Notes

Sure, creating a word-wise vocabulary involves tokenizing your text into words and then creating a vocabulary from those words. Here's how you can do it using Python:

1. **Load and Preprocess Text:**
Read your Hindi and English text files, and preprocess them by removing unnecessary characters and splitting the text into sentences.

```python
def preprocess_text(text):
# Remove unnecessary characters and split into sentences
sentences = text.split('n')
return sentences

with open('hindi_file.txt', 'r', encoding='utf-8') as hindi_file:
hindi_text = hindi_file.read()

with open('english_file.txt', 'r', encoding='utf-8') as english_file:
english_text = english_file.read()

hindi_sentences = preprocess_text(hindi_text)
english_sentences = preprocess_text(english_text)
```

2. **Tokenize Sentences:**
Tokenize each sentence into words.

```python
hindi_words = [sentence.split() for sentence in hindi_sentences]
english_words = [sentence.split() for sentence in english_sentences]
```

3. **Build Vocabulary:**
Create a vocabulary by collecting all unique words from both languages.

```python
def build_vocabulary(sentences):
vocabulary = set()
for sentence in sentences:
vocabulary.update(sentence)
return vocabulary

hindi_vocabulary = build_vocabulary(hindi_words)
english_vocabulary = build_vocabulary(english_words)
```

4. **Save Vocabulary:**
Save the vocabularies to files.

```python
def save_vocabulary(vocabulary, filename):
with open(filename, 'w', encoding='utf-8') as vocab_file:
for word in vocabulary:
vocab_file.write(word + 'n')

save_vocabulary(hindi_vocabulary, 'hindi_vocab.txt')
save_vocabulary(english_vocabulary, 'english_vocab.txt')
```

Now you have created word-wise vocabularies for both Hindi and English languages. Each vocabulary file (`hindi_vocab.txt` and `english_vocab.txt`) contains one word per line.

Note: This is a basic outline, and you might need to handle special characters, punctuation, and other preprocessing steps based on your specific requirements. Additionally, consider using libraries like NLTK or spaCy for more advanced tokenization and preprocessing.