use std::io::BufRead; use ctclib::{ BeamSearchDecoder, BeamSearchDecoderOptions, Decoder, Dict, GreedyDecoder, KenLM, ZeroLM, }; fn load_logits() -> (usize, usize, Vec) { let file = std::io::BufReader::new(std::fs::File::open("data/logit.txt").unwrap()); let lines = file.lines().collect::, _>>().unwrap(); let step = lines.len(); let mut logits = Vec::new(); for line in lines { let iter = line.split(' ').map(|x| x.parse::().unwrap()); logits.extend(iter); } let vocab = logits.len() / step; (step, vocab, logits) } fn load_letter_dicts() -> Vec { let file = std::io::BufReader::new(std::fs::File::open("data/letter.dict").unwrap()); file.lines().map(|x| x.unwrap()).collect::>() } #[test] fn greedy_decoder_decodes_sequence_greedy() { let (steps, n_vocab, data) = load_logits(); let vocab = load_letter_dicts(); let blank = (n_vocab - 1) as i32; let mut decoder = GreedyDecoder; let outputs = decoder.decode(&data, steps, n_vocab, blank); let output = &outputs[0]; let tokens = output.reduced_tokens(blank); let text = tokens .into_iter() .map(|i| vocab[i as usize].as_str()) .collect::>() .join(""); assert_eq!(text, "MISTE|QUILTER|T|IS|TH|E|APOSTLESR|OF|THE|RIDDLE|CLASHES|AND|WEHARE|GOLADB|TO|WELCOME|HIS|GOSUPEL|N|"); } #[test] fn beam_search_decoder_decodes_sequence() { let (steps, n_vocab, data) = load_logits(); let vocab = load_letter_dicts(); let blank = (n_vocab - 1) as i32; let mut decoder = BeamSearchDecoder::new( BeamSearchDecoderOptions { beam_size: 100, beam_size_token: 2000000, beam_threshold: f32::MAX, lm_weight: 0.0, }, ZeroLM, ); let outputs = decoder.decode(&data, steps, n_vocab, blank); let output = &outputs[0]; let tokens = output.reduced_tokens(blank); let text = tokens .into_iter() .map(|i| vocab[i as usize].as_str()) .collect::>() .join(""); assert_eq!(text, "MISTE|QUILTER|T|IS|TH|E|APOSTLESR|OF|THE|RIDDLE|CLASHES|AND|WEHARE|GOLADB|TO|WELCOME|HIS|GOSPEL|N|"); } #[test] fn beam_search_decoder_decodes_sequence_with_kenlm() { let (steps, n_vocab, data) = load_logits(); let vocab = load_letter_dicts(); let blank = (n_vocab - 1) as i32; let dict = Dict::read("data/letter.dict").unwrap(); let mut decoder = BeamSearchDecoder::new( BeamSearchDecoderOptions { beam_size: 100, beam_size_token: 2000000, beam_threshold: f32::MAX, lm_weight: 0.5, }, KenLM::new("data/overfit.arpa", &dict), ); let outputs = decoder.decode(&data, steps, n_vocab, blank); let output = &outputs[0]; let tokens = output.reduced_tokens(blank); let text = tokens .into_iter() .map(|i| vocab[i as usize].as_str()) .collect::>() .join(""); assert_eq!(text, "MISTE|QUILTER|T|IS|THE|APOSTLES|OF|THE|RIDDLE|CLASHES|AND|WEHARE|GOLAD|TO|WECOME|HIS|GOSPEL|"); }