xls-r-300m-sv-robust / join_datasets_asr_ctc.py

add more debugging, shuffle dataset max train

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	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and

	""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition"""

	import datetime
	import functools
	import json
	import logging
	import os
	import re
	import sys
	import warnings
	from dataclasses import dataclass, field
	from typing import Dict, List, Optional, Union

	import datasets
	import numpy as np
	import pandas as pd
	import torch
	import wandb
	from datasets import DatasetDict, concatenate_datasets, load_dataset, load_metric

	import transformers
	from transformers import (
	AutoConfig,
	AutoFeatureExtractor,
	AutoModelForCTC,
	AutoProcessor,
	AutoTokenizer,
	HfArgumentParser,
	Trainer,
	TrainingArguments,
	Wav2Vec2Processor,
	set_seed,
	)
	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.dev0")

	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_name: str = field(
	metadata={
	"help": "The name of the dataset to use (via the datasets library)."
	" To use multiple datasets, specify them separated by a comma."
	" e.g.: 'mozilla-foundation/common_voice_7_0,marinone94/nst_sv'"
	}
	)
	dataset_config_name: str = field(
	default=None, metadata={
	"help": "The configuration name of the dataset to use (via the datasets library)."
	" To use multiple datasets, specify them separated by a comma."
	" e.g.: 'sv-SE,sv'"
	}
	)
	train_split_name: str = field(
	default="train+validation",
	metadata={
	"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train+validation'"
	" To use multiple datasets, specify them separated by a comma."
	" e.g.: 'train+validation,all'"
	},
	)
	eval_split_name: str = field(
	default="test",
	metadata={
	"help": "The name of the training data set split to use (via the datasets library). Defaults to 'test'"
	" To use multiple datasets, specify them separated by a comma."
	" e.g.: 'test,None'"
	},
	)
	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'"},
	)
	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"
	},
	)
	use_auth_token: bool = field(
	default=False,
	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."
	},
	)
	dataset_seed: Optional[int] = field(
	default=None,
	metadata={"help": "Seed for shuffling training data"},
	)


	@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

	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": 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,
	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, vocab):
	all_text = " ".join(batch)
	return list(set(list(set(all_text)) + vocab))

	batch_size = 10000
	vocab = []
	for i in range(0, datasets["train"].num_rows, 10000):
	batch = datasets["train"].select(range(i, min(datasets["train"].num_rows, i+batch_size)))
	vocab = extract_all_chars(batch["target_text"], vocab)
	for i in range(0, datasets["eval"].num_rows, 10000):
	batch = datasets["eval"].select(range(i, min(datasets["eval"].num_rows, i+batch_size)))
	vocab = extract_all_chars(batch["target_text"], vocab)

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

	# 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 init_wandb(training_args):
	# Adds report to wandb in training args if login succeds
	# TODO: Replace with check of wandb env vars
	try:
	repo_name = os.getcwd().split("/")[-1]
	run_name = f"{datetime.datetime.utcnow()}".replace(" ", "T")
	os.environ["WANDB_PROJECT"] = repo_name
	wandb.login()
	training_args.report_to = ["wandb"]
	training_args.run_name = run_name
	# wandb.init()
	except:
	pass

	return training_args

	def detect_last_checkpoint(training_args):
	# Get last checkpoint if training mode and no overwrite flag is set
	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."
	)

	return last_checkpoint


	def set_logging(training_args):
	# Set logging level
	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)


	def load_raw_datasets(training_args, data_args):

	raw_datasets = DatasetDict()

	def common_cols(columns_a, columns_b):
	col_a = set(columns_a)
	col_b = set(columns_b)
	return [col for col in col_a if col in col_b]

	if training_args.do_train:

	# Multiple datasets might need to be loaded from HF
	# It assumes they all follow the common voice format
	for (dataset_name, dataset_config_name, train_split_name) in zip(
	data_args.dataset_name.split(","),
	data_args.dataset_config_name.split(","),
	data_args.train_split_name.split(","),
	):

	if train_split_name != "None":
	if "train" not in raw_datasets:
	raw_datasets["train"] = load_dataset(
	dataset_name,
	dataset_config_name,
	split=train_split_name,
	use_auth_token=data_args.use_auth_token,
	)
	min_columns_train = raw_datasets["train"].column_names
	dataset_sampling_rate = raw_datasets["train"].features[data_args.audio_column_name].sampling_rate
	print(f"Dataset sampling rate: {dataset_sampling_rate}")
	else:
	new_dataset = DatasetDict()
	new_dataset["train"] = load_dataset(
	dataset_name,
	dataset_config_name,
	split=train_split_name,
	use_auth_token=data_args.use_auth_token,
	)
	new_dataset_sampling_rate = next(iter(new_dataset.values())).features[data_args.audio_column_name].sampling_rate
	if new_dataset_sampling_rate != dataset_sampling_rate:
	print(f"New dataset sampling rate casted from {dataset_sampling_rate} to {dataset_sampling_rate}")
	new_dataset["train"] = new_dataset["train"].cast_column(
	data_args.audio_column_name, datasets.features.Audio(sampling_rate=dataset_sampling_rate)
	)
	raw_datasets["train"] = concatenate_datasets(
	[
	raw_datasets["train"],
	new_dataset["train"]
	]
	)
	min_columns_train = common_cols(min_columns_train, new_dataset.column_names)
	else:
	logging.warning(f"{dataset_name} {dataset_config_name} train not loaded as split is {train_split_name}")

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

	# If dataset_seed is set, shuffle train
	if data_args.dataset_seed is not None:
	raw_datasets["train"] = raw_datasets["train"].shuffle(seed=data_args.dataset_seed)


	if data_args.max_train_samples is not None:
	raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
	other_columns_train = [col for col in raw_datasets["train"].column_names if col not in min_columns_train]
	raw_datasets["train"].remove_columns(other_columns_train)

	# pd_train_head = raw_datasets["train"].select(range(10)).to_pandas()
	# pd_train_tail = raw_datasets["train"].select(range(raw_datasets["train"].num_rows-10, raw_datasets["train"].num_rows)).to_pandas()
	# pd_train = pd.concat([pd_train_head, pd_train_tail])
	# print(pd_train["audio"])

	if training_args.do_eval:
	# Multiple datasets might need to be loaded from HF
	# It assumes they all follow the common voice format
	for (dataset_name, dataset_config_name, eval_split_name) in zip(
	data_args.dataset_name.split(","),
	data_args.dataset_config_name.split(","),
	data_args.eval_split_name.split(","),
	):

	if eval_split_name != "None":
	if "eval" not in raw_datasets:
	raw_datasets["eval"] = load_dataset(
	dataset_name,
	dataset_config_name,
	split=eval_split_name,
	use_auth_token=data_args.use_auth_token,
	)
	min_columns_eval = raw_datasets["eval"].column_names
	dataset_sampling_rate = raw_datasets["eval"].features[data_args.audio_column_name].sampling_rate
	print(f"Dataset sampling rate: {dataset_sampling_rate}")
	else:
	new_dataset = DatasetDict()
	new_dataset["eval"] = load_dataset(
	dataset_name,
	dataset_config_name,
	split=train_split_name,
	use_auth_token=data_args.use_auth_token,
	)
	new_dataset_sampling_rate = new_dataset["eval"].features[data_args.audio_column_name].sampling_rate
	if new_dataset_sampling_rate != dataset_sampling_rate:
	print(f"New dataset sampling rate casted from {new_dataset_sampling_rate} to {dataset_sampling_rate}")
	new_dataset["eval"] = new_dataset["eval"].cast_column(
	data_args.audio_column_name, datasets.features.Audio(sampling_rate=dataset_sampling_rate)
	)
	raw_datasets["eval"] = concatenate_datasets(
	[
	raw_datasets["eval"],
	new_dataset["eval"]
	]
	)
	min_columns_eval = common_cols(min_columns_eval, new_dataset.column_names)
	else:
	logging.warning(f"{dataset_name} {dataset_config_name} eval not loaded as split is {eval_split_name}")

	if data_args.max_eval_samples is not None:
	raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
	other_columns_eval = [col for col in raw_datasets["eval"].column_names if col not in min_columns_eval]
	raw_datasets["eval"].remove_columns(other_columns_eval)

