From 45da88f0b80a72b0b6c4d8beeb5d4939ca170c1a Mon Sep 17 00:00:00 2001 From: liangym Date: Tue, 25 Apr 2023 09:22:35 +0000 Subject: [PATCH] adapt paddle2.5, test=tts --- .../t2s/models/diffsinger/fastspeech2midi.py | 6 +++--- paddlespeech/t2s/models/ernie_sat/ernie_sat.py | 12 ++++++------ .../t2s/models/fastspeech2/fastspeech2.py | 8 ++++---- paddlespeech/t2s/models/jets/jets.py | 4 ++-- paddlespeech/t2s/models/melgan/style_melgan.py | 2 +- .../parallel_wavegan/parallel_wavegan.py | 2 +- paddlespeech/t2s/models/tacotron2/tacotron2.py | 8 ++++---- .../t2s/models/vits/duration_predictor.py | 4 ++-- paddlespeech/t2s/models/vits/flow.py | 2 +- paddlespeech/t2s/models/vits/vits.py | 6 +++--- paddlespeech/t2s/models/wavernn/wavernn.py | 6 +++--- paddlespeech/t2s/modules/losses.py | 6 +++--- paddlespeech/t2s/modules/nets_utils.py | 6 +++--- .../t2s/modules/predictor/length_regulator.py | 4 ++-- .../t2s/modules/tacotron2/attentions.py | 10 +++++----- paddlespeech/t2s/modules/tacotron2/decoder.py | 16 ++++++++-------- paddlespeech/t2s/modules/tacotron2/encoder.py | 2 +- .../t2s/modules/transformer/attention.py | 8 ++++---- .../t2s/modules/transformer/embedding.py | 18 +++++++++--------- 19 files changed, 65 insertions(+), 65 deletions(-) diff --git a/paddlespeech/t2s/models/diffsinger/fastspeech2midi.py b/paddlespeech/t2s/models/diffsinger/fastspeech2midi.py index cce88d8a0..7fbb0acf8 100644 --- a/paddlespeech/t2s/models/diffsinger/fastspeech2midi.py +++ b/paddlespeech/t2s/models/diffsinger/fastspeech2midi.py @@ -308,7 +308,7 @@ class FastSpeech2MIDI(FastSpeech2): zs, _ = self.decoder(hs, h_masks) # (B, Lmax, odim) before_outs = self.feat_out(zs).reshape( - (paddle.shape(zs)[0], -1, self.odim)) + (paddle.shape(zs)[0:1], -1, self.odim)) # postnet -> (B, Lmax//r * r, odim) if self.postnet is None: @@ -334,7 +334,7 @@ class FastSpeech2MIDI(FastSpeech2): note_dur = paddle.cast(note_dur, 'float32').unsqueeze(0) is_slur = paddle.cast(is_slur, 'int64').unsqueeze(0) # setup batch axis - ilens = paddle.shape(xs)[1] + ilens = paddle.shape(xs)[1:2] if spk_emb is not None: spk_emb = spk_emb.unsqueeze(0) @@ -449,7 +449,7 @@ class FastSpeech2MIDI(FastSpeech2): is_slur = paddle.cast(is_slur, 'int64').unsqueeze(0) d, p, e = durations, pitch, energy # setup batch axis - ilens = paddle.shape(xs)[1] + ilens = paddle.shape(xs)[1:2] if spk_emb is not None: spk_emb = spk_emb.unsqueeze(0) diff --git a/paddlespeech/t2s/models/ernie_sat/ernie_sat.py b/paddlespeech/t2s/models/ernie_sat/ernie_sat.py index eb42b33ed..78d24cfd9 100644 --- a/paddlespeech/t2s/models/ernie_sat/ernie_sat.py +++ b/paddlespeech/t2s/models/ernie_sat/ernie_sat.py @@ -485,11 +485,11 @@ class MLMEncAsDecoder(MLM): zs, _ = self.decoder(encoder_out, h_masks) else: zs = encoder_out - speech_hidden_states = zs[:, :paddle.shape(speech)[1], :] + speech_hidden_states = zs[:, :paddle.shape(speech)[1:2], :] if self.sfc is not None: before_outs = paddle.reshape( self.sfc(speech_hidden_states), - (paddle.shape(speech_hidden_states)[0], -1, self.odim)) + (paddle.shape(speech_hidden_states)[0:1], -1, self.odim)) else: before_outs = speech_hidden_states if self.postnet is not None: @@ -524,16 +524,16 @@ class MLMDualMaksing(MLM): zs, _ = self.decoder(encoder_out, h_masks) else: zs = encoder_out - speech_hidden_states = zs[:, :paddle.shape(speech)[1], :] + speech_hidden_states = zs[:, :paddle.shape(speech)[1:2], :] if self.text_sfc: - text_hiddent_states = zs[:, paddle.shape(speech)[1]:, :] + text_hiddent_states = zs[:, paddle.shape(speech)[1:2]:, :] text_outs = paddle.reshape( self.text_sfc(text_hiddent_states), - (paddle.shape(text_hiddent_states)[0], -1, self.vocab_size)) + (paddle.shape(text_hiddent_states)[0:1], -1, self.vocab_size)) if self.sfc is not None: before_outs = paddle.reshape( self.sfc(speech_hidden_states), - (paddle.shape(speech_hidden_states)[0], -1, self.odim)) + (paddle.shape(speech_hidden_states)[0:1], -1, self.odim)) else: before_outs = speech_hidden_states if self.postnet is not None: diff --git a/paddlespeech/t2s/models/fastspeech2/fastspeech2.py b/paddlespeech/t2s/models/fastspeech2/fastspeech2.py index 8ce19795e..18adfe510 100644 --- a/paddlespeech/t2s/models/fastspeech2/fastspeech2.py +++ b/paddlespeech/t2s/models/fastspeech2/fastspeech2.py @@ -696,7 +696,7 @@ class FastSpeech2(nn.Layer): zs, _ = self.decoder(hs, h_masks) # (B, Lmax, odim) before_outs = self.feat_out(zs).reshape( - (paddle.shape(zs)[0], -1, self.odim)) + (paddle.shape(zs)[0:1], -1, self.odim)) # postnet -> (B, Lmax//r * r, odim) if self.postnet is None: @@ -718,7 +718,7 @@ class FastSpeech2(nn.Layer): # input of embedding must be int64 x = paddle.cast(text, 'int64') # setup batch axis - ilens = paddle.shape(x)[0] + ilens = paddle.shape(x)[0:1] xs = x.unsqueeze(0) @@ -783,7 +783,7 @@ class FastSpeech2(nn.Layer): x = paddle.cast(text, 'int64') d, p, e = durations, pitch, energy # setup batch axis - ilens = paddle.shape(x)[0] + ilens = paddle.shape(x)[0:1] xs = x.unsqueeze(0) @@ -843,7 +843,7 @@ class FastSpeech2(nn.Layer): elif self.spk_embed_integration_type == "concat": # concat hidden states with spk embeds and then apply projection spk_emb = F.normalize(spk_emb).unsqueeze(1).expand( - shape=[-1, paddle.shape(hs)[1], -1]) + shape=[-1, paddle.shape(hs)[1:2], -1]) hs = self.spk_projection(paddle.concat([hs, spk_emb], axis=-1)) else: raise NotImplementedError("support only add or concat.") diff --git a/paddlespeech/t2s/models/jets/jets.py b/paddlespeech/t2s/models/jets/jets.py index 4346c65b4..77e4931b2 100644 --- a/paddlespeech/t2s/models/jets/jets.py +++ b/paddlespeech/t2s/models/jets/jets.py @@ -512,13 +512,13 @@ class JETS(nn.Layer): """ # setup text = text[None] - text_lengths = paddle.to_tensor(paddle.shape(text)[1]) + text_lengths = paddle.to_tensor(paddle.shape(text)[1:2]) # inference if use_alignment_module: