fix Voc5/Jets with CSMSC

examples/csmsc/jets/README.md

@@ -3,7 +3,15 @@ This example contains code used to train a [JETS](https://arxiv.org/abs/2203.168
 
 ## Dataset
 ### Download and Extract
-Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/source).
+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 and durations for JETS.

examples/csmsc/voc5/README.md

@@ -4,6 +4,14 @@ This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.
 ### 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) results to cut silence at the edge of audio.
 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.

examples/csmsc/voc5/iSTFTNet.md

@@ -6,6 +6,14 @@ This example contains code used to train a [iSTFTNet](https://arxiv.org/abs/2203
 ### 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) results to cut silence at the edge of audio.
 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/gan_vocoder/preprocess.py

@@ -206,8 +206,9 @@ def main():
     # split data into 3 sections
     if args.dataset == "baker":
         wav_files = sorted(list((rootdir / "Wave").rglob("*.wav")))
-        num_train = 9800
+        # split data into 3 sections, the max number of dev/test is 10% or 100
-        num_dev = 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/models/jets/length_regulator.py

@@ -55,7 +55,9 @@ class GaussianUpsampling(nn.Layer):
         if h_masks is not None:
             t = t * paddle.to_tensor(h_masks, dtype="float32")
 
-        c = ds.cumsum(axis=-1) - ds / 2
+        ds_cumsum = ds.cumsum(axis=-1)
+        ds_half = ds / 2
+        c = ds_cumsum.astype(ds_half.dtype) - ds_half
         energy = -1 * self.delta * (t.unsqueeze(-1) - c.unsqueeze(1))**2
         if d_masks is not None:
             d_masks = ~(d_masks.unsqueeze(1))

paddlespeech/t2s/modules/nets_utils.py

@@ -348,7 +348,9 @@ def get_random_segments(
     """
     b, c, t = paddle.shape(x)
     max_start_idx = x_lengths - segment_size
-    start_idxs = paddle.cast(paddle.rand([b]) * max_start_idx, 'int64')
+    rand_number = paddle.rand([b])
+    start_idxs = paddle.cast(rand_number *
+                             max_start_idx.astype(rand_number.dtype), 'int64')
     segments = get_segments(x, start_idxs, segment_size)
 
     return segments, start_idxs