#!/usr/bin/env python3 import logging import pathlib import re import sys import time import csv from dataclasses import dataclass, field from typing import Any, Callable, Dict, List, Optional, Set, Union import datasets import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from packaging import version from torch.cuda.amp import GradScaler, autocast import librosa from lang_trans import arabic from datasets import Dataset import soundfile as sf from model import Wav2Vec2ForCTCnCLS from transformers.trainer_utils import get_last_checkpoint from transformers import ( HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, is_apex_available, trainer_utils, ) local_model_path = "local_model" 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 logger = logging.getLogger(__name__) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default="local_model", 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=False, metadata={"help": "Whether to freeze the feature extractor layers of the model."} ) verbose_logging: Optional[bool] = field( default=False, metadata={"help": "Whether to log verbose messages or not."}, ) tokenizer: Optional[str] = field( default="checkpoint-33000", metadata={"help": "Path to pretrained tokenizer"} ) def configure_logger(model_args: ModelArguments, training_args: TrainingArguments): logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logging_level = logging.WARNING if model_args.verbose_logging: logging_level = logging.DEBUG elif trainer_utils.is_main_process(training_args.local_rank): logging_level = logging.INFO logger.setLevel(logging_level) @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='emotion', 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'" }, ) validation_split_name: Optional[str] = field( default="validation", metadata={ "help": "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'" }, ) target_text_column: Optional[str] = field( default="text", metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"}, ) speech_file_column: Optional[str] = field( default="file", metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}, ) target_feature_extractor_sampling_rate: Optional[bool] = field( default=False, metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."}, ) max_duration_in_seconds: Optional[float] = field( default=None, metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."}, ) orthography: Optional[str] = field( default="librispeech", metadata={ "help": "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or 'buckwalter'." }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) preprocessing_num_workers: Optional[int] = field( default=8, metadata={"help": "The number of processes to use for the preprocessing."}, ) output_file: Optional[str] = field( default=None, metadata={"help": "Output file."}, ) @dataclass class Orthography: """ Orthography scheme used for text normalization and tokenization. Args: do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to accept lowercase input and lowercase the output when decoding. vocab_file (:obj:`str`, `optional`, defaults to :obj:`None`): File containing the vocabulary. word_delimiter_token (:obj:`str`, `optional`, defaults to :obj:`"|"`): The token used for delimiting words; it needs to be in the vocabulary. translation_table (:obj:`Dict[str, str]`, `optional`, defaults to :obj:`{}`): Table to use with `str.translate()` when preprocessing text (e.g., "-" -> " "). words_to_remove (:obj:`Set[str]`, `optional`, defaults to :obj:`set()`): Words to remove when preprocessing text (e.g., "sil"). untransliterator (:obj:`Callable[[str], str]`, `optional`, defaults to :obj:`None`): Function that untransliterates text back into native writing system. tokenizer (:obj:`str`, `optional`, defaults to :obj:`None`): Tokenizer type, e.g., 'jieba' for Chinese. """ do_lower_case: bool = False vocab_file: Optional[str] = None word_delimiter_token: Optional[str] = "|" translation_table: Optional[Dict[str, str]] = field(default_factory=dict) words_to_remove: Optional[Set[str]] = field(default_factory=set) tokenizer: Optional[str] = None untransliterator: Optional[Callable[[str], str]] = None @classmethod def from_name(cls, name: str): if name == "librispeech": return cls() else: raise ValueError(f"Unsupported orthography: '{name}'.") def create_processor(self, model_args: ModelArguments) -> Wav2Vec2Processor: feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( local_model_path, cache_dir=model_args.cache_dir ) if self.vocab_file: tokenizer = Wav2Vec2CTCTokenizer( self.vocab_file, cache_dir=model_args.cache_dir, do_lower_case=self.do_lower_case, word_delimiter_token=self.word_delimiter_token, ) else: tokenizer = Wav2Vec2CTCTokenizer.from_pretrained( local_model_path, # self.tokenizer, cache_dir=model_args.cache_dir, do_lower_case=self.do_lower_case, word_delimiter_token=self.word_delimiter_token, device_map="cuda:0", ) return Wav2Vec2Processor(feature_extractor, tokenizer) @dataclass class TrainingArguments(TrainingArguments): output_dir: str = field( default="output/angry_tmp", metadata={"help": "The store of your output."