assert feats is not None feats = feats[None] - feats_lengths = paddle.to_tensor(paddle.shape(feats)[1]) + feats_lengths = paddle.to_tensor(paddle.shape(feats)[1:2]) pitch = pitch[None] energy = energy[None] wav, dur = self.generator.inference( diff --git a/paddlespeech/t2s/models/melgan/style_melgan.py b/paddlespeech/t2s/models/melgan/style_melgan.py index d902a4b01..6b8dd31b2 100644 --- a/paddlespeech/t2s/models/melgan/style_melgan.py +++ b/paddlespeech/t2s/models/melgan/style_melgan.py @@ -156,7 +156,7 @@ class StyleMelGANGenerator(nn.Layer): """ # batch_max_steps(24000) == noise_upsample_factor(80) * upsample_factor(300) if z is None: - z = paddle.randn([paddle.shape(c)[0], self.in_channels, 1]) + z = paddle.randn([paddle.shape(c)[0:1], self.in_channels, 1]) # (B, in_channels, noise_upsample_factor). x = self.noise_upsample(z) for block in self.blocks: diff --git a/paddlespeech/t2s/models/parallel_wavegan/parallel_wavegan.py b/paddlespeech/t2s/models/parallel_wavegan/parallel_wavegan.py index be306d9cc..525667771 100644 --- a/paddlespeech/t2s/models/parallel_wavegan/parallel_wavegan.py +++ b/paddlespeech/t2s/models/parallel_wavegan/parallel_wavegan.py @@ -223,7 +223,7 @@ class PWGGenerator(nn.Layer): """ # when to static, can not input x, see https://github.com/PaddlePaddle/Parakeet/pull/132/files x = paddle.randn( - [1, self.in_channels, paddle.shape(c)[0] * self.upsample_factor]) + [1, self.in_channels, paddle.shape(c)[0:1] * self.upsample_factor]) c = paddle.transpose(c, [1, 0]).unsqueeze(0) # pseudo batch c = nn.Pad1D(self.aux_context_window, mode='replicate')(c) out = self(x, c).squeeze(0).transpose([1, 0]) diff --git a/paddlespeech/t2s/models/tacotron2/tacotron2.py b/paddlespeech/t2s/models/tacotron2/tacotron2.py index 25b5c932a..804a81ac0 100644 --- a/paddlespeech/t2s/models/tacotron2/tacotron2.py +++ b/paddlespeech/t2s/models/tacotron2/tacotron2.py @@ -286,7 +286,7 @@ class Tacotron2(nn.Layer): text = text[:, :text_lengths.max()] speech = speech[:, :speech_lengths.max()] - batch_size = paddle.shape(text)[0] + batch_size = paddle.shape(text)[0:1] # Add eos at the last of sequence xs = F.pad(text, [0, 0, 0, 1], "constant", self.padding_idx) @@ -413,8 +413,8 @@ class Tacotron2(nn.Layer): xs, ys = x.unsqueeze(0), y.unsqueeze(0) spk_emb = None if spk_emb is None else spk_emb.unsqueeze(0) - ilens = paddle.shape(xs)[1] - olens = paddle.shape(ys)[1] + ilens = paddle.shape(xs)[1:2] + olens = paddle.shape(ys)[1:2] outs, _, _, att_ws = self._forward( xs=xs, ilens=ilens, @@ -470,7 +470,7 @@ class Tacotron2(nn.Layer): elif self.spk_embed_integration_type == "concat": # concat hidden states with spk embeds spk_emb = F.normalize(spk_emb).unsqueeze(1).expand( - shape=[-1, paddle.shape(hs)[1], -1]) + shape=[-1, paddle.shape(hs)[1:2], -1]) hs = paddle.concat([hs, spk_emb], axis=-1) else: raise NotImplementedError("support only add or concat.") diff --git