WIP: baker

examples/transformer_tts/synthesize.py

@@ -17,13 +17,15 @@ import time
 from pathlib import Path
 import numpy as np
 import paddle
+import matplotlib
+from matplotlib import pyplot as plt
 
 import parakeet
 from parakeet.frontend import English
 from parakeet.models.transformer_tts import TransformerTTS
 from parakeet.utils import scheduler
 from parakeet.training.cli import default_argument_parser
-from parakeet.utils.display import add_attention_plots
+from parakeet.utils.display import add_attention_plots, pack_attention_images
 
 from config import get_cfg_defaults
 
@@ -49,7 +51,16 @@ def main(config, args):
     for i, sentence in enumerate(sentences):
         outputs = model.predict(sentence, verbose=args.verbose)
         mel_output = outputs["mel_output"]
-        # cross_attention_weights = outputs["cross_attention_weights"]
+        cross_attention_weights = outputs["cross_attention_weights"]
+        attns = [attn for attn in cross_attention_weights]
+        
+        fig = plt.figure(figsize=(40, 40))
+        for j, attn in enumerate(attns):
+            plt.subplot(1, 4, j+1)
+            plt.imshow(attn[0])
+        plt.tight_layout()
+        plt.savefig(str(output_dir / f"sentence_{i}.png"))
+
         mel_output = mel_output.T  #(C, T)
         np.save(str(output_dir / f"sentence_{i}"), mel_output)
         if args.verbose:

examples/waveflow/synthesize.py

@@ -21,6 +21,7 @@ import paddle
 import parakeet
 from parakeet.models.waveflow import UpsampleNet, WaveFlow, ConditionalWaveFlow
 from parakeet.utils import layer_tools, checkpoint
+import time
 
 from config import get_cfg_defaults
 
@@ -34,9 +35,10 @@ def main(config, args):
     mel_dir = Path(args.input).expanduser()
     output_dir = Path(args.output).expanduser()
     output_dir.mkdir(parents=True, exist_ok=True)
-    for file_path in mel_dir.iterdir():
+    for file_path in mel_dir.glob("*.npy"):
         mel = np.load(str(file_path))
-        audio = model.predict(mel)
+        with paddle.amp.auto_cast():
+            audio = model.predict(mel)
         audio_path = output_dir / (
             os.path.splitext(file_path.name)[0] + ".wav")
         sf.write(audio_path, audio, config.data.sample_rate)

parakeet/frontend/punctuation.py

@@ -18,6 +18,7 @@ import string
 __all__ = ["get_punctuations"]
 
 EN_PUNCT = [
+    " ",
     "-",
     "...",
     ",",

parakeet/models/tacotron2.py

@@ -22,6 +22,7 @@ from parakeet.modules.conv import Conv1dBatchNorm
 from parakeet.modules.attention import LocationSensitiveAttention
 from parakeet.modules import masking
 from parakeet.utils import checkpoint
+from tqdm import trange
 
 __all__ = ["Tacotron2", "Tacotron2Loss"]
 
@@ -475,10 +476,11 @@ class Tacotron2Decoder(nn.Layer):
             dtype=key.dtype)  #[B, C]
 
         self._initialize_decoder_states(key)
+        T_enc = key.shape[1]
         self.mask = None
 
         mel_outputs, stop_logits, alignments = [], [], []
-        while True:
+        for _ in trange(max_decoder_steps):
             query = self.prenet(query)
             mel_output, stop_logit, alignment = self._decode(query)
 
@@ -487,8 +489,12 @@ class Tacotron2Decoder(nn.Layer):
             alignments += [alignment]
 
             if F.sigmoid(stop_logit) > stop_threshold:
+                print("hits stop condition!")
                 break
-            elif len(mel_outputs) == max_decoder_steps:
+            if int(paddle.argmax(alignment[0])) == T_enc - 1:
+                print("content exhausted!")
+                break
+            if len(mel_outputs) == max_decoder_steps:
                 print("Warning! Reached max decoder steps!!!")
                 break
 
