1 files changed, 10 insertions, 14 deletions
diff --git a/tensorflow/contrib/signal/python/ops/mel_ops.py b/tensorflow/contrib/signal/python/ops/mel_ops.py
index 1e84006116..062d84aea1 100644
--- a/tensorflow/contrib/signal/python/ops/mel_ops.py
+++ b/tensorflow/contrib/signal/python/ops/mel_ops.py
@@ -151,22 +151,21 @@ def linear_to_mel_weight_matrix(num_mel_bins=20,
     _validate_arguments(num_mel_bins, sample_rate,
                         lower_edge_hertz, upper_edge_hertz, dtype)
 
-    # To preserve accuracy, we compute the matrix at float64 precision and then
-    # cast to `dtype` at the end. This function can be constant folded by graph
-    # optimization since there are no Tensor inputs.
+    # This function can be constant folded by graph optimization since there are
+    # no Tensor inputs.
     sample_rate = ops.convert_to_tensor(
-        sample_rate, dtypes.float64, name='sample_rate')
+        sample_rate, dtype, name='sample_rate')
     lower_edge_hertz = ops.convert_to_tensor(
-        lower_edge_hertz, dtypes.float64, name='lower_edge_hertz')
+        lower_edge_hertz, dtype, name='lower_edge_hertz')
     upper_edge_hertz = ops.convert_to_tensor(
-        upper_edge_hertz, dtypes.float64, name='upper_edge_hertz')
-    zero_float64 = ops.convert_to_tensor(0.0, dtypes.float64)
+        upper_edge_hertz, dtype, name='upper_edge_hertz')
+    zero = ops.convert_to_tensor(0.0, dtype)
 
     # HTK excludes the spectrogram DC bin.
     bands_to_zero = 1
     nyquist_hertz = sample_rate / 2.0
     linear_frequencies = math_ops.linspace(
-        zero_float64, nyquist_hertz, num_spectrogram_bins)[bands_to_zero:]
+        zero, nyquist_hertz, num_spectrogram_bins)[bands_to_zero:]
     spectrogram_bins_mel = array_ops.expand_dims(
         _hertz_to_mel(linear_frequencies), 1)
 
@@ -193,11 +192,8 @@ def linear_to_mel_weight_matrix(num_mel_bins=20,
 
     # Intersect the line segments with each other and zero.
     mel_weights_matrix = math_ops.maximum(
-        zero_float64, math_ops.minimum(lower_slopes, upper_slopes))
+        zero, math_ops.minimum(lower_slopes, upper_slopes))
 
     # Re-add the zeroed lower bins we sliced out above.
-    mel_weights_matrix = array_ops.pad(
-        mel_weights_matrix, [[bands_to_zero, 0], [0, 0]])
-
-    # Cast to the desired type.
-    return math_ops.cast(mel_weights_matrix, dtype, name=name)
+    return array_ops.pad(
+        mel_weights_matrix, [[bands_to_zero, 0], [0, 0]], name=name)