usrbinenv python codingutf8 2021 the huggingface inc team 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 orglicenseslicense2 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 finetuning a transformers ctc model for automatic speech recognition 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 bitsandbytes as bnb import datasets import numpy as np import torch from datasets import datasetdict loaddataset loadmetric import transformers from transformers import autoconfig autofeatureextractor automodelforctc autoprocessor autotokenizer hfargumentparser trainer trainingarguments wav2vec2processor setseed from transformers trainerptutils import getparameternames from transformers trainerutils import getlastcheckpoint ismainprocess from transformers utils import checkminversion from transformers utils versions import requireversion will error if the minimal version of transformers is not installed remove at your own risks checkminversion4 16 0 dev0 requireversiondatasets1 13 3 to fix pip install r examplespytorchtextclassificationrequirements txt logger logging getloggername def listfielddefaultnone metadatanone return fielddefaultfactorylambda default metadatametadata dataclass class modelarguments modelnameorpath str field metadatahelp path to pretrained model or model identifier from huggingface comodels tokenizernameorpath optionalstr field defaultnone metadatahelp path to pretrained tokenizer or tokenizer identifier from huggingface comodels cachedir optionalstr field defaultnone metadatahelp where do you want to store the pretrained models downloaded from huggingface co freezefeatureencoder bool field defaulttrue metadatahelp whether to freeze the feature encoder layers of the model attentiondropout float field default0 0 metadatahelp the dropout ratio for the attention probabilities activationdropout float field default0 0 metadatahelp the dropout ratio for activations inside the fully connected layer featprojdropout float fielddefault0 0 metadatahelp the dropout ratio for the projected features hiddendropout float field default0 0 metadata help the dropout probability for all fully connected layers in the embeddings encoder and pooler finaldropout float field default0 0 metadatahelp the dropout probability for the final projection layer masktimeprob float field default0 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 masktimeprob sequencelength masktimelength feature vectors will be masked along the time axis masktimelength int field default10 metadatahelp length of vector span to mask along the time axis maskfeatureprob float field default0 0 metadata help probability of each feature vector along the feature axis to be chosen as the start of the vectorspan to be masked approximately maskfeatureprob sequencelength maskfeaturelength feature bins will be masked along the time axis maskfeaturelength int field default10 metadatahelp length of vector span to mask along the feature axis layerdrop float fielddefault0 0 metadatahelp the layerdrop probability ctclossreduction optionalstr field defaultmean metadatahelp the way the ctc loss should be reduced should be one of mean or sum dataclass class datatrainingarguments datasetname str field metadatahelp the configuration name of the dataset to use via the datasets library datasetconfigname str field defaultnone metadatahelp the configuration name of the dataset to use via the datasets library trainsplitname str field defaulttrainvalidation metadata help the name of the training data set split to use via the datasets library defaults to train evalsplitname str field defaulttest metadata help the name of the training data set split to use via the datasets library defaults to train audiocolumnname str field defaultaudio metadatahelp the name of the dataset column containing the audio data defaults to audio textcolumnname str field defaulttext metadatahelp the name of the dataset column containing the text data defaults to text overwritecache bool field defaultfalse metadatahelp overwrite the cached preprocessed datasets or not preprocessingnumworkers optionalint field defaultnone metadatahelp the number of processes to use for the preprocessing maxtrainsamples optionalint field defaultnone metadata help for debugging purposes or quicker training truncate the number of training examples to this value if set maxevalsamples optionalint field defaultnone metadata help for debugging purposes or quicker training truncate the number of validation examples to this value if set charstoignore optionalliststr listfield defaultnone metadatahelp a list of characters to remove from the transcripts evalmetrics liststr listfield defaultwer metadatahelp a list of metrics the model should be evaluated on e g wer cer maxdurationinseconds float field default20 0 metadata help filter audio files that are longer than maxdurationinseconds seconds to maxdurationinseconds mindurationinseconds float field default0 0 metadatahelp filter audio files that are shorter than mindurationinseconds seconds preprocessingonly bool field defaultfalse 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 nondistributed setup with preprocessingonlytrue so that the cached datasets can consequently be loaded in distributed training useauthtoken bool field defaultfalse metadata help if obj true will use the token generated when running obj huggingfacecli login as http bearer ization for remote files unktoken str field defaultunk metadatahelp the unk token for the tokenizer padtoken str field defaultpad metadatahelp