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	# File: open_asr_leaderboard-main/ctranslate2/run_eval.py
	""""""
	import argparse
	import os
	import time
	import evaluate
	from faster_whisper import WhisperModel
	from tqdm import tqdm
	from normalizer import data_utils
	wer_metric = evaluate.load('wer')

	def main(args) -> None:
	asr_model = WhisperModel(model_size_or_path=args.model_id, compute_type='float16', device='cuda', device_index=args.device)

	def benchmark(batch):
	start_time = time.time()
	(segments, _) = asr_model.transcribe(batch['audio']['array'], language='en')
	outputs = [segment._asdict() for segment in segments]
	batch['transcription_time_s'] = time.time() - start_time
	batch['predictions'] = data_utils.normalizer(''.join([segment['text'] for segment in outputs])).strip()
	batch['references'] = batch['norm_text']
	return batch
	if args.warmup_steps is not None:
	dataset = data_utils.load_data(args)
	dataset = data_utils.prepare_data(dataset)
	if args.streaming:
	warmup_dataset = dataset.take(args.warmup_steps)
	else:
	warmup_dataset = dataset.select(range(min(args.warmup_steps, len(dataset))))
	warmup_dataset = iter(warmup_dataset.map(benchmark, remove_columns=['audio']))
	for _ in tqdm(warmup_dataset, desc='Warming up...'):
	continue
	dataset = data_utils.load_data(args)
	if args.max_eval_samples is not None and args.max_eval_samples > 0:
	print(f'Subsampling dataset to first {args.max_eval_samples} samples!')
	if args.streaming:
	dataset = dataset.take(args.max_eval_samples)
	else:
	dataset = dataset.select(range(min(args.max_eval_samples, len(dataset))))
	dataset = data_utils.prepare_data(dataset)
	dataset = dataset.map(benchmark, remove_columns=['audio'])
	all_results = {'audio_length_s': [], 'transcription_time_s': [], 'predictions': [], 'references': []}
	result_iter = iter(dataset)
	for result in tqdm(result_iter, desc='Samples...'):
	for key in all_results:
	all_results[key].append(result[key])
	manifest_path = data_utils.write_manifest(all_results['references'], all_results['predictions'], args.model_id, args.dataset_path, args.dataset, args.split, audio_length=all_results['audio_length_s'], transcription_time=all_results['transcription_time_s'])
	print('Results saved at path:', os.path.abspath(manifest_path))
	wer = wer_metric.compute(references=all_results['references'], predictions=all_results['predictions'])
	wer = round(100 * wer, 2)
	rtfx = round(sum(all_results['audio_length_s']) / sum(all_results['transcription_time_s']), 2)
	print('WER:', wer, '%', 'RTFx:', rtfx)
	if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--model_id', type=str, required=True, help='Model identifier. Should be loadable with faster-whisper')
	parser.add_argument('--dataset_path', type=str, default='esb/datasets', help='Dataset path. By default, it is `esb/datasets`')
	parser.add_argument('--dataset', type=str, required=True, help="Dataset name. E.g. `'librispeech_asr` for the LibriSpeech ASR dataset, or `'common_voice'` for Common Voice. The full list of dataset names can be found at `https://huggingface.co/datasets/esb/datasets`")
	parser.add_argument('--split', type=str, default='test', help="Split of the dataset. E.g. `'validation`' for the dev split, or `'test'` for the test split.")
	parser.add_argument('--device', type=int, default=-1, help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.')
	parser.add_argument('--max_eval_samples', type=int, default=None, help='Number of samples to be evaluated. Put a lower number e.g. 64 for testing this script.')
	parser.add_argument('--no-streaming', dest='streaming', action='store_false', help="Choose whether you'd like to download the entire dataset or stream it during the evaluation.")
	parser.add_argument('--warmup_steps', type=int, default=5, help='Number of warm-up steps to run before launching the timed runs.')
	args = parser.parse_args()
	parser.set_defaults(streaming=False)
	main(args)

	# File: open_asr_leaderboard-main/nemo_asr/run_eval.py
	import argparse
	import os
	import torch
	import evaluate
	import soundfile
	from tqdm import tqdm
	from normalizer import data_utils
	import numpy as np
	from nemo.collections.asr.models import ASRModel
	import time
	wer_metric = evaluate.load('wer')

	def main(args):
	DATA_CACHE_DIR = os.path.join(os.getcwd(), 'audio_cache')
	DATASET_NAME = args.dataset
	SPLIT_NAME = args.split
	CACHE_DIR = os.path.join(DATA_CACHE_DIR, DATASET_NAME, SPLIT_NAME)
	if not os.path.exists(CACHE_DIR):
	os.makedirs(CACHE_DIR)
	if args.device >= 0:
	device = torch.device(f'cuda:{args.device}')
	compute_dtype = torch.bfloat16
	else:
	device = torch.device('cpu')
	compute_dtype = torch.float32
	if args.model_id.endswith('.nemo'):
	asr_model = ASRModel.restore_from(args.model_id, map_location=device)
	else:
	asr_model = ASRModel.from_pretrained(args.model_id, map_location=device)
	asr_model.to(compute_dtype)
	asr_model.eval()
	dataset = data_utils.load_data(args)

	def download_audio_files(batch):
	audio_paths = []
	durations = []
	for (id, sample) in zip(batch['id'], batch['audio']):
	audio_path = os.path.join(CACHE_DIR, f'{id}.wav')
	if not os.path.exists(audio_path):
	os.makedirs(os.path.dirname(audio_path), exist_ok=True)
	soundfile.write(audio_path, np.float32(sample['array']), 16000)
	audio_paths.append(audio_path)
	durations.append(len(sample['array']) / 16000)
	batch['references'] = batch['norm_text']
	batch['audio_filepaths'] = audio_paths
	batch['durations'] = durations
	return batch
	if args.max_eval_samples is not None and args.max_eval_samples > 0:
	print(f'Subsampling dataset to first {args.max_eval_samples} samples !')
	dataset = dataset.take(args.max_eval_samples)
	dataset = data_utils.prepare_data(dataset)
	if asr_model.cfg.decoding.strategy != 'beam':
	asr_model.cfg.decoding.strategy = 'greedy_batch'
	asr_model.change_decoding_strategy(asr_model.cfg.decoding)
	dataset = dataset.map(download_audio_files, batch_size=args.batch_size, batched=True, remove_columns=['audio'])
	all_data = {'audio_filepaths': [], 'durations': [], 'references': []}
	data_itr = iter(dataset)
	for data in tqdm(data_itr, desc='Downloading Samples'):
	for key in all_data:
	all_data[key].append(data[key])
	sorted_indices = sorted(range(len(all_data['durations'])), key=lambda k: all_data['durations'][k], reverse=True)
	all_data['audio_filepaths'] = [all_data['audio_filepaths'][i] for i in sorted_indices]
	all_data['references'] = [all_data['references'][i] for i in sorted_indices]
	all_data['durations'] = [all_data['durations'][i] for i in sorted_indices]
	total_time = 0
	for _ in range(2):
	if _ == 0:
	audio_files = all_data['audio_filepaths'][:args.batch_size * 4]
	else:
	audio_files = all_data['audio_filepaths']
	start_time = time.time()
	with torch.cuda.amp.autocast(enabled=False, dtype=compute_dtype), torch.inference_mode(), torch.no_grad():
	if 'canary' in args.model_id:
	transcriptions = asr_model.transcribe(audio_files, batch_size=args.batch_size, verbose=False, pnc='no', num_workers=1)
	else:
	transcriptions = asr_model.transcribe(audio_files, batch_size=args.batch_size, verbose=False, num_workers=1)
	end_time = time.time()
	if _ == 1:
	total_time += end_time - start_time
	total_time = total_time
	if isinstance(transcriptions, tuple) and len(transcriptions) == 2:
	transcriptions = transcriptions[0]
	predictions = [data_utils.normalizer(pred) for pred in transcriptions]
	avg_time = total_time / len(all_data['audio_filepaths'])
	manifest_path = data_utils.write_manifest(all_data['references'], predictions, args.model_id, args.dataset_path, args.dataset, args.split, audio_length=all_data['durations'], transcription_time=[avg_time] * len(all_data['audio_filepaths']))
	print('Results saved at path:', os.path.abspath(manifest_path))
	wer = wer_metric.compute(references=all_data['references'], predictions=predictions)
	wer = round(100 * wer, 2)
	audio_length = sum(all_data['durations'])
	rtfx = audio_length / total_time
	rtfx = round(rtfx, 2)
	print('RTFX:', rtfx)
	print('WER:', wer, '%')
	if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--model_id', type=str, required=True, help='Model identifier. Should be loadable with NVIDIA NeMo.')
	parser.add_argument('--dataset_path', type=str, default='esb/datasets', help='Dataset path. By default, it is `esb/datasets`')
	parser.add_argument('--dataset', type=str, required=True, help="Dataset name. E.g. `'librispeech_asr` for the LibriSpeech ASR dataset, or `'common_voice'` for Common Voice. The full list of dataset names can be found at `https://huggingface.co/datasets/esb/datasets`")
	parser.add_argument('--split', type=str, default='test', help="Split of the dataset. E.g. `'validation`' for the dev split, or `'test'` for the test split.")
	parser.add_argument('--device', type=int, default=-1, help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.')
	parser.add_argument('--batch_size', type=int, default=32, help='Number of samples to go through each streamed batch.')
	parser.add_argument('--max_eval_samples', type=int, default=None, help='Number of samples to be evaluated. Put a lower number e.g. 64 for testing this script.')
	parser.add_argument('--no-streaming', dest='streaming', action='store_false', help="Choose whether you'd like to download the entire dataset or stream it during the evaluation.")
	args = parser.parse_args()
	parser.set_defaults(streaming=True)
	main(args)

