fix wavernn dygraph to static , test=tts

examples/csmsc/tts3/local/inference.sh

@@ -48,4 +48,15 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/pd_infer_out \
         --phones_dict=dump/phone_id_map.txt
+fi
+
+# wavernn
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    python3 ${BIN_DIR}/../inference.py \
+        --inference_dir=${train_output_path}/inference \
+        --am=fastspeech2_csmsc \
+        --voc=wavernn_csmsc \
+        --text=${BIN_DIR}/../sentences.txt \
+        --output_dir=${train_output_path}/pd_infer_out \
+        --phones_dict=dump/phone_id_map.txt
 fi
\ No newline at end of file

examples/csmsc/tts3/local/synthesize_e2e.sh

@@ -108,5 +108,6 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --phones_dict=dump/phone_id_map.txt
+        --phones_dict=dump/phone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi

paddlespeech/t2s/exps/inference.py

@@ -54,7 +54,7 @@ def main():
         default='pwgan_csmsc',
         choices=[
             'pwgan_csmsc', 'mb_melgan_csmsc', 'hifigan_csmsc', 'pwgan_aishell3',
-            'pwgan_vctk'
+            'pwgan_vctk', 'wavernn_csmsc'
         ],
         help='Choose vocoder type of tts task.')
     # other

paddlespeech/t2s/models/wavernn/wavernn.py

@@ -76,6 +76,7 @@ class MelResNet(nn.Layer):
         Tensor
             Output tensor (B, res_out_dims, T).
         '''
+
         x = self.conv_in(x)
         x = self.batch_norm(x)
         x = F.relu(x)
@@ -230,6 +231,7 @@ class WaveRNN(nn.Layer):
 
         self.rnn1 = nn.GRU(rnn_dims, rnn_dims)
         self.rnn2 = nn.GRU(rnn_dims + self.aux_dims, rnn_dims)
+
         self._to_flatten += [self.rnn1, self.rnn2]
 
         self.fc1 = nn.Linear(rnn_dims + self.aux_dims, fc_dims)
@@ -326,17 +328,17 @@ class WaveRNN(nn.Layer):
 
         output = []
         start = time.time()
-        rnn1 = self.get_gru_cell(self.rnn1)
-        rnn2 = self.get_gru_cell(self.rnn2)
+
         # pseudo batch
         # (T, C_aux) -> (1, C_aux, T)
         c = paddle.transpose(c, [1, 0]).unsqueeze(0)
-
-        wave_len = (paddle.shape(c)[-1] - 1) * self.hop_length
+        T = paddle.shape(c)[-1]
+        wave_len = (T - 1) * self.hop_length
         # TODO remove two transpose op by modifying function pad_tensor
         c = self.pad_tensor(
             c.transpose([0, 2, 1]), pad=self.aux_context_window,
             side='both').transpose([0, 2, 1])
+
         c, aux = self.upsample(c)
 
         if batched:
@@ -344,7 +346,13 @@ class WaveRNN(nn.Layer):
             c = self.fold_with_overlap(c, target, overlap)
             aux = self.fold_with_overlap(aux, target, overlap)
 
-        b_size, seq_len, _ = paddle.shape(c)
+        # for dygraph to static graph, if use seq_len of `b_size, seq_len, _ = paddle.shape(c)` in for
+        # will not get TensorArray
+        # see https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/case_analysis_cn.html#list-lodtensorarray
+        # b_size, seq_len, _ = paddle.shape(c)
+        b_size = paddle.shape(c)[0]
+        seq_len = paddle.shape(c)[1]
+
         h1 = paddle.zeros([b_size, self.rnn_dims])
         h2 = paddle.zeros([b_size, self.rnn_dims])
         x = paddle.zeros([b_size, 1])
@@ -354,14 +362,20 @@ class WaveRNN(nn.Layer):
 
         for i in range(seq_len):
             m_t = c[:, i, :]
-
-            a1_t, a2_t, a3_t, a4_t = (a[:, i, :] for a in aux_split)
+            # for dygraph to static graph
+            # a1_t, a2_t, a3_t, a4_t = (a[:, i, :] for a in aux_split)
+            a1_t = aux_split[0][:, i, :]
+            a2_t = aux_split[1][:, i, :]
+            a3_t = aux_split[2][:, i, :]
+            a4_t = aux_split[3][:, i, :]
             x = paddle.concat([x, m_t, a1_t], axis=1)
             x = self.I(x)
-            h1, _ = rnn1(x, h1)
+            # use GRUCell here
+            h1, _ = self.rnn1[0].cell(x, h1)
             x = x + h1
             inp = paddle.concat([x, a2_t], axis=1)
-            h2, _ = rnn2(inp, h2)
+            # use GRUCell here
+            h2, _ = self.rnn2[0].cell(inp, h2)
 
