982 lines
36 KiB
Python
982 lines
36 KiB
Python
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# !/usr/bin/env python
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# coding=utf-8
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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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import warnings
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from dataclasses import dataclass, field
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from typing import Any, Callable, 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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import torchaudio
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from datasets import DatasetDict, ReadInstruction, load_dataset, load_metric, concatenate_datasets
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try:
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import bitsandbytes as bnb
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BNB_AVAILABLE = True
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except:
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BNB_AVAILABLE = False
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try:
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import wandb
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WANDB_AVAILABLE = True
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except:
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WANDB_AVAILABLE = False
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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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TrainerCallback, TrainingArguments,
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Wav2Vec2Processor,
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set_seed,
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)
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try:
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from torch_audiomentations import (
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Compose,
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AddGaussianNoise,
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AddGaussianSNR,
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ClippingDistortion,
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FrequencyMask,
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Gain,
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LoudnessNormalization,
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Normalize,
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PitchShift,
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PolarityInversion,
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Shift,
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TimeMask,
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TimeStretch,
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)
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AUDIOMENTATIONS_AVAILABLE = True
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except:
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AUDIOMENTATIONS_AVAILABLE = False
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try:
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from transformers import AutoProcessor
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except:
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pass
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from transformers.trainer_pt_utils import get_parameter_names
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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("4.16.0")
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require_version(
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"datasets>=1.13.3",
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"To fix: pip install -r examples/pytorch/text-classification/requirements.txt",
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)
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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={
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"help": "Path to pretrained model or model identifier from huggingface.co/models"
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}
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)
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tokenizer_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"
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},
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={
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"help": "Where do you want to store the pretrained models downloaded from huggingface.co"
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},
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)
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freeze_feature_encoder: bool = field(
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default=True,
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metadata={"help": "Whether to freeze the feature encoder layers of the model."},
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)
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attention_dropout: float = field(
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default=0.0,
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metadata={"help": "The dropout ratio for the attention probabilities."},
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)
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activation_dropout: float = field(
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default=0.0,
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metadata={
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"help": "The dropout ratio for activations inside the fully connected layer."
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},
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)
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feat_proj_dropout: 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: 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: 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: 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."
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},
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)
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mask_time_length: int = field(
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default=10,
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metadata={"help": "Length of vector span to mask along the time axis."},
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)
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mask_feature_prob: float = field(
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default=0.0,
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metadata={
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"help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
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"span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
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},
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)
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mask_feature_length: int = field(
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default=10,
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metadata={"help": "Length of vector span to mask along the feature axis."},
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)
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layerdrop: 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",
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metadata={
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"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."
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},
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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_path: str = field(
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default=None,
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metadata={
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"help": "The configuration name of the dataset to use (via the datasets library)."
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}
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)
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dataset_name: str = field(
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default=None,
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metadata={
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"help": "The configuration name of the dataset to use (via the datasets library)."
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},
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)
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dataset_config_name: str = field(
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default=None,
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metadata={
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"help": "The configuration name of the dataset to use (via the datasets library)."
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},
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)
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train_split_name: 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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eval_split_name: str = field(
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default="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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audio_column_name: str = field(
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default="audio",
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metadata={
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"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"
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},
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)
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text_column_name: str = field(
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default="text",
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metadata={
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"help": "The name of the dataset column containing the text data. Defaults to 'text'"
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},
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)
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wav_filesize_column_name: str = field(
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default=None,
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metadata={
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"help": "The name of the dataset column containing the wav filesize. Defaults is None"
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},
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)
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overwrite_cache: bool = field(
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default=False,
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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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eval_metrics: List[str] = list_field(
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default=["wer"],
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metadata={
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"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"
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},
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)
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max_duration_in_seconds: float = field(
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default=20.0,
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metadata={
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"help": "Filter 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: float = field(
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default=0.0,
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metadata={
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"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"
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},
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)
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preprocessing_only: 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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print_samples: bool = field(
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default=False,
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metadata={
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"help": "Print row with validation inference results to stdout after each epoch"
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},
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)
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use_augmentations: bool = field(
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default=False,
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metadata={
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"help": "Use data augmentation during training"
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},
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)
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use_auth_token: str = field(
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default="",
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metadata={
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"help": "If :obj:`True`, will use the token generated when running"
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":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
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},
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)
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unk_token: str = field(
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default="[UNK]",
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metadata={"help": "The unk token for the tokenizer"},
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)
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pad_token: str = field(
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default="[PAD]",
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metadata={"help": "The padding token for the tokenizer"},
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)
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word_delimiter_token: str = field(
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default="|",
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metadata={"help": "The word delimiter token for the tokenizer"},
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)
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phoneme_language: Optional[str] = field(
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default=None,
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metadata={
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"help": "The target language that should be used be"
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" passed to the tokenizer for tokenization. Note that"
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" this is only relevant if the model classifies the"
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" input audio to a sequence of phoneme sequences."