	# File: open_asr_leaderboard-main/normalizer/data_utils.py
	from datasets import load_dataset, Audio
	from normalizer import EnglishTextNormalizer
	from .eval_utils import read_manifest, write_manifest

	def is_target_text_in_range(ref):
	if ref.strip() == 'ignore time segment in scoring':
	return False
	else:
	return ref.strip() != ''

	def get_text(sample):
	if 'text' in sample:
	return sample['text']
	elif 'sentence' in sample:
	return sample['sentence']
	elif 'normalized_text' in sample:
	return sample['normalized_text']
	elif 'transcript' in sample:
	return sample['transcript']
	elif 'transcription' in sample:
	return sample['transcription']
	else:
	raise ValueError(f"Expected transcript column of either 'text', 'sentence', 'normalized_text' or 'transcript'. Got sample of .join{{sample.keys()}}. Ensure a text column name is present in the dataset.")
	normalizer = EnglishTextNormalizer()

	def normalize(batch):
	batch['original_text'] = get_text(batch)
	batch['norm_text'] = normalizer(batch['original_text'])
	return batch

	def load_data(args):
	dataset = load_dataset(args.dataset_path, args.dataset, split=args.split, streaming=args.streaming, token=True)
	return dataset

	def prepare_data(dataset):
	dataset = dataset.cast_column('audio', Audio(sampling_rate=16000))
	dataset = dataset.map(normalize)
	dataset = dataset.filter(is_target_text_in_range, input_columns=['norm_text'])
	return dataset