@@ -718,7 +724,7 @@ class Tacotron2(nn.Layer):
         """
         embedded_inputs = self.embedding(text_inputs)
         if self.toned:
-             embedded_inputs = paddle.concat([embedded_inputs, self.embedding_tones(tones)], -1)
+            embedded_inputs += self.embedding_tones(tones)
         encoder_outputs = self.encoder(embedded_inputs)
         mel_outputs, stop_logits, alignments = self.decoder.infer(
             encoder_outputs,

parakeet/models/transformer_tts.py

@@ -340,13 +340,15 @@ class CNNPostNet(nn.Layer):
             c_in = d_input if i == 0 else d_hidden
             c_out = d_output if i == n_layers - 1 else d_hidden
             self.convs.append(
-                nn.Conv1D(
+                Conv1dBatchNorm(
                     c_in,
                     c_out,
                     kernel_size,
                     weight_attr=I.XavierUniform(),
-                    padding=padding))
+                    padding=padding,
-        # self.last_bn = nn.BatchNorm1D(d_output)
+                    momentum=0.99,
+                    epsilon=1e-03))
+        self.last_bn = nn.BatchNorm1D(d_output, momentum=0.99, epsilon=1e-3)
         # for a layer that ends with a normalization layer that is targeted to
         # output a non zero-central output, it may take a long time to 
         # train the scale and bias
@@ -359,8 +361,8 @@ class CNNPostNet(nn.Layer):
             if i != (len(self.convs) - 1):
                 x = F.tanh(x)
         # TODO: check it
-        x = x_in + x
+        # x = x_in + x
-        # x = self.last_bn(x_in + x)
+        x = self.last_bn(x_in + x)
         return x
 
 
@@ -567,8 +569,13 @@ class TransformerTTS(nn.Layer):
         text_ids = paddle.to_tensor(self.frontend(input))
         input = paddle.unsqueeze(text_ids, 0)  # (1, T)
         outputs = self.infer(input, max_length=max_length, verbose=verbose)
-        outputs = {k: v[0].numpy() for k, v in outputs.items()}
+        npy_outputs = {
-        return outputs
+            "mel_output": outputs["mel_output"][0].numpy(),
+            "encoder_attention_weights": [item[0].numpy() for item in outputs["encoder_attention_weights"]],
+            "cross_attention_weights": [item[0].numpy() for item in outputs["cross_attention_weights"]],
+        }
+        
+        return npy_outputs
 
     def set_constants(self, reduction_factor, drop_n_heads):
         self.r = reduction_factor

parakeet/models/waveflow.py

@@ -19,6 +19,7 @@ import paddle
 from paddle import nn
 from paddle.nn import functional as F
 from paddle.nn import initializer as I
+import time
 
 from parakeet.utils import checkpoint
 from parakeet.modules import geometry as geo
@@ -798,10 +799,13 @@ class ConditionalWaveFlow(nn.LayerList):
         Tensor : [shape=(B, T)] 
             The synthesized audio, where``T <= T_mel \* upsample_factors``.
         """
+        start = time.time()
         condition = self.encoder(mel, trim_conv_artifact=True)  #(B, C, T)
         batch_size, _, time_steps = condition.shape
         z = paddle.randn([batch_size, time_steps], dtype=mel.dtype)
         x = self.decoder.inverse(z, condition)
+        end = time.time()
+        print("time: {}s".format(end - start))
         return x
 
     @paddle.no_grad()

parakeet/utils/display.py

@@ -14,6 +14,8 @@
 
 import numpy as np
 import matplotlib
+import librosa
+import librosa.display
 matplotlib.use("Agg")
 import matplotlib.pylab as plt
 from matplotlib import cm, pyplot
@@ -69,6 +71,7 @@ def plot_alignment(alignment, title=None):
     plt.xlabel(xlabel)
     plt.ylabel('Encoder timestep')
     plt.tight_layout()
+    return fig
 
     fig.canvas.draw()
     data = save_figure_to_numpy(fig)