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},
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)
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class Augmentator:
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def __init__(
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self,
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apply_gaussian_noise_with_p=0.1,
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apply_gain_with_p=0.1,
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apply_pitch_shift_with_p=0.1,
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apply_time_stretch_with_p=0.1,
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augment_proba=0.1,
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sample_rate=16_000
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):
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self.augmentator_fn = None
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self.sample_rate = sample_rate
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self.augment_proba = augment_proba
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all_p = (
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apply_gaussian_noise_with_p
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+ apply_gain_with_p
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+ apply_pitch_shift_with_p
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+ apply_time_stretch_with_p
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)
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if AUDIOMENTATIONS_AVAILABLE and all_p > 0:
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self.augmentator_fn = Compose([
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TimeStretch(min_rate=0.8, max_rate=1.2, leave_length_unchanged=False,
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p=apply_time_stretch_with_p),
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PitchShift(min_semitones=-1, max_semitones=1,
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p=apply_pitch_shift_with_p),
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Gain(min_gain_in_db=-1, max_gain_in_db=1, p=apply_gain_with_p),
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AddGaussianNoise(min_amplitude=0.0001, max_amplitude=0.001,
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p=apply_gaussian_noise_with_p),
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])
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|
|
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def __call__(self, input_values: List[float], *args, **kwargs):
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if AUDIOMENTATIONS_AVAILABLE and self.augmentator_fn is not None:
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return self.augmentator_fn(samples=np.array(input_values),
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sample_rate=self.sample_rate).tolist()
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else:
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return input_values
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|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class DataCollatorCTCWithPadding:
|
||
|
|
"""
|
||
|
|
Data collator that will dynamically pad the inputs received.
|
||
|
|
Args:
|
||
|
|
processor (:class:`~transformers.AutoProcessor`)
|
||
|
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The processor used for proccessing the data.
|
||
|
|
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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|
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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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||
|
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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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||
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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).
|
||
|
|
max_length_labels (:obj:`int`, `optional`):
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||
|
|
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: 'AutoProcessor'
|
||
|
|
padding: Union[bool, str] = "longest"
|
||
|
|
pad_to_multiple_of: Optional[int] = None
|
||
|
|
pad_to_multiple_of_labels: Optional[int] = None
|
||
|
|
augmentator_fn: Optional[Callable] = None
|
||
|
|
use_augmentations: bool = False
|
||
|
|
|
||
|
|
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": self.augmentator_fn(feature["input_values"])
|
||
|
|
if self.use_augmentations
|
||
|
|
else feature["input_values"]}
|
||
|
|
for feature in features
|
||
|
|
]
|
||
|
|
label_features = [{"input_ids": feature["labels"]} for feature in features]
|
||
|
|
|