	# File: open_asr_leaderboard-main/normalizer/english_abbreviations.py
	english_spelling_normalizer = {'accessorise': 'accessorize', 'accessorised': 'accessorized', 'accessorises': 'accessorizes', 'accessorising': 'accessorizing', 'acclimatisation': 'acclimatization', 'acclimatise': 'acclimatize', 'acclimatised': 'acclimatized', 'acclimatises': 'acclimatizes', 'acclimatising': 'acclimatizing', 'accoutrements': 'accouterments', 'aeon': 'eon', 'aeons': 'eons', 'aerogramme': 'aerogram', 'aerogrammes': 'aerograms', 'aeroplane': 'airplane', 'aeroplanes': 'airplanes', 'aesthete': 'esthete', 'aesthetes': 'esthetes', 'aesthetic': 'esthetic', 'aesthetically': 'esthetically', 'aesthetics': 'esthetics', 'aetiology': 'etiology', 'ageing': 'aging', 'aggrandisement': 'aggrandizement', 'agonise': 'agonize', 'agonised': 'agonized', 'agonises': 'agonizes', 'agonising': 'agonizing', 'agonisingly': 'agonizingly', 'almanack': 'almanac', 'almanacks': 'almanacs', 'aluminium': 'aluminum', 'amortisable': 'amortizable', 'amortisation': 'amortization', 'amortisations': 'amortizations', 'amortise': 'amortize', 'amortised': 'amortized', 'amortises': 'amortizes', 'amortising': 'amortizing', 'amphitheatre': 'amphitheater', 'amphitheatres': 'amphitheaters', 'anaemia': 'anemia', 'anaemic': 'anemic', 'anaesthesia': 'anesthesia', 'anaesthetic': 'anesthetic', 'anaesthetics': 'anesthetics', 'anaesthetise': 'anesthetize', 'anaesthetised': 'anesthetized', 'anaesthetises': 'anesthetizes', 'anaesthetising': 'anesthetizing', 'anaesthetist': 'anesthetist', 'anaesthetists': 'anesthetists', 'anaesthetize': 'anesthetize', 'anaesthetized': 'anesthetized', 'anaesthetizes': 'anesthetizes', 'anaesthetizing': 'anesthetizing', 'analogue': 'analog', 'analogues': 'analogs', 'analyse': 'analyze', 'analysed': 'analyzed', 'analyses': 'analyzes', 'analysing': 'analyzing', 'anglicise': 'anglicize', 'anglicised': 'anglicized', 'anglicises': 'anglicizes', 'anglicising': 'anglicizing', 'annualised': 'annualized', 'antagonise': 'antagonize', 'antagonised': 'antagonized', 'antagonises': 'antagonizes', 'antagonising': 'antagonizing', 'apologise': 'apologize', 'apologised': 'apologized', 'apologises': 'apologizes', 'apologising': 'apologizing', 'appal': 'appall', 'appals': 'appalls', 'appetiser': 'appetizer', 'appetisers': 'appetizers', 'appetising': 'appetizing', 'appetisingly': 'appetizingly', 'arbour': 'arbor', 'arbours': 'arbors', 'archaeologically': 'archeologically', 'archaeologist': 'archeologist', 'archaeologists': 'archeologists', 'archaeology': 'archeology</span>', 'archeological': 'archaeological', 'ardour': 'ardor', 'armour': 'armor', 'armoured': 'armored', 'armourer': 'armorer', 'armourers': 'armorers', 'armouries': 'armories', 'armoury': 'armory', 'artefact': 'artifact', 'artefacts': 'artifacts', 'authorise': 'authorize', 'authorised': 'authorized', 'authorises': 'authorizes', 'authorising': 'authorizing', 'axe': 'ax', 'backpedalled': 'backpedaled', 'backpedalling': 'backpedaling', 'bannister': 'banister', 'bannisters': 'banisters', 'baptise': 'baptize', 'baptised': 'baptized', 'baptises': 'baptizes', 'baptising': 'baptizing', 'bastardise': 'bastardize', 'bastardised': 'bastardized', 'bastardises': 'bastardizes', 'bastardising': 'bastardizing', 'battleax': 'battleaxe', 'baulk': 'balk', 'baulked': 'balked', 'baulking': 'balking', 'baulks': 'balks', 'bedevilled': 'bedeviled', 'bedevilling': 'bedeviling', 'behaviour': 'behavior', 'behavioural': 'behavioral', 'behaviourism': 'behaviorism', 'behaviourist': 'behaviorist', 'behaviourists': 'behaviorists', 'behaviours': 'behaviors', 'behove': 'behoove', 'behoved': 'behooved', 'behoves': 'behooves', 'bejewelled': 'bejeweled', 'belabour': 'belabor', 'belaboured': 'belabored', 'belabouring': 'belaboring', 'belabours': 'belabors', 'bevelled': 'beveled', 'bevvies': 'bevies', 'bevvy': 'bevy', 'biassed': 'biased', 'biassing': 'biasing', 'bingeing': 'binging', 'bougainvillaea': 'bougainvillea', 'bougainvillaeas': 'bougainvilleas', 'bowdlerise': 'bowdlerize', 'bowdlerised': 'bowdlerized', 'bowdlerises': 'bowdlerizes', 'bowdlerising': 'bowdlerizing', 'breathalyse': 'breathalyze', 'breathalysed': 'breathalyzed', 'breathalyser': 'breathalyzer', 'breathalysers': 'breathalyzers', 'breathalyses': 'breathalyzes', 'breathalysing': 'breathalyzing', 'brutalise': 'brutalize', 'brutalised': 'brutalized', 'brutalises': 'brutalizes', 'brutalising': 'brutalizing', 'busses': 'buses', 'bussing': 'busing', 'caesarean': 'cesarean', 'caesareans': 'cesareans', 'calibre': 'caliber', 'calibres': 'calibers', 'calliper': 'caliper', 'callipers': 'calipers', 'callisthenics': 'calisthenics', 'canalise': 'canalize', 'canalised': 'canalized', 'canalises': 'canalizes', 'canalising': 'canalizing', 'cancelation': 'cancellation', 'cancelations': 'cancellations', 'cancelled': 'canceled', 'cancelling': 'canceling', 'candour': 'candor', 'cannibalise': 'cannibalize', 'cannibalised': 'cannibalized', 'cannibalises': 'cannibalizes', 'cannibalising': 'cannibalizing', 'canonise': 'canonize', 'canonised': 'canonized', 'canonises': 'canonizes', 'canonising': 'canonizing', 'capitalise': 'capitalize', 'capitalised': 'capitalized', 'capitalises': 'capitalizes', 'capitalising': 'capitalizing', 'caramelise': 'caramelize', 'caramelised': 'caramelized', 'caramelises': 'caramelizes', 'caramelising': 'caramelizing', 'carbonise': 'carbonize', 'carbonised': 'carbonized', 'carbonises': 'carbonizes', 'carbonising': 'carbonizing', 'carolled': 'caroled', 'carolling': 'caroling', 'catalogue': 'catalog', 'catalogued': 'cataloged', 'catalogues': 'catalogs', 'cataloguing': 'cataloging', 'catalyse': 'catalyze', 'catalysed': 'catalyzed', 'catalyses': 'catalyzes', 'catalysing': 'catalyzing', 'categorise': 'categorize', 'categorised': 'categorized', 'categorises': 'categorizes', 'categorising': 'categorizing', 'cauterise': 'cauterize', 'cauterised': 'cauterized', 'cauterises': 'cauterizes', 'cauterising': 'cauterizing', 'cavilled': 'caviled', 'cavilling': 'caviling', 'centigramme': 'centigram', 'centigrammes': 'centigrams', 'centilitre': 'centiliter', 'centilitres': 'centiliters', 'centimetre': 'centimeter', 'centimetres': 'centimeters', 'centralise': 'centralize', 'centralised': 'centralized', 'centralises': 'centralizes', 'centralising': 'centralizing', 'centre': 'center', 'centred': 'centered', 'centrefold': 'centerfold', 'centrefolds': 'centerfolds', 'centrepiece': 'centerpiece', 'centrepieces': 'centerpieces', 'centres': 'centers', 'channelled': 'channeled', 'channelling': 'channeling', 'characterise': 'characterize', 'characterised': 'characterized', 'characterises': 'characterizes', 'characterising': 'characterizing', 'cheque': 'check', 'chequebook': 'checkbook', 'chequebooks': 'checkbooks', 'chequered': 'checkered', 'cheques': 'checks', 'chilli': 'chili', 'chimaera': 'chimera', 'chimaeras': 'chimeras', 'chiselled': 'chiseled', 'chiselling': 'chiseling', 'circularise': 'circularize', 'circularised': 'circularized', 'circularises': 'circularizes', 'circularising': 'circularizing', 'civilise': 'civilize', 'civilised': 'civilized', 'civilises': 'civilizes', 'civilising': 'civilizing', 'clamour': 'clamor', 'clamoured': 'clamored', 'clamouring': 'clamoring', 'clamours': 'clamors', 'clangour': 'clangor', 'clarinettist': 'clarinetist', 'clarinettists': 'clarinetists', 'collectivise': 'collectivize', 'collectivised': 'collectivized', 'collectivises': 'collectivizes', 'collectivising': 'collectivizing', 'colonisation': 'colonization', 'colonise': 'colonize', 'colonised': 'colonized', 'coloniser': 'colonizer', 'colonisers': 'colonizers', 'colonises': 'colonizes', 'colonising': 'colonizing', 'colour': 'color', 'colourant': 'colorant', 'colourants': 'colorants', 'coloured': 'colored', 'coloureds': 'coloreds', 'colourful': 'colorful', 'colourfully': 'colorfully', 'colouring': 'coloring', 'colourize': 'colorize', 'colourized': 'colorized', 'colourizes': 'colorizes', 'colourizing': 'colorizing', 'colourless': 'colorless', 'colours': 'colors', 'commercialise': 'commercialize', 'commercialised': 'commercialized', 'commercialises': 'commercializes', 'commercialising': 'commercializing', 'compartmentalise': 'compartmentalize', 'compartmentalised': 'compartmentalized', 'compartmentalises': 'compartmentalizes', 'compartmentalising': 'compartmentalizing', 'computerise': 'computerize', 'computerised': 'computerized', 'computerises': 'computerizes', 'computerising': 'computerizing', 'conceptualise': 'conceptualize', 'conceptualised': 'conceptualized', 'conceptualises': 'conceptualizes', 'conceptualising': 'conceptualizing', 'connexion': 'connection', 'connexions': 'connections', 'contextualise': 'contextualize', 'contextualised': 'contextualized', 'contextualises': 'contextualizes', 'contextualising': 'contextualizing', 'cosier': 'cozier', 'cosies': 'cozies', 'cosiest': 'coziest', 'cosily': 'cozily', 'cosiness': 'coziness', 'cosy': 'cozy', 'councillor': 'councilor', 'councillors': 'councilors', 'counselled': 'counseled', 'counselling': 'counseling', 'counsellor': 'counselor', 'counsellors': 'counselors', 'crenelated': 'crenellated', 'criminalise': 'criminalize', 'criminalised': 'criminalized', 'criminalises': 'criminalizes', 'criminalising': 'criminalizing', 'criticise': 'criticize', 'criticised': 'criticized', 'criticises': 'criticizes', 'criticising': 'criticizing', 'crueller': 'crueler', 'cruellest': 'cruelest', 'crystallisation': 'crystallization', 'crystallise': 'crystallize', 'crystallised': 'crystallized', 'crystallises': 'crystallizes', 'crystallising': 'crystallizing', 'cudgelled': 'cudgeled', 'cudgelling': 'cudgeling', 'customise': 'customize', 'customised': 'customized', 'customises': 'customizes', 'customising': 'customizing', 'cypher': 'cipher', 'cyphers': 'ciphers', 'decentralisation': 'decentralization', 'decentralise': 'decentralize', 'decentralised': 'decentralized', 'decentralises': 'decentralizes', 'decentralising': 'decentralizing', 'decriminalisation': 'decriminalization', 'decriminalise': 'decriminalize', 'decriminalised': 'decriminalized', 'decriminalises': 'decriminalizes', 'decriminalising': 'decriminalizing', 'defence': 'defense', 'defenceless': 'defenseless', 'defences': 'defenses', 'dehumanisation': 'dehumanization', 'dehumanise': 'dehumanize', 'dehumanised': 'dehumanized', 'dehumanises': 'dehumanizes', 'dehumanising': 'dehumanizing', 'demeanour': 'demeanor', 'demilitarisation': 'demilitarization', 'demilitarise': 'demilitarize', 'demilitarised': 'demilitarized', 'demilitarises': 'demilitarizes', 'demilitarising': 'demilitarizing', 'demobilisation': 'demobilization', 'demobilise': 'demobilize', 'demobilised': 'demobilized', 'demobilises': 'demobilizes', 'demobilising': 'demobilizing', 'democratisation': 'democratization', 'democratise': 'democratize', 'democratised': 'democratized', 'democratises': 'democratizes', 'democratising': 'democratizing', 'demonise': 'demonize', 'demonised': 'demonized', 'demonises': 'demonizes', 'demonising': 'demonizing', 'demoralisation': 'demoralization', 'demoralise': 'demoralize', 'demoralised': 'demoralized', 'demoralises': 'demoralizes', 'demoralising': 'demoralizing', 'denationalisation': 'denationalization', 'denationalise': 'denationalize', 'denationalised': 'denationalized', 'denationalises': 'denationalizes', 'denationalising': 'denationalizing', 'deodorise': 'deodorize', 'deodorised': 'deodorized', 'deodorises': 'deodorizes', 'deodorising': 'deodorizing', 'depersonalise': 'depersonalize', 'depersonalised': 'depersonalized', 'depersonalises': 'depersonalizes', 'depersonalising': 'depersonalizing', 'deputise': 'deputize', 'deputised': 'deputized', 'deputises': 'deputizes', 'deputising': 'deputizing', 'desensitisation': 'desensitization', 'desensitise': 'desensitize', 'desensitised': 'desensitized', 'desensitises': 'desensitizes', 'desensitising': 'desensitizing', 'destabilisation': 'destabilization', 'destabilise': 'destabilize', 'destabilised': 'destabilized', 'destabilises': 'destabilizes', 'destabilising': 'destabilizing', 'dialled': 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'pedophile', 'paedophiles': 'pedophiles', 'paedophilia': 'pedophilia', 'palaeolithic': 'paleolithic', 'palaeontologist': 'paleontologist', 'palaeontologists': 'paleontologists', 'palaeontology': 'paleontology', 'panelled': 'paneled', 'panelling': 'paneling', 'panellist': 'panelist', 'panellists': 'panelists', 'paralyse': 'paralyze', 'paralysed': 'paralyzed', 'paralyses': 'paralyzes', 'paralysing': 'paralyzing', 'parcelled': 'parceled', 'parcelling': 'parceling', 'parlour': 'parlor', 'parlours': 'parlors', 'particularise': 'particularize', 'particularised': 'particularized', 'particularises': 'particularizes', 'particularising': 'particularizing', 'passivisation': 'passivization', 'passivise': 'passivize', 'passivised': 'passivized', 'passivises': 'passivizes', 'passivising': 'passivizing', 'pasteurisation': 'pasteurization', 'pasteurise': 'pasteurize', 'pasteurised': 'pasteurized', 'pasteurises': 'pasteurizes', 'pasteurising': 'pasteurizing', 'patronise': 'patronize', 'patronised': 'patronized', 'patronises': 'patronizes', 'patronising': 'patronizing', 'patronisingly': 'patronizingly', 'pedalled': 'pedaled', 'pedalling': 'pedaling', 'pedestrianisation': 'pedestrianization', 'pedestrianise': 'pedestrianize', 'pedestrianised': 'pedestrianized', 'pedestrianises': 'pedestrianizes', 'pedestrianising': 'pedestrianizing', 'penalise': 'penalize', 'penalised': 'penalized', 'penalises': 'penalizes', 'penalising': 'penalizing', 'pencilled': 'penciled', 'pencilling': 'penciling', 'personalise': 'personalize', 'personalised': 'personalized', 'personalises': 'personalizes', 'personalising': 'personalizing', 'pharmacopoeia': 'pharmacopeia', 'pharmacopoeias': 'pharmacopeias', 'philosophise': 'philosophize', 'philosophised': 'philosophized', 'philosophises': 'philosophizes', 'philosophising': 'philosophizing', 'philtre': 'filter', 'philtres': 'filters', 'phoney': 'phony', 'plagiarise': 'plagiarize', 'plagiarised': 'plagiarized', 'plagiarises': 'plagiarizes', 'plagiarising': 'plagiarizing', 'plough': 'plow', 'ploughed': 'plowed', 'ploughing': 'plowing', 'ploughman': 'plowman', 'ploughmen': 'plowmen', 'ploughs': 'plows', 'ploughshare': 'plowshare', 'ploughshares': 'plowshares', 'polarisation': 'polarization', 'polarise': 'polarize', 'polarised': 'polarized', 'polarises': 'polarizes', 'polarising': 'polarizing', 'politicisation': 'politicization', 'politicise': 'politicize', 'politicised': 'politicized', 'politicises': 'politicizes', 'politicising': 'politicizing', 'popularisation': 'popularization', 'popularise': 'popularize', 'popularised': 'popularized', 'popularises': 'popularizes', 'popularising': 