run with aishell/asr3 (#3904)

paddlespeech/s2t/exps/wav2vec2/bin/test.py

@@ -37,8 +37,6 @@ if __name__ == "__main__":
     # save asr result to
     parser.add_argument(
         '--dict-path', type=str, default=None, help='dict path.')
-    parser.add_argument(
-        "--result_file", type=str, help="path of save the asr result")
     args = parser.parse_args()
     print_arguments(args, globals())
 

paddlespeech/s2t/exps/wav2vec2/bin/test_wav.py

@@ -104,11 +104,6 @@ def main(config, args):
 
 if __name__ == "__main__":
     parser = default_argument_parser()
-    # save asr result to
-    parser.add_argument(
-        "--result_file", type=str, help="path of save the asr result")
-    parser.add_argument(
-        "--audio_file", type=str, help="path of the input audio file")
     args = parser.parse_args()
 
     config = CfgNode(new_allowed=True)

paddlespeech/s2t/models/wav2vec2/modules/wav2vec2_model.py

@@ -714,13 +714,13 @@ class MultiheadAttention(nn.Layer):
             else:
                 if self.beam_size > 1 and bsz == key.size(1):
                     # key is [T, bsz*beam_size, C], reduce to [T, bsz, C]
-                    key = key.view(
+                    key = key.reshape(
-                        key.size(0), -1, self.beam_size,
+                        [key.size(0), -1, self.beam_size,
-                        key.size(2))[:, :, 0, :]
+                         key.size(2)])[:, :, 0, :]
                     if key_padding_mask is not None:
-                        key_padding_mask = key_padding_mask.view(
+                        key_padding_mask = key_padding_mask.reshape(
-                            -1, self.beam_size,
+                            [-1, self.beam_size,
-                            key_padding_mask.size(1))[:, 0, :]
+                             key_padding_mask.size(1)])[:, 0, :]
                 k = self.k_proj(key)
                 v = self.v_proj(key)
 

paddlespeech/s2t/models/wav2vec2/processing/signal_processing.py

@@ -88,7 +88,7 @@ def compute_amplitude(waveforms, lengths=None, amp_type="avg", scale="linear"):
             out = paddle.mean(paddle.abs(waveforms), axis=1, keepdim=True)
         else:
             wav_sum = paddle.sum(paddle.abs(waveforms), axis=1, keepdim=True)
-            out = wav_sum / lengths
+            out = wav_sum / lengths.astype(wav_sum.dtype)
     elif amp_type == "peak":
         out = paddle.max(paddle.abs(waveforms), axis=1, keepdim=True)[0]
     else:
@@ -248,4 +248,4 @@ def notch_filter(notch_freq, filter_width=101, notch_width=0.05):
     hhpf[pad] += 1
 
     # Adding filters creates notch filter
-    return (hlpf + hhpf).view(1, -1, 1)
+    return (hlpf + hhpf).reshape([1, -1, 1])

paddlespeech/s2t/models/wav2vec2/processing/speech_augmentation.py

@@ -743,7 +743,7 @@ class SpecAugment(paddle.nn.Layer):
 
         time = x.shape[2]
         if time - window <= window:
-            return x.view(*original_size)
+            return x.reshape([*original_size])
 
         # compute center and corresponding window
         c = paddle.randint(window, time - window, (1, ))[0]
@@ -762,7 +762,7 @@ class SpecAugment(paddle.nn.Layer):
 
         x[:, :, :w] = left
         x[:, :, w:] = right
-        return x.view(*original_size)
+        return x.reshape([*original_size])
 
     def mask_along_axis(self, x, dim):
         """Mask along time or frequency axis.
@@ -775,7 +775,7 @@ class SpecAugment(paddle.nn.Layer):
         """
         original_size = x.shape
         if x.dim() == 4:
-            x = x.view(-1, x.shape[2], x.shape[3])
+            x = x.reshape([-1, x.shape[2], x.shape[3]])
 
         batch, time, fea = x.shape
 
@@ -795,7 +795,7 @@ class SpecAugment(paddle.nn.Layer):
                                   (batch, n_mask)).unsqueeze(2)
 
         # compute masks
-        arange = paddle.arange(end=D).view(1, 1, -1)
+        arange = paddle.arange(end=D).reshape([1, 1, -1])
         mask = (mask_pos <= arange) * (arange < (mask_pos + mask_len))
         mask = mask.any(axis=1)
 
@@ -811,7 +811,7 @@ class SpecAugment(paddle.nn.Layer):
         # same to x.masked_fill_(mask, val)
         y = paddle.full(x.shape, val, x.dtype)
         x = paddle.where(mask, y, x)
-        return x.view(*original_size)
+        return x.reshape([*original_size])
 
 
 class TimeDomainSpecAugment(nn.Layer):