}) do_predict: bool = field( default=True, metadata={"help": "The store of your output."}) do_eval: bool = field( default=False, metadata={"help": "The store of your output."}) overwrite_output_dir: str = field( default='overwrite_output_dir', metadata={"help": "The store of your output."} ) per_device_eval_batch_size: int = field( default=2, metadata={"help": "The store of your output."}) warmup_ratio: float = field( default=0.1, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."} ) @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 audio_only = False duration = 6 sample_rate = 16000 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] batch = self.processor.pad( input_features, padding=self.padding, # max_length=self.max_length, max_length=self.duration*self.sample_rate, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) return batch class CTCTrainer(Trainer): def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]: self.use_amp = False self.use_apex = False self.deepspeed = False self.scaler = GradScaler() for k, v in inputs.items(): if isinstance(v, torch.Tensor): kwargs = dict(device=self.args.device) if self.deepspeed and inputs[k].dtype != torch.int64: kwargs.update(dict(dtype=self.args.hf_deepspeed_config.dtype())) inputs[k] = v.to(**kwargs) if self.args.past_index >= 0 and self._past is not None: inputs["mems"] = self._past return inputs def create_dataset(audio_path): data = { 'file': [audio_path] } dataset = Dataset.from_dict(data) return dataset def exeute_angry_predict(audio_path): # 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. target_sr = 16000 parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() configure_logger(model_args, training_args) orthography = Orthography.from_name(data_args.orthography.lower()) orthography.tokenizer = model_args.tokenizer processor = orthography.create_processor(model_args) if data_args.dataset_name == 'emotion': val_dataset = create_dataset(audio_path) cls_label_map = {"neutral":0, "angry":1} model = Wav2Vec2ForCTCnCLS.from_pretrained( local_model_path, gradient_checkpointing=True, # training_args.gradient_checkpointing, cls_len=len(cls_label_map), ) def prepare_example(example, audio_only=False): # TODO(elgeish) make use of multiprocessing? example["speech"], example["sampling_rate"] = librosa.load(example[data_args.speech_file_column], sr=target_sr) orig_sample_rate = example["sampling_rate"] target_sample_rate = target_sr if orig_sample_rate != target_sample_rate: example["speech"] = librosa.resample(example["speech"], orig_sr=orig_sample_rate, target_sr=target_sample_rate) if data_args.max_duration_in_seconds is not None: example["duration_in_seconds"] = len(example["speech"]) / example["sampling_rate"] return example if training_args.do_predict: val_dataset = val_dataset.map(prepare_example, fn_kwargs={'audio_only':True}) def prepare_dataset(batch, audio_only=False): # 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 return batch if training_args.do_predict: val_dataset = val_dataset.map( prepare_dataset, fn_kwargs={'audio_only':True}, batch_size=training_args.per_device_eval_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) if model_args.freeze_feature_extractor: model.freeze_feature_extractor() trainer = CTCTrainer( model=model, args=training_args, eval_dataset=val_dataset, tokenizer=processor.feature_extractor, ) if training_args.do_predict: logger.info('******* Predict ********') data_collator.audio_only=True results= {} result= '' predictions, labels, metrics = trainer.predict(val_dataset, metric_key_prefix="predict") logits_ctc, logits_cls = predictions pred_ids = np.argmax(logits_cls, axis=-1) if pred_ids==0: result = "非愤怒" if pred_ids==1: result = "愤怒" results[audio_path] = result print("results", results) if __name__ == "__main__": audio_path = 'audio.mp3' exeute_angry_predict(audio_path)