mirror of
https://github.com/huggingface/transformers.git
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5c673efad7
help for `ModelArguments.gradient_checkpointing` should be "If True, use gradient checkpointing to save memory at the expense of slower backward pass." not "Whether to freeze the feature extractor layers of the model." (which is duplicated from `freeze_feature_extractor` arg)
480 lines
20 KiB
Python
Executable File
480 lines
20 KiB
Python
Executable File
#!/usr/bin/env python3
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import logging
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import pathlib
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import re
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import sys
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from dataclasses import dataclass, field
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from typing import Any, Callable, Dict, List, Optional, Set, Union
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import datasets
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import numpy as np
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import torch
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from packaging import version
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from torch import nn
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import librosa
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from lang_trans import arabic
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from transformers import (
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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Wav2Vec2CTCTokenizer,
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Wav2Vec2FeatureExtractor,
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Wav2Vec2ForCTC,
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Wav2Vec2Processor,
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is_apex_available,
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trainer_utils,
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)
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if is_apex_available():
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from apex import amp
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if version.parse(torch.__version__) >= version.parse("1.6"):
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_is_native_amp_available = True
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from torch.cuda.amp import autocast
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logger = logging.getLogger(__name__)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
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)
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freeze_feature_extractor: Optional[bool] = field(
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default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
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)
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gradient_checkpointing: Optional[bool] = field(
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default=False,
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metadata={
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"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
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},
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)
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verbose_logging: Optional[bool] = field(
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default=False,
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metadata={"help": "Whether to log verbose messages or not."},
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)
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def configure_logger(model_args: ModelArguments, training_args: TrainingArguments):
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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logging_level = logging.WARNING
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if model_args.verbose_logging:
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logging_level = logging.DEBUG
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elif trainer_utils.is_main_process(training_args.local_rank):
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logging_level = logging.INFO
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logger.setLevel(logging_level)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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Using `HfArgumentParser` we can turn this class
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into argparse arguments to be able to specify them on
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the command line.
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"""
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dataset_name: str = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_split_name: Optional[str] = field(
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default="train",
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metadata={
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"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
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},
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)
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validation_split_name: Optional[str] = field(
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default="validation",
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metadata={
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"help": "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
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},
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)
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target_text_column: Optional[str] = field(
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default="text",
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metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"},
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)
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speech_file_column: Optional[str] = field(
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default="file",
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metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"},
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)
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target_feature_extractor_sampling_rate: Optional[bool] = field(
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default=False,
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metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."},
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)
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max_duration_in_seconds: Optional[float] = field(
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default=None,
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metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."},
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)
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orthography: Optional[str] = field(
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default="librispeech",
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metadata={
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"help": "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or 'buckwalter'."
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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@dataclass
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class Orthography:
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"""
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Orthography scheme used for text normalization and tokenization.
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Args:
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do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to accept lowercase input and lowercase the output when decoding.
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vocab_file (:obj:`str`, `optional`):
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File containing the vocabulary.
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word_delimiter_token (:obj:`str`, `optional`, defaults to :obj:`"|"`):
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The token used for delimiting words; it needs to be in the vocabulary.
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translation_table (:obj:`Dict[str, str]`, `optional`, defaults to :obj:`{}`):
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Table to use with `str.translate()` when preprocessing text (e.g., "-" -> " ").
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words_to_remove (:obj:`Set[str]`, `optional`, defaults to :obj:`set()`):
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Words to remove when preprocessing text (e.g., "sil").
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untransliterator (:obj:`Callable[[str], str]`, `optional`):
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Function that untransliterates text back into native writing system.
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"""
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do_lower_case: bool = False
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vocab_file: Optional[str] = None
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word_delimiter_token: Optional[str] = "|"
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translation_table: Optional[Dict[str, str]] = field(default_factory=dict)
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words_to_remove: Optional[Set[str]] = field(default_factory=set)
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untransliterator: Optional[Callable[[str], str]] = None
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@classmethod
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def from_name(cls, name: str):
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if name == "librispeech":
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return cls()
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if name == "timit":
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return cls(
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do_lower_case=True,
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# break compounds like "quarter-century-old" and replace pauses "--"
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translation_table=str.maketrans({"-": " "}),
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)
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if name == "buckwalter":
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translation_table = {
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"-": " ", # sometimes used to represent pauses
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"^": "v", # fixing "tha" in arabic_speech_corpus dataset
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}
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return cls(
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vocab_file=pathlib.Path(__file__).parent.joinpath("vocab/buckwalter.json"),
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word_delimiter_token="/", # "|" is Arabic letter alef with madda above
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translation_table=str.maketrans(translation_table),
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words_to_remove={"sil"}, # fixing "sil" in arabic_speech_corpus dataset
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untransliterator=arabic.buckwalter.untransliterate,
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)
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raise ValueError(f"Unsupported orthography: '{name}'.")