a/paddlespeech/t2s/models/vits/duration_predictor.py b/paddlespeech/t2s/models/vits/duration_predictor.py index 12177fbc2..749a8cf1f 100644 --- a/paddlespeech/t2s/models/vits/duration_predictor.py +++ b/paddlespeech/t2s/models/vits/duration_predictor.py @@ -145,7 +145,7 @@ class StochasticDurationPredictor(nn.Layer): h_w = self.post_pre(w) h_w = self.post_dds(h_w, x_mask) h_w = self.post_proj(h_w) * x_mask - e_q = (paddle.randn([paddle.shape(w)[0], 2, paddle.shape(w)[2]]) * + e_q = (paddle.randn([paddle.shape(w)[0:1], 2, paddle.shape(w)[2]]) * x_mask) z_q = e_q logdet_tot_q = 0.0 @@ -174,7 +174,7 @@ class StochasticDurationPredictor(nn.Layer): flows = list(reversed(self.flows)) # remove a useless vflow flows = flows[:-2] + [flows[-1]] - z = (paddle.randn([paddle.shape(x)[0], 2, paddle.shape(x)[2]]) * + z = (paddle.randn([paddle.shape(x)[0:1], 2, paddle.shape(x)[2]]) * noise_scale) for flow in flows: z = flow(z, x_mask, g=x, inverse=inverse) diff --git a/paddlespeech/t2s/models/vits/flow.py b/paddlespeech/t2s/models/vits/flow.py index 94df968a0..bae643f12 100644 --- a/paddlespeech/t2s/models/vits/flow.py +++ b/paddlespeech/t2s/models/vits/flow.py @@ -46,7 +46,7 @@ class FlipFlow(nn.Layer): """ x = paddle.flip(x, [1]) if not inverse: - logdet = paddle.zeros(paddle.shape(x)[0], dtype=x.dtype) + logdet = paddle.zeros(paddle.shape(x)[0:1], dtype=x.dtype) return x, logdet else: return x diff --git a/paddlespeech/t2s/models/vits/vits.py b/paddlespeech/t2s/models/vits/vits.py index 7013e06c0..ccce27362 100644 --- a/paddlespeech/t2s/models/vits/vits.py +++ b/paddlespeech/t2s/models/vits/vits.py @@ -420,7 +420,7 @@ class VITS(nn.Layer): """ # setup text = text[None] - text_lengths = paddle.to_tensor(paddle.shape(text)[1]) + text_lengths = paddle.to_tensor(paddle.shape(text)[1:2]) if durations is not None: durations = paddle.reshape(durations, [1, 1, -1]) @@ -429,7 +429,7 @@ class VITS(nn.Layer): if use_teacher_forcing: assert feats is not None feats = feats[None].transpose([0, 2, 1]) - feats_lengths = paddle.to_tensor(paddle.shape(feats)[2]) + feats_lengths = paddle.to_tensor(paddle.shape(feats)[2:3]) wav, att_w, dur = self.generator.inference( text=text, text_lengths=text_lengths, @@ -484,7 +484,7 @@ class VITS(nn.Layer): """ assert feats is not None feats = feats[None].transpose([0, 2, 1]) - feats_lengths = paddle.to_tensor(paddle.shape(feats)[2]) + feats_lengths = paddle.to_tensor(paddle.shape(feats)[2:3]) sids_none = sids_src is None and sids_tgt is None spembs_none = spembs_src is None and spembs_tgt is None diff --git a/paddlespeech/t2s/models/wavernn/wavernn.py b/paddlespeech/t2s/models/wavernn/wavernn.py index 44e9f2d8d..b09e367e3 100644 --- a/paddlespeech/t2s/models/wavernn/wavernn.py +++ b/paddlespeech/t2s/models/wavernn/wavernn.py @@ -255,7 +255,7 @@ class WaveRNN(nn.Layer): # weights are contiguous in GPU memory. Hence, we must call it again self._flatten_parameters() - bsize = paddle.shape(x)[0] + bsize = paddle.shape(x)[0:1] h1 = paddle.zeros([1, bsize, self.rnn_dims]) h2 = paddle.zeros([1, bsize, self.rnn_dims]) # c: [B, T, C_aux] @@ -339,8 +339,8 @@ class WaveRNN(nn.Layer): # 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] + b_size = paddle.shape(c)[0:1] + seq_len = paddle.shape(c)[1:2] h1 = paddle.zeros([b_size, self.rnn_dims]) h2 = paddle.zeros([b_size, self.rnn_dims]) diff --git a/paddlespeech/t2s/modules/losses.py b/paddlespeech/t2s/modules/losses.py index b4d78364c..f7ed6ae5f 100644 --- a/paddlespeech/t2s/modules/losses.py +++ b/paddlespeech/t2s/modules/losses.py @@ -433,8 +433,8 @@ class Tacotron2Loss(nn.Layer): masks = make_non_pad_mask(olens).unsqueeze(-1) weights = masks.float() / masks.sum(axis=1, keepdim=True).float() out_weights = weights.divide( - paddle.shape(ys)[0] * paddle.shape(ys)[2]) - logit_weights = weights.divide(paddle.shape(ys)[0]) + paddle.shape(ys)[0:1] * paddle.shape(ys)[2:3]) + logit_weights = weights.divide(paddle.shape(ys)[0:1]) # apply weight l1_loss = l1_loss.multiply(out_weights) @@ -907,7 +907,7 @@ class MelSpectrogram(nn.Layer): """ if len(x.shape) == 3: # (B, C, T) -> (B*C, T) - x = x.reshape([-1, paddle.shape(x)[2]]) + x = x.reshape([-1, paddle.shape(x)[2:3]]) if self.window is not None: # calculate window diff --git a/paddlespeech/t2s/modules/nets_utils.py b/paddlespeech/t2s/modules/nets_utils.py index 3d1b48dec..f140494b5 100644 --- a/paddlespeech/t2s/modules/nets_utils.py +++ b/paddlespeech/t2s/modules/nets_utils.py @@ -181,11 +181,11 @@ def make_pad_mask(lengths, xs=None, length_dim=-1): if length_dim == 0: raise ValueError("length_dim cannot be 0: {}".format(length_dim)) - bs = paddle.shape(lengths)[0] + bs = paddle.shape(lengths)[0:1] if xs is None: maxlen = paddle.cast(lengths.max(), dtype=bs.dtype) else: - maxlen = paddle.shape(xs)[length_dim] + maxlen = paddle.shape(xs)[length_dim:length_dim+1] seq_range = paddle.arange(0, maxlen, dtype=paddle.int64) # VITS 最后一个 expand 的位置 @@ -194,7 +194,7 @@ def make_pad_mask(lengths, xs=None, length_dim=-1): mask = seq_range_expand >= seq_length_expand.cast(seq_range_expand.dtype) if xs is not None: - assert paddle.shape(xs)[0] == bs, (paddle.shape(xs)[0], bs) + assert paddle.shape(xs)[0:1] == bs, (paddle.shape(xs)[0:1], bs) if length_dim < 0: length_dim = len(paddle.shape(xs)) + length_dim # ind = (:, None, ..., None, :, , None, ..., None) diff --git a/paddlespeech/t2s/modules/predictor/length_regulator.py b/paddlespeech/t2s/modules/predictor/length_regulator.py index bdfa18391..df4415b87 100644 --- a/paddlespeech/t2s/modules/predictor/length_regulator.py +++ b/paddlespeech/t2s/modules/predictor/length_regulator.py @@ -51,8 +51,8 @@ class LengthRegulator(nn.Layer): durations: (B, T) """ #batch_size, t_enc = durations.shape # linux - batch_size = paddle.shape(durations)[0] # windows and mac - t_enc = paddle.shape(durations)[1] # windows and mac + batch_size = paddle.shape(durations)[0:1] # windows and mac + t_enc = paddle.shape(durations)[1:2] # windows and mac durations = durations.numpy() slens = np.sum(durations, -1) t_dec = np.max(slens) diff --git a/paddlespeech/t2s/modules/tacotron2/attentions.py b/paddlespeech/t2s/modules/tacotron2/attentions.py index 5d1a24845..396e86b19 100644 --- a/paddlespeech/t2s/modules/tacotron2/attentions.py +++ b/paddlespeech/t2s/modules/tacotron2/attentions.py @@ -56,7 +56,7 @@ def _apply_attention_constraint(e, forward_idx = paddle.cast(last_attended_idx + forward_window, dtype='int64') if backward_idx > 0: e[:, :backward_idx] = -float("inf") - if forward_idx < paddle.shape(e)[1]: + if forward_idx < paddle.shape(e)[1:2]: e[:, forward_idx:] = -float("inf") return e @@ -153,12 +153,12 @@ class AttLoc(nn.Layer): Tensor: previous attention weights (B, T_max) """ - batch = paddle.shape(enc_hs_pad)[0] + batch = paddle.shape(enc_hs_pad)[0:1] # pre-compute all h outside the decoder loop if self.pre_compute_enc_h is None or self.han_mode: # (utt, frame, hdim) self.enc_h = enc_hs_pad - self.h_length = paddle.shape(self.enc_h)[1] + self.h_length = paddle.shape(self.enc_h)[1:2] # (utt, frame, att_dim) self.pre_compute_enc_h = self.mlp_enc(self.enc_h) @@ -294,7 +294,7 @@ class AttForward(nn.Layer): # pre-compute all h outside the decoder loop if self.pre_compute_enc_h is None: self.enc_h = enc_hs_pad # utt x frame x hdim - self.h_length = paddle.shape(self.enc_h)[1] + self.h_length = paddle.shape(self.enc_h)[1:2] # utt x frame x att_dim self.pre_compute_enc_h = self.mlp_enc(self.enc_h) @@ -445,7 +445,7 @@ class AttForwardTA(nn.Layer): # pre-compute all h outside the decoder loop if self.pre_compute_enc_h is None: self.enc_h = enc_hs_pad # utt x frame x hdim - self.h_length = paddle.shape(self.enc_h)[1] + self.h_length = paddle.shape(self.enc_h)[1:2] # utt x frame x att_dim self.pre_compute_enc_h = self.mlp_enc(self.enc_h) diff --git a/paddlespeech/t2s/modules/tacotron2/decoder.py b/paddlespeech/t2s/modules/tacotron2/decoder.py index 15e29194d..4d4147e89 100644 --- a/paddlespeech/t2s/modules/tacotron2/decoder.py +++ b/paddlespeech/t2s/modules/tacotron2/decoder.py @@ -375,7 +375,7 @@ class Decoder(nn.Layer): self.prob_out = nn.Linear(iunits, reduction_factor) def _zero_state(self, hs): - init_hs = paddle.zeros([paddle.shape(hs)[0], self.lstm[0].hidden_size]) + init_hs = paddle.zeros([paddle.shape(hs)[0:1], self.lstm[0].hidden_size]) return init_hs def forward(self, hs, hlens, ys): @@ -415,7 +415,7 @@ class Decoder(nn.Layer): for _ in range(1, len(self.lstm)): c_list.append(self._zero_state(hs)) z_list.append(self._zero_state(hs)) - prev_out = paddle.zeros([paddle.shape(hs)[0], self.odim]) + prev_out = paddle.zeros([paddle.shape(hs)[0:1], self.odim]) # initialize attention prev_att_ws = [] @@ -445,7 +445,7 @@ class Decoder(nn.Layer): zcs = (paddle.concat([z_list[-1], att_c], axis=1) if self.use_concate