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wav2vec2-xls-r-300m-hebrew

Automatic Speech Recognition Transformers PyTorch Safetensors Hebrew wav2vec2 generated_from_trainer hf-asr-leaderboard robust-speech-event Eval Results Inference Endpoints

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wav2vec2-xls-r-300m-hebrew / run_train.py

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	# !/usr/bin/env python
	# coding=utf-8
	import functools
	import json
	import logging
	import os
	import re
	import sys
	import warnings
	from dataclasses import dataclass, field
	from typing import Any, Callable, Dict, List, Optional, Union

	import datasets
	import numpy as np
	import torch
	import torchaudio
	from datasets import DatasetDict, ReadInstruction, load_dataset, load_metric, concatenate_datasets

	try:
	import bitsandbytes as bnb

	BNB_AVAILABLE = True
	except:
	BNB_AVAILABLE = False
	try:
	import wandb

	WANDB_AVAILABLE = True
	except:
	WANDB_AVAILABLE = False
	import transformers
	from transformers import (
	AutoConfig,
	AutoFeatureExtractor,
	AutoModelForCTC,
	AutoTokenizer,
	HfArgumentParser,
	Trainer,
	TrainerCallback, TrainingArguments,
	Wav2Vec2Processor,
	set_seed,
	)

	try:
	from torch_audiomentations import (
	Compose,
	AddGaussianNoise,
	AddGaussianSNR,
	ClippingDistortion,
	FrequencyMask,
	Gain,
	LoudnessNormalization,
	Normalize,
	PitchShift,
	PolarityInversion,
	Shift,
	TimeMask,
	TimeStretch,
	)

	AUDIOMENTATIONS_AVAILABLE = True
	except:
	AUDIOMENTATIONS_AVAILABLE = False
	try:
	from transformers import AutoProcessor
	except:
	pass
	from transformers.trainer_pt_utils import get_parameter_names
	from transformers.trainer_utils import get_last_checkpoint, is_main_process
	from transformers.utils import check_min_version
	from transformers.utils.versions import require_version

	# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
	check_min_version("4.16.0")

	require_version(
	"datasets>=1.13.3",
	"To fix: pip install -r examples/pytorch/text-classification/requirements.txt",
	)

	logger = logging.getLogger(__name__)


	def list_field(default=None, metadata=None):
	return field(default_factory=lambda: default, metadata=metadata)


	@dataclass
	class ModelArguments:
	"""
	Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
	"""

	model_name_or_path: str = field(
	metadata={
	"help": "Path to pretrained model or model identifier from huggingface.co/models"
	}
	)
	tokenizer_name_or_path: Optional[str] = field(
	default=None,
	metadata={
	"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"
	},
	)
	cache_dir: Optional[str] = field(
	default=None,
	metadata={
	"help": "Where do you want to store the pretrained models downloaded from huggingface.co"
	},
	)
	freeze_feature_encoder: bool = field(
	default=True,
	metadata={"help": "Whether to freeze the feature encoder layers of the model."},
	)
	attention_dropout: float = field(
	default=0.0,
	metadata={"help": "The dropout ratio for the attention probabilities."},
	)
	activation_dropout: float = field(
	default=0.0,
	metadata={
	"help": "The dropout ratio for activations inside the fully connected layer."
	},
	)
	feat_proj_dropout: float = field(
	default=0.0, metadata={"help": "The dropout ratio for the projected features."}
	)
	hidden_dropout: float = field(
	default=0.0,
	metadata={
	"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
	},
	)
	final_dropout: float = field(
	default=0.0,
	metadata={"help": "The dropout probability for the final projection layer."},
	)
	mask_time_prob: float = field(
	default=0.05,
	metadata={
	"help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
	"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
	"vectors will be masked along the time axis."
	},
	)
	mask_time_length: int = field(
	default=10,
	metadata={"help": "Length of vector span to mask along the time axis."},
	)
	mask_feature_prob: float = field(
	default=0.0,
	metadata={
	"help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
	"span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
	},
	)
	mask_feature_length: int = field(
	default=10,
	metadata={"help": "Length of vector span to mask along the feature axis."},
	)
	layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
	ctc_loss_reduction: Optional[str] = field(
	default="mean",
	metadata={
	"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."
	},
	)


	@dataclass
	class DataTrainingArguments:
	"""
	Arguments pertaining to what data we are going to input our model for training and eval.