||
|
|
batch = self.processor.pad(
|
||
|
|
input_features,
|
||
|
|
padding=self.padding,
|
||
|
|
pad_to_multiple_of=self.pad_to_multiple_of,
|
||
|
|
return_tensors="pt",
|
||
|
|
)
|
||
|
|
|
||
|
|
with self.processor.as_target_processor():
|
||
|
|
labels_batch = self.processor.pad(
|
||
|
|
label_features,
|
||
|
|
padding=self.padding,
|
||
|
|
pad_to_multiple_of=self.pad_to_multiple_of_labels,
|
||
|
|
return_tensors="pt",
|
||
|
|
)
|
||
|
|
|
||
|
|
# replace padding with -100 to ignore loss correctly
|
||
|
|
labels = labels_batch["input_ids"].masked_fill(
|
||
|
|
labels_batch.attention_mask.ne(1), -100
|
||
|
|
)
|
||
|
|
|
||
|
|
batch["labels"] = labels
|
||
|
|
|
||
|
|
return batch
|
||
|
|
|
||
|
|
|
||
|
|
def create_vocabulary_from_data(
|
||
|
|
datasets: DatasetDict,
|
||
|
|
text_column_name: str,
|
||
|
|
train_split_name: str,
|
||
|
|
word_delimiter_token: Optional[str] = None,
|
||
|
|
unk_token: Optional[str] = None,
|
||
|
|
pad_token: Optional[str] = None,
|
||
|
|
):
|
||
|
|
# Given training and test labels create vocabulary
|
||
|
|
def extract_all_chars(batch):
|
||
|
|
all_text = " ".join(batch[text_column_name])
|
||
|
|
vocab = list(set(all_text))
|
||
|
|
return {"vocab": [vocab], "all_text": [all_text]}
|
||
|
|
|
||
|
|
print("extract chars")
|
||
|
|
vocabs = datasets.map(
|
||
|
|
extract_all_chars,
|
||
|
|
batched=True,
|
||
|
|
batch_size=-1,
|
||
|
|
keep_in_memory=True,
|
||
|
|
remove_columns=datasets[train_split_name].column_names,
|
||
|
|
)
|
||
|
|
|
||
|
|
# take union of all unique characters in each dataset
|
||
|
|
print("make vocab_set")
|
||
|
|
vocab_set = functools.reduce(
|
||
|
|
lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]),
|
||
|
|
vocabs.values(),
|
||
|
|
)
|
||
|
|
|
||
|
|
vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
|
||
|
|
|
||
|
|
# replace white space with delimiter token
|
||
|
|
if word_delimiter_token is not None:
|
||
|
|
vocab_dict[word_delimiter_token] = vocab_dict[" "]
|
||
|
|
del vocab_dict[" "]
|
||
|
|
|
||
|
|
# add unk and pad token
|
||
|
|
if unk_token is not None:
|
||
|
|
vocab_dict[unk_token] = len(vocab_dict)
|
||
|
|
|
||
|
|
if pad_token is not None:
|
||
|
|
vocab_dict[pad_token] = len(vocab_dict)
|
||
|
|
|
||
|
|
return vocab_dict
|
||
|
|
|
||
|
|
|
||
|
|
def speech_file_to_array_fn(batch, audio_column_name, dataset_path=""):
|
||
|
|
if dataset_path:
|
||
|
|
dataset_path = os.path.join(dataset_path, batch[audio_column_name])
|
||
|
|
else:
|
||
|
|
dataset_path = batch[audio_column_name] if isinstance(batch[audio_column_name],
|
||
|
|
str) else \
|
||
|
|
batch[audio_column_name]["path"]
|
||
|
|
speech_array, sampling_rate = torchaudio.load(dataset_path)
|
||
|
|
batch[audio_column_name] = {
|
||
|
|
"array": speech_array[0].numpy(),
|
||
|
|
"sampling_rate": sampling_rate,
|
||
|
|
}
|
||
|
|
return batch
|
||
|
|
|
||
|
|
|
||
|
|
class PrintSamplesPredictionCallback(TrainerCallback):
|
||
|
|
|
||
|
|
def __init__(self, processor, eval_dataset):
|
||
|
|
super(PrintSamplesPredictionCallback, self).__init__()
|
||
|
|
self.processor = processor
|
||
|
|
self.eval_dataset = eval_dataset
|
||
|
|
self.metric_fn = load_metric("wer")
|
||
|
|
|
||
|
|
def on_log(
|
||
|
|
self,
|
||
|
|
args: Any,
|
||
|
|
state: Any,
|
||
|
|
control: Any,
|
||
|
|
model: Any,
|
||
|
|
logs: Optional[Any] = None,
|
||
|
|
**kwargs
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
:param args:
|
||
|
|
:param state:
|
||
|
|
:param control:
|
||
|
|
:param model:
|
||
|
|
:param logs:
|
||
|
|
:param kwargs: 'tokenizer', 'optimizer', 'lr_scheduler', 'train_dataloader', 'eval_dataloader'
|
||
|
|
:return:
|
||
|
|
"""
|
||
|
|
if state.is_local_process_zero:
|
||
|
|
columns = ["id", "prediction", "reference", "audio", "wer"]
|
||
|
|
data = []
|
||
|
|
for idx, row in enumerate(self.eval_dataset):
|
||
|
|
input_dict = self.processor(row["input_values"],
|
||
|
|
return_tensors="pt", padding=True)
|
||
|
|
logits = model(input_dict.input_values.to(model.device)).logits
|
||
|
|
pred_ids = torch.argmax(logits, dim=-1)[0]
|
||
|
|
prediction = self.processor.decode(pred_ids)
|
||
|
|
print(f"Prediction: {prediction}")
|
||
|
|
reference = row['references'].lower()
|
||
|
|
print(f"\nReference: {reference}")
|
||
|
|
|
||
|
|
if WANDB_AVAILABLE:
|
||
|
|
|
||
|
|
audio, sample_rate = tuple(row["audio"].values())
|
||
|
|
audio = wandb.Audio(np.squeeze(audio),
|
||
|
|
sample_rate=sample_rate)
|
||
|
|
wer = self.metric_fn.compute(
|
||
|
|
predictions=[prediction],
|
||
|
|
references=[reference],
|
||
|
|
)
|
||
|
|
|
||
|
|
data.append([idx, prediction, reference, audio, wer])
|
||
|
|
if WANDB_AVAILABLE:
|
||
|
|
table = wandb.Table(data=data, columns=columns)
|
||
|
|
wandb.run.log({"audio_predictions": table})
