usrbinenv python3 arguments pertaining to which modelconfigtokenizer we are going to finetune 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 wav2vec2processor the processor used for proccessing the data padding obj bool obj str or class transformers tokenizationutilsbase 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 maxlength pad to a maximum length specified with the argument obj maxlength or to the maximum acceptable input length for the model if that argument is not provided obj false or obj donotpad default no padding i e can output a batch with sequences of different lengths maxlength obj int optional maximum length of the inputvalues of the returned list and optionally padding length see above maxlengthlabels obj int optional maximum length of the labels returned list and optionally padding length see above padtomultipleof 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 perform a training step on a batch of inputs subclass and override to inject custom behavior args model obj nn module the model to train inputs obj dictstr uniontorch tensor any the inputs and targets of the model the dictionary will be unpacked before being fed to the model most models expect the targets under the argument obj labels check your model s documentation for all accepted arguments return obj torch tensor the tensor with training loss on this batch 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 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 get the datasets create and save tokenizer load pretrained model and tokenizer distributed training the frompretrained methods guarantee that only one local process can concurrently download model vocab preprocessing the datasets we need to read the aduio files as arrays and tokenize the targets check that all files have the correct sampling rate metric we do not want to group tokens when computing the metrics data collator initialize our trainer training save the featureextractor and the tokenizer evaluation usr bin env python3 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 wav2vec2processor 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 perform a training step on a batch of inputs subclass and override to inject custom behavior args model obj nn module the model to train inputs obj dict str union torch tensor any the inputs and targets of the model the dictionary will be unpacked before being fed to the model most models expect the targets under the argument obj labels check your model s documentation for all accepted arguments return obj torch tensor the tensor with training loss on this batch 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 get the datasets create and save tokenizer load pretrained model and tokenizer distributed training the from_pretrained methods guarantee that only one local process can concurrently download model vocab preprocessing the datasets we need to read the aduio files as arrays and tokenize the targets check that all files have the correct sampling rate metric we do not want to group tokens when computing the metrics data collator initialize our trainer training save the feature_extractor and the tokenizer evaluation
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    Wav2Vec2CTCTokenizer,
    Wav2Vec2FeatureExtractor,
    Wav2Vec2ForCTC,
    Wav2Vec2Processor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
    from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
    _is_native_amp_available = True
    from torch.cuda.amp import autocast

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"}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )
    freeze_feature_extractor: Optional[bool] = field(
        default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
    )
    attention_dropout: Optional[float] = field(
        default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."}
    )
    activation_dropout: Optional[float] = field(
        default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
    )
    hidden_dropout: Optional[float] = field(
        default=0.1,
        metadata={
            "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
        },
    )
    feat_proj_dropout: Optional[float] = field(
        default=0.1,
        metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."},
    )
    mask_time_prob: Optional[float] = field(
        default=0.05,
        metadata={
            "help": (
                "Propability 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. This is only relevant if ``apply_spec_augment is True``."
            )
        },
    )
    layerdrop: Optional[float] = field(default=0.0, metadata={"help": "The LayerDrop probability."})


@dataclass
class DataTrainingArguments:
    

    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    train_split_name: Optional[str] = field(
        default="train+validation",
        metadata={
            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
        },
    )
    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_val_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: List[str] = list_field(
        default=[",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�"],
        metadata={"help": "A list of characters to remove from the transcripts."},
    )


@dataclass
class DataCollatorCTCWithPadding:
    

    processor: Wav2Vec2Processor
    padding: Union[bool, str] = True
    max_length: Optional[int] = None
    max_length_labels: Optional[int] = None
    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,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )
        labels_batch = self.processor.pad(
            labels=label_features,
            padding=self.padding,
            max_length=self.max_length_labels,
            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


class CTCTrainer(Trainer):
    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
        

        model.train()
        inputs = self._prepare_inputs(inputs)

        if self.use_amp:
            with autocast():
                loss = self.compute_loss(model, inputs)
        else:
            loss = self.compute_loss(model, inputs)

        if self.args.n_gpu > 1:
            if model.module.config.ctc_loss_reduction == "mean":
                loss = loss.mean()
            elif model.module.config.ctc_loss_reduction == "sum":
                loss = loss.sum() / (inputs["labels"] >= 0).sum()
            else:
                raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']")

        if self.args.gradient_accumulation_steps > 1:
            loss = loss / self.args.gradient_accumulation_steps

        if self.use_amp:
            self.scaler.scale(loss).backward()
        elif self.use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        elif self.deepspeed:
            self.deepspeed.backward(loss)
        else:
            loss.backward()

        return loss.detach()