'popularizing', 'pouffe': 'pouf', 'pouffes': 'poufs', 'practise': 'practice', 'practised': 'practiced', 'practises': 'practices', 'practising': 'practicing', 'praesidium': 'presidium', 'praesidiums': 'presidiums', 'pressurisation': 'pressurization', 'pressurise': 'pressurize', 'pressurised': 'pressurized', 'pressurises': 'pressurizes', 'pressurising': 'pressurizing', 'pretence': 'pretense', 'pretences': 'pretenses', 'primaeval': 'primeval', 'prioritisation': 'prioritization', 'prioritise': 'prioritize', 'prioritised': 'prioritized', 'prioritises': 'prioritizes', 'prioritising': 'prioritizing', 'privatisation': 'privatization', 'privatisations': 'privatizations', 'privatise': 'privatize', 'privatised': 'privatized', 'privatises': 'privatizes', 'privatising': 'privatizing', 'professionalisation': 'professionalization', 'professionalise': 'professionalize', 'professionalised': 'professionalized', 'professionalises': 'professionalizes', 'professionalising': 'professionalizing', 'programme': 'program', 'programmes': 'programs', 'prologue': 'prolog', 'prologues': 'prologs', 'propagandise': 'propagandize', 'propagandised': 'propagandized', 'propagandises': 'propagandizes', 'propagandising': 'propagandizing', 'proselytise': 'proselytize', 'proselytised': 'proselytized', 'proselytiser': 'proselytizer', 'proselytisers': 'proselytizers', 'proselytises': 'proselytizes', 'proselytising': 'proselytizing', 'psychoanalyse': 'psychoanalyze', 'psychoanalysed': 'psychoanalyzed', 'psychoanalyses': 'psychoanalyzes', 'psychoanalysing': 'psychoanalyzing', 'publicise': 'publicize', 'publicised': 'publicized', 'publicises': 'publicizes', 'publicising': 'publicizing', 'pulverisation': 'pulverization', 'pulverise': 'pulverize', 'pulverised': 'pulverized', 'pulverises': 'pulverizes', 'pulverising': 'pulverizing', 'pummelled': 'pummel', 'pummelling': 'pummeled', 'pyjama': 'pajama', 'pyjamas': 'pajamas', 'pzazz': 'pizzazz', 'quarrelled': 'quarreled', 'quarrelling': 'quarreling', 'radicalise': 'radicalize', 'radicalised': 'radicalized', 'radicalises': 'radicalizes', 'radicalising': 'radicalizing', 'rancour': 'rancor', 'randomise': 'randomize', 'randomised': 'randomized', 'randomises': 'randomizes', 'randomising': 'randomizing', 'rationalisation': 'rationalization', 'rationalisations': 'rationalizations', 'rationalise': 'rationalize', 'rationalised': 'rationalized', 'rationalises': 'rationalizes', 'rationalising': 'rationalizing', 'ravelled': 'raveled', 'ravelling': 'raveling', 'realisable': 'realizable', 'realisation': 'realization', 'realisations': 'realizations', 'realise': 'realize', 'realised': 'realized', 'realises': 'realizes', 'realising': 'realizing', 'recognisable': 'recognizable', 'recognisably': 'recognizably', 'recognisance': 'recognizance', 'recognise': 'recognize', 'recognised': 'recognized', 'recognises': 'recognizes', 'recognising': 'recognizing', 'reconnoitre': 'reconnoiter', 'reconnoitred': 'reconnoitered', 'reconnoitres': 'reconnoiters', 'reconnoitring': 'reconnoitering', 'refuelled': 'refueled', 'refuelling': 'refueling', 'regularisation': 'regularization', 'regularise': 'regularize', 'regularised': 'regularized', 'regularises': 'regularizes', 'regularising': 'regularizing', 'remodelled': 'remodeled', 'remodelling': 'remodeling', 'remould': 'remold', 'remoulded': 'remolded', 'remoulding': 'remolding', 'remoulds': 'remolds', 'reorganisation': 'reorganization', 'reorganisations': 'reorganizations', 'reorganise': 'reorganize', 'reorganised': 'reorganized', 'reorganises': 'reorganizes', 'reorganising': 'reorganizing', 'revelled': 'reveled', 'reveller': 'reveler', 'revellers': 'revelers', 'revelling': 'reveling', 'revitalise': 'revitalize', 'revitalised': 'revitalized', 'revitalises': 'revitalizes', 'revitalising': 'revitalizing', 'revolutionise': 'revolutionize', 'revolutionised': 'revolutionized', 'revolutionises': 'revolutionizes', 'revolutionising': 'revolutionizing', 'rhapsodise': 'rhapsodize', 'rhapsodised': 'rhapsodized', 'rhapsodises': 'rhapsodizes', 'rhapsodising': 'rhapsodizing', 'rigour': 'rigor', 'rigours': 'rigors', 'ritualised': 'ritualized', 'rivalled': 'rivaled', 'rivalling': 'rivaling', 'romanticise': 'romanticize', 'romanticised': 'romanticized', 'romanticises': 'romanticizes', 'romanticising': 'romanticizing', 'rumour': 'rumor', 'rumoured': 'rumored', 'rumours': 'rumors', 'sabre': 'saber', 'sabres': 'sabers', 'saltpetre': 'saltpeter', 'sanitise': 'sanitize', 'sanitised': 'sanitized', 'sanitises': 'sanitizes', 'sanitising': 'sanitizing', 'satirise': 'satirize', 'satirised': 'satirized', 'satirises': 'satirizes', 'satirising': 'satirizing', 'saviour': 'savior', 'saviours': 'saviors', 'savour': 'savor', 'savoured': 'savored', 'savouries': 'savories', 'savouring': 'savoring', 'savours': 'savors', 'savoury': 'savory', 'scandalise': 'scandalize', 'scandalised': 'scandalized', 'scandalises': 'scandalizes', 'scandalising': 'scandalizing', 'sceptic': 'skeptic', 'sceptical': 'skeptical', 'sceptically': 'skeptically', 'scepticism': 'skepticism', 'sceptics': 'skeptics', 'sceptre': 'scepter', 'sceptres': 'scepters', 'scrutinise': 'scrutinize', 'scrutinised': 'scrutinized', 'scrutinises': 'scrutinizes', 'scrutinising': 'scrutinizing', 'secularisation': 'secularization', 'secularise': 'secularize', 'secularised': 'secularized', 'secularises': 'secularizes', 'secularising': 'secularizing', 'sensationalise': 'sensationalize', 'sensationalised': 'sensationalized', 'sensationalises': 'sensationalizes', 'sensationalising': 'sensationalizing', 'sensitise': 'sensitize', 'sensitised': 'sensitized', 'sensitises': 'sensitizes', 'sensitising': 'sensitizing', 'sentimentalise': 'sentimentalize', 'sentimentalised': 'sentimentalized', 'sentimentalises': 'sentimentalizes', 'sentimentalising': 'sentimentalizing', 'sepulchre': 'sepulcher', 'sepulchres': 'sepulchers', 'serialisation': 'serialization', 'serialisations': 'serializations', 'serialise': 'serialize', 'serialised': 'serialized', 'serialises': 'serializes', 'serialising': 'serializing', 'sermonise': 'sermonize', 'sermonised': 'sermonized', 'sermonises': 'sermonizes', 'sermonising': 'sermonizing', 'sheikh': 'sheik', 'shovelled': 'shoveled', 'shovelling': 'shoveling', 'shrivelled': 'shriveled', 'shrivelling': 'shriveling', 'signalise': 'signalize', 'signalised': 'signalized', 'signalises': 'signalizes', 'signalising': 'signalizing', 'signalled': 'signaled', 'signalling': 'signaling', 'smoulder': 'smolder', 'smouldered': 'smoldered', 'smouldering': 'smoldering', 'smoulders': 'smolders', 'snivelled': 'sniveled', 'snivelling': 'sniveling', 'snorkelled': 'snorkeled', 'snorkelling': 'snorkeling', 'snowplough': 'snowplow', 'snowploughs': 'snowplow', 'socialisation': 'socialization', 'socialise': 'socialize', 'socialised': 'socialized', 'socialises': 'socializes', 'socialising': 'socializing', 'sodomise': 'sodomize', 'sodomised': 'sodomized', 'sodomises': 'sodomizes', 'sodomising': 'sodomizing', 'solemnise': 'solemnize', 'solemnised': 'solemnized', 'solemnises': 'solemnizes', 'solemnising': 'solemnizing', 'sombre': 'somber', 'specialisation': 'specialization', 'specialisations': 'specializations', 'specialise': 'specialize', 'specialised': 'specialized', 'specialises': 'specializes', 'specialising': 'specializing', 'spectre': 'specter', 'spectres': 'specters', 'spiralled': 'spiraled', 'spiralling': 'spiraling', 'splendour': 'splendor', 'splendours': 'splendors', 'squirrelled': 'squirreled', 'squirrelling': 'squirreling', 'stabilisation': 'stabilization', 'stabilise': 'stabilize', 'stabilised': 'stabilized', 'stabiliser': 'stabilizer', 'stabilisers': 'stabilizers', 'stabilises': 'stabilizes', 'stabilising': 'stabilizing', 'standardisation': 'standardization', 'standardise': 'standardize', 'standardised': 'standardized', 'standardises': 'standardizes', 'standardising': 'standardizing', 'stencilled': 'stenciled', 'stencilling': 'stenciling', 'sterilisation': 'sterilization', 'sterilisations': 'sterilizations', 'sterilise': 'sterilize', 'sterilised': 'sterilized', 'steriliser': 'sterilizer', 'sterilisers': 'sterilizers', 'sterilises': 'sterilizes', 'sterilising': 'sterilizing', 'stigmatisation': 'stigmatization', 'stigmatise': 'stigmatize', 'stigmatised': 'stigmatized', 'stigmatises': 'stigmatizes', 'stigmatising': 'stigmatizing', 'storey': 'story', 'storeys': 'stories', 'subsidisation': 'subsidization', 'subsidise': 'subsidize', 'subsidised': 'subsidized', 'subsidiser': 'subsidizer', 'subsidisers': 'subsidizers', 'subsidises': 'subsidizes', 'subsidising': 'subsidizing', 'succour': 'succor', 'succoured': 'succored', 'succouring': 'succoring', 'succours': 'succors', 'sulphate': 'sulfate', 'sulphates': 'sulfates', 'sulphide': 'sulfide', 'sulphides': 'sulfides', 'sulphur': 'sulfur', 'sulphurous': 'sulfurous', 'summarise': 'summarize', 'summarised': 'summarized', 'summarises': 'summarizes', 'summarising': 'summarizing', 'swivelled': 'swiveled', 'swivelling': 'swiveling', 'symbolise': 'symbolize', 'symbolised': 'symbolized', 'symbolises': 'symbolizes', 'symbolising': 'symbolizing', 'sympathise': 'sympathize', 'sympathised': 'sympathized', 'sympathiser': 'sympathizer', 'sympathisers': 'sympathizers', 'sympathises': 'sympathizes', 'sympathising': 'sympathizing', 'synchronisation': 'synchronization', 'synchronise': 'synchronize', 'synchronised': 'synchronized', 'synchronises': 'synchronizes', 'synchronising': 'synchronizing', 'synthesise': 'synthesize', 'synthesised': 'synthesized', 'synthesiser': 'synthesizer', 'synthesisers': 'synthesizers', 'synthesises': 'synthesizes', 'synthesising': 'synthesizing', 'syphon': 'siphon', 'syphoned': 'siphoned', 'syphoning': 'siphoning', 'syphons': 'siphons', 'systematisation': 'systematization', 'systematise': 'systematize', 'systematised': 'systematized', 'systematises': 'systematizes', 'systematising': 'systematizing', 'tantalise': 'tantalize', 'tantalised': 'tantalized', 'tantalises': 'tantalizes', 'tantalising': 'tantalizing', 'tantalisingly': 'tantalizingly', 'tasselled': 'tasseled', 'technicolour': 'technicolor', 'temporise': 'temporize', 'temporised': 'temporized', 'temporises': 'temporizes', 'temporising': 'temporizing', 'tenderise': 'tenderize', 'tenderised': 'tenderized', 'tenderises': 'tenderizes', 'tenderising': 'tenderizing', 'terrorise': 'terrorize', 'terrorised': 'terrorized', 'terrorises': 'terrorizes', 'terrorising': 'terrorizing', 'theatre': 'theater', 'theatregoer': 'theatergoer', 'theatregoers': 'theatergoers', 'theatres': 'theaters', 'theorise': 'theorize', 'theorised': 'theorized', 'theorises': 'theorizes', 'theorising': 'theorizing', 'tonne': 'ton', 'tonnes': 'tons', 'towelled': 'toweled', 'towelling': 'toweling', 'toxaemia': 'toxemia', 'tranquillise': 'tranquilize', 'tranquillised': 'tranquilized', 'tranquilliser': 'tranquilizer', 'tranquillisers': 'tranquilizers', 'tranquillises': 'tranquilizes', 'tranquillising': 'tranquilizing', 'tranquillity': 'tranquility', 'tranquillize': 'tranquilize', 'tranquillized': 'tranquilized', 'tranquillizer': 'tranquilizer', 'tranquillizers': 'tranquilizers', 'tranquillizes': 'tranquilizes', 'tranquillizing': 'tranquilizing', 'tranquilly': 'tranquility', 'transistorised': 'transistorized', 'traumatise': 'traumatize', 'traumatised': 'traumatized', 'traumatises': 'traumatizes', 'traumatising': 'traumatizing', 'travelled': 'traveled', 'traveller': 'traveler', 'travellers': 'travelers', 'travelling': 'traveling', 'travelog': 'travelogue', 'travelogs': 'travelogues', 'trialled': 'trialed', 'trialling': 'trialing', 'tricolour': 'tricolor', 'tricolours': 'tricolors', 'trivialise': 'trivialize', 'trivialised': 'trivialized', 'trivialises': 'trivializes', 'trivialising': 'trivializing', 'tumour': 'tumor', 'tumours': 'tumors', 'tunnelled': 'tunneled', 'tunnelling': 'tunneling', 'tyrannise': 'tyrannize', 'tyrannised': 'tyrannized', 'tyrannises': 'tyrannizes', 'tyrannising': 'tyrannizing', 'tyre': 'tire', 'tyres': 'tires', 'unauthorised': 'unauthorized', 'uncivilised': 'uncivilized', 'underutilised': 'underutilized', 'unequalled': 'unequaled', 'unfavourable': 'unfavorable', 'unfavourably': 'unfavorably', 'unionisation': 'unionization', 'unionise': 'unionize', 'unionised': 'unionized', 'unionises': 'unionizes', 'unionising': 'unionizing', 'unorganised': 'unorganized', 'unravelled': 'unraveled', 'unravelling': 'unraveling', 'unrecognisable': 'unrecognizable', 'unrecognised': 'unrecognized', 'unrivalled': 'unrivaled', 'unsavoury': 'unsavory', 'untrammelled': 'untrammeled', 'urbanisation': 'urbanization', 'urbanise': 'urbanize', 'urbanised': 'urbanized', 'urbanises': 'urbanizes', 'urbanising': 'urbanizing', 'utilisable': 'utilizable', 'utilisation': 'utilization', 'utilise': 'utilize', 'utilised': 'utilized', 'utilises': 'utilizes', 'utilising': 'utilizing', 'valour': 'valor', 'vandalise': 'vandalize', 'vandalised': 'vandalized', 'vandalises': 'vandalizes', 'vandalising': 'vandalizing', 'vaporisation': 'vaporization', 'vaporise': 'vaporize', 'vaporised': 'vaporized', 'vaporises': 'vaporizes', 'vaporising': 'vaporizing', 'vapour': 'vapor', 'vapours': 'vapors', 'verbalise': 'verbalize', 'verbalised': 'verbalized', 'verbalises': 'verbalizes', 'verbalising': 'verbalizing', 'victimisation': 'victimization', 'victimise': 'victimize', 'victimised': 'victimized', 'victimises': 'victimizes', 'victimising': 'victimizing', 'videodisc': 'videodisk', 'videodiscs': 'videodisks', 'vigour': 'vigor', 'visualisation': 'visualization', 'visualisations': 'visualizations', 'visualise': 'visualize', 'visualised': 'visualized', 'visualises': 'visualizes', 'visualising': 'visualizing', 'vocalisation': 'vocalization', 'vocalisations': 'vocalizations', 'vocalise': 'vocalize', 'vocalised': 'vocalized', 'vocalises': 'vocalizes', 'vocalising': 'vocalizing', 'vulcanised': 'vulcanized', 'vulgarisation': 'vulgarization', 'vulgarise': 'vulgarize', 'vulgarised': 'vulgarized', 'vulgarises': 'vulgarizes', 'vulgarising': 'vulgarizing', 'waggon': 'wagon', 'waggons': 'wagons', 'watercolour': 'watercolor', 'watercolours': 'watercolors', 'weaselled': 'weaseled', 'weaselling': 'weaseling', 'westernisation': 'westernization', 'westernise': 'westernize', 'westernised': 'westernized', 'westernises': 'westernizes', 'westernising': 'westernizing', 'womanise': 'womanize', 'womanised': 'womanized', 'womaniser': 'womanizer', 'womanisers': 'womanizers', 'womanises': 'womanizes', 'womanising': 'womanizing', 'woollen': 'woolen', 'woollens': 'woolens', 'woollies': 'woolies', 'woolly': 'wooly', 'worshipped': 'worshiped', 'worshipper': 'worshiper', 'worshipping': 'worshiping', 'yodelled': 'yodeled', 'yodelling': 'yodeling', 'yoghourt': 'yogurt', 'yoghourts': 'yogurts', 'yoghurt': 'yogurt', 'yoghurts': 'yogurts'}