the padding token for the tokenizer worddelimitertoken str field default metadatahelp the word delimiter token for the tokenizer phonemelanguage optionalstr field defaultnone 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 dataclass class datacollatorctcwithpadding processor autoprocessor padding unionbool str longest padtomultipleof optionalint none padtomultipleoflabels optionalint none def callself features listdictstr unionlistint torch tensor dictstr torch tensor split inputs and labels since they have to be of different lengths and need different padding methods inputfeatures inputvalues featureinputvalues for feature in features labelfeatures inputids featurelabels for feature in features batch self processor pad inputfeatures paddingself padding padtomultipleofself padtomultipleof returntensorspt labelsbatch self processor pad labelslabelfeatures paddingself padding padtomultipleofself padtomultipleoflabels returntensorspt replace padding with 100 to ignore loss correctly labels labelsbatchinputids maskedfilllabelsbatch attentionmask ne1 100 batchlabels labels return batch def createvocabularyfromdata datasets datasetdict worddelimitertoken optionalstr none unktoken optionalstr none padtoken optionalstr none given training and test labels create vocabulary def extractallcharsbatch alltext joinbatchtargettext vocab listsetalltext return vocab vocab alltext alltext vocabs datasets map extractallchars batchedtrue batchsize1 keepinmemorytrue removecolumnsdatasetstrain columnnames take union of all unique characters in each dataset vocabset functools reduce lambda vocab1 vocab2 setvocab1vocab0 setvocab2vocab0 vocabs values vocabdict v k for k v in enumeratesortedvocabset replace white space with delimiter token if worddelimitertoken is not none vocabdictworddelimitertoken vocabdict del vocabdict add unk and pad token if unktoken is not none vocabdictunktoken lenvocabdict if padtoken is not none vocabdictpadtoken lenvocabdict return vocabdict def main see all possible arguments in srctransformerstrainingargs py or by passing the help flag to this script we now keep distinct sets of args for a cleaner separation of concerns parser hfargumentparsermodelarguments datatrainingarguments trainingarguments if lensys argv 2 and sys argv1 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 modelargs dataargs trainingargs parser parsejsonfilejsonfileos path abspathsys argv1 else modelargs dataargs trainingargs parser parseargsintodataclasses detecting last checkpoint lastcheckpoint none if os path isdirtrainingargs outputdir and trainingargs dotrain and not trainingargs overwriteoutputdir lastcheckpoint getlastcheckpointtrainingargs outputdir if lastcheckpoint is none and lenos listdirtrainingargs outputdir 0 raise valueerror foutput directory trainingargs outputdir already exists and is not empty use overwriteoutputdir to overcome elif lastcheckpoint is not none logger info fcheckpoint detected resuming training at lastcheckpoint to avoid this behavior change the outputdir or add overwriteoutputdir to train from scratch setup logging logging basicconfig formatasctimes levelnames names messages datefmtmdy h m s handlerslogging streamhandlersys stdout logger setlevellogging info if ismainprocesstrainingargs localrank else logging warn log on each process the small summary logger warning fprocess rank trainingargs localrank device trainingargs device ngpu trainingargs ngpu fdistributed training booltrainingargs localrank 1 16bits training trainingargs fp16 set the verbosity to info of the transformers logger on main process only if ismainprocesstrainingargs localrank transformers utils logging setverbosityinfo logger infotrainingevaluation parameters s trainingargs set seed before initializing model setseedtrainingargs seed 1 first let s load the dataset rawdatasets datasetdict if trainingargs dotrain rawdatasetstrain loaddataset dataargs datasetname dataargs datasetconfigname splitdataargs trainsplitname tokendataargs useauthtoken if dataargs audiocolumnname not in rawdatasetstrain columnnames raise valueerror faudiocolumnname dataargs audiocolumnname not found in dataset dataargs datasetname make sure to set audiocolumnname to the correct audio column one of f joinrawdatasets train columnnames if dataargs textcolumnname not in rawdatasetstrain columnnames raise valueerror ftextcolumnname dataargs textcolumnname not found in dataset dataargs datasetname make sure to set textcolumnname to the correct text column one of f joinrawdatasets train columnnames if dataargs maxtrainsamples is not none rawdatasetstrain rawdatasetstrain selectrangedataargs maxtrainsamples if trainingargs doeval rawdatasetseval loaddataset dataargs datasetname dataargs datasetconfigname splitdataargs evalsplitname tokendataargs useauthtoken if dataargs maxevalsamples is not none rawdatasetseval rawdatasetseval selectrangedataargs maxevalsamples 2 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 charstoignoreregex f joindataargs charstoignore if dataargs charstoignore is not none else none textcolumnname dataargs textcolumnname def removespecialcharactersbatch if charstoignoreregex is not none batchtargettext re subcharstoignoreregex batchtextcolumnname lower else batchtargettext batchtextcolumnname lower return batch with trainingargs mainprocessfirstdescdataset map special characters removal rawdatasets rawdatasets map removespecialcharacters removecolumnstextcolumnname descremove special characters from datasets save special tokens for tokenizer worddelimitertoken