	# pd_eval_head = raw_datasets["eval"].select(range(10)).to_pandas()
	# pd_eval_tail = raw_datasets["eval"].select(range(raw_datasets["eval"].num_rows-10, raw_datasets["eval"].num_rows)).to_pandas()
	# pd_eval = pd.concat([pd_eval_head, pd_eval_tail])
	# print(pd_eval["audio"])

	return raw_datasets


	def preprocess_text_datasets(raw_datasets, training_args, data_args):

	chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", "\"", "“", "%", "‘", "”", "�", "—", "’", "…", "–"]

	chars_to_ignore_regex = (
	f'[{"".join(chars_to_ignore)}]'
	)
	text_column_name = data_args.text_column_name

	def is_text_valid(text):
	for token in text.split():
	if len(token) > 1:
	return True
	return False

	def remove_special_characters(batch):
	if chars_to_ignore_regex is not None:
	batch["target_text"] = \
	re.sub(chars_to_ignore_regex, "", batch[text_column_name]) \
	.replace("\\\\Punkt", "") \
	.replace("\\\\Komma", "") \
	.replace("è", "e") \
	.replace("é", "e") \
	.replace("î", "i") \
	.replace("ü", "u") \
	.replace("ÿ", "y") \
	.replace("ô", "o") \
	.replace("\\", "") \
	.replace("/", "") \
	.replace("\|", "") \
	.lower() + " "
	else:
	batch["target_text"] = batch[text_column_name] \
	.replace("\\\\Punkt", "") \
	.replace("\\\\Komma", "") \
	.replace("è", "e") \
	.replace("é", "e") \
	.replace("î", "i") \
	.replace("ü", "u") \
	.replace("ÿ", "y") \
	.replace("ô", "o") \
	.replace("\\", "") \
	.replace("/", "") \
	.replace("\|", "") \
	.lower() + " "
	return batch

	num_workers = data_args.preprocessing_num_workers
	with training_args.main_process_first(desc="dataset map special characters removal"):
	raw_datasets = raw_datasets.map(
	remove_special_characters,
	remove_columns=[text_column_name],
	desc="remove special characters from datasets",
	)

	raw_datasets = raw_datasets.filter(
	is_text_valid,
	num_proc=num_workers,
	input_columns=["target_text"],
	desc="remove single words, single chars and 'W O R D S'",
	)

	# 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

	return raw_datasets, word_delimiter_token, unk_token, pad_token


	def create_vocab(raw_datasets, config, training_args, model_args, word_delimiter_token, unk_token, pad_token):

	tokenizer_name_or_path = model_args.tokenizer_name_or_path
	tokenizer_kwargs = {}
	if tokenizer_name_or_path is None:
	# save vocab in training output dir
	tokenizer_name_or_path = training_args.output_dir

	vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")

	with training_args.main_process_first():
	if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
	os.remove(vocab_file)

	with training_args.main_process_first(desc="dataset map vocabulary creation"):
	if not os.path.isfile(vocab_file):
	os.makedirs(tokenizer_name_or_path, exist_ok=True)
	vocab_dict = create_vocabulary_from_data(
	raw_datasets,
	word_delimiter_token=word_delimiter_token,
	unk_token=unk_token,
	pad_token=pad_token,
	)

	# save vocab dict to be loaded into tokenizer
	with open(vocab_file, "w") as file:
	json.dump(vocab_dict, file)

	# if tokenizer has just been created
	# it is defined by `tokenizer_class` if present in config else by `model_type`
	tokenizer_kwargs = {
	"config": config if config.tokenizer_class is not None else None,
	"tokenizer_type": config.model_type if config.tokenizer_class is None else None,
	"unk_token": unk_token,
	"pad_token": pad_token,
	"word_delimiter_token": word_delimiter_token,
	}
	return tokenizer_name_or_path, tokenizer_kwargs


	def inst_model_tokenizer_feature_extractor(
	training_args,
	model_args,
	data_args,
	tokenizer_name_or_path,
	tokenizer_kwargs,
	config
	):
	# 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()

	return model, tokenizer, feature_extractor, config


	def preprocess_audio_datasets(raw_datasets, tokenizer, feature_extractor, training_args, data_args):

	num_workers = data_args.preprocessing_num_workers

	# make sure that dataset decodes audio with correct sampling rate
	dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
	if dataset_sampling_rate != feature_extractor.sampling_rate:
	raw_datasets = raw_datasets.cast_column(
	data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
	)

	# derive max & min input length for sample rate & max duration
	max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
	min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
	audio_column_name = data_args.audio_column_name