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)

    
    train_dataset = datasets.load_dataset(
        "common_voice", data_args.dataset_config_name, split=data_args.train_split_name
    )
    eval_dataset = datasets.load_dataset("common_voice", data_args.dataset_config_name, split="test")

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

    def remove_special_characters(batch):
        batch["text"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower() + " "
        return batch

    train_dataset = train_dataset.map(remove_special_characters, remove_columns=["sentence"])
    eval_dataset = eval_dataset.map(remove_special_characters, remove_columns=["sentence"])

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

    vocab_train = train_dataset.map(
        extract_all_chars,
        batched=True,
        batch_size=-1,
        keep_in_memory=True,
        remove_columns=train_dataset.column_names,
    )
    vocab_test = train_dataset.map(
        extract_all_chars,
        batched=True,
        batch_size=-1,
        keep_in_memory=True,
        remove_columns=eval_dataset.column_names,
    )

    vocab_list = list(set(vocab_train["vocab"][0]) | set(vocab_test["vocab"][0]))
    vocab_dict = {v: k for k, v in enumerate(vocab_list)}
    vocab_dict["|"] = vocab_dict[" "]
    del vocab_dict[" "]
    vocab_dict["[UNK]"] = len(vocab_dict)
    vocab_dict["[PAD]"] = len(vocab_dict)

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

    
    
    
    
    
    tokenizer = Wav2Vec2CTCTokenizer(
        "vocab.json",
        unk_token="[UNK]",
        pad_token="[PAD]",
        word_delimiter_token="|",
    )
    feature_extractor = Wav2Vec2FeatureExtractor(
        feature_size=1, sampling_rate=16_000, padding_value=0.0, do_normalize=True, return_attention_mask=True
    )
    processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
    model = Wav2Vec2ForCTC.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        activation_dropout=model_args.activation_dropout,
        attention_dropout=model_args.attention_dropout,
        hidden_dropout=model_args.hidden_dropout,
        feat_proj_dropout=model_args.feat_proj_dropout,
        mask_time_prob=model_args.mask_time_prob,
        gradient_checkpointing=training_args.gradient_checkpointing,
        layerdrop=model_args.layerdrop,
        ctc_loss_reduction="mean",
        pad_token_id=processor.tokenizer.pad_token_id,
        vocab_size=len(processor.tokenizer),
    )

    if data_args.max_train_samples is not None:
        max_train_samples = min(len(train_dataset), data_args.max_train_samples)
        train_dataset = train_dataset.select(range(max_train_samples))

    if data_args.max_val_samples is not None:
        eval_dataset = eval_dataset.select(range(data_args.max_val_samples))

    resampler = torchaudio.transforms.Resample(48_000, 16_000)

    
    
    def speech_file_to_array_fn(batch):
        speech_array, sampling_rate = torchaudio.load(batch["path"])
        batch["speech"] = resampler(speech_array).squeeze().numpy()
        batch["sampling_rate"] = 16_000
        batch["target_text"] = batch["text"]
        return batch

    train_dataset = train_dataset.map(
        speech_file_to_array_fn,
        remove_columns=train_dataset.column_names,
        num_proc=data_args.preprocessing_num_workers,
    )
    eval_dataset = eval_dataset.map(
        speech_file_to_array_fn,
        remove_columns=eval_dataset.column_names,
        num_proc=data_args.preprocessing_num_workers,
    )

    def prepare_dataset(batch):
        
        assert (
            len(set(batch["sampling_rate"])) == 1
        ), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

        processed_batch = processor(
            audio=batch["speech"], text=batch["target_text"], sampling_rate=batch["sampling_rate"][0]
        )
        batch.update(processed_batch)
        return batch

    train_dataset = train_dataset.map(
        prepare_dataset,
        remove_columns=train_dataset.column_names,
        batch_size=training_args.per_device_train_batch_size,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
    )
    eval_dataset = eval_dataset.map(
        prepare_dataset,
        remove_columns=eval_dataset.column_names,
        batch_size=training_args.per_device_train_batch_size,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
    )

    
    wer_metric = datasets.load_metric("wer")

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

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

        pred_str = processor.batch_decode(pred_ids)
        
        label_str = processor.batch_decode(pred.label_ids, group_tokens=False)

        wer = wer_metric.compute(predictions=pred_str, references=label_str)

        return {"wer": wer}

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    
    data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)

    
    trainer = CTCTrainer(
        model=model,
        data_collator=data_collator,
        args=training_args,
        compute_metrics=compute_metrics,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=processor.feature_extractor,
    )

    
    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

        
        if is_main_process(training_args.local_rank):
            processor.save_pretrained(training_args.output_dir)

        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(train_dataset)
        )
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        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_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset)
        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    return results


if __name__ == "__main__":
    main()