dataargs worddelimitertoken unktoken dataargs unktoken padtoken dataargs padtoken 3 next let s load the config as we might need it to create the tokenizer load config config autoconfig frompretrained modelargs modelnameorpath cachedirmodelargs cachedir tokendataargs useauthtoken 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 tokenizernameorpath modelargs tokenizernameorpath tokenizerkwargs if tokenizernameorpath is none save vocab in training output dir tokenizernameorpath trainingargs outputdir vocabfile os path jointokenizernameorpath vocab json with trainingargs mainprocessfirst if trainingargs overwriteoutputdir and os path isfilevocabfile os removevocabfile with trainingargs mainprocessfirstdescdataset map vocabulary creation if not os path isfilevocabfile os makedirstokenizernameorpath existoktrue vocabdict createvocabularyfromdata rawdatasets worddelimitertokenworddelimitertoken unktokenunktoken padtokenpadtoken save vocab dict to be loaded into tokenizer with openvocabfile w as file json dumpvocabdict file if tokenizer has just been created it is defined by tokenizerclass if present in config else by modeltype tokenizerkwargs config config if config tokenizerclass is not none else none tokenizertype config modeltype if config tokenizerclass is none else none unktoken unktoken padtoken padtoken worddelimitertoken worddelimitertoken 5 now we can instantiate the feature extractor tokenizer and model note for distributed training the frompretrained methods guarantee that only one local process can concurrently download model vocab load featureextractor and tokenizer tokenizer autotokenizer frompretrained tokenizernameorpath tokendataargs useauthtoken tokenizerkwargs featureextractor autofeatureextractor frompretrained modelargs modelnameorpath cachedirmodelargs cachedir tokendataargs useauthtoken adapt config config update featprojdropout modelargs featprojdropout attentiondropout modelargs attentiondropout hiddendropout modelargs hiddendropout finaldropout modelargs finaldropout masktimeprob modelargs masktimeprob masktimelength modelargs masktimelength maskfeatureprob modelargs maskfeatureprob maskfeaturelength modelargs maskfeaturelength gradientcheckpointing trainingargs gradientcheckpointing layerdrop modelargs layerdrop ctclossreduction modelargs ctclossreduction padtokenid tokenizer padtokenid vocabsize lentokenizer activationdropout modelargs activationdropout create model model automodelforctc frompretrained modelargs modelnameorpath cachedirmodelargs cachedir configconfig tokendataargs useauthtoken freeze encoder if modelargs freezefeatureencoder model freezefeatureencoder 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 featureextractor make sure that dataset decodes audio with correct sampling rate datasetsamplingrate nextiterrawdatasets values featuresdataargs audiocolumnname samplingrate if datasetsamplingrate featureextractor samplingrate rawdatasets rawdatasets castcolumn dataargs audiocolumnname datasets features audiosamplingratefeatureextractor samplingrate derive max min input length for sample rate max duration maxinputlength dataargs maxdurationinseconds featureextractor samplingrate mininputlength dataargs mindurationinseconds featureextractor samplingrate audiocolumnname dataargs audiocolumnname numworkers dataargs preprocessingnumworkers phonemelanguage is only relevant if the model is finetuned on phoneme classification phonemelanguage dataargs phonemelanguage preprocessing the datasets we need to read the audio files as arrays and tokenize the targets def preparedatasetbatch load audio sample batchaudiocolumnname inputs featureextractorsamplearray samplingratesamplesamplingrate batchinputvalues inputs inputvalues0 batchinputlength lenbatchinputvalues encode targets additionalkwargs if phonemelanguage is not none additionalkwargsphonemizerlang phonemelanguage batchlabels tokenizerbatchtargettext additionalkwargs inputids return batch with trainingargs mainprocessfirstdescdataset map preprocessing vectorizeddatasets rawdatasets map preparedataset removecolumnsnextiterrawdatasets values columnnames numprocnumworkers descpreprocess datasets def isaudioinlengthrangelength return length mininputlength and length maxinputlength filter data that is shorter than mininputlength vectorizeddatasets vectorizeddatasets filter isaudioinlengthrange numprocnumworkers inputcolumnsinputlength 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 evalmetrics metric loadmetricmetric for metric in dataargs evalmetrics for large datasets it is advised to run the preprocessing on a single machine first with args preprocessingonly since there will mostly likely be a timeout when running the script in distributed mode in a second step args preprocessingonly can then be set to false to load the cached dataset if dataargs preprocessingonly logger infofdata preprocessing finished files cached at vectorizeddatasets cachefiles return def computemetricspred predlogits pred predictions predids np argmaxpredlogits axis1 pred labelidspred labelids 100 tokenizer padtokenid predstr tokenizer batchdecodepredids we do not want to group tokens when computing the metrics labelstr tokenizer batchdecodepred labelids grouptokensfalse metrics k v computepredictionspredstr referenceslabelstr for k v in evalmetrics items return metrics now save everything to be able to create a single processor later if ismainprocesstrainingargs localrank save feature extractor tokenizer and config featureextractor savepretrainedtrainingargs outputdir tokenizer