shithub: opus

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Fine-tuning the scripts

--- a/dnn/training_tf2/fec_encoder.py

+++ b/dnn/training_tf2/fec_encoder.py

@@ -108,8 +108,8 @@

 features = features[:, :num_subframes, :]

 #variable quantizer depending on the delay

-q0 = 2

-q1 = 10

+q0 = 3

+q1 = 15

 quant_id = np.round(q1 + (q0-q1)*np.arange(args.num_redundancy_frames//2)/args.num_redundancy_frames).astype('int16')

 #print(quant_id)

@@ -154,7 +154,10 @@

 fake_lambda = np.ones((sym_batch.shape[0], sym_batch.shape[1], 1), dtype='float32')

 rate_input = np.concatenate((sym_batch, hard_distr_embed, fake_lambda), axis=-1)

 rates = sq_rate_metric(None, rate_input, reduce=False).numpy()

-print("rate = ", np.mean(rates))

+print(rates.shape)

+print("average rate = ", np.mean(rates[args.num_redundancy_frames:,:]))

+

+#sym_batch.tofile('qsyms.f32')

 sym_batch = sym_batch / quant_scale

 print(sym_batch.shape, quant_state.shape)

--- a/dnn/training_tf2/rdovae.py

+++ b/dnn/training_tf2/rdovae.py

@@ -167,8 +167,8 @@

         abs_kx = tf.abs(kx)

         kk=tf.reduce_sum(abs_y, axis=-1)

         #print("sums = ", kk)

-        plus = 1.0001*tf.reduce_min((abs_y+.5)/(abs_kx+1e-15), axis=-1)

-        minus = .9999*tf.reduce_max((abs_y-.5)/(abs_kx+1e-15), axis=-1)

+        plus = 1.000001*tf.reduce_min((abs_y+.5)/(abs_kx+1e-15), axis=-1)

+        minus = .999999*tf.reduce_max((abs_y-.5)/(abs_kx+1e-15), axis=-1)

         #print("plus = ", plus)

         #print("minus = ", minus)

         factor = tf.where(kk>k, minus, plus)

@@ -183,7 +183,7 @@

         y = tf.round(kx)

     #print(y)

-    

+    #print(K.mean(K.sum(K.abs(y), axis=-1)))

     return y

 def pvq_quantize(x, k):

@@ -281,7 +281,7 @@

     range_select = Lambda(lambda x: x[0][:,x[1]:x[2],:])

     elem_select = Lambda(lambda x: x[0][:,x[1],:])

-    points = [0, 64, 128, 192, 256]

+    points = [0, 100, 200, 300, 400]

     outputs = []

     for i in range(len(points)-1):

         begin = points[i]//bunch

-- 

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