#!/usr/bin/env python3 ############################################################# # eval.sh contains the commands to run evaluation properly ############################################################ import argparse import sys import re from typing import Dict import torch from datasets import Audio, Dataset, load_dataset, load_metric from pyctcdecode import BeamSearchDecoderCTC from transformers import AutoFeatureExtractor, AutoTokenizer, pipeline import transformers import Levenshtein import hunspell dutch_unigrams = set(open('language_model/unigrams.txt').read().splitlines()) dutch_hobj = hunspell.HunSpell('dictionaries/nl.dic', 'dictionaries/nl.aff') MOST_COMMON_WORDS = 'ik|je|het|de|is|dat|een|niet|en|wat|van|we|in|ze|op|te|hij|zijn|er|maar|me|die|heb|voor|met|als|ben|was|n|mijn|u|dit|aan|hier|om|naar|dan|jij|weet|ja|kan|geen|zo|nog|wil|wel|moet|goed|hem|hebben|nee|heeft|waar|nu|hoe|ga|t|kom|uit|gaan|bent|haar|doen|ook|mij|over|of|daar|zou|al|jullie|bij|ons|zal|gaat|hebt|meer|waarom|iets|laat|deze|had|doe|wie|jou|moeten|alles|denk|kunnen|eens|echt|man|weg|door|oké|toch|zien|alleen|s|nou'.split('|') def log_results(result: Dataset, args: Dict[str, str]): """DO NOT CHANGE. This function computes and logs the result metrics.""" log_outputs = args.log_outputs dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split]) # load metric wer = load_metric("wer") cer = load_metric("cer") # compute metrics wer_result = wer.compute(references=result["target"], predictions=result["prediction"]) cer_result = cer.compute(references=result["target"], predictions=result["prediction"]) # print & log results result_str = f"WER: {wer_result}\n" f"CER: {cer_result}" print(result_str) with open(f"{dataset_id}_eval_results.txt", "w") as f: f.write(result_str) # log all results in text file. Possibly interesting for analysis if log_outputs is not None: pred_file = f"log_{dataset_id}_predictions.txt" target_file = f"log_{dataset_id}_targets.txt" with open(pred_file, "w") as p, open(target_file, "w") as t: # mapping function to write output def write_to_file(batch, i): p.write(f"{i}" + "\n") p.write(batch["prediction"] + "\n") t.write(f"{i}" + "\n") t.write(batch["target"] + "\n") result.map(write_to_file, with_indices=True) def normalize_text(text: str) -> str: """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text.""" chars_to_ignore_regex = '[,?.!\-\;\:"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training text = re.sub(chars_to_ignore_regex, "", text.lower()) text = re.sub(r'[\n\s]+', ' ', text) return text def main(args): # load dataset dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True) # for testing: only process the first two examples as a test # dataset = dataset.select(range(10)) # load processor feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id) sampling_rate = feature_extractor.sampling_rate # resample audio dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate)) # load eval pipeline if args.device is None: args.device = 0 if torch.cuda.is_available() else -1 config = transformers.PretrainedConfig.from_pretrained(args.model_id) model=transformers.Wav2Vec2ForCTC.from_pretrained(args.model_id) tokenizer = AutoTokenizer.from_pretrained(args.model_id) processor = transformers.AutoProcessor.from_pretrained(args.model_id) language_model = BeamSearchDecoderCTC.model_container[processor.decoder._model_key]._kenlm_model #asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device) asr = pipeline("automatic-speech-recognition", config=config, model=model, tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=processor.decoder, device=args.device) # map function to decode audio def map_to_pred(batch): prediction = asr( batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s ) text = prediction["text"] #print('### STARTING TO FIND TYPOS') text_words = text.split(' ') is_known_word = lambda word: (len(word) == 0) or (word in dutch_unigrams) or (dutch_hobj.spell(word)) for index in range(len(text_words)): curr_word = text_words[index] if is_known_word(curr_word): continue prev_word = text_words[index-1] if index>0 else '' next_word = text_words[index+1] if index' BASE_PENALITY = -2 EDIT_PENALITY = -0.5 curr_word_letters = curr_word.replace("-",'').replace("'",'') best_word = curr_word best_score = language_model.score(prev_word + ' ' + curr_word + ' ' + next_word) + BASE_PENALITY #print(prev_word + ' ' + curr_word + ' ' + next_word + ' == ' + str(best_score)) # typos suggestions by hunspell all_suggestions = list(dutch_hobj.suggest(curr_word)) # diphtongs flattened: a common faillure mode of pyctcdecode for dutch if curr_word.endswith('lik'): all_suggestions.append(curr_word[0:-3] + 'lijk') # words merged: a common failure mode of pyctcdecode for dutch for most_common_word in MOST_COMMON_WORDS: if curr_word.endswith(most_common_word): all_suggestions.append(curr_word[0:-len(most_common_word)] + ' ' + most_common_word) # look at all the suggestions and see if somethings look better for sugg_word in all_suggestions: sugg_word = sugg_word.lower() #if sugg_word == curr_word or sugg_word == best_word: continue sugg_word_letters = sugg_word.replace("-",'').replace("'",'') sugg_distance = Levenshtein.distance(curr_word_letters, sugg_word_letters) sugg_distance = sugg_distance if sugg_distance > 0 else -3 # bonus for perfect match sugg_score = language_model.score(prev_word + ' ' + sugg_word + ' ' + next_word) + EDIT_PENALITY * sugg_distance #print(prev_word + ' ' + sugg_word + ' ' + next_word + ' == ' + str(sugg_score)) if sugg_score > best_score: best_score = sugg_score best_word = sugg_word if best_word != curr_word: text_words[index] = best_word #print(curr_word + ' ===> ' + best_word) #print('### DONE FINDING TYPOS') text = " ".join(text_words) batch["prediction"] = text batch["target"] = normalize_text(batch["sentence"]) return batch # run inference on all examples result = dataset.map(map_to_pred, remove_columns=dataset.column_names) # compute and log_results # do not change function below log_results(result, args) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers" ) parser.add_argument( "--dataset", type=str, required=True, help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets", ) parser.add_argument( "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice" ) parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`") parser.add_argument( "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds." ) parser.add_argument( "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second." ) parser.add_argument( "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis." ) parser.add_argument( "--device", type=int, default=None, help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.", ) args = parser.parse_args() main(args)