else z_list[-1]) outs.append( - self.feat_out(zcs).reshape([paddle.shape(hs)[0], self.odim, -1 + self.feat_out(zcs).reshape([paddle.shape(hs)[0:1], self.odim, -1 ])) logits.append(self.prob_out(zcs)) att_ws.append(att_w) @@ -466,7 +466,7 @@ class Decoder(nn.Layer): if self.reduction_factor > 1: # (B, odim, Lmax) before_outs = before_outs.reshape( - [paddle.shape(before_outs)[0], self.odim, -1]) + [paddle.shape(before_outs)[0:1], self.odim, -1]) if self.postnet is not None: # (B, odim, Lmax) @@ -530,10 +530,10 @@ class Decoder(nn.Layer): assert len(paddle.shape(h)) == 2 hs = h.unsqueeze(0) - ilens = paddle.shape(h)[0] + ilens = paddle.shape(h)[0:1] # 本来 maxlen 和 minlen 外面有 int()，防止动转静的问题此处删除 - maxlen = paddle.shape(h)[0] * maxlenratio - minlen = paddle.shape(h)[0] * minlenratio + maxlen = paddle.shape(h)[0:1] * maxlenratio + minlen = paddle.shape(h)[0:1] * minlenratio # 本来是直接使用 threshold 的，此处为了防止动转静的问题把 threshold 转成 tensor threshold = paddle.ones([1]) * threshold @@ -690,7 +690,7 @@ class Decoder(nn.Layer): for _ in range(1, len(self.lstm)): c_list.append(self._zero_state(hs)) z_list.append(self._zero_state(hs)) - prev_out = paddle.zeros([paddle.shape(hs)[0], self.odim]) + prev_out = paddle.zeros([paddle.shape(hs)[0:1], self.odim]) # initialize attention prev_att_w = None diff --git a/paddlespeech/t2s/modules/tacotron2/encoder.py b/paddlespeech/t2s/modules/tacotron2/encoder.py index 224c82400..0ab6277db 100644 --- a/paddlespeech/t2s/modules/tacotron2/encoder.py +++ b/paddlespeech/t2s/modules/tacotron2/encoder.py @@ -184,6 +184,6 @@ class Encoder(nn.Layer): """ xs = x.unsqueeze(0) - ilens = paddle.shape(x)[0] + ilens = paddle.shape(x)[0:1] return self.forward(xs, ilens)[0][0] diff --git a/paddlespeech/t2s/modules/transformer/attention.py b/paddlespeech/t2s/modules/transformer/attention.py index 3237be1b6..30074534d 100644 --- a/paddlespeech/t2s/modules/transformer/attention.py +++ b/paddlespeech/t2s/modules/transformer/attention.py @@ -66,7 +66,7 @@ class MultiHeadedAttention(nn.Layer): Tensor: Transformed value tensor (#batch, n_head, time2, d_k). """ - n_batch = paddle.shape(query)[0] + n_batch = paddle.shape(query)[0:1] q = paddle.reshape( self.linear_q(query), [n_batch, -1, self.h, self.d_k]) @@ -96,7 +96,7 @@ class MultiHeadedAttention(nn.Layer): Returns: Tensor: Transformed value (#batch, time1, d_model) weighted by the attention score (#batch, time1, time2). """ - n_batch = paddle.shape(value)[0] + n_batch = paddle.shape(value)[0:1] softmax = paddle.nn.Softmax(axis=-1) if mask is not None: mask = mask.unsqueeze(1) @@ -220,7 +220,7 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention): q, k, v = self.forward_qkv(query, key, value) # (batch, time1, head, d_k) q = q.transpose([0, 2, 1, 3]) - n_batch_pos = paddle.shape(pos_emb)[0] + n_batch_pos = paddle.shape(pos_emb)[0:1] p = self.linear_pos(pos_emb).reshape( [n_batch_pos, -1, self.h, self.d_k]) # (batch, head, 