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def preprocess_for_training(self, text: str) -> str:
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# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
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if len(self.translation_table) > 0:
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text = text.translate(self.translation_table)
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if len(self.words_to_remove) == 0:
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text = " ".join(text.split()) # clean up whitespaces
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else:
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text = " ".join(w for w in text.split() if w not in self.words_to_remove) # and clean up whilespaces
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return text
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def create_processor(self, model_args: ModelArguments) -> Wav2Vec2Processor:
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir
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)
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if self.vocab_file:
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tokenizer = Wav2Vec2CTCTokenizer(
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self.vocab_file,
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cache_dir=model_args.cache_dir,
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do_lower_case=self.do_lower_case,
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word_delimiter_token=self.word_delimiter_token,
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)
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else:
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tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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do_lower_case=self.do_lower_case,
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word_delimiter_token=self.word_delimiter_token,
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)
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return Wav2Vec2Processor(feature_extractor, tokenizer)
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@dataclass
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class DataCollatorCTCWithPadding:
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"""
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Data collator that will dynamically pad the inputs received.
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Args:
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processor (:class:`~transformers.Wav2Vec2Processor`)
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The processor used for proccessing the data.
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padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
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among:
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* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
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maximum acceptable input length for the model if that argument is not provided.
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* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
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different lengths).
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max_length (:obj:`int`, `optional`):
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Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
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max_length_labels (:obj:`int`, `optional`):
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Maximum length of the ``labels`` returned list and optionally padding length (see above).
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pad_to_multiple_of (:obj:`int`, `optional`):
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If set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
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7.5 (Volta).
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"""
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processor: Wav2Vec2Processor
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padding: Union[bool, str] = True
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max_length: Optional[int] = None
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max_length_labels: Optional[int] = None
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pad_to_multiple_of: Optional[int] = None
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pad_to_multiple_of_labels: Optional[int] = None
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def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
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# split inputs and labels since they have to be of different lenghts and need
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# different padding methods
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input_features = [{"input_values": feature["input_values"]} for feature in features]
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label_features = [{"input_ids": feature["labels"]} for feature in features]
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batch = self.processor.pad(
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input_features,
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padding=self.padding,
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max_length=self.max_length,
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pad_to_multiple_of=self.pad_to_multiple_of,
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return_tensors="pt",
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)
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with self.processor.as_target_processor():
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labels_batch = self.processor.pad(
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label_features,
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padding=self.padding,
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max_length=self.max_length_labels,
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pad_to_multiple_of=self.pad_to_multiple_of_labels,
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return_tensors="pt",
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)
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# replace padding with -100 to ignore loss correctly
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labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
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batch["labels"] = labels
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return batch
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class CTCTrainer(Trainer):
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def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
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"""
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Perform a training step on a batch of inputs.
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Subclass and override to inject custom behavior.
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Args:
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model (:obj:`nn.Module`):
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The model to train.
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inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
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The inputs and targets of the model.
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The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
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argument :obj:`labels`. Check your model's documentation for all accepted arguments.
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Return:
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:obj:`torch.Tensor`: The tensor with training loss on this batch.
