255 lines
10 KiB
Python
255 lines
10 KiB
Python
#!/usr/bin/env python3
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import argparse
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import re
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from typing import Dict
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import torch
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from datasets import Audio, Dataset, load_dataset, load_metric
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from num2words import num2words as n2w
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from slugify import slugify
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from transformers import AutoFeatureExtractor, AutoModelForCTC, pipeline, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2FeatureExtractor
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# from pyctcdecode import BeamSearchDecoderCTC
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from cardinal_numbers import convert_nums
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def log_results(result: Dataset, args: Dict[str, str]):
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"""DO NOT CHANGE. This function computes and logs the result metrics."""
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log_outputs = args.log_outputs
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lm = "withLM" if args.use_lm else "noLM"
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model_id = args.model_id.replace("/", "_").replace(".", "")
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if args.filter:
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extra_args = [args.config, slugify(args.filter), args.split, lm]
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else:
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extra_args = [args.config, args.split, lm]
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dataset_id = "_".join([model_id] + args.dataset.split("/") + extra_args)
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# load metric
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wer = load_metric("wer")
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cer = load_metric("cer")
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# compute metrics
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wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
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cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
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# print & log results
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result_str = f"{dataset_id}\nWER: {wer_result}\nCER: {cer_result}"
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print(result_str)
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with open(f"{dataset_id}_eval_results.txt", "w") as f:
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f.write(result_str)
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with open(f"{dataset_id}_eval_results.tsv", "w") as f:
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f.write("\t".join([args.model_id, args.dataset, args.config, args.filter, args.split, str(lm), str(wer_result), str(cer_result)]))
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# log all results in text file. Possibly interesting for analysis
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if log_outputs is not None:
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pred_file = f"log_{dataset_id}_predictions.txt"
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target_file = f"log_{dataset_id}_targets.txt"
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with open(pred_file, "w") as p, open(target_file, "w") as t:
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# mapping function to write output
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def write_to_file(batch, i):
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p.write(f"{i}" + "\n")
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p.write(batch["prediction"] + "\n")
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t.write(f"{i}" + "\n")
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t.write(batch["target"] + "\n")
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result.map(write_to_file, with_indices=True)
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def normalize_text(original_text: str, dataset: str) -> str:
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"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
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text = original_text.lower()
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if dataset.lower().endswith("fleurs"):
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replacements = (
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(r"\be\.kr", "etter kristus fødsel"),
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(r"\bf\.kr", "før kristi fødsel"),
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(r"\bca[.]?\b", "circa"),
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(r"(\d)\s*km/t", r"\1 kilometer i timen"),
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(r"(\d)\s*km", r"\1 kilometer"),
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(r"(\d)\s*cm", r"\1 centimeter"),
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(r"(\d)\s*mm", r"\1 millimeter"),
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(r"kl\.", "klokka"),
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(r"f\.eks", "for eksempel"),
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)
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for abrev, expasion in replacements:
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text = re.sub(abrev, expasion, text)
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text = re.sub(r'(\d+)[-–](\d+)', r'\1 til \2', text) # 1-89, 70-90
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text = re.sub(r'(\d{2}):00', r'\1', text) # 21:00
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text = re.sub(r"(\d{2}):0(\d{1})", r"\1 null \2", text) # 17:03
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text = re.sub(r"(\d{1,2}):(\d{1,2})", r"\1 \2", text) # 17:23 (time), 4:3 (aspect ratios)
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text = re.sub(r"(1[1-9])00", r"\1 hundre", text) # 1800, 1900
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text = re.sub(r"(1[1-9])0([1-9])", r"\1 null \2 ", text) # 1901, 1909
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text = re.sub(r"(1[1-9])([1-9]\d)", r"\1 \2 ", text) # 1911, 1987
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text = re.sub(r"(20)0([1-9])", r"\1 null \2 ", text) # 2009
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text = re.sub(r"(20)(\d{2})", r"\1 \2 ", text) # 2009
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text = re.sub(r"(\d{1,3})[.](\d{1,2})", r"\1 dot \2 ", text) # 802.11n, 2.5ghz (in English)
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text = re.sub(r"(\d{1,2})[ .](\d{3})", r"\1\2", text) # 10 000, 32.000
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text = re.sub(r'(\w+)-(\w+)', r'\1 \2', text) # n-standard
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# text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: n2w(x.group(0), lang="no"), text.replace(".", ""))
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text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: convert_nums(int(x.group(0)), nn=True), text.replace(".", ""))
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\<EFBFBD>\'\–\_\\\+\#\/]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
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text = re.sub(chars_to_ignore_regex, "", text) + " "
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if dataset.lower().endswith("nst"):
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text = text.lower()
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text = text.replace("(...vær stille under dette opptaket...)", "")
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text = re.sub('[áàâ]', 'a', text)
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text = re.sub('[ä]', 'æ', text)
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text = re.sub('[éèëê]', 'e', text)
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text = re.sub('[íìïî]', 'i', text)
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text = re.sub('[óòöô]', 'o', text)
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text = re.sub('[ö]', 'ø', text)
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text = re.sub('[ç]', 'c', text)
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text = re.sub('[úùüû]', 'u', text)
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# text = re.sub('\\(?=(Punktum|Komma|Utropstegn|Spørsmålstegn))', ' ', text)
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text = re.sub('\s+', ' ', text)
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elif dataset.lower().endswith("npsc"):
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text = re.sub('[áàâ]', 'a', text)
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text = re.sub('[ä]', 'æ', text)
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text = re.sub('[éèëê]', 'e', text)
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text = re.sub('[íìïî]', 'i', text)
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text = re.sub('[óòöô]', 'o', text)
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text = re.sub('[ö]', 'ø', text)
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text = re.sub('[ç]', 'c', text)
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text = re.sub('[úùüû]', 'u', text)
