = read(full_path) - assert sr == sampling_rate, "{} SR doesn't match {} on path {}".format( - sr, sampling_rate, full_path) - return torch.FloatTensor(data.astype(np.float32)) + return torch.FloatTensor(data.astype(np.float32)), sampling_rate -def load_filepaths_and_text(filename, sort_by_length, split="|"): +def load_filepaths_and_text(filename, split="|"): with open(filename, encoding='utf-8') as f: filepaths_and_text = [line.strip().split(split) for line in f] - - if sort_by_length: - filepaths_and_text.sort(key=lambda x: len(x[1])) - return filepaths_and_text def to_gpu(x): - x = x.contiguous().cuda(async=True) + x = x.contiguous() + + if torch.cuda.is_available(): + x = x.cuda(non_blocking=True) return torch.autograd.Variable(x)