	Using `HfArgumentParser` we can turn this class
	into argparse arguments to be able to specify them on
	the command line.
	"""

	dataset_path: str = field(
	default=None,
	metadata={
	"help": "The configuration name of the dataset to use (via the datasets library)."
	}
	)
	dataset_name: str = field(
	default=None,
	metadata={
	"help": "The configuration name of the dataset to use (via the datasets library)."
	},
	)
	dataset_config_name: str = field(
	default=None,
	metadata={
	"help": "The configuration name of the dataset to use (via the datasets library)."
	},
	)
	train_split_name: str = field(
	default="train",
	metadata={
	"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
	},
	)
	eval_split_name: str = field(
	default="validation",
	metadata={
	"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
	},
	)
	audio_column_name: str = field(
	default="audio",
	metadata={
	"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"
	},
	)
	text_column_name: str = field(
	default="text",
	metadata={
	"help": "The name of the dataset column containing the text data. Defaults to 'text'"
	},
	)
	wav_filesize_column_name: str = field(
	default=None,
	metadata={
	"help": "The name of the dataset column containing the wav filesize. Defaults is None"
	},
	)
	overwrite_cache: bool = field(
	default=False,
	metadata={"help": "Overwrite the cached preprocessed datasets or not."},
	)
	preprocessing_num_workers: Optional[int] = field(
	default=None,
	metadata={"help": "The number of processes to use for the preprocessing."},
	)
	max_train_samples: Optional[int] = field(
	default=None,
	metadata={
	"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
	"value if set."
	},
	)
	max_eval_samples: Optional[int] = field(
	default=None,
	metadata={
	"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
	"value if set."
	},
	)
	chars_to_ignore: Optional[List[str]] = list_field(
	default=None,
	metadata={"help": "A list of characters to remove from the transcripts."},
	)
	eval_metrics: List[str] = list_field(
	default=["wer"],
	metadata={
	"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"
	},
	)
	max_duration_in_seconds: float = field(
	default=20.0,
	metadata={
	"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
	},
	)
	min_duration_in_seconds: float = field(
	default=0.0,
	metadata={
	"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"
	},
	)
	preprocessing_only: bool = field(
	default=False,
	metadata={
	"help": "Whether to only do data preprocessing and skip training. "
	"This is especially useful when data preprocessing errors out in distributed training due to timeout. "
	"In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
	"so that the cached datasets can consequently be loaded in distributed training"
	},
	)
	print_samples: bool = field(
	default=False,
	metadata={
	"help": "Print row with validation inference results to stdout after each epoch"
	},
	)
	use_augmentations: bool = field(
	default=False,
	metadata={
	"help": "Use data augmentation during training"
	},
	)
	use_auth_token: str = field(
	default="",
	metadata={
	"help": "If :obj:`True`, will use the token generated when running"
	":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
	},
	)
	unk_token: str = field(
	default="[UNK]",
	metadata={"help": "The unk token for the tokenizer"},
	)
	pad_token: str = field(
	default="[PAD]",
	metadata={"help": "The padding token for the tokenizer"},
	)
	word_delimiter_token: str = field(
	default="\|",
	metadata={"help": "The word delimiter token for the tokenizer"},
	)
	phoneme_language: Optional[str] = field(
	default=None,
	metadata={
	"help": "The target language that should be used be"
	" passed to the tokenizer for tokenization. Note that"
	" this is only relevant if the model classifies the"
	" input audio to a sequence of phoneme sequences."
	},
	)


	class Augmentator:

	def __init__(
	self,
	apply_gaussian_noise_with_p=0.1,
	apply_gain_with_p=0.1,
	apply_pitch_shift_with_p=0.1,
	apply_time_stretch_with_p=0.1,
	augment_proba=0.1,
	sample_rate=16_000
	):
	self.augmentator_fn = None
	self.sample_rate = sample_rate
	self.augment_proba = augment_proba
	all_p = (
	apply_gaussian_noise_with_p
	+ apply_gain_with_p
	+ apply_pitch_shift_with_p
	+ apply_time_stretch_with_p
	)
	if AUDIOMENTATIONS_AVAILABLE and all_p > 0:
	self.augmentator_fn = Compose([
	TimeStretch(min_rate=0.8, max_rate=1.2, leave_length_unchanged=False,
	p=apply_time_stretch_with_p),
	PitchShift(min_semitones=-1, max_semitones=1,
	p=apply_pitch_shift_with_p),
	Gain(min_gain_in_db=-1, max_gain_in_db=1, p=apply_gain_with_p),
	AddGaussianNoise(min_amplitude=0.0001, max_amplitude=0.001,
	p=apply_gaussian_noise_with_p),
	])

	def __call__(self, input_values: List[float], args, *kwargs):
	if AUDIOMENTATIONS_AVAILABLE and self.augmentator_fn is not None:
	return self.augmentator_fn(samples=np.array(input_values),
	sample_rate=self.sample_rate).tolist()
	else:
	return input_values


	@dataclass
	class DataCollatorCTCWithPadding:
	"""
	Data collator that will dynamically pad the inputs received.
	Args:
	processor (:class:`~transformers.AutoProcessor`)
	The processor used for proccessing the data.
	padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
	Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
	among:
	* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
	sequence if provided).
	* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
	maximum acceptable input length for the model if that argument is not provided.
	* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
	different lengths).
	max_length (:obj:`int`, `optional`):
	Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
	max_length_labels (:obj:`int`, `optional`):
	Maximum length of the ``labels`` returned list and optionally padding length (see above).
	pad_to_multiple_of (:obj:`int`, `optional`):
	If set will pad the sequence to a multiple of the provided value.
	This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
	7.5 (Volta).
	"""

	processor: 'AutoProcessor'
	padding: Union[bool, str] = "longest"
	pad_to_multiple_of: Optional[int] = None
	pad_to_multiple_of_labels: Optional[int] = None
	augmentator_fn: Optional[Callable] = None
	use_augmentations: bool = False

	def __call__(
	self, features: List[Dict[str, Union[List[int], torch.Tensor]]]
	) -> Dict[str, torch.Tensor]:
	# split inputs and labels since they have to be of different lenghts and need
	# different padding methods
	input_features = [
	{
	"input_values": self.augmentator_fn(feature["input_values"])
	if self.use_augmentations
	else feature["input_values"]}
	for feature in features
	]
	label_features = [{"input_ids": feature["labels"]} for feature in features]

	batch = self.processor.pad(
	input_features,
	padding=self.padding,
	pad_to_multiple_of=self.pad_to_multiple_of,
	return_tensors="pt",
	)

	with self.processor.as_target_processor():
	labels_batch = self.processor.pad(
	label_features,
	padding=self.padding,
	pad_to_multiple_of=self.pad_to_multiple_of_labels,
	return_tensors="pt",
	)

	# replace padding with -100 to ignore loss correctly
	labels = labels_batch["input_ids"].masked_fill(
	labels_batch.attention_mask.ne(1), -100
	)

	batch["labels"] = labels

	return batch


	def create_vocabulary_from_data(
	datasets: DatasetDict,
	text_column_name: str,
	train_split_name: str,
	word_delimiter_token: Optional[str] = None,
	unk_token: Optional[str] = None,
	pad_token: Optional[str] = None,
	):
	# Given training and test labels create vocabulary
	def extract_all_chars(batch):
	all_text = " ".join(batch[text_column_name])
	vocab = list(set(all_text))
	return {"vocab": [vocab], "all_text": [all_text]}

	print("extract chars")
	vocabs = datasets.map(
	extract_all_chars,
	batched=True,
	batch_size=-1,
	keep_in_memory=True,
	remove_columns=datasets[train_split_name].column_names,
	)

	# take union of all unique characters in each dataset
	print("make vocab_set")
	vocab_set = functools.reduce(
	lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) \| set(vocab_2["vocab"][0]),
	vocabs.values(),
	)

	vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}

	# replace white space with delimiter token
	if word_delimiter_token is not None:
	vocab_dict[word_delimiter_token] = vocab_dict[" "]
	del vocab_dict[" "]