	# File: open_asr_leaderboard-main/normalizer/eval_utils.py
	import os
	import glob
	import json
	import evaluate
	from collections import defaultdict

	def read_manifest(manifest_path: str):
	data = []
	with open(manifest_path, 'r', encoding='utf-8') as f:
	for line in f:
	if len(line) > 0:
	datum = json.loads(line)
	data.append(datum)
	return data

	def write_manifest(references: list, transcriptions: list, model_id: str, dataset_path: str, dataset_name: str, split: str, audio_length: list=None, transcription_time: list=None):
	model_id = model_id.replace('/', '-')
	dataset_path = dataset_path.replace('/', '-')
	dataset_name = dataset_name.replace('/', '-')
	if len(references) != len(transcriptions):
	raise ValueError(f'The number of samples in `references` ({len(references)}) must match `transcriptions` ({len(transcriptions)}).')
	if audio_length is not None and len(audio_length) != len(references):
	raise ValueError(f'The number of samples in `audio_length` ({len(audio_length)}) must match `references` ({len(references)}).')
	if transcription_time is not None and len(transcription_time) != len(references):
	raise ValueError(f'The number of samples in `transcription_time` ({len(transcription_time)}) must match `references` ({len(references)}).')
	audio_length = audio_length if audio_length is not None else len(references) * [None]
	transcription_time = transcription_time if transcription_time is not None else len(references) * [None]
	basedir = './results/'
	if not os.path.exists(basedir):
	os.makedirs(basedir)
	manifest_path = os.path.join(basedir, f'MODEL_{model_id}_DATASET_{dataset_path}_{dataset_name}_{split}.jsonl')
	with open(manifest_path, 'w', encoding='utf-8') as f:
	for (idx, (text, transcript, audio_length, transcription_time)) in enumerate(zip(references, transcriptions, audio_length, transcription_time)):
	datum = {'audio_filepath': f'sample_{idx}', 'duration': audio_length, 'time': transcription_time, 'text': text, 'pred_text': transcript}
	f.write(f'{json.dumps(datum, ensure_ascii=False)}\n')
	return manifest_path

	def score_results(directory: str, model_id: str=None):
	if directory.endswith(os.pathsep):
	directory = directory[:-1]
	result_files = list(glob.glob(f'{directory}/*/.jsonl', recursive=True))
	result_files = list(sorted(result_files))
	if model_id is not None and model_id != '':
	print('Filtering models by id:', model_id)
	model_id = model_id.replace('/', '-')
	result_files = [fp for fp in result_files if model_id in fp]
	if len(result_files) == 0:
	raise ValueError(f'No result files found in {directory}')