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

	# 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["target_text"], **additional_kwargs).input_ids
	return batch

	with training_args.main_process_first(desc="dataset map preprocessing"):
	vectorized_datasets = DatasetDict()
	vectorized_datasets["train"] = raw_datasets["train"].map(
	prepare_dataset,
	remove_columns=raw_datasets["train"].column_names,
	num_proc=num_workers,
	desc="preprocess datasets",
	)
	vectorized_datasets["eval"] = raw_datasets["eval"].map(
	prepare_dataset,
	remove_columns=raw_datasets["eval"].column_names,
	num_proc=num_workers,
	desc="preprocess datasets",
	)

	def is_audio_in_length_range(length):
	return length > min_input_length and length < max_input_length

	# filter data that is shorter than min_input_length
	vectorized_datasets = vectorized_datasets.filter(
	is_audio_in_length_range,
	num_proc=num_workers,
	input_columns=["input_length"],
	)

	# TODO: Log sample of datasets in the right way (see wandb docs)
	pd_train = vectorized_datasets["train"].select(range(10)).to_pandas()
	pd_eval = vectorized_datasets["eval"].select(range(10)).to_pandas()
	# wandb.log({"train_sample": pd_train})
	# wandb.log({"eval_sample": pd_eval})

	return vectorized_datasets




	def main():
	# 0. Initialize script

	# 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()

	# Adds report to wandb in training args if login succeds
	training_args = init_wandb(training_args=training_args)

	last_checkpoint = detect_last_checkpoint(training_args=training_args)

	set_logging(training_args=training_args)

	set_seed(training_args.seed)

	# 1. Load and compose the datasets
	raw_datasets = load_raw_datasets(
	training_args=training_args,
	data_args=data_args
	)

	# 2. Preprocess the datasets
	#
	# We remove some special characters from the datasets
	# that make training complicated and do not help in transcribing the speech
	# E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
	# that could be easily picked up by the model
	raw_datasets, word_delimiter_token, unk_token, pad_token = preprocess_text_datasets(
	raw_datasets= raw_datasets,
	training_args=training_args,
	data_args=data_args
	)

	# 3.Load the config to create the tokenizer
	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, tokenizer_kwargs = create_vocab(
	raw_datasets= raw_datasets,
	config=config,
	training_args=training_args,
	model_args=model_args,
	word_delimiter_token=word_delimiter_token,
	unk_token=unk_token,
	pad_token=pad_token,
	)

	# 5. 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.
	model, tokenizer, feature_extractor, config = inst_model_tokenizer_feature_extractor(
	training_args=training_args,
	model_args=model_args,
	data_args=data_args,
	tokenizer_name_or_path=tokenizer_name_or_path,
	tokenizer_kwargs=tokenizer_kwargs,
	config=config
	)


	# 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`
	vectorized_datasets = preprocess_audio_datasets(
	raw_datasets=raw_datasets,
	tokenizer=tokenizer,
	feature_extractor=feature_extractor,
	training_args=training_args,
	data_args=data_args
	)

	# Inspect datasets
	logger.info("Inspect datasets")
	avg = []
	std = []
	import numpy as np
	for input_ in vectorized_datasets["train"][:10]["input_values"]:
	avg.append(np.average(input_))
	std.append(np.std(input_))
	for input_ in vectorized_datasets["eval"][:10]["input_values"]:
	avg.append(np.average(input_))
	std.append(np.std(input_))

	logger.info(f"Average values: {avg}")
	logger.info(f"Std values: {std}")

	# 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

	# 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

	# 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}
	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)

	# Initialize Trainer
	trainer = Trainer(
	model=model,
	data_collator=data_collator,
	args=training_args,
	compute_metrics=compute_metrics,
	train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
	eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
	tokenizer=feature_extractor,
	)

	# 8. Finally, we can start 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"])
	)
	metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))

	trainer.log_metrics("train", metrics)
	trainer.save_metrics("train", 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"])
	)
	metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))

	trainer.log_metrics("eval", metrics)
	trainer.save_metrics("eval", 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 = {
	"finetuned_from": model_args.model_name_or_path,
	"tasks": "speech-recognition",
	"tags": ["automatic-speech-recognition", data_args.dataset_name],
	"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
	"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
	}
	if "common_voice" in data_args.dataset_name:
	kwargs["language"] = config_name

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

	return results


	if __name__ == "__main__":
	main()