	# add unk and pad token
	if unk_token is not None:
	vocab_dict[unk_token] = len(vocab_dict)

	if pad_token is not None:
	vocab_dict[pad_token] = len(vocab_dict)

	return vocab_dict


	def speech_file_to_array_fn(batch, audio_column_name, dataset_path=""):
	if dataset_path:
	dataset_path = os.path.join(dataset_path, batch[audio_column_name])
	else:
	dataset_path = batch[audio_column_name] if isinstance(batch[audio_column_name],
	str) else \
	batch[audio_column_name]["path"]
	speech_array, sampling_rate = torchaudio.load(dataset_path)
	batch[audio_column_name] = {
	"array": speech_array[0].numpy(),
	"sampling_rate": sampling_rate,
	}
	return batch


	class PrintSamplesPredictionCallback(TrainerCallback):

	def __init__(self, processor, eval_dataset):
	super(PrintSamplesPredictionCallback, self).__init__()
	self.processor = processor
	self.eval_dataset = eval_dataset
	self.metric_fn = load_metric("wer")

	def on_log(
	self,
	args: Any,
	state: Any,
	control: Any,
	model: Any,
	logs: Optional[Any] = None,
	**kwargs
	):
	"""
	:param args:
	:param state:
	:param control:
	:param model:
	:param logs:
	:param kwargs: 'tokenizer', 'optimizer', 'lr_scheduler', 'train_dataloader', 'eval_dataloader'
	:return:
	"""
	if state.is_local_process_zero:
	columns = ["id", "prediction", "reference", "audio", "wer"]
	data = []
	for idx, row in enumerate(self.eval_dataset):
	input_dict = self.processor(row["input_values"],
	return_tensors="pt", padding=True)
	logits = model(input_dict.input_values.to(model.device)).logits
	pred_ids = torch.argmax(logits, dim=-1)[0]
	prediction = self.processor.decode(pred_ids)
	print(f"Prediction: {prediction}")
	reference = row['references'].lower()
	print(f"\nReference: {reference}")

	if WANDB_AVAILABLE:

	audio, sample_rate = tuple(row["audio"].values())
	audio = wandb.Audio(np.squeeze(audio),
	sample_rate=sample_rate)
	wer = self.metric_fn.compute(
	predictions=[prediction],
	references=[reference],
	)

	data.append([idx, prediction, reference, audio, wer])
	if WANDB_AVAILABLE:
	table = wandb.Table(data=data, columns=columns)
	wandb.run.log({"audio_predictions": table})


	def main():
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
	if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
	# If we pass only one argument to the script and it's the path to a json file,
	# let's parse it to get our arguments.
	model_args, data_args, training_args = parser.parse_json_file(
	json_file=os.path.abspath(sys.argv[1])
	)
	else:
	model_args, data_args, training_args = parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	last_checkpoint = None
	if (
	os.path.isdir(training_args.output_dir)
	and training_args.do_train
	and not training_args.overwrite_output_dir
	):
	last_checkpoint = get_last_checkpoint(training_args.output_dir)
	if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
	raise ValueError(
	f"Output directory ({training_args.output_dir}) already exists and is not empty. "
	"Use --overwrite_output_dir to overcome."
	)
	elif last_checkpoint is not None:
	logger.info(
	f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
	"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
	)

	# Setup logging
	logging.basicConfig(
	format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
	datefmt="%m/%d/%Y %H:%M:%S",
	handlers=[logging.StreamHandler(sys.stdout)],
	)
	logger.setLevel(
	logging.INFO if is_main_process(training_args.local_rank) else logging.WARN
	)

	# Log on each process the small summary:
	logger.warning(
	f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
	f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
	)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank):
	transformers.utils.logging.set_verbosity_info()
	logger.info("Training/evaluation parameters %s", training_args)

	# Set seed before initializing model.
	set_seed(training_args.seed)

	train_split_name = data_args.train_split_name
	eval_split_name = data_args.eval_split_name

