#!/usr/bin/env python3 from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Union import datasets import numpy as np import torch import torch.nn as nn from packaging import version import soundfile as sf from transformers import ( HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2ForCTC, Wav2Vec2Processor, is_apex_available, ) if is_apex_available(): from apex import amp if version.parse(torch.__version__) >= version.parse("1.6"): _is_native_amp_available = True from torch.cuda.amp import autocast @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"} ) 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."} ) @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( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) 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", 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."}, ) @dataclass class DataCollatorCTCWithPadding: """ 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). """ 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]: # 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, max_length=self.max_length, 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, max_length=self.max_length_labels, 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 class CTCTrainer(Trainer): def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor: """ 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. """ 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(): # 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)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() model = Wav2Vec2ForCTC.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) processor = Wav2Vec2Processor.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) train_dataset = datasets.load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name ) val_dataset = datasets.load_dataset(data_args.dataset_name, data_args.dataset_config_name, split="validation") wer_metric = datasets.load_metric("wer") def map_to_array(batch): speech_array, sampling_rate = sf.read(batch["file"]) batch["speech"] = speech_array batch["sampling_rate"] = sampling_rate return batch train_dataset = train_dataset.map(map_to_array, remove_columns=["file"]) val_dataset = val_dataset.map(map_to_array, remove_columns=["file"]) def prepare_dataset(batch): # check that all files have the correct sampling rate assert ( len(set(batch["sampling_rate"])) == 1 ), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}." batch["input_values"] = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0]).input_values with processor.as_target_processor(): batch["labels"] = processor(batch["text"]).input_ids return batch train_dataset = train_dataset.map( prepare_dataset, batch_size=training_args.per_device_train_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) val_dataset = val_dataset.map( prepare_dataset, batch_size=training_args.per_device_train_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) 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) # we do not want to group tokens when computing the metrics 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() trainer = CTCTrainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=train_dataset, eval_dataset=val_dataset, tokenizer=processor.feature_extractor, ) trainer.train() if __name__ == "__main__": main()