mirror of
https://github.com/huggingface/transformers.git
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dbaf49203e
* Update SEW integration test tolerance * Update audio classification * Update test * Remove torchaudio * Add dataset revision * Hub branch naming * Revert dataset revisions * Update datasets
610 lines
25 KiB
Python
Executable File
610 lines
25 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition"""
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import functools
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import json
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import logging
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import os
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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 Dict, List, Optional, 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 datasets import DatasetDict, load_dataset, load_metric
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import transformers
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from transformers import (
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AutoConfig,
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AutoFeatureExtractor,
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AutoModelForCTC,
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AutoTokenizer,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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Wav2Vec2Processor,
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set_seed,
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)
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from transformers.trainer_utils import get_last_checkpoint, is_main_process
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.12.0.dev0")
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require_version("datasets>=1.13.3", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
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logger = logging.getLogger(__name__)
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def list_field(default=None, metadata=None):
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return field(default_factory=lambda: default, metadata=metadata)
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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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attention_dropout: Optional[float] = field(
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default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
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)
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activation_dropout: Optional[float] = field(
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default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
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)
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feat_proj_dropout: Optional[float] = field(
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default=0.0, metadata={"help": "The dropout ratio for the projected features."}
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)
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hidden_dropout: Optional[float] = field(
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default=0.0,
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metadata={
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"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
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},
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)
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final_dropout: Optional[float] = field(
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default=0.0,
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metadata={"help": "The dropout probability for the final projection layer."},
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)
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mask_time_prob: Optional[float] = field(
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default=0.05,
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metadata={
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"help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
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"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
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"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
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},
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)
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layerdrop: Optional[float] = field(default=0.0, metadata={"help": "The LayerDrop probability."})
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ctc_loss_reduction: Optional[str] = field(
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default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
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)
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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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metadata={"help": "The configuration 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+validation",
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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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eval_split_name: Optional[str] = field(
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default="test",
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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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audio_column_name: Optional[str] = field(
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default="audio",
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metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
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)
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text_column_name: Optional[str] = field(
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default="text",
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metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
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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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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
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"value if set."
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},
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)
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chars_to_ignore: Optional[List[str]] = list_field(
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default=None,
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metadata={"help": "A list of characters to remove from the transcripts."},
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)
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max_duration_in_seconds: Optional[float] = field(
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default=20.0,
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metadata={
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"help": "Truncate audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
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},
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)
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min_duration_in_seconds: Optional[float] = field(
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default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
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)
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preprocessing_only: Optional[bool] = field(
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default=False,
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metadata={
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"help": "Whether to only do data preprocessing and skip training. "
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"This is especially useful when data preprocessing errors out in distributed training due to timeout. "
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"In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
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"so that the cached datasets can consequently be loaded in distributed training"
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},
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)
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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] = "longest"
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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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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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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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def create_vocabulary_from_data(datasets: DatasetDict):
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# Given training and test labels create vocabulary
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def extract_all_chars(batch):
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all_text = " ".join(batch["target_text"])
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vocab = list(set(all_text))
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return {"vocab": [vocab], "all_text": [all_text]}
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vocabs = datasets.map(
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extract_all_chars,
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batched=True,
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batch_size=-1,
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keep_in_memory=True,
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remove_columns=datasets["train"].column_names,
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)
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# take union of all unique characters in each dataset
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vocab_set = functools.reduce(
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lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
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)
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vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
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# replace white space with delimiter token
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vocab_dict["|"] = vocab_dict[" "]
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del vocab_dict[" "]
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# add unk and pad token
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vocab_dict["[UNK]"] = len(vocab_dict)
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vocab_dict["[PAD]"] = len(vocab_dict)
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return vocab_dict
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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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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to overcome."
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)
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elif last_checkpoint is not None:
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logger.info(
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f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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# Setup logging
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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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logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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# Set the verbosity to info of the Transformers logger (on main process only):
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if is_main_process(training_args.local_rank):
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transformers.utils.logging.set_verbosity_info()
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logger.info("Training/evaluation parameters %s", training_args)
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# 1. First, let's load the dataset
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raw_datasets = DatasetDict()
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raw_datasets["train"] = 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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raw_datasets["eval"] = load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
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)
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if data_args.audio_column_name not in raw_datasets["train"].column_names:
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raise ValueError(
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f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
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"Make sure to set `--audio_column_name` to the correct audio column - one of "
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f"{', '.join(raw_datasets['train'].column_names)}."
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)
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if data_args.text_column_name not in raw_datasets["train"].column_names:
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raise ValueError(
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f"--text_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
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"Make sure to set `--text_column_name` to the correct text column - one of "
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f"{', '.join(raw_datasets['train'].column_names)}."