	# 1. First, let's load the dataset
	raw_datasets = DatasetDict({
	train_split_name: None,
	eval_split_name: None,
	})

	if data_args.dataset_path:
	raw_datasets = load_dataset(
	"csv",
	data_files={
	train_split_name: os.path.join(data_args.dataset_path, "train-all.csv"),
	eval_split_name: os.path.join(data_args.dataset_path, "eval-all.csv"),
	},
	)

	if training_args.do_train:
	if raw_datasets[train_split_name] is None:
	raw_datasets[train_split_name] = load_dataset(
	data_args.dataset_name,
	data_args.dataset_config_name,
	split=data_args.train_split_name,
	use_auth_token=data_args.use_auth_token,
	)

	if data_args.audio_column_name not in raw_datasets[train_split_name].column_names:
	raise ValueError(
	f"--audio_column_name '{data_args.audio_column_name}' not found in dataset. "
	"Make sure to set `--audio_column_name` to the correct audio column - one of "
	f"{', '.join(raw_datasets['train'].column_names)}."
	)

	if data_args.text_column_name not in raw_datasets[train_split_name].column_names:
	raise ValueError(
	f"--text_column_name {data_args.text_column_name} not found in dataset. "
	"Make sure to set `--text_column_name` to the correct text column - one of "
	f"{', '.join(raw_datasets['train'].column_names)}."
	)

	if data_args.max_train_samples is not None:
	raw_datasets[train_split_name] = raw_datasets[train_split_name].select(
	range(data_args.max_train_samples)
	)

	if data_args.wav_filesize_column_name is not None:
	raw_datasets[train_split_name] = raw_datasets[train_split_name].sort(
	data_args.wav_filesize_column_name, reverse=True)

	if training_args.do_eval:
	if raw_datasets[eval_split_name] is None:
	raw_datasets[eval_split_name] = load_dataset(
	data_args.dataset_name,
	data_args.dataset_config_name,
	split=data_args.eval_split_name,
	use_auth_token=data_args.use_auth_token,
	)

	if data_args.max_eval_samples is not None:
	raw_datasets[eval_split_name] = raw_datasets[eval_split_name].select(
	range(data_args.max_eval_samples)
	)
	if data_args.wav_filesize_column_name is not None:
	raw_datasets[eval_split_name] = raw_datasets[eval_split_name].sort(
	data_args.wav_filesize_column_name, reverse=True)

	# save special tokens for tokenizer
	word_delimiter_token = data_args.word_delimiter_token
	unk_token = data_args.unk_token
	pad_token = data_args.pad_token

	# 3. Next, let's load the config as we might need it to create
	# the tokenizer
	# load config
	config = AutoConfig.from_pretrained(
	model_args.model_name_or_path,
	cache_dir=model_args.cache_dir,
	use_auth_token=data_args.use_auth_token,
	)

	# 4. Next, if no tokenizer file is defined,
	# we create the vocabulary of the model by extracting all unique characters from
	# the training and evaluation datasets
	# We need to make sure that only first rank saves vocabulary
	# make sure all processes wait until vocab is created
	tokenizer_name_or_path = model_args.tokenizer_name_or_path
	tokenizer_kwargs = {}

	# 5. Now we can instantiate the feature extractor, tokenizer and model
	# Note for distributed training, the .from_pretrained methods guarantee that only
	# one local process can concurrently download model & vocab.
	with open(os.path.join(tokenizer_name_or_path, "vocab.json"), "r") as fin:
	print("loading tokenizer")
	print(fin.read())

	# load feature_extractor and tokenizer
	tokenizer = AutoTokenizer.from_pretrained(
	tokenizer_name_or_path,
	use_auth_token=data_args.use_auth_token,
	**tokenizer_kwargs,
	)
	feature_extractor = AutoFeatureExtractor.from_pretrained(
	model_args.model_name_or_path,
	cache_dir=model_args.cache_dir,
	use_auth_token=data_args.use_auth_token,
	)