2*time1-1, d_k) @@ -318,7 +318,7 @@ class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention): # (batch, time1, head, d_k) q = paddle.transpose(q, [0, 2, 1, 3]) - n_batch_pos = paddle.shape(pos_emb)[0] + n_batch_pos = paddle.shape(pos_emb)[0:1] p = paddle.reshape( self.linear_pos(pos_emb), [n_batch_pos, -1, self.h, self.d_k]) # (batch, head, time1, d_k) diff --git a/paddlespeech/t2s/modules/transformer/embedding.py b/paddlespeech/t2s/modules/transformer/embedding.py index f90eb44a4..d20bab08d 100644 --- a/paddlespeech/t2s/modules/transformer/embedding.py +++ b/paddlespeech/t2s/modules/transformer/embedding.py @@ -80,7 +80,7 @@ class PositionalEncoding(nn.Layer): Tensor: Encoded tensor (batch, time, `*`). """ self.extend_pe(x) - T = paddle.shape(x)[1] + T = paddle.shape(x)[1:2] x = x * self.xscale + self.pe[:, :T] return self.dropout(x) @@ -127,7 +127,7 @@ class ScaledPositionalEncoding(PositionalEncoding): Tensor: Encoded tensor (batch, time, `*`). """ self.extend_pe(x) - T = paddle.shape(x)[1] + T = paddle.shape(x)[1:2] x = x + self.alpha * self.pe[:, :T] return self.dropout(x) @@ -161,7 +161,7 @@ class RelPositionalEncoding(nn.Layer): if self.pe is not None: # self.pe contains both positive and negative parts # the length of self.pe is 2 * input_len - 1 - if paddle.shape(self.pe)[1] >= paddle.shape(x)[1] * 2 - 1: + if paddle.shape(self.pe)[1:2] >= paddle.shape(x)[1:2] * 2 - 1: return # Suppose `i` means to the position of query vecotr and `j` means the # position of key vector. We use position relative positions when keys @@ -196,7 +196,7 @@ class RelPositionalEncoding(nn.Layer): """ self.extend_pe(x) x = x * self.xscale - T = paddle.shape(x)[1] + T = paddle.shape(x)[1:2] pe_size = paddle.shape(self.pe) tmp = paddle.cast(paddle.floor(pe_size[1] / 2), dtype='int32') pos_emb = self.pe[:, tmp - T + 1:tmp + T, ] @@ -235,16 +235,16 @@ class LegacyRelPositionalEncoding(PositionalEncoding): def extend_pe(self, x): """Reset the positional encodings.""" if self.pe is not None: - if paddle.shape(self.pe)[1] >= paddle.shape(x)[1]: + if paddle.shape(self.pe)[1:2] >= paddle.shape(x)[1:2]: return - pe = paddle.zeros((paddle.shape(x)[1], self.d_model)) + pe = paddle.zeros((paddle.shape(x)[1:2], self.d_model)) if self.reverse: position = paddle.arange( - paddle.shape(x)[1] - 1, -1, -1.0, + paddle.shape(x)[1:2] - 1, -1, -1.0, dtype=paddle.float32).unsqueeze(1) else: position = paddle.arange( - 0, paddle.shape(x)[1], dtype=paddle.float32).unsqueeze(1) + 0, paddle.shape(x)[1:2], dtype=paddle.float32).unsqueeze(1) div_term = paddle.exp( paddle.arange(0, self.d_model, 2, dtype=paddle.float32) * -(math.log(10000.0) / self.d_model)) @@ -266,5 +266,5 @@ class LegacyRelPositionalEncoding(PositionalEncoding): """ self.extend_pe(x) x = x * self.xscale - pos_emb = self.pe[:, :paddle.shape(x)[1]] + pos_emb = self.pe[:, :paddle.shape(x)[1:2]] return self.dropout(x), self.dropout(pos_emb)