fix Tacotron2 with CSMSC

examples/csmsc/tts0/README.md

@@ -5,6 +5,14 @@ This example contains code used to train a [Tacotron2](https://arxiv.org/abs/171
 ### Download and Extract
 Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/TNtts/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/BZNSYP`.
 
+The structure of the folder is listed below.
+
+```text
+datasets/BZNSYP
+└── Wave
+    └── .wav files
+```
+
 ### Get MFA Result and Extract
 We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes for Tacotron2, the durations of MFA are not needed here.
 You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo.

paddlespeech/t2s/exps/tacotron2/preprocess.py

@@ -228,9 +228,9 @@ def main():
 
     if args.dataset == "baker":
         wav_files = sorted(list((rootdir / "Wave").rglob("*.wav")))
-        # split data into 3 sections
-        num_train = 9800
-        num_dev = 100
+        # split data into 3 sections, the max number of dev/test is 10% or 100
+        num_dev = min(int(len(wav_files) * 0.1), 100)
+        num_train = len(wav_files) - num_dev * 2
         train_wav_files = wav_files[:num_train]
         dev_wav_files = wav_files[num_train:num_train + num_dev]
         test_wav_files = wav_files[num_train + num_dev:]

paddlespeech/t2s/modules/nets_utils.py

@@ -181,6 +181,9 @@ def make_pad_mask(lengths, xs=None, length_dim=-1):
     if length_dim == 0:
         raise ValueError("length_dim cannot be 0: {}".format(length_dim))
 
+    # check if ilens is 0-dim tensor, if so, add a dimension
+    if lengths.ndim == 0:
+        lengths = lengths.unsqueeze(0)
     bs = paddle.shape(lengths)
     if xs is None:
         maxlen = paddle.cast(lengths.max(), dtype=bs.dtype)

paddlespeech/t2s/modules/tacotron2/attentions.py

@@ -171,7 +171,8 @@ class AttLoc(nn.Layer):
         if paddle.sum(att_prev) == 0:
             # if no bias, 0 0-pad goes 0
             att_prev = 1.0 - make_pad_mask(enc_hs_len)
-            att_prev = att_prev / enc_hs_len.unsqueeze(-1)
+            att_prev = att_prev / enc_hs_len.unsqueeze(-1).astype(
+                att_prev.dtype)
 
         # att_prev: (utt, frame) -> (utt, 1, 1, frame)
         # -> (utt, att_conv_chans, 1, frame)

paddlespeech/t2s/modules/tacotron2/encoder.py

@@ -162,6 +162,9 @@ class Encoder(nn.Layer):
             return xs.transpose([0, 2, 1])
         if not isinstance(ilens, paddle.Tensor):
             ilens = paddle.to_tensor(ilens)
+        # check if ilens is 0-dim tensor, if so, add a dimension
+        if ilens.ndim == 0:
+            ilens = ilens.unsqueeze(0)
         xs = xs.transpose([0, 2, 1])
         # for dygraph to static graph
         # self.blstm.flatten_parameters()