savepretrainedtrainingargs outputdir config savepretrainedtrainingargs outputdir try processor autoprocessor frompretrainedtrainingargs outputdir except oserror keyerror warnings warn loading a processor from a feature extractor config that does not include a processorclass attribute is deprecated and will be removed in v5 please add the following attribute to your preprocessorconfig json file to suppress this warning processorclass wav2vec2processor futurewarning processor wav2vec2processor frompretrainedtrainingargs outputdir instantiate custom data collator datacollator datacollatorctcwithpaddingprocessorprocessor decayparameters getparameternamesmodel torch nn layernorm decayparameters name for name in decayparameters if bias not in name optimizergroupedparameters params p for n p in model namedparameters if n in decayparameters weightdecay trainingargs weightdecay params p for n p in model namedparameters if n not in decayparameters weightdecay 0 0 optimizer bnb optim adam8bit paramsoptimizergroupedparameters lrtrainingargs learningrate betastrainingargs adambeta1 trainingargs adambeta2 epstrainingargs adamepsilon optimizers optimizer none initialize trainer trainer trainer modelmodel datacollatordatacollator argstrainingargs computemetricscomputemetrics traindatasetvectorizeddatasetstrain if trainingargs dotrain else none evaldatasetvectorizeddatasetseval if trainingargs doeval else none tokenizerfeatureextractor optimizersoptimizers 8 finally we can start training training if trainingargs dotrain use last checkpoint if exist if lastcheckpoint is not none checkpoint lastcheckpoint elif os path isdirmodelargs modelnameorpath checkpoint modelargs modelnameorpath else checkpoint none trainresult trainer trainresumefromcheckpointcheckpoint trainer savemodel metrics trainresult metrics maxtrainsamples dataargs maxtrainsamples if dataargs maxtrainsamples is not none else lenvectorizeddatasetstrain metricstrainsamples minmaxtrainsamples lenvectorizeddatasetstrain trainer logmetricstrain metrics trainer savemetricstrain metrics trainer savestate evaluation results if trainingargs doeval logger info evaluate metrics trainer evaluate maxevalsamples dataargs maxevalsamples if dataargs maxevalsamples is not none else lenvectorizeddatasetseval metricsevalsamples minmaxevalsamples lenvectorizeddatasetseval trainer logmetricseval metrics trainer savemetricseval metrics write model card and optionally push to hub configname dataargs datasetconfigname if dataargs datasetconfigname is not none else na kwargs finetunedfrom modelargs modelnameorpath tasks automaticspeechrecognition tags automaticspeechrecognition dataargs datasetname datasetargs fconfig configname training split dataargs trainsplitname eval split f dataargs evalsplitname dataset fdataargs datasetname upper configname upper if commonvoice in dataargs datasetname kwargslanguage configname if trainingargs pushtohub trainer pushtohubkwargs else trainer createmodelcardkwargs return results if name main main usr bin env python coding utf 8 2021 the huggingface inc team 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 will error if the minimal version of transformers is not installed remove at your own risks arguments pertaining to which model config tokenizer we are going to fine tune from 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 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 split inputs and labels since they have to be of different lengths and need different padding methods replace padding with 100 to ignore loss correctly given training and test labels create vocabulary take union of all unique characters in each dataset replace white space with delimiter token add unk and pad token 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 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 detecting last checkpoint setup logging log on each process the small summary set the verbosity to info of the transformers logger on main process only set seed before initializing model 1 first let s load the dataset 2 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 save special tokens for tokenizer 3 next let s load the config as we might need it to create the tokenizer load config 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 save vocab in training output dir save vocab dict to be loaded into tokenizer if tokenizer has just been created it is defined by tokenizer_class if present in config else by model_type 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 load feature_extractor and tokenizer adapt config create model freeze 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 phoneme_language is only relevant if the model is fine tuned on phoneme classification preprocessing the datasets we need to read the audio files as arrays and tokenize the targets load audio encode targets filter data that is shorter than min_input_length 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 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 we do not want to group tokens when computing the metrics now save everything to be able to create a single processor later save feature extractor tokenizer and config instantiate custom data collator initialize trainer 8 finally we can start training training use last checkpoint if exist evaluation write model card and optionally push to hub
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 bitsandbytes as bnb
import datasets
import numpy as np
import torch
from datasets import DatasetDict, load_dataset, load_metric