	# adapt config
	config.update(
	{
	"feat_proj_dropout": model_args.feat_proj_dropout,
	"attention_dropout": model_args.attention_dropout,
	"hidden_dropout": model_args.hidden_dropout,
	"final_dropout": model_args.final_dropout,
	"mask_time_prob": model_args.mask_time_prob,
	"mask_time_length": model_args.mask_time_length,
	"mask_feature_prob": model_args.mask_feature_prob,
	"mask_feature_length": model_args.mask_feature_length,
	"gradient_checkpointing": training_args.gradient_checkpointing,
	"layerdrop": model_args.layerdrop,
	"ctc_loss_reduction": model_args.ctc_loss_reduction,
	"pad_token_id": tokenizer.pad_token_id,
	"vocab_size": len(tokenizer),
	"activation_dropout": model_args.activation_dropout,
	}
	)

	# create model
	model = AutoModelForCTC.from_pretrained(
	model_args.model_name_or_path,
	cache_dir=model_args.cache_dir,
	config=config,
	use_auth_token=data_args.use_auth_token,
	)

	# freeze encoder
	if model_args.freeze_feature_encoder:
	model.freeze_feature_encoder()

	# 6. Now we preprocess the datasets including loading the audio, resampling and normalization
	# Thankfully, `datasets` takes care of automatically loading and resampling the audio,
	# so that we just need to set the correct target sampling rate and normalize the input
	# via the `feature_extractor`

	# make sure that dataset decodes audio with correct sampling rate

	# derive max & min input length for sample rate & max duration
	audio_column_name = data_args.audio_column_name
	num_workers = data_args.preprocessing_num_workers

	# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
	phoneme_language = data_args.phoneme_language

	raw_datasets[train_split_name] = raw_datasets[train_split_name].map(
	speech_file_to_array_fn,
	num_proc=num_workers,
	fn_kwargs={"dataset_path": data_args.dataset_path,
	"audio_column_name": audio_column_name},
	)
	raw_datasets[eval_split_name] = raw_datasets[eval_split_name].map(
	speech_file_to_array_fn,
	num_proc=num_workers,
	fn_kwargs={"dataset_path": data_args.dataset_path,
	"audio_column_name": audio_column_name},
	)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays and tokenize the targets.
	def prepare_dataset(batch):
	# load audio
	sample = batch[audio_column_name]

	inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
	batch["input_values"] = inputs.input_values[0]
	batch["input_length"] = len(batch["input_values"])

	# encode targets
	additional_kwargs = {}
	if phoneme_language is not None:
	additional_kwargs["phonemizer_lang"] = phoneme_language

	batch["labels"] = tokenizer(batch[data_args.text_column_name],
	**additional_kwargs).input_ids
	return batch

	print(f"Vectorizing")

	with training_args.main_process_first(desc="dataset map preprocessing"):
	vectorized_datasets = raw_datasets.map(
	prepare_dataset,
	remove_columns=next(iter(raw_datasets.values())).column_names,
	num_proc=num_workers,
	desc="preprocess datasets",
	)

	# 7. Next, we can prepare the training.
	# Let's use word error rate (WER) as our evaluation metric,
	# instantiate a data collator and the trainer

	# Define evaluation metrics during training, i.e. word error rate, character error rate
	eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}

	# for large datasets it is advised to run the preprocessing on a
	# single machine first with ``args.preprocessing_only`` since there will mostly likely
	# be a timeout when running the script in distributed mode.
	# In a second step ``args.preprocessing_only`` can then be set to `False` to load the
	# cached dataset
	if data_args.preprocessing_only:
	logger.info(
	f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}"
	)
	return

	def compute_metrics(pred):
	pred_logits = pred.predictions
	pred_ids = np.argmax(pred_logits, axis=-1)

	pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id

	pred_str = tokenizer.batch_decode(pred_ids)
	# we do not want to group tokens when computing the metrics
	label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)

	metrics = {
	k: v.compute(predictions=pred_str, references=label_str)
	for k, v in eval_metrics.items()
	}

	return metrics

	# Now save everything to be able to create a single processor later
	if is_main_process(training_args.local_rank):
	# save feature extractor, tokenizer and config
	feature_extractor.save_pretrained(training_args.output_dir)
	tokenizer.save_pretrained(training_args.output_dir)
	config.save_pretrained(training_args.output_dir)

	try:
	processor = AutoProcessor.from_pretrained(training_args.output_dir)
	except (OSError, KeyError):
	warnings.warn(
	"Loading a processor from a feature extractor config that does not"
	" include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
	" attribute to your `preprocessor_config.json` file to suppress this warning: "
	" `'processor_class': 'Wav2Vec2Processor'`",
	FutureWarning,
	)
	processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)