	def parse_filepath(fp: str):
	model_index = fp.find('MODEL_')
	fp = fp[model_index:]
	ds_index = fp.find('DATASET_')
	model_id = fp[:ds_index].replace('MODEL_', '').rstrip('_')
	author_index = model_id.find('-')
	model_id = model_id[:author_index] + '/' + model_id[author_index + 1:]
	ds_fp = fp[ds_index:]
	dataset_id = ds_fp.replace('DATASET_', '').rstrip('.jsonl')
	return (model_id, dataset_id)
	results = {}
	wer_metric = evaluate.load('wer')
	for result_file in result_files:
	manifest = read_manifest(result_file)
	(model_id_of_file, dataset_id) = parse_filepath(result_file)
	references = [datum['text'] for datum in manifest]
	predictions = [datum['pred_text'] for datum in manifest]
	time = [datum['time'] for datum in manifest]
	duration = [datum['duration'] for datum in manifest]
	compute_rtfx = all(time) and all(duration)
	wer = wer_metric.compute(references=references, predictions=predictions)
	wer = round(100 * wer, 2)
	if compute_rtfx:
	audio_length = sum(duration)
	inference_time = sum(time)
	rtfx = round(sum(duration) / sum(time), 4)
	else:
	audio_length = inference_time = rtfx = None
	result_key = f'{model_id_of_file} \| {dataset_id}'
	results[result_key] = {'wer': wer, 'audio_length': audio_length, 'inference_time': inference_time, 'rtfx': rtfx}
	print('' 80)
	print('Results per dataset:')
	print('' 80)
	for (k, v) in results.items():
	metrics = f"{k}: WER = {v['wer']:0.2f} %"
	if v['rtfx'] is not None:
	metrics += f", RTFx = {v['rtfx']:0.2f}"
	print(metrics)
	composite_wer = defaultdict(float)
	composite_audio_length = defaultdict(float)
	composite_inference_time = defaultdict(float)
	count_entries = defaultdict(int)
	for (k, v) in results.items():
	key = k.split('\|')[0].strip()
	composite_wer[key] += v['wer']
	if v['rtfx'] is not None:
	composite_audio_length[key] += v['audio_length']
	composite_inference_time[key] += v['inference_time']
	else:
	composite_audio_length[key] = composite_inference_time[key] = None
	count_entries[key] += 1
	print()
	print('' 80)
	print('Composite Results:')
	print('' 80)
	for (k, v) in composite_wer.items():
	wer = v / count_entries[k]
	print(f'{k}: WER = {wer:0.2f} %')
	for k in composite_audio_length:
	if composite_audio_length[k] is not None:
	rtfx = composite_audio_length[k] / composite_inference_time[k]
	print(f'{k}: RTFx = {rtfx:0.2f}')
	print('' 80)
	return (composite_wer, results)

	# File: open_asr_leaderboard-main/normalizer/normalizer.py
	import re
	import unicodedata
	from fractions import Fraction
	from typing import Iterator, List, Match, Optional, Union
	from .english_abbreviations import english_spelling_normalizer
	import regex
	ADDITIONAL_DIACRITICS = {'œ': 'oe', 'Œ': 'OE', 'ø': 'o', 'Ø': 'O', 'æ': 'ae', 'Æ': 'AE', 'ß': 'ss', 'ẞ': 'SS', 'đ': 'd', 'Đ': 'D', 'ð': 'd', 'Ð': 'D', 'þ': 'th', 'Þ': 'th', 'ł': 'l', 'Ł': 'L'}

	def remove_symbols_and_diacritics(s: str, keep=''):

	def replace_character(char):
	if char in keep:
	return char
	elif char in ADDITIONAL_DIACRITICS:
	return ADDITIONAL_DIACRITICS[char]
	elif unicodedata.category(char) == 'Mn':
	return ''
	elif unicodedata.category(char)[0] in 'MSP':
	return ' '
	return char
	return ''.join((replace_character(c) for c in unicodedata.normalize('NFKD', s)))

	def remove_symbols(s: str):
	return ''.join((' ' if unicodedata.category(c)[0] in 'MSP' else c for c in unicodedata.normalize('NFKC', s)))

	class BasicTextNormalizer:

	def __init__(self, remove_diacritics: bool=False, split_letters: bool=False):
	self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
	self.split_letters = split_letters

	def __call__(self, s: str):
	s = s.lower()
	s = re.sub('[<\\[][^>\\]]*[>\\]]', '', s)
	s = re.sub('\$([^)]+?)\$', '', s)
	s = self.clean(s).lower()
	if self.split_letters:
	s = ' '.join(regex.findall('\\X', s, regex.U))
	s = re.sub('\\s+', ' ', s)
	return s

	class EnglishNumberNormalizer:

	def __init__(self):
	super().__init__()
	self.zeros = {'o', 'oh', 'zero'}
	self.ones = {name: i for (i, name) in enumerate(['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten', 'eleven', 'twelve', 'thirteen', 'fourteen', 'fifteen', 'sixteen', 'seventeen', 'eighteen', 'nineteen'], start=1)}
	self.ones_plural = {'sixes' if name == 'six' else name + 's': (value, 's') for (name, value) in self.ones.items()}
	self.ones_ordinal = {'zeroth': (0, 'th'), 'first': (1, 'st'), 'second': (2, 'nd'), 'third': (3, 'rd'), 'fifth': (5, 'th'), 'twelfth': (12, 'th'), **{name + ('h' if name.endswith('t') else 'th'): (value, 'th') for (name, value) in self.ones.items() if value > 3 and value != 5 and (value != 12)}}
	self.ones_suffixed = {self.ones_plural, self.ones_ordinal}
	self.tens = {'twenty': 20, 'thirty': 30, 'forty': 40, 'fifty': 50, 'sixty': 60, 'seventy': 70, 'eighty': 80, 'ninety': 90}
	self.tens_plural = {name.replace('y', 'ies'): (value, 's') for (name, value) in self.tens.items()}
	self.tens_ordinal = {name.replace('y', 'ieth'): (value, 'th') for (name, value) in self.tens.items()}
	self.tens_suffixed = {self.tens_plural, self.tens_ordinal}
	self.multipliers = {'hundred': 100, 'thousand': 1000, 'million': 1000000, 'billion': 1000000000, 'trillion': 1000000000000, 'quadrillion': 1000000000000000, 'quintillion': 1000000000000000000, 'sextillion': 1000000000000000000000, 'septillion': 1000000000000000000000000, 'octillion': 1000000000000000000000000000, 'nonillion': 1000000000000000000000000000000, 'decillion': 1000000000000000000000000000000000}
	self.multipliers_plural = {name + 's': (value, 's') for (name, value) in self.multipliers.items()}
	self.multipliers_ordinal = {name + 'th': (value, 'th') for (name, value) in self.multipliers.items()}
	self.multipliers_suffixed = {self.multipliers_plural, self.multipliers_ordinal}
	self.decimals = {self.ones, self.tens, *self.zeros}
	self.preceding_prefixers = {'minus': '-', 'negative': '-', 'plus': '+', 'positive': '+'}
	self.following_prefixers = {'pound': '£', 'pounds': '£', 'euro': '€', 'euros': '€', 'dollar': '$', 'dollars': '$', 'cent': '¢', 'cents': '¢'}
	self.prefixes = set(list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()))
	self.suffixers = {'per': {'cent': '%'}, 'percent': '%'}
	self.specials = {'and', 'double', 'triple', 'point'}
	self.words = {key for mapping in [self.zeros, self.ones, self.ones_suffixed, self.tens, self.tens_suffixed, self.multipliers, self.multipliers_suffixed, self.preceding_prefixers, self.following_prefixers, self.suffixers, self.specials] for key in mapping}
	self.literal_words = {'one', 'ones'}

	def process_words(self, words: List[str]) -> Iterator[str]:
	prefix: Optional[str] = None
	value: Optional[Union[str, int]] = None
	skip = False

	def to_fraction(s: str):
	try:
	return Fraction(s)
	except ValueError:
	return None

