Transformer training code

Author: pythonlessons

Tutorials/09_translation_transformer/test.py

@@ -1,10 +1,7 @@
-from mltu.inferenceModel import OnnxInferenceModel
-import tensorflow as tf
-try: [tf.config.experimental.set_memory_growth(gpu, True) for gpu in tf.config.experimental.list_physical_devices("GPU")]
-except: pass
 import numpy as np
 
 from mltu.tokenizers import CustomTokenizer
+from mltu.inferenceModel import OnnxInferenceModel
 
 class PtEnTranslator(OnnxInferenceModel):
     def __init__(self, *args, **kwargs):
@@ -13,8 +10,6 @@ def __init__(self, *args, **kwargs):
         self.new_inputs = self.model.get_inputs()
         self.tokenizer = CustomTokenizer.load(self.metadata["tokenizer"])
         self.detokenizer = CustomTokenizer.load(self.metadata["detokenizer"])
-        # self.eng_tokenizer = CustomTokenizer.load("Tutorials/09_transformers/eng_tokenizer.json")
-        # self.pt_tokenizer = CustomTokenizer.load("Tutorials/09_transformers/pt_tokenizer.json")  
 
     def predict(self, sentence):
         tokenized_sentence = self.tokenizer.texts_to_sequences([sentence])[0]
@@ -53,19 +48,12 @@ def read_files(path):
 # Consider only sentences with length <= 500
 max_lenght = 500
 val_examples = [[es_sentence, en_sentence] for es_sentence, en_sentence in zip(es_validation_data, en_validation_data) if len(es_sentence) <= max_lenght and len(en_sentence) <= max_lenght]
-# es_validation_data, en_validation_data = zip(*val_dataset)
-
-
-
-
 
 translator = PtEnTranslator("Models/09_translation_transformer/202307241748/model.onnx")
 
-
 val_dataset = []
 for es, en in val_examples:
     results = translator.predict(es)
     print(en)
     print(results)
-    print()
-    # val_dataset.append([pt.numpy().decode('utf-8'), en.numpy().decode('utf-8')])
+    print()

mltu/transformers.py

@@ -159,7 +159,7 @@ def __init__(
         self.padding_value = padding_value
 
     def __call__(self, spectrogram: np.ndarray, label: np.ndarray):
-        padded_spectrogram = np.pad(spectrogram, (0, (self.max_spectrogram_length - spectrogram.shape[0]),(0,0)), mode="constant", constant_values=self.padding_value)
+        padded_spectrogram = np.pad(spectrogram, ((0, self.max_spectrogram_length - spectrogram.shape[0]),(0,0)), mode="constant", constant_values=self.padding_value)
 
         return padded_spectrogram, label