             x = x + h2
             x = paddle.concat([x, a3_t], axis=1)
@@ -413,15 +427,6 @@ class WaveRNN(nn.Layer):
         # 增加 C_out 维度
         return output.unsqueeze(-1)
 
-    def get_gru_cell(self, gru):
-        gru_cell = nn.GRUCell(gru.input_size, gru.hidden_size)
-        gru_cell.weight_hh = gru.weight_hh_l0
-        gru_cell.weight_ih = gru.weight_ih_l0
-        gru_cell.bias_hh = gru.bias_hh_l0
-        gru_cell.bias_ih = gru.bias_ih_l0
-
-        return gru_cell
-
     def _flatten_parameters(self):
         [m.flatten_parameters() for m in self._to_flatten]
 
@@ -438,7 +443,9 @@ class WaveRNN(nn.Layer):
         ----------
         Tensor
         '''
-        b, t, c = paddle.shape(x)
+        b, t, _ = paddle.shape(x)
+        # for dygraph to static graph
+        c = x.shape[-1]
         total = t + 2 * pad if side == 'both' else t + pad
         padded = paddle.zeros([b, total, c])
         if side == 'before' or side == 'both':
@@ -516,7 +523,7 @@ class WaveRNN(nn.Layer):
         y : Tensor
             Batched sequences of audio samples
             shape=(num_folds, target + 2 * overlap)
-            dtype=paddle.float64
+            dtype=paddle.float32
         overlap : int
             Timesteps for both xfade and rnn warmup
 
@@ -525,7 +532,7 @@ class WaveRNN(nn.Layer):
         Tensor
             audio samples in a 1d array
             shape=(total_len)
-            dtype=paddle.float64
+            dtype=paddle.float32
 
         Details
         ----------
@@ -545,19 +552,19 @@ class WaveRNN(nn.Layer):
 
         '''
         # num_folds = (total_len - overlap) // (target + overlap)
-        num_folds, length = y.shape
+        num_folds, length = paddle.shape(y)
         target = length - 2 * overlap
         total_len = num_folds * (target + overlap) + overlap
 
         # Need some silence for the run warmup
         slience_len = overlap // 2
         fade_len = overlap - slience_len
-        slience = paddle.zeros([slience_len], dtype=paddle.float64)
-        linear = paddle.ones([fade_len], dtype=paddle.float64)
+        slience = paddle.zeros([slience_len], dtype=paddle.float32)
+        linear = paddle.ones([fade_len], dtype=paddle.float32)
 
         # Equal power crossfade
         # fade_in increase from 0 to 1, fade_out reduces from 1 to 0
-        t = paddle.linspace(-1, 1, fade_len, dtype=paddle.float64)
+        t = paddle.linspace(-1, 1, fade_len, dtype=paddle.float32)
         fade_in = paddle.sqrt(0.5 * (1 + t))
         fade_out = paddle.sqrt(0.5 * (1 - t))
         # Concat the silence to the fades
@@ -568,7 +575,7 @@ class WaveRNN(nn.Layer):
         y[:, :overlap] *= fade_in
         y[:, -overlap:] *= fade_out
 
-        unfolded = paddle.zeros([total_len], dtype=paddle.float64)
+        unfolded = paddle.zeros([total_len], dtype=paddle.float32)
 
         # Loop to add up all the samples
         for i in range(num_folds):
@@ -606,11 +613,13 @@ class WaveRNNInference(nn.Layer):
                 mu_law: bool=True,
                 gen_display: bool=False):
         normalized_mel = self.normalizer(logmel)
+
         wav = self.wavernn.generate(
-            normalized_mel,
-            batched=batched,
-            target=target,
-            overlap=overlap,
-            mu_law=mu_law,
-            gen_display=gen_display)
+            normalized_mel, )
+        # batched=batched,
+        # target=target,
+        # overlap=overlap,
+        # mu_law=mu_law,
+        # gen_display=gen_display)
+
         return wav