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"""
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model.train()
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inputs = self._prepare_inputs(inputs)
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if self.use_amp:
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with autocast():
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loss = self.compute_loss(model, inputs)
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else:
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loss = self.compute_loss(model, inputs)
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if self.args.n_gpu > 1:
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if model.module.config.ctc_loss_reduction == "mean":
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loss = loss.mean()
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elif model.module.config.ctc_loss_reduction == "sum":
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loss = loss.sum() / (inputs["labels"] >= 0).sum()
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else:
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raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']")
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if self.args.gradient_accumulation_steps > 1:
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loss = loss / self.args.gradient_accumulation_steps
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if self.use_amp:
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self.scaler.scale(loss).backward()
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elif self.use_apex:
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with amp.scale_loss(loss, self.optimizer) as scaled_loss:
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scaled_loss.backward()
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elif self.deepspeed:
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self.deepspeed.backward(loss)
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else:
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loss.backward()
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return loss.detach()
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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configure_logger(model_args, training_args)
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orthography = Orthography.from_name(data_args.orthography.lower())
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processor = orthography.create_processor(model_args)
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model = Wav2Vec2ForCTC.from_pretrained(
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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gradient_checkpointing=model_args.gradient_checkpointing,
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vocab_size=len(processor.tokenizer),
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)
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train_dataset = datasets.load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
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)
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val_dataset = datasets.load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, split=data_args.validation_split_name
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)
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wer_metric = datasets.load_metric("wer")
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target_sr = processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
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vocabulary_chars_str = "".join(t for t in processor.tokenizer.get_vocab().keys() if len(t) == 1)
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vocabulary_text_cleaner = re.compile( # remove characters not in vocabulary
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f"[^\s{re.escape(vocabulary_chars_str)}]", # allow space in addition to chars in vocabulary
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flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0,
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)
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text_updates = []
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def prepare_example(example): # TODO(elgeish) make use of multiprocessing?
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example["speech"], example["sampling_rate"] = librosa.load(example[data_args.speech_file_column], sr=target_sr)
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if data_args.max_duration_in_seconds is not None:
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example["duration_in_seconds"] = len(example["speech"]) / example["sampling_rate"]
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# Normalize and clean up text; order matters!
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updated_text = orthography.preprocess_for_training(example[data_args.target_text_column])
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updated_text = vocabulary_text_cleaner.sub("", updated_text)
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if updated_text != example[data_args.target_text_column]:
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text_updates.append((example[data_args.target_text_column], updated_text))
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example[data_args.target_text_column] = updated_text
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return example
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train_dataset = train_dataset.map(prepare_example, remove_columns=[data_args.speech_file_column])
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val_dataset = val_dataset.map(prepare_example, remove_columns=[data_args.speech_file_column])
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if data_args.max_duration_in_seconds is not None:
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def filter_by_max_duration(example):
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return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
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old_train_size = len(train_dataset)
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old_val_size = len(val_dataset)
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train_dataset = train_dataset.filter(filter_by_max_duration, remove_columns=["duration_in_seconds"])
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val_dataset = val_dataset.filter(filter_by_max_duration, remove_columns=["duration_in_seconds"])
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if len(train_dataset) > old_train_size:
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logger.warning(
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f"Filtered out {len(train_dataset) - old_train_size} train example(s) longer than {data_args.max_duration_in_seconds} second(s)."
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)
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if len(val_dataset) > old_val_size:
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logger.warning(
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f"Filtered out {len(val_dataset) - old_val_size} validation example(s) longer than {data_args.max_duration_in_seconds} second(s)."
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)
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logger.info(f"Split sizes: {len(train_dataset)} train and {len(val_dataset)} validation.")
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logger.warning(f"Updated {len(text_updates)} transcript(s) using '{data_args.orthography}' orthography rules.")
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if logger.isEnabledFor(logging.DEBUG):
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for original_text, updated_text in text_updates:
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logger.debug(f'Updated text: "{original_text}" -> "{updated_text}"')
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text_updates = None
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def prepare_dataset(batch):
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# check that all files have the correct sampling rate
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assert (
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len(set(batch["sampling_rate"])) == 1
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), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."
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batch["input_values"] = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0]).input_values
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with processor.as_target_processor():
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batch["labels"] = processor(batch[data_args.target_text_column]).input_ids
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return batch
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train_dataset = train_dataset.map(
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prepare_dataset,
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batch_size=training_args.per_device_train_batch_size,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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)
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val_dataset = val_dataset.map(
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prepare_dataset,
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batch_size=training_args.per_device_train_batch_size,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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)
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data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)
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def compute_metrics(pred):
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pred_logits = pred.predictions
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pred_ids = np.argmax(pred_logits, axis=-1)
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pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
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pred_str = processor.batch_decode(pred_ids)
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# we do not want to group tokens when computing the metrics
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label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
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if logger.isEnabledFor(logging.DEBUG):
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|
for reference, predicted in zip(label_str, pred_str):
|
|
logger.debug(f'reference: "{reference}"')
|
|
logger.debug(f'predicted: "{predicted}"')
|
|
if orthography.untransliterator is not None:
|
|
logger.debug(f'reference (untransliterated): "{orthography.untransliterator(reference)}"')
|
|
logger.debug(f'predicted (untransliterated): "{orthography.untransliterator(predicted)}"')
|
|
|
|
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()
|