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text = re.sub('\s+', ' ', text)
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elif dataset.lower().endswith("fleurs"):
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text = re.sub('[áàâ]', 'a', text)
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text = re.sub('[ä]', 'æ', text)
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text = re.sub('[éèëê]', 'e', text)
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text = re.sub('[íìïî]', 'i', text)
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text = re.sub('[óòöô]', 'o', text)
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text = re.sub('[ö]', 'ø', text)
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text = re.sub('[ç]', 'c', text)
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text = re.sub('[úùüû]', 'u', text)
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text = re.sub('[«»]', '', text)
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text = re.sub('\s+', ' ', text)
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text = re.sub('<ee>', 'eee', text)
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text = re.sub('<qq>', 'qqq', text)
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text = re.sub('<mm>', 'mmm', text)
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text = re.sub('<inaudible>', 'xxx', text)
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# # In addition, we can normalize the target text, e.g. removing new lines characters etc...
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# # note that order is important here!
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# token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
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# for t in token_sequences_to_ignore:
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# text = " ".join(text.split(t))
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return text
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def main(args):
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# load dataset
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dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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if args.filter:
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attribute, value = list(map(str.strip, args.filter.split(":")))
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dataset = dataset.filter(
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lambda x: x[attribute] == value,
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desc=f"Filtering on {args.filter}",
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)
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# for testing: only process the first two examples as a test
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# dataset = dataset.select(range(10))
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# load processor
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feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
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sampling_rate = feature_extractor.sampling_rate
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# resample audio
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dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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# load eval pipeline
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if args.device is None:
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args.device = 0 if torch.cuda.is_available() else -1
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# asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
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model_instance = AutoModelForCTC.from_pretrained(args.model_id)
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if args.use_lm:
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
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decoder = processor.decoder
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else:
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processor = Wav2Vec2Processor.from_pretrained(args.model_id)
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decoder = None
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asr = pipeline(
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"automatic-speech-recognition",
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model=model_instance,
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tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor,
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decoder=decoder,
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device=args.device
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)
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# feature_extractor_dict, _ = Wav2Vec2FeatureExtractor.get_feature_extractor_dict(args.model_id)
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# feature_extractor_dict["processor_class"] = "Wav2Vec2Processor" if not args.use_lm else "Wav2Vec2ProcessorWithLM"
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# feature_extractor = Wav2Vec2FeatureExtractor.from_dict(feature_extractor_dict)
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# asr = pipeline("automatic-speech-recognition", model=args.model_id, feature_extractor=feature_extractor, device=args.device, decoder=BeamSearchDecoderCTC.load_from_dir("./"))
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# map function to decode audio
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def map_to_pred(batch):
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prediction = asr(
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batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
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)
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batch["prediction"] = prediction["text"]
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batch["target"] = normalize_text(batch[args.text_column], args.dataset)
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return batch
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# run inference on all examples
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result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
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# compute and log_results
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# do not change function below
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log_results(result, args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
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)
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parser.add_argument(
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"--dataset",
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type=str,
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required=True,
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help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
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)
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parser.add_argument(
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"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
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)
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parser.add_argument(
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"--filter", type=str, default="", help="Simple filter on attributes. *E.g.* `region_of_youth:Troms` would pnly keep those samplesfor which the condition is met"
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)
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parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
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parser.add_argument(
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"--text_column", type=str, default="text", help="Column name containing the transcription."
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)
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parser.add_argument(
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"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
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)
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parser.add_argument(
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"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
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)
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parser.add_argument(
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"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
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)
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parser.add_argument(
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"--device",
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type=int,
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default=None,
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help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
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)
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parser.add_argument(
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"--use_lm", action="store_true", help="If defined, use included language model as the decoder."
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)
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args = parser.parse_args()
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main(args)
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