shithub: opus

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Avoiding feature copies

--- a/dnn/training_tf2/train_lpcnet.py

+++ b/dnn/training_tf2/train_lpcnet.py

@@ -112,15 +112,14 @@

 # u for unquantised, load 16 bit PCM samples and convert to mu-law

 data = np.memmap(pcm_file, dtype='uint8', mode='r')

-nb_frames = len(data)//(4*pcm_chunk_size)//batch_size*batch_size

+nb_frames = (len(data)//(4*pcm_chunk_size)-1)//batch_size*batch_size

 features = np.memmap(feature_file, dtype='float32', mode='r')

 # limit to discrete number of frames

+data = data[4*2*frame_size:]

 data = data[:nb_frames*4*pcm_chunk_size]

-features = features[:nb_frames*feature_chunk_size*nb_features].copy()

-features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))

 data = np.reshape(data, (nb_frames, pcm_chunk_size, 4))

 in_data = data[:,:,:3]

@@ -128,12 +127,10 @@

 print("ulaw std = ", np.std(out_exc))

-features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))

+sizeof = features.strides[-1]

+features = np.lib.stride_tricks.as_strided(features, shape=(nb_frames, feature_chunk_size+4, nb_features),

+                                           strides=(feature_chunk_size*nb_features*sizeof, nb_features*sizeof, sizeof))

 features = features[:, :, :nb_used_features]

-

-fpad1 = np.concatenate([features[0:1, 0:2, :], features[:-1, -2:, :]], axis=0)

-fpad2 = np.concatenate([features[1:, :2, :], features[0:1, -2:, :]], axis=0)

-features = np.concatenate([fpad1, features, fpad2], axis=1)

 periods = (.1 + 50*features[:,:,18:19]+100).astype('int16')

-- 

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