


"""simple docstring"""






import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def      lowerCamelCase       (     _UpperCamelCase		:      Dataset       ,			_UpperCamelCase		:      Dict[str, str]							)		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						str      							=					args.log_outputs
							__UpperCAmelCase   :						Optional[Any]      							=					"""_""".join(args.dataset.split("""/"""							) + [args.config, args.split]							)

							# load metric
							__UpperCAmelCase   :						Optional[int]      							=					load_metric("""wer"""							)
							__UpperCAmelCase   :						str      							=					load_metric("""cer"""							)

							# compute metrics
							__UpperCAmelCase   :						Optional[Any]      							=					wer.compute(references=result["""target"""]       ,			predictions=result["""prediction"""]							)
							__UpperCAmelCase   :						str      							=					cer.compute(references=result["""target"""]       ,			predictions=result["""prediction"""]							)

							# print & log results
							__UpperCAmelCase   :						Tuple      							=					f'''WER: {wer_result}\nCER: {cer_result}'''
							print(_UpperCamelCase							)

							with open(f'''{dataset_id}_eval_results.txt'''       ,			"""w"""							) as f:
														f.write(_UpperCamelCase							)

							# log all results in text file. Possibly interesting for analysis
							if log_outputs is not None:
														__UpperCAmelCase   :						Dict      							=					f'''log_{dataset_id}_predictions.txt'''
														__UpperCAmelCase   :						Union[str, Any]      							=					f'''log_{dataset_id}_targets.txt'''

														with open(_UpperCamelCase       ,			"""w"""							) as p, open(_UpperCamelCase       ,			"""w"""							) as t:
																					# mapping function to write output
																					def write_to_file(_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Optional[int]							):
																												p.write(f'''{i}''' + """\n"""							)
																												p.write(batch["""prediction"""] + """\n"""							)
																												t.write(f'''{i}''' + """\n"""							)
																												t.write(batch["""target"""] + """\n"""							)

																					result.map(_UpperCamelCase       ,			with_indices=_UpperCamelCase							)
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

							__UpperCAmelCase   :						List[str]      							=					re.sub(_UpperCamelCase       ,			""""""       ,			text.lower()							)

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

							for t in token_sequences_to_ignore:
														__UpperCAmelCase   :						Union[str, Any]      							=					""" """.join(text.split(_UpperCamelCase							)							)

							return text
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						List[str]      							=					load_dataset(args.dataset       ,			args.config       ,			split=args.split       ,			use_auth_token=_UpperCamelCase							)

							# for testing: only process the first two examples as a test
							# dataset = dataset.select(range(10))

							# load processor
							__UpperCAmelCase   :						Tuple      							=					AutoFeatureExtractor.from_pretrained(args.model_id							)
							__UpperCAmelCase   :						str      							=					feature_extractor.sampling_rate

							# resample audio
							__UpperCAmelCase   :						int      							=					dataset.cast_column("""audio"""       ,			Audio(sampling_rate=_UpperCamelCase							)							)

							# load eval pipeline
							if args.device is None:
														__UpperCAmelCase   :						Any      							=					0 if torch.cuda.is_available() else -1
							__UpperCAmelCase   :						Tuple      							=					pipeline("""automatic-speech-recognition"""       ,			model=args.model_id       ,			device=args.device							)

							# map function to decode audio
							def map_to_pred(_UpperCamelCase		:      Union[str, Any]							):
														__UpperCAmelCase   :						int      							=					asr(
														    batch["""audio"""]["""array"""]       ,			chunk_length_s=args.chunk_length_s       ,			stride_length_s=args.stride_length_s							)

														__UpperCAmelCase   :						List[Any]      							=					prediction["""text"""]
														__UpperCAmelCase   :						Optional[int]      							=					normalize_text(batch["""sentence"""]							)
														return batch

							# run inference on all examples
							__UpperCAmelCase   :						Union[str, Any]      							=					dataset.map(_UpperCamelCase       ,			remove_columns=dataset.column_names							)

							# compute and log_results
							# do not change function below
							log_results(_UpperCamelCase       ,			_UpperCamelCase							)


if __name__ == "__main__":
							UpperCAmelCase    :     Tuple   						=		argparse.ArgumentParser()

							parser.add_argument(
							    '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
							)
							parser.add_argument(
							    '--dataset',
							    type=str,
							    required=True,
							    help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
							)
							parser.add_argument(
							    '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
							)
							parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
							parser.add_argument(
							    '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
							)
							parser.add_argument(
							    '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
							)
							parser.add_argument(
							    '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
							)
							parser.add_argument(
							    '--device',
							    type=int,
							    default=None,
							    help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
							)
							UpperCAmelCase    :     Any   						=		parser.parse_args()

							main(args)




"""simple docstring"""






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
							import ctypes
							import msvcrt  # noqa





							class      lowerCamelCase__   (     ctypes.Structure				):







												"""simple docstring"""


												__a	       =							[("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def      lowerCamelCase       (     )		->       Optional[int]:

							'''simple docstring'''



							if os.name == "nt":
														__UpperCAmelCase   :						Dict      							=					CursorInfo()
														__UpperCAmelCase   :						Any      							=					ctypes.windll.kernelaa.GetStdHandle(-1_1							)
														ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
														__UpperCAmelCase   :						Tuple      							=					False
														ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
							elif os.name == "posix":
														sys.stdout.write("""\033[?25l"""							)
														sys.stdout.flush()
def      lowerCamelCase       (     )		->       Optional[int]:

							'''simple docstring'''



							if os.name == "nt":
														__UpperCAmelCase   :						str      							=					CursorInfo()
														__UpperCAmelCase   :						int      							=					ctypes.windll.kernelaa.GetStdHandle(-1_1							)
														ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
														__UpperCAmelCase   :						Union[str, Any]      							=					True
														ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
							elif os.name == "posix":
														sys.stdout.write("""\033[?25h"""							)
														sys.stdout.flush()
@contextmanager
def      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



							try:
														hide_cursor()
														yield
							finally:
														show_cursor()
