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Merge pull request #5 from LEEYOONHYUNG/master
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final
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LEEYOONHYUNG authored Feb 26, 2020
2 parents 4960826 + ab703e3 commit 64d07b0
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13 changes: 5 additions & 8 deletions README.md
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# FastSpeech
Implementation of ["FastSpeech: Fast, Robust and Controllable Text to Speech"](https://arxiv.org/abs/1905.09263)

(2020/02/10)
- I was able to finish the implementation of [Transformer-TTS](https://github.com/Deepest-Project/Transformer-TTS) by completing the Stop token prediction and remove the concatenation of inputs and outputs of multihead attention.
- However, the alignments of the implementation are less diagonal, so it can not generate proper alignments for fastspeech.
- As a result, I failed to train the fastspeech with the new transformer-tts implementation :(

## Training
0. Set `data_path` in `hparams.py` as the LJSpeech folder
1. Set `teacher_dir` in `hparams.py` as the data directory where the alignments and melspectrogram targets are saved
2. Set `teacer_model` as the pre-trained [transfomer-tts model](https://github.com/Deepest-Project/Transformer-TTS) for pretrained_embedding
3. `python train.py --gpu='0'`
2. Put checkpoint of the pre-trained transformer-tts (weights of the embedding/encoder layers are used)
3. `python train.py`

## Training curves (orange: / blue: )
## Training curves (orange: character / blue: phoneme)
##### The size of the train dataset is different because transformer-tts trained with phoneme shows more diagonal attention
##### train:val:test=8:1:1, total => character:1126 / phoneme:3412
<img src="figures/duration_loss.JPG" height="200">
<img src="figures/train_loss.JPG" height="200">
<img src="figures/val_loss.JPG" height="200">
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14 changes: 7 additions & 7 deletions generate_samples.ipynb
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T06:05:50.543482Z",
Expand All @@ -19,7 +19,7 @@
"outputs": [],
"source": [
"import os\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = '3'\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"for data_type in ['char']:\n",
"for data_type in ['char', 'phone']:\n",
" for step in ['20000', '50000', '100000', '200000']:\n",
" checkpoint_path = f\"training_log/fastspeech-{data_type}/checkpoint_{step}\"\n",
" state_dict = {}\n",
Expand All @@ -131,7 +131,7 @@
"\n",
" for i in [1, 6, 22]:\n",
" file_name, _, text = lines[i]\n",
" sequence = np.array(text2seq(text,data_type))[None, :]\n",
" sequence = np.array(text2seq(text, data_type))[None, :]\n",
" sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()\n",
"\n",
" for alpha in [0.8, 0.9, 1.0, 1.1, 1.2]:\n",
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6 changes: 3 additions & 3 deletions hparams.py
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seed=1234
n_gpus=2
output_directory = 'training_log'
log_directory = 'fastspeech-char'
log_directory = 'fastspeech-phone'
data_path = '/media/disk1/lyh/LJSpeech-1.1/preprocessed'
teacher_dir = '/media/disk1/lyh/fastspeech_char'
teacher_dir = '/media/disk1/lyh/fastspeech_phone'

training_files='filelists/ljs_audio_text_train_filelist.txt'
validation_files='filelists/ljs_audio_text_val_filelist.txt'
Expand All @@ -30,7 +30,7 @@
# Model Parameters #
################################
n_symbols=len(symbols)
data_type='char_seq' # 'phone_seq'
data_type='phone_seq' # 'phone_seq'
symbols_embedding_dim=256
hidden_dim=256
dprenet_dim=256
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213 changes: 210 additions & 3 deletions index.html
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<body>
<div>
<h2>LJ001-0029.wav</h2>
<h2>LJ001-0029.wav (character)</h2>
<table>
<thead>
<tr>
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</tbody>
</table>

<h2>LJ001-0085.wav</h2>
<h2>LJ001-0085.wav (character)</h2>
<table>
<thead>
<tr>
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</tbody>
</table>