	def output(result: Union[str, int]):
	nonlocal prefix, value
	result = str(result)
	if prefix is not None:
	result = prefix + result
	value = None
	prefix = None
	return result
	if len(words) == 0:
	return
	for (i, current) in enumerate(words):
	prev = words[i - 1] if i != 0 else None
	next = words[i + 1] if i != len(words) - 1 else None
	if skip:
	skip = False
	continue
	next_is_numeric = next is not None and re.match('^\\d+(\\.\\d+)?$', next)
	has_prefix = current[0] in self.prefixes
	current_without_prefix = current[1:] if has_prefix else current
	if re.match('^\\d+(\\.\\d+)?$', current_without_prefix):
	f = to_fraction(current_without_prefix)
	if f is None:
	raise ValueError('Converting the fraction failed')
	if value is not None:
	if isinstance(value, str) and value.endswith('.'):
	value = str(value) + str(current)
	continue
	else:
	yield output(value)
	prefix = current[0] if has_prefix else prefix
	if f.denominator == 1:
	value = f.numerator
	else:
	value = current_without_prefix
	elif current not in self.words:
	if value is not None:
	yield output(value)
	yield output(current)
	elif current in self.zeros:
	value = str(value or '') + '0'
	elif current in self.ones:
	ones = self.ones[current]
	if value is None:
	value = ones
	elif isinstance(value, str) or prev in self.ones:
	if prev in self.tens and ones < 10:
	value = value[:-1] + str(ones)
	else:
	value = str(value) + str(ones)
	elif ones < 10:
	if value % 10 == 0:
	value += ones
	else:
	value = str(value) + str(ones)
	elif value % 100 == 0:
	value += ones
	else:
	value = str(value) + str(ones)
	elif current in self.ones_suffixed:
	(ones, suffix) = self.ones_suffixed[current]
	if value is None:
	yield output(str(ones) + suffix)
	elif isinstance(value, str) or prev in self.ones:
	if prev in self.tens and ones < 10:
	yield output(value[:-1] + str(ones) + suffix)
	else:
	yield output(str(value) + str(ones) + suffix)
	elif ones < 10:
	if value % 10 == 0:
	yield output(str(value + ones) + suffix)
	else:
	yield output(str(value) + str(ones) + suffix)
	elif value % 100 == 0:
	yield output(str(value + ones) + suffix)
	else:
	yield output(str(value) + str(ones) + suffix)
	value = None
	elif current in self.tens:
	tens = self.tens[current]
	if value is None:
	value = tens
	elif isinstance(value, str):
	value = str(value) + str(tens)
	elif value % 100 == 0:
	value += tens
	else:
	value = str(value) + str(tens)
	elif current in self.tens_suffixed:
	(tens, suffix) = self.tens_suffixed[current]
	if value is None:
	yield output(str(tens) + suffix)
	elif isinstance(value, str):
	yield output(str(value) + str(tens) + suffix)
	elif value % 100 == 0:
	yield output(str(value + tens) + suffix)
	else:
	yield output(str(value) + str(tens) + suffix)
	elif current in self.multipliers:
	multiplier = self.multipliers[current]
	if value is None:
	value = multiplier
	elif isinstance(value, str) or value == 0:
	f = to_fraction(value)
	p = f * multiplier if f is not None else None
	if f is not None and p.denominator == 1:
	value = p.numerator
	else:
	yield output(value)
	value = multiplier
	else:
	before = value // 1000 * 1000
	residual = value % 1000
	value = before + residual * multiplier
	elif current in self.multipliers_suffixed:
	(multiplier, suffix) = self.multipliers_suffixed[current]
	if value is None:
	yield output(str(multiplier) + suffix)
	elif isinstance(value, str):
	f = to_fraction(value)
	p = f * multiplier if f is not None else None
	if f is not None and p.denominator == 1:
	yield output(str(p.numerator) + suffix)
	else:
	yield output(value)
	yield output(str(multiplier) + suffix)
	else:
	before = value // 1000 * 1000
	residual = value % 1000
	value = before + residual * multiplier
	yield output(str(value) + suffix)
	value = None
	elif current in self.preceding_prefixers:
	if value is not None:
	yield output(value)
	if next in self.words or next_is_numeric:
	prefix = self.preceding_prefixers[current]
	else:
	yield output(current)
	elif current in self.following_prefixers:
	if value is not None:
	prefix = self.following_prefixers[current]
	yield output(value)
	else:
	yield output(current)
	elif current in self.suffixers:
	if value is not None:
	suffix = self.suffixers[current]
	if isinstance(suffix, dict):
	if next in suffix:
	yield output(str(value) + suffix[next])
	skip = True
	else:
	yield output(value)
	yield output(current)
	else:
	yield output(str(value) + suffix)
	else:
	yield output(current)
	elif current in self.specials:
	if next not in self.words and (not next_is_numeric):
	if value is not None:
	yield output(value)
	yield output(current)
	elif current == 'and':
	if prev not in self.multipliers:
	if value is not None:
	yield output(value)
	yield output(current)
	elif current == 'double' or current == 'triple':
	if next in self.ones or next in self.zeros:
	repeats = 2 if current == 'double' else 3
	ones = self.ones.get(next, 0)
	value = str(value or '') + str(ones) * repeats
	skip = True
	else:
	if value is not None:
	yield output(value)
	yield output(current)
	elif current == 'point':
	if next in self.decimals or next_is_numeric:
	value = str(value or '') + '.'
	else:
	raise ValueError(f'Unexpected token: {current}')
	else:
	raise ValueError(f'Unexpected token: {current}')
	if value is not None:
	yield output(value)

	def preprocess(self, s: str):
	results = []
	segments = re.split('\\band\\s+a\\s+half\\b', s)
	for (i, segment) in enumerate(segments):
	if len(segment.strip()) == 0:
	continue
	if i == len(segments) - 1:
	results.append(segment)
	else:
	results.append(segment)
	last_word = segment.rsplit(maxsplit=2)[-1]
	if last_word in self.decimals or last_word in self.multipliers:
	results.append('point five')
	else:
	results.append('and a half')
	s = ' '.join(results)
	s = re.sub('([a-z])([0-9])', '\\1 \\2', s)
	s = re.sub('([0-9])([a-z])', '\\1 \\2', s)
	s = re.sub('([0-9])\\s+(st\|nd\|rd\|th\|s)\\b', '\\1\\2', s)
	return s

	def postprocess(self, s: str):

	def combine_cents(m: Match):
	try:
	currency = m.group(1)
	integer = m.group(2)
	cents = int(m.group(3))
	return f'{currency}{integer}.{cents:02d}'
	except ValueError:
	return m.string

	def extract_cents(m: Match):
	try:
	return f'¢{int(m.group(1))}'
	except ValueError:
	return m.string
	s = re.sub('([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\\b', combine_cents, s)
	s = re.sub('[€£$]0.([0-9]{1,2})\\b', extract_cents, s)
	s = re.sub('\\b1(s?)\\b', 'one\\1', s)
	return s

	def __call__(self, s: str):
	s = self.preprocess(s)
	s = ' '.join((word for word in self.process_words(s.split()) if word is not None))
	s = self.postprocess(s)
	return s

	class EnglishSpellingNormalizer:

	def __init__(self, english_spelling_mapping):
	self.mapping = english_spelling_mapping

	def __call__(self, s: str):
	return ' '.join((self.mapping.get(word, word) for word in s.split()))

	class EnglishTextNormalizer:

	def __init__(self, english_spelling_mapping=english_spelling_normalizer):
	self.ignore_patterns = '\\b(hmm\|mm\|mhm\|mmm\|uh\|um)\\b'
	self.replacers = {"\\bwon't\\b": 'will not', "\\bcan't\\b": 'can not', "\\blet's\\b": 'let us', "\\bain't\\b": 'aint', "\\by'all\\b": 'you all', '\\bwanna\\b': 'want to', '\\bgotta\\b': 'got to', '\\bgonna\\b': 'going to', "\\bi'ma\\b": 'i am going to', '\\bimma\\b': 'i am going to', '\\bwoulda\\b': 'would have', '\\bcoulda\\b': 'could have', '\\bshoulda\\b': 'should have', "\\bma'am\\b": 'madam', '\\bmr\\b': 'mister ', '\\bmrs\\b': 'missus ', '\\bst\\b': 'saint ', '\\bdr\\b': 'doctor ', '\\bprof\\b': 'professor ', '\\bcapt\\b': 'captain ', '\\bgov\\b': 'governor ', '\\bald\\b': 'alderman ', '\\bgen\\b': 'general ', '\\bsen\\b': 'senator ', '\\brep\\b': 'representative ', '\\bpres\\b': 'president ', '\\brev\\b': 'reverend ', '\\bhon\\b': 'honorable ', '\\basst\\b': 'assistant ', '\\bassoc\\b': 'associate ', '\\blt\\b': 'lieutenant ', '\\bcol\\b': 'colonel ', '\\bjr\\b': 'junior ', '\\bsr\\b': 'senior ', '\\besq\\b': 'esquire ', "'d been\\b": ' had been', "'s been\\b": ' has been', "'d gone\\b": ' had gone', "'s gone\\b": ' has gone', "'d done\\b": ' had done', "'s got\\b": ' has got', "n't\\b": ' not', "'re\\b": ' are', "'s\\b": ' is', "'d\\b": ' would', "'ll\\b": ' will', "'t\\b": ' not', "'ve\\b": ' have', "'m\\b": ' am'}
	self.standardize_numbers = EnglishNumberNormalizer()
	self.standardize_spellings = EnglishSpellingNormalizer(english_spelling_mapping)

	def __call__(self, s: str):
	s = s.lower()
	s = re.sub('[<\\[][^>\\]]*[>\\]]', '', s)
	s = re.sub('\$([^)]+?)\$', '', s)
	s = re.sub(self.ignore_patterns, '', s)
	s = re.sub("\\s+'", "'", s)
	for (pattern, replacement) in self.replacers.items():
	s = re.sub(pattern, replacement, s)
	s = re.sub('(\\d),(\\d)', '\\1\\2', s)
	s = re.sub('\\.([^0-9]\|$)', ' \\1', s)
	s = remove_symbols_and_diacritics(s, keep='.%$¢€£')
	s = self.standardize_numbers(s)
	s = self.standardize_spellings(s)
	s = re.sub('[.$¢€£]([^0-9])', ' \\1', s)
	s = re.sub('([^0-9])%', '\\1 ', s)
	s = re.sub('\\s+', ' ', s)
	return s

	# File: open_asr_leaderboard-main/speechbrain/run_eval.py
	""""""
	import argparse
	import time
	import evaluate
	from normalizer import data_utils
	from tqdm import tqdm
	import torch
	import speechbrain.inference.ASR as ASR
	from speechbrain.utils.data_utils import batch_pad_right
	import os

	def get_model(speechbrain_repository: str, speechbrain_pretrained_class_name: str, **kwargs):
	run_opt_defaults = {'device': 'cpu', 'data_parallel_count': -1, 'data_parallel_backend': False, 'distributed_launch': False, 'distributed_backend': 'nccl', 'jit_module_keys': None}
	run_opts = {run_opt_defaults, kwargs}
	kwargs = {'source': f'{speechbrain_repository}', 'savedir': f'pretrained_models/{speechbrain_repository}', 'run_opts': run_opts}
	try:
	model_class = getattr(ASR, speechbrain_pretrained_class_name)
	except AttributeError:
	raise AttributeError(f'SpeechBrain Pretrained class: {speechbrain_pretrained_class_name} not found in pretrained.py')
	return model_class.from_hparams(**kwargs)

	def main(args):
	if args.device == -1:
	device = 'cpu'
	else:
	device = f'cuda:{args.device}'
	model = get_model(args.source, args.speechbrain_pretrained_class_name, device=device)