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)
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# prepare dataset
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if data_args.max_train_samples is not None:
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raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
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if data_args.max_eval_samples is not None:
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raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
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# 2. We remove some special characters from the datasets
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# that make training complicated and do not help in transcribing the speech
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# E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
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# that could be easily picked up by the model
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chars_to_ignore_regex = (
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f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
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)
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def remove_special_characters(batch):
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if chars_to_ignore_regex is not None:
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batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[data_args.text_column_name]).lower() + " "
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else:
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batch["target_text"] = batch[data_args.text_column_name].lower() + " "
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return batch
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with training_args.main_process_first(desc="dataset map special characters removal"):
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raw_datasets = raw_datasets.map(
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remove_special_characters,
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remove_columns=[data_args.text_column_name],
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desc="remove special characters from datasets",
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)
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# 3. Next, we create the vocabulary of the model by extracting all unique characters from
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# the training and evaluation datasets
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# We need to make sure that only first rank saves vocabulary
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# make sure all processes wait until vocab is created
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with training_args.main_process_first(desc="dataset map vocabulary creation"):
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vocab_dict = create_vocabulary_from_data(raw_datasets)
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vocab_file = os.path.join(training_args.output_dir, "vocab.json")
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# save vocab dict to be loaded into tokenizer
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os.makedirs(training_args.output_dir, exist_ok=True)
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if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
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os.remove(vocab_file)
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if not os.path.isfile(vocab_file):
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with open(vocab_file, "w") as vocab_file:
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json.dump(vocab_dict, vocab_file)
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# 4. Now we can instantiate the configuration, feature extractor, tokenizer and model
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# Note for distributed training, the .from_pretrained methods guarantee that only
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# one local process can concurrently download model & vocab.
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# load config
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
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# load feature_extractor, tokenizer and create processor
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tokenizer = AutoTokenizer.from_pretrained(
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training_args.output_dir,
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tokenizer_type=config.model_type,
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unk_token="[UNK]",
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pad_token="[PAD]",
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word_delimiter_token="|",
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)
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feature_extractor = AutoFeatureExtractor.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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processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
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# adapt config
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config.update(
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{
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"feat_proj_dropout": model_args.feat_proj_dropout,
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"attention_dropout": model_args.attention_dropout,
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"hidden_dropout": model_args.hidden_dropout,
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"final_dropout": model_args.final_dropout,
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"mask_time_prob": model_args.mask_time_prob,
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"gradient_checkpointing": training_args.gradient_checkpointing,
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"layerdrop": model_args.layerdrop,
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"ctc_loss_reduction": model_args.ctc_loss_reduction,
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"pad_token_id": processor.tokenizer.pad_token_id,
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"vocab_size": len(processor.tokenizer),
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"activation_dropout": model_args.activation_dropout,
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}
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)
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# create model
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model = AutoModelForCTC.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir, config=config
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)
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# freeze encoder
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if model_args.freeze_feature_extractor:
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model.freeze_feature_extractor()
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# 5. Now we preprocess the datasets including loading the audio, resampling and normalization
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# 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
|
|
raw_datasets = raw_datasets.cast_column(
|
|
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
|
|
)
|
|
|
|
# derive max & min input length for sample rate & max duration
|
|
max_input_length = data_args.max_duration_in_seconds * processor.feature_extractor.sampling_rate
|
|
min_input_length = data_args.min_duration_in_seconds * processor.feature_extractor.sampling_rate
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to read the audio files as arrays and tokenize the targets.
|
|
def prepare_dataset(batch):
|
|
# load audio
|
|
sample = batch[data_args.audio_column_name]
|
|
|
|
batch["input_values"] = processor(
|
|
sample["array"], sampling_rate=sample["sampling_rate"], truncate=True, max_length=max_input_length
|
|
).input_values[0]
|
|
batch["input_length"] = len(batch["input_values"])
|
|
|
|
# Setup the processor for targets
|
|
with processor.as_target_processor():
|
|
batch["labels"] = processor(batch["target_text"]).input_ids
|
|
return batch
|
|
|
|
with training_args.main_process_first(desc="dataset map preprocessing"):
|
|
vectorized_datasets = raw_datasets.map(
|
|
prepare_dataset,
|
|
remove_columns=raw_datasets["train"].column_names,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
desc="preprocess datasets",
|
|
)
|
|
|
|
if min_input_length > 0.0:
|
|
# filter data that is shorter than min_input_length
|
|
vectorized_datasets = vectorized_datasets.filter(
|
|
lambda x: x > min_input_length,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
input_columns=["input_length"],
|
|
)
|
|
|
|
vectorized_datasets = vectorized_datasets.remove_columns("input_length")
|
|
|
|
# 6. 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 Metric during training
|
|
wer_metric = load_metric("wer")
|
|
|
|
# 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
|
|
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] = 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}
|
|
|
|
# Instantiate custom data collator
|
|
data_collator = DataCollatorCTCWithPadding(processor=processor)
|
|
|
|
# Initialize Trainer
|
|
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=processor.feature_extractor,
|
|
)
|
|
|
|
# 7. Finally, we can start training
|
|
|
|
# Training
|
|
if training_args.do_train:
|
|
|
|
# use last checkpoint if exist
|
|
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
|
|
|
|
# Save the feature_extractor and the tokenizer
|
|
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(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()
|
|
|
|
# Evaluation
|
|
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)
|
|
|
|
# Write model card and (optionally) push to hub
|
|
kwargs = {
|
|
"finetuned_from": model_args.model_name_or_path,
|
|
"tasks": "speech-recognition",
|
|
"tags": ["automatic-speech-recognition", data_args.dataset_name],
|
|
"dataset_args": f"Config: {data_args.dataset_config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
|
|
"dataset": f"{data_args.dataset_name.upper()} - {data_args.dataset_config_name.upper()}",
|
|
}
|
|
if "common_voice" in data_args.dataset_name:
|
|
kwargs["language"] = data_args.dataset_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()
|