<h2>LJ002-0106.wav</h2>
<h2>LJ002-0106.wav (character)</h2>
<table>
<thead>
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</table>

</div>

<div>
<h2>LJ001-0029.wav (phoneme)</h2>
<table>
<thead>
<tr>
<th>Steps</th>
<th>alpha=0.8</th>
<th>alpha=0.9</th>
<th>alpha=1.0</th>
<th>alpha=1.1</th>
<th>alpha=1.2</th>
</tr>
</thead>
<tbody>
<tr>
<td>Steps 20000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone20000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone20000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone20000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone20000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone20000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 50000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone50000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone50000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone50000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone50000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone50000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 100000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone100000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone100000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone100000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone100000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone100000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 200000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone200000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone200000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone200000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone200000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0029_phone200000_12.wav" type="audio/wav"></audio></td>
</tr>
</tbody>
</table>

<h2>LJ001-0085.wav (phoneme)</h2>
<table>
<thead>
<tr>
<th>Steps</th>
<th>alpha=0.8</th>
<th>alpha=0.9</th>
<th>alpha=1.0</th>
<th>alpha=1.1</th>
<th>alpha=1.2</th>
</tr>
</thead>
<tbody>
<tr>
<td>Steps 20000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone20000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone20000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone20000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone20000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone20000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 50000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone50000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone50000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone50000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone50000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone50000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 100000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone100000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone100000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone100000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone100000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone100000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 200000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone200000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone200000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone200000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone200000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ001-0085_phone200000_12.wav" type="audio/wav"></audio></td>
</tr>
</tbody>
</table>

<h2>LJ002-0106.wav (phoneme)</h2>
<table>
<thead>
<tr>
<th>Steps</th>
<th>alpha=0.8</th>
<th>alpha=0.9</th>
<th>alpha=1.0</th>
<th>alpha=1.1</th>
<th>alpha=1.2</th>
</tr>
</thead>
<tbody>
<tr>
<td>Steps 20000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone20000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone20000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone20000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone20000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone20000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 50000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone50000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone50000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone50000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone50000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone50000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 100000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone100000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone100000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone100000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone100000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone100000_12.wav" type="audio/wav"></audio></td>
</tr>
<tr>
<td>Steps 200000</td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone200000_8.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone200000_9.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone200000_10.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone200000_11.wav" type="audio/wav"></audio></td>
<td><audio controls class="audio-player" preload="metadata">
<source src="wavs/LJ002-0106_phone200000_12.wav" type="audio/wav"></audio></td>
</tr>
</tbody>
</table>

</div>
</body>

</html>
3,218 changes: 3,210 additions & 8 deletions inference.ipynb

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7 changes: 4 additions & 3 deletions modules/model.py
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Expand Up @@ -160,7 +160,7 @@ def inference(self, text, alpha=1.0):

### Duration Predictor ###
durations = self.Duration(hidden_states.permute(1,2,0))
hidden_states_expanded = self.LR(hidden_states, durations, alpha)
hidden_states_expanded = self.LR(hidden_states, durations, alpha, inference=True)
hidden_states_expanded += self.alpha2*self.pe[:hidden_states_expanded.size(0)].unsqueeze(1)
mel_mask = text.new_zeros(1, hidden_states_expanded.size(0)).to(torch.bool)

Expand All @@ -185,10 +185,11 @@ def align2duration(self, alignments, mel_mask):
return durations


def LR(self, hidden_states, durations, alpha=1.0):
def LR(self, hidden_states, durations, alpha=1.0, inference=False):
L, B, D = hidden_states.size()
durations = torch.round(durations*alpha).to(torch.long)
durations[durations<=0]=1
if inference:
durations[durations<=0]=1
T=int(torch.sum(durations, dim=-1).max().item())
expanded = hidden_states.new_zeros(T, B, D)

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