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



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:
    

    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 vectorspan"
                " 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:
    

    dataset_name: str = field(
        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+validation",
        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="test",
        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'"},
    )
    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:`huggingface-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."
            )
        },
    )


@dataclass
class DataCollatorCTCWithPadding:
    

    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]:
        
        
        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",
        )

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

        
        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,
):
    
    def extract_all_chars(batch):
        all_text = " ".join(batch["target_text"])
        vocab = list(set(all_text))
        return {"vocab": [vocab], "all_text": [all_text]}

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

    
    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(vocab_set))}

    
    if word_delimiter_token is not None:
        vocab_dict[word_delimiter_token] = vocab_dict[" "]
        del vocab_dict[" "]

    
    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 main():
    
    
    

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        
        
        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()

    
    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."
            )

    
    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)

    
    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}"
    )
    
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    
    set_seed(training_args.seed)

    
    raw_datasets = DatasetDict()

    if training_args.do_train:
        raw_datasets["train"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.train_split_name,
            token=data_args.use_auth_token,
        )

        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 data_args.max_train_samples is not None:
            raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))

    if training_args.do_eval:
        raw_datasets["eval"] = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            split=data_args.eval_split_name,
            token=data_args.use_auth_token,
        )

        if data_args.max_eval_samples is not None:
            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))

    
    
    
    
    chars_to_ignore_regex = (
        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
    )
    text_column_name = data_args.text_column_name

    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]).lower() + " "
        else:
            batch["target_text"] = batch[text_column_name].lower() + " "
        return batch

    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",
        )

    
    word_delimiter_token = data_args.word_delimiter_token
    unk_token = data_args.unk_token
    pad_token = data_args.pad_token

    
    
    
    config = AutoConfig.from_pretrained(
        model_args.model_name_or_path, cache_dir=model_args.cache_dir, token=data_args.use_auth_token
    )

    
    
    
    
    
    tokenizer_name_or_path = model_args.tokenizer_name_or_path
    tokenizer_kwargs = {}
    if tokenizer_name_or_path is None:
        
        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,
                )

                
                with open(vocab_file, "w") as file:
                    json.dump(vocab_dict, file)

        
        
        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,
        }

    
    
    

    
    tokenizer = AutoTokenizer.from_pretrained(
        tokenizer_name_or_path,
        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, token=data_args.use_auth_token
    )

    
    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,
        }
    )

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

    
    if model_args.freeze_feature_encoder:
        model.freeze_feature_encoder()

    
    
    
    

    
    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)
        )

    
    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
    num_workers = data_args.preprocessing_num_workers

    
    phoneme_language = data_args.phoneme_language

    
    
    def prepare_dataset(batch):
        
        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"])

        
        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 = raw_datasets.map(
            prepare_dataset,
            remove_columns=next(iter(raw_datasets.values())).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

        
        vectorized_datasets = vectorized_datasets.filter(
            is_audio_in_length_range,
            num_proc=num_workers,
            input_columns=["input_length"],
        )

    
    
    

    
    eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}

    
    
    
    
    
    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)
        
        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

    
    if is_main_process(training_args.local_rank):
        
        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)

    
    data_collator = DataCollatorCTCWithPadding(processor=processor)

    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,
        },
    ]
    optimizer = bnb.optim.Adam8bit(
        params=optimizer_grouped_parameters,
        lr=training_args.learning_rate,
        betas=(training_args.adam_beta1, training_args.adam_beta2),
        eps=training_args.adam_epsilon,
    )

    optimizers = (optimizer, None)

    
    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,
        optimizers=optimizers,
    )

    

    
    if training_args.do_train:
        
        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()

    
    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)

    
    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": "automatic-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:"
            f" {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()