Update eval.py

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	#!/usr/bin/env python3
	import argparse
	import re
	from typing import Dict

	import torch
	from datasets import Audio, Dataset, load_dataset, load_metric
	from num2words import num2words as n2w
	from slugify import slugify

	from transformers import AutoFeatureExtractor, AutoModelForCTC, pipeline, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2FeatureExtractor
	# from pyctcdecode import BeamSearchDecoderCTC

	from cardinal_numbers import convert_nums


	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
	lm = "withLM" if args.use_lm else "noLM"
	model_id = args.model_id.replace("/", "_").replace(".", "")
	if args.filter:
	extra_args = [args.config, slugify(args.filter), args.split, lm]
	else:
	extra_args = [args.config, args.split, lm]
	dataset_id = "_".join([model_id] + args.dataset.split("/") + extra_args)

	# 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"{dataset_id}\nWER: {wer_result}\nCER: {cer_result}"
	print(result_str)

	with open(f"{dataset_id}_eval_results.txt", "w") as f:
	f.write(result_str)
	with open(f"{dataset_id}_eval_results.tsv", "w") as f:
	f.write("\t".join([args.model_id, args.dataset, args.config, args.filter, args.split, str(lm), str(wer_result), str(cer_result)]))

	# 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(original_text: str, dataset: str) -> str:
	"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""

	text = original_text.lower()
	if dataset.lower().endswith("fleurs"):
	replacements = (
	(r"\be\.kr", "etter kristus fødsel"),
	(r"\bf\.kr", "før kristi fødsel"),
	(r"\bca[.]?\b", "circa"),
	(r"(\d)\s*km/t", r"\1 kilometer i timen"),
	(r"(\d)\s*km", r"\1 kilometer"),
	(r"(\d)\s*cm", r"\1 centimeter"),
	(r"(\d)\s*mm", r"\1 millimeter"),
	(r"kl\.", "klokka"),
	(r"f\.eks", "for eksempel"),
	)
	for abrev, expasion in replacements:
	text = re.sub(abrev, expasion, text)
	text = re.sub(r'(\d+)[-–](\d+)', r'\1 til \2', text) # 1-89, 70-90
	text = re.sub(r'(\d{2}):00', r'\1', text) # 21:00
	text = re.sub(r"(\d{2}):0(\d{1})", r"\1 null \2", text) # 17:03
	text = re.sub(r"(\d{1,2}):(\d{1,2})", r"\1 \2", text) # 17:23 (time), 4:3 (aspect ratios)
	text = re.sub(r"(1[1-9])00", r"\1 hundre", text) # 1800, 1900
	text = re.sub(r"(1[1-9])0([1-9])", r"\1 null \2 ", text) # 1901, 1909
	text = re.sub(r"(1[1-9])([1-9]\d)", r"\1 \2 ", text) # 1911, 1987
	text = re.sub(r"(20)0([1-9])", r"\1 null \2 ", text) # 2009
	text = re.sub(r"(20)(\d{2})", r"\1 \2 ", text) # 2009
	text = re.sub(r"(\d{1,3})[.](\d{1,2})", r"\1 dot \2 ", text) # 802.11n, 2.5ghz (in English)
	text = re.sub(r"(\d{1,2})[ .](\d{3})", r"\1\2", text) # 10 000, 32.000
	text = re.sub(r'(\w+)-(\w+)', r'\1 \2', text) # n-standard
	# text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: n2w(x.group(0), lang="no"), text.replace(".", ""))
	text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: convert_nums(int(x.group(0)), nn=True), text.replace(".", ""))