	# Instantiate custom data collator
	data_collator = DataCollatorCTCWithPadding(
	processor=processor,
	augmentator_fn=Augmentator(),
	use_augmentations=data_args.use_augmentations
	)

	decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm])
	decay_parameters = [name for name in decay_parameters if "bias" not in name]
	optimizer_grouped_parameters = [
	{
	"params": [p for n, p in model.named_parameters() if n in decay_parameters],
	"weight_decay": training_args.weight_decay,
	},
	{
	"params": [
	p for n, p in model.named_parameters() if n not in decay_parameters
	],
	"weight_decay": 0.0,
	},
	]
	trainer_kwargs = {}
	if BNB_AVAILABLE:
	optimizer = bnb.optim.Adam8bit(
	params=optimizer_grouped_parameters,
	betas=(training_args.adam_beta1, training_args.adam_beta2),
	eps=training_args.adam_epsilon,
	)
	trainer_kwargs["optimizers"] = (optimizer, None)

	samples_to_log = [
	{
	**vectorized_datasets[eval_split_name][i],
	"references": raw_datasets[eval_split_name][i][data_args.text_column_name],
	"audio": raw_datasets[eval_split_name][i][data_args.audio_column_name],
	} for i in range(5)
	]

	trainer = Trainer(
	model=model,
	data_collator=data_collator,
	args=training_args,
	compute_metrics=compute_metrics,
	train_dataset=vectorized_datasets[
	train_split_name] if training_args.do_train else None,
	eval_dataset=vectorized_datasets[
	eval_split_name] if training_args.do_eval else None,
	tokenizer=feature_extractor,
	**trainer_kwargs,
	callbacks=[PrintSamplesPredictionCallback(
	processor=processor,
	eval_dataset=samples_to_log)] if data_args.print_samples and training_args.do_eval else None,
	)

	# 8. Finally, we can start training

	# Training
	if training_args.do_train:

	# use last checkpoint if exist
	if last_checkpoint is not None:
	checkpoint = last_checkpoint
	elif os.path.isdir(model_args.model_name_or_path):
	checkpoint = model_args.model_name_or_path
	else:
	checkpoint = None

	train_result = trainer.train(resume_from_checkpoint=checkpoint)
	trainer.save_model()

	metrics = train_result.metrics
	max_train_samples = (
	data_args.max_train_samples
	if data_args.max_train_samples is not None
	else len(vectorized_datasets[train_split_name])
	)
	metrics["train_samples"] = min(
	max_train_samples, len(vectorized_datasets[train_split_name])
	)

	trainer.log_metrics(train_split_name, metrics)
	trainer.save_metrics(train_split_name, metrics)
	trainer.save_state()

	# Evaluation
	results = {}
	if training_args.do_eval:
	logger.info("* Evaluate *")
	metrics = trainer.evaluate()
	max_eval_samples = (
	data_args.max_eval_samples
	if data_args.max_eval_samples is not None
	else len(vectorized_datasets[eval_split_name])
	)
	metrics["eval_samples"] = min(max_eval_samples,
	len(vectorized_datasets[eval_split_name]))

	trainer.log_metrics(eval_split_name, metrics)
	trainer.save_metrics(eval_split_name, metrics)

	# Write model card and (optionally) push to hub
	config_name = (
	data_args.dataset_config_name
	if data_args.dataset_config_name is not None
	else "na"
	)
	kwargs = {
	"language": "he",
	"finetuned_from": model_args.model_name_or_path,
	"tasks": "speech-recognition",
	"tags": ["automatic-speech-recognition", "robust-speech-event", "he"],
	"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
	}

	if training_args.push_to_hub:
	trainer.push_to_hub(**kwargs)
	else:
	trainer.create_model_card(**kwargs)

	return results


	if __name__ == "__main__":
	main()