	def benchmark(batch):
	audios = [torch.from_numpy(sample['array']) for sample in batch['audio']]
	minibatch_size = len(audios)
	start_time = time.time()
	(audios, audio_lens) = batch_pad_right(audios)
	audios = audios.to(device)
	audio_lens = audio_lens.to(device)
	(predictions, _) = model.transcribe_batch(audios, audio_lens)
	runtime = time.time() - start_time
	batch['transcription_time_s'] = minibatch_size * [runtime / minibatch_size]
	batch['predictions'] = [data_utils.normalizer(pred) for pred in predictions]
	batch['references'] = batch['norm_text']
	return batch
	if args.warmup_steps is not None:
	dataset = data_utils.load_data(args)
	dataset = data_utils.prepare_data(dataset)
	num_warmup_samples = args.warmup_steps * args.batch_size
	if args.streaming:
	warmup_dataset = dataset.take(num_warmup_samples)
	else:
	warmup_dataset = dataset.select(range(min(num_warmup_samples, len(dataset))))
	warmup_dataset = iter(warmup_dataset.map(benchmark, batch_size=args.batch_size, batched=True))
	for _ in tqdm(warmup_dataset, desc='Warming up...'):
	continue
	dataset = data_utils.load_data(args)
	if args.max_eval_samples is not None and args.max_eval_samples > 0:
	print(f'Subsampling dataset to first {args.max_eval_samples} samples!')
	if args.streaming:
	dataset = dataset.take(args.max_eval_samples)
	else:
	dataset = dataset.select(range(min(args.max_eval_samples, len(dataset))))
	dataset = data_utils.prepare_data(dataset)
	dataset = dataset.map(benchmark, batch_size=args.batch_size, batched=True, remove_columns=['audio'])
	all_results = {'audio_length_s': [], 'transcription_time_s': [], 'predictions': [], 'references': []}
	result_iter = iter(dataset)
	for result in tqdm(result_iter, desc='Samples...'):
	for key in all_results:
	all_results[key].append(result[key])
	manifest_path = data_utils.write_manifest(all_results['references'], all_results['predictions'], args.source, args.dataset_path, args.dataset, args.split, audio_length=all_results['audio_length_s'], transcription_time=all_results['transcription_time_s'])
	print('Results saved at path:', os.path.abspath(manifest_path))
	wer_metric = evaluate.load('wer')
	wer = wer_metric.compute(references=all_results['references'], predictions=all_results['predictions'])
	wer = round(100 * wer, 2)
	rtfx = round(sum(all_results['audio_length_s']) / sum(all_results['transcription_time_s']), 2)
	print('WER:', wer, '%', 'RTFx:', rtfx)
	if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--source', type=str, required=True, help='SpeechBrain model repository. E.g. `asr-crdnn-rnnlm-librispeech`')
	parser.add_argument('--speechbrain_pretrained_class_name', type=str, required=True, help='SpeechBrain pretrained class name. E.g. `EncoderASR`')
	parser.add_argument('--dataset_path', type=str, default='hf-audio/esb-datasets-test-only-sorted', help='Dataset path. By default, it is `esb/datasets`')
	parser.add_argument('--dataset', type=str, required=True, help="Dataset name. E.g. `'librispeech_asr` for the LibriSpeech ASR dataset, or `'common_voice'` for Common Voice. The full list of dataset names can be found at `https://huggingface.co/datasets/esb/datasets`")
	parser.add_argument('--split', type=str, default='test', help="Split of the dataset. E.g. `'validation`' for the dev split, or `'test'` for the test split.")
	parser.add_argument('--device', type=int, default=-1, help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.')
	parser.add_argument('--batch_size', type=int, default=16, help='Number of samples to go through each streamed batch.')
	parser.add_argument('--max_eval_samples', type=int, default=None, help='Number of samples to be evaluated. Put a lower number e.g. 64 for testing this script.')
	parser.add_argument('--no-streaming', dest='streaming', action='store_false', help="Choose whether you'd like to download the entire dataset or stream it during the evaluation.")
	parser.add_argument('--warmup_steps', type=int, default=5, help='Number of warm-up steps to run before launching the timed runs.')
	args = parser.parse_args()
	parser.set_defaults(streaming=True)
	main(args)

	# File: open_asr_leaderboard-main/transformers/run_eval.py
	import argparse
	import os
	import torch
	from torch.nn.attention import sdpa_kernel, SDPBackend
	from transformers import AutoConfig, AutoModelForSpeechSeq2Seq, AutoModelForCTC, AutoProcessor, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
	import evaluate
	from normalizer import data_utils
	import time
	from tqdm import tqdm
	wer_metric = evaluate.load('wer')
	torch.set_float32_matmul_precision('high')

	def main(args):
	config = AutoConfig.from_pretrained(args.model_id)
	cls_model = AutoModelForSpeechSeq2Seq if type(config) in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING else AutoModelForCTC
	model = cls_model.from_pretrained(args.model_id, torch_dtype=torch.bfloat16, attn_implementation='sdpa').to(args.device)
	processor = AutoProcessor.from_pretrained(args.model_id)
	model_input_name = processor.model_input_names[0]
	if model.can_generate():
	gen_kwargs = {'max_new_tokens': args.max_new_tokens}
	if getattr(model.generation_config, 'is_multilingual'):
	gen_kwargs['language'] = 'en'
	gen_kwargs['task'] = 'transcribe'
	elif args.max_new_tokens:
	raise ValueError('`max_new_tokens` should only be set for auto-regressive models, but got a CTC model.')
	if args.torch_compile:
	model.forward = torch.compile(model.forward, mode=args.compile_mode, fullgraph=True)
	if model.can_generate():
	model.generation_config.cache_implementation = 'static'

	def benchmark(batch, min_new_tokens=None):
	audios = [audio['array'] for audio in batch['audio']]
	minibatch_size = len(audios)
	start_time = time.time()
	padding_size = None
	if minibatch_size != args.batch_size and args.torch_compile:
	padding_size = args.batch_size - minibatch_size
	padding_audios = [audios[-1] for _ in range(padding_size)]
	audios.extend(padding_audios)
	if not model.can_generate():
	inputs = processor(audios, sampling_rate=16000, truncation=False, padding='longest', return_tensors='pt', return_attention_mask=True)
	else:
	inputs = processor(audios, sampling_rate=16000, return_tensors='pt', device=args.device)
	inputs = inputs.to(args.device)
	inputs[model_input_name] = inputs[model_input_name].to(torch.bfloat16)
	with sdpa_kernel(SDPBackend.MATH if args.torch_compile else SDPBackend.FLASH_ATTENTION):
	if model.can_generate():
	pred_ids = model.generate(inputs, gen_kwargs, min_new_tokens=min_new_tokens)
	else:
	with torch.no_grad():
	logits = model(**inputs).logits
	pred_ids = logits.argmax(-1)
	if padding_size is not None:
	pred_ids = pred_ids[:-padding_size, ...]
	pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True)
	runtime = time.time() - start_time
	batch['transcription_time_s'] = minibatch_size * [runtime / minibatch_size]
	batch['predictions'] = [data_utils.normalizer(pred) for pred in pred_text]
	batch['references'] = batch['norm_text']
	return batch
	if args.warmup_steps is not None:
	dataset = data_utils.load_data(args)
	dataset = data_utils.prepare_data(dataset)
	num_warmup_samples = args.warmup_steps * args.batch_size
	if args.streaming:
	warmup_dataset = dataset.take(num_warmup_samples)
	else:
	warmup_dataset = dataset.select(range(min(num_warmup_samples, len(dataset))))
	warmup_dataset = iter(warmup_dataset.map(benchmark, batch_size=args.batch_size, batched=True, fn_kwargs={'min_new_tokens': args.max_new_tokens}))
	for _ in tqdm(warmup_dataset, desc='Warming up...'):
	continue
	dataset = data_utils.load_data(args)
	if args.max_eval_samples is not None and args.max_eval_samples > 0:
	print(f'Subsampling dataset to first {args.max_eval_samples} samples!')
	if args.streaming:
	dataset = dataset.take(args.max_eval_samples)
	else:
	dataset = dataset.select(range(min(args.max_eval_samples, len(dataset))))
	dataset = data_utils.prepare_data(dataset)
	dataset = dataset.map(benchmark, batch_size=args.batch_size, batched=True, remove_columns=['audio'])
	all_results = {'audio_length_s': [], 'transcription_time_s': [], 'predictions': [], 'references': []}
	result_iter = iter(dataset)
	for result in tqdm(result_iter, desc='Samples...'):
	for key in all_results:
	all_results[key].append(result[key])
	manifest_path = data_utils.write_manifest(all_results['references'], all_results['predictions'], args.model_id, args.dataset_path, args.dataset, args.split, audio_length=all_results['audio_length_s'], transcription_time=all_results['transcription_time_s'])
	print('Results saved at path:', os.path.abspath(manifest_path))
	wer = wer_metric.compute(references=all_results['references'], predictions=all_results['predictions'])
	wer = round(100 * wer, 2)
	rtfx = round(sum(all_results['audio_length_s']) / sum(all_results['transcription_time_s']), 2)
	print('WER:', wer, '%', 'RTFx:', rtfx)
	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_path', type=str, default='esb/datasets', help='Dataset path. By default, it is `esb/datasets`')
	parser.add_argument('--dataset', type=str, required=True, help="Dataset name. E.g. `'librispeech_asr` for the LibriSpeech ASR dataset, or `'common_voice'` for Common Voice. The full list of dataset names can be found at `https://huggingface.co/datasets/esb/datasets`")
	parser.add_argument('--split', type=str, default='test', help="Split of the dataset. E.g. `'validation`' for the dev split, or `'test'` for the test split.")
	parser.add_argument('--device', type=int, default=-1, help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.')
	parser.add_argument('--batch_size', type=int, default=16, help='Number of samples to go through each streamed batch.')
	parser.add_argument('--max_eval_samples', type=int, default=None, help='Number of samples to be evaluated. Put a lower number e.g. 64 for testing this script.')
	parser.add_argument('--no-streaming', dest='streaming', action='store_false', help="Choose whether you'd like to download the entire dataset or stream it during the evaluation.")
	parser.add_argument('--max_new_tokens', type=int, default=None, help='Maximum number of tokens to generate (for auto-regressive models).')
	parser.add_argument('--torch_compile', action='store_true', help='Whether to JIT compile the forward pass of the model.')
	parser.add_argument('--compile_mode', type=str, default='max-autotune', help="Mode for torch compiling model forward pass. Can be either 'default', 'reduce-overhead', 'max-autotune' or 'max-autotune-no-cudagraphs'.")
	parser.add_argument('--warmup_steps', type=int, default=10, help='Number of warm-up steps to run before launching the timed runs.')
	args = parser.parse_args()
	parser.set_defaults(streaming=False)
	main(args)