	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) + " "

	if dataset.lower().endswith("nst"):
	text = text.lower()
	text = text.replace("(...vær stille under dette opptaket...)", "")
	text = re.sub('[áàâ]', 'a', text)
	text = re.sub('[ä]', 'æ', text)
	text = re.sub('[éèëê]', 'e', text)
	text = re.sub('[íìïî]', 'i', text)
	text = re.sub('[óòöô]', 'o', text)
	text = re.sub('[ö]', 'ø', text)
	text = re.sub('[ç]', 'c', text)
	text = re.sub('[úùüû]', 'u', text)
	# text = re.sub('\\(?=(Punktum\|Komma\|Utropstegn\|Spørsmålstegn))', ' ', text)
	text = re.sub('\s+', ' ', text)
	elif dataset.lower().endswith("npsc"):
	text = re.sub('[áàâ]', 'a', text)
	text = re.sub('[ä]', 'æ', text)
	text = re.sub('[éèëê]', 'e', text)
	text = re.sub('[íìïî]', 'i', text)
	text = re.sub('[óòöô]', 'o', text)
	text = re.sub('[ö]', 'ø', text)
	text = re.sub('[ç]', 'c', text)
	text = re.sub('[úùüû]', 'u', text)
	text = re.sub('\s+', ' ', text)
	elif dataset.lower().endswith("fleurs"):
	text = re.sub('[áàâ]', 'a', text)
	text = re.sub('[ä]', 'æ', text)
	text = re.sub('[éèëê]', 'e', text)
	text = re.sub('[íìïî]', 'i', text)
	text = re.sub('[óòöô]', 'o', text)
	text = re.sub('[ö]', 'ø', text)
	text = re.sub('[ç]', 'c', text)
	text = re.sub('[úùüû]', 'u', text)
	text = re.sub('[«»]', '', text)
	text = re.sub('\s+', ' ', text)
	text = re.sub('<e+h?>', 'eee', text)
	text = re.sub('<m+>', 'mmm', text)
	text = re.sub('<q+>', 'qqq', text)
	text = re.sub('<inaudible>', 'xxx', text)
	text = re.sub('[<>]', '', text)

	# # In addition, we can normalize the target text, e.g. removing new lines characters etc...
	# # note that order is important here!
	# token_sequences_to_ignore = ["\n\n", "\n", " ", " "]

	# for t in token_sequences_to_ignore:
	# text = " ".join(text.split(t))

	return text.strip()


	def main(args):
	# load dataset
	dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
	if args.filter:
	attribute, value = list(map(str.strip, args.filter.split(":")))
	dataset = dataset.filter(
	lambda x: x[attribute] == value,
	desc=f"Filtering on {args.filter}",
	)
	# 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
	# asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)

	model_instance = AutoModelForCTC.from_pretrained(args.model_id)
	if args.use_lm:
	processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
	decoder = processor.decoder
	else:
	processor = Wav2Vec2Processor.from_pretrained(args.model_id)
	decoder = None
	asr = pipeline(
	"automatic-speech-recognition",
	model=model_instance,
	tokenizer=processor.tokenizer,
	feature_extractor=processor.feature_extractor,
	decoder=decoder,
	device=args.device
	)

	# feature_extractor_dict, _ = Wav2Vec2FeatureExtractor.get_feature_extractor_dict(args.model_id)
	# feature_extractor_dict["processor_class"] = "Wav2Vec2Processor" if not args.use_lm else "Wav2Vec2ProcessorWithLM"
	# feature_extractor = Wav2Vec2FeatureExtractor.from_dict(feature_extractor_dict)

	# asr = pipeline("automatic-speech-recognition", model=args.model_id, feature_extractor=feature_extractor, device=args.device, decoder=BeamSearchDecoderCTC.load_from_dir("./"))

	# 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
	)

	batch["prediction"] = prediction["text"]
	batch["target"] = normalize_text(batch[args.text_column], args.dataset)
	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(
	"--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"
	)
	parser.add_argument("--split", type=str, required=True, help="Split of the dataset. E.g. `'test'`")
	parser.add_argument(
	"--text_column", type=str, default="text", help="Column name containing the transcription."
	)
	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.",
	)
	parser.add_argument(
	"--use_lm", action="store_true", help="If defined, use included language model as the decoder."
	)
	args = parser.parse_args()

	main(args)