Upload Transformer.py

Transformer.py

@@ -0,0 +1,97 @@
+import torch
+import torch.nn as nn
+from torch.nn import Transformer
+from UpdatedTransformer import neko_TransformerEncoderLayer
+
+# To speed-up training process
+torch.autograd.set_detect_anomaly(False)
+torch.autograd.profiler.profile(False)
+torch.autograd.profiler.emit_nvtx(False)
+
+import math
+import json
+
+with open("config.json") as json_data_file:
+    data = json.load(json_data_file)
+
+N_enc = data['N_encoder']
+N_heads_enc = data['N_heads_enc']
+
+N_dec = data['N_decoder']
+N_heads_dec = data['N_heads_dec']
+
+gen_emb = data['Gen_Embed']
+fwd_exp = data['Forward_Expansion']
+
+dropout = data['Dropout']
+device = 'cpu'
+
+       
+class BiLSTM(nn.Module):
+    def __init__(self, phonemed, speaker, gender, seq_len):
+
+        super(BiLSTM, self).__init__()
+
+        self.phonemed = phonemed
+        self.speakered = speaker
+        self.gendered = gender
+        self.device = device
+
+        self.seq_len = seq_len
+
+        factor = (self.phonemed * 1) + (self.speakered * 1) + (self.gendered * 1) + 1
+
+        self.embed_mfcc = nn.Sequential(
+            nn.Linear(13, gen_emb),
+            nn.ReLU(),
+            nn.Linear(gen_emb, gen_emb))
+        
+        self.emb_phoneme = nn.Sequential(
+            nn.Embedding(40, gen_emb),
+            nn.ReLU(),
+            nn.Linear(gen_emb, gen_emb))
+
+        self.emb_speaker = nn.Sequential(
+            nn.Embedding(38, gen_emb),
+            nn.ReLU(),
+            nn.Linear(gen_emb, gen_emb))
+
+        self.emb_gender = nn.Sequential(
+            nn.Embedding(2, gen_emb),
+            nn.ReLU(),
+            nn.Linear(gen_emb, gen_emb))
+
+        self.pre_position = nn.Sequential(
+            nn.ReLU(),
+            nn.Linear(gen_emb * factor, gen_emb))
+
+        self.aai_rnn = nn.LSTM(gen_emb, fwd_exp, 3, bidirectional = True, batch_first = True)
+
+        self.artic_linear = nn.Sequential(
+            nn.ReLU(),
+            nn.Linear(fwd_exp * 2, 32),
+            nn.ReLU(),
+            nn.Linear(32, 12))
+
+
+    def forward(self, mfcc, pho, spk, gnd):
+
+        mfcc_embedded = self.embed_mfcc(mfcc)
+        cat_embedded = mfcc_embedded
+
+        if self.phonemed:
+            pho_embedded = self.emb_phoneme(pho)
+            cat_embedded = torch.concat((mfcc_embedded, pho_embedded), -1)
+        
+
+        cat_embedded = self.pre_position(cat_embedded)
+
+        final, (hidden, cell) = self.aai_rnn(cat_embedded)
+
+        artic = self.artic_linear(final)
+
+        if torch.sum(artic[0]).isnan():
+            print('Encountered Nan. Exiting program ...')
+            exit(0)
+
+        return artic