Generating Random Sentences with Fairseq Dictionaries

This function uses the Fairseq library's Dictionary, SourceDataset, TargetDataset, and LanguagePairDataset classes to generate a random sentence. It first randomly generates sequences of tokens for the source and target languages, then converts these sequences into actual words. Next, it places these words into the SourceDataset and TargetDataset, and finally combines them into a LanguagePairDataset. It then randomly samples a sentence from the LanguagePairDataset as the output.

Technology Stack : Fairseq, Dictionary, SourceDataset, TargetDataset, LanguagePairDataset

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
import random
import torch
from fairseq.data import Dictionary, LanguagePairDataset, SourceDataset, TargetDataset

def sample_random_sentence(dictionary, src_dict, tgt_dict):
    """
    Generate a random sentence using the Fairseq dictionary and dictionaries for source and target languages.
    """
    # Generate a random sequence of source tokens
    random_src_tokens = [random.randint(1, len(src_dict)) for _ in range(10)]
    # Generate a random sequence of target tokens
    random_tgt_tokens = [random.randint(1, len(tgt_dict)) for _ in range(10)]
    
    # Create source and target datasets
    src_dataset = SourceDataset(dictionary, random_src_tokens)
    tgt_dataset = TargetDataset(dictionary, random_tgt_tokens)
    
    # Combine the source and target datasets into a language pair dataset
    paired_dataset = LanguagePairDataset(src_dataset, tgt_dataset, src_dict, tgt_dict)
    
    # Sample a random sentence from the dataset
    for src, tgt in paired_dataset:
        # Convert tokens to strings
        src_str = ' '.join(dictionary.index_to_word(src))
        tgt_str = ' '.join(dictionary.index_to_word(tgt))
        break  # Break after the first sample for demonstration purposes

    return src_str, tgt_str

# Example usage
dictionary = Dictionary()
src_dict = Dictionary()
tgt_dict = Dictionary()

# For demonstration purposes, we need to create some dummy dictionaries and datasets
dictionary.add_word('the')
dictionary.add_word('cat')
dictionary.add_word('sat')
dictionary.add_word('on')
dictionary.add_word('the')
dictionary.add_word('mat')
src_dict.add_word('the')
src_dict.add_word('cat')
src_dict.add_word('sat')
src_dict.add_word('on')
tgt_dict.add_word('the')
tgt_dict.add_word('mat')
tgt_dict.add_word('the')
tgt_dict.add_word('cat')
tgt_dict.add_word('sat')
tgt_dict.add_word('on')

src, tgt = sample_random_sentence(dictionary, src_dict, tgt_dict)
print(f"Source: {src}")
print(f"Target: {tgt}")