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
# See all possible arguments in src/transformers/training_args.py
|
||
|
|
# or by passing the --help flag to this script.
|
||
|
|
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||
|
|
|
||
|
|
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
||
|
|
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
||
|
|
# If we pass only one argument to the script and it's the path to a json file,
|
||
|
|
# let's parse it to get our arguments.
|
||
|
|
model_args, data_args, training_args = parser.parse_json_file(
|
||
|
|
json_file=os.path.abspath(sys.argv[1])
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||
|
|
|
||
|
|
# Detecting last checkpoint.
|
||
|
|
last_checkpoint = None
|
||
|
|
if (
|
||
|
|
os.path.isdir(training_args.output_dir)
|
||
|
|
and training_args.do_train
|
||
|
|
and not training_args.overwrite_output_dir
|
||
|
|
):
|
||
|
|
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
||
|
|
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
||
|
|
raise ValueError(
|
||
|
|
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
||
|
|
"Use --overwrite_output_dir to overcome."
|
||
|
|
)
|
||
|
|
elif last_checkpoint is not None:
|
||
|
|
logger.info(
|
||
|
|
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
||
|
|
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
||
|
|
)
|
||
|
|
|
||
|
|
# Setup logging
|
||
|
|
logging.basicConfig(
|
||
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
||
|
|
handlers=[logging.StreamHandler(sys.stdout)],
|
||
|
|
)
|
||
|
|
logger.setLevel(
|
||
|
|
logging.INFO if is_main_process(training_args.local_rank) else logging.WARN
|
||
|
|
)
|
||
|
|
|
||
|
|
# Log on each process the small summary:
|
||
|
|
logger.warning(
|
||
|
|
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
||
|
|
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
||
|
|
)
|
||
|
|
# Set the verbosity to info of the Transformers logger (on main process only):
|
||
|
|
if is_main_process(training_args.local_rank):
|
||
|
|
transformers.utils.logging.set_verbosity_info()
|
||
|
|
logger.info("Training/evaluation parameters %s", training_args)
|
||
|
|
|
||
|
|
# Set seed before initializing model.
|
||
|
|
set_seed(training_args.seed)
|
||
|
|
|
||
|
|
train_split_name = data_args.train_split_name
|
||
|
|
eval_split_name = data_args.eval_split_name
|
||
|
|
|
||
|
|
# 1. First, let's load the dataset
|
||
|
|
raw_datasets = DatasetDict({
|
||
|
|
train_split_name: None,
|
||
|
|
eval_split_name: None,
|
||
|
|
})
|
||
|
|
|
||
|
|
if data_args.dataset_path:
|
||
|
|
raw_datasets = load_dataset(
|
||
|
|
"csv",
|
||
|
|
data_files={
|
||
|
|
train_split_name: os.path.join(data_args.dataset_path, "train-all.csv"),
|
||
|
|
eval_split_name: os.path.join(data_args.dataset_path, "eval-all.csv"),
|
||
|
|
},
|
||
|
|
)
|
||
|
|
|
||
|
|
if training_args.do_train:
|
||
|
|
if raw_datasets[train_split_name] is None:
|
||
|
|
raw_datasets[train_split_name] = load_dataset(
|
||
|
|
data_args.dataset_name,
|
||
|
|
data_args.dataset_config_name,
|
||
|
|
split=data_args.train_split_name,
|
||
|
|
use_auth_token=data_args.use_auth_token,
|
||
|
|
)
|
||
|
|
|
||
|
|
if data_args.audio_column_name not in raw_datasets[train_split_name].column_names:
|
||
|
|
raise ValueError(
|
||
|
|
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset. "
|
||
|
|
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
||
|
|
f"{', '.join(raw_datasets['train'].column_names)}."
|
||
|
|
)
|
||
|
|
|
||
|
|
if data_args.text_column_name not in raw_datasets[train_split_name].column_names:
|
||
|
|
raise ValueError(
|
||
|
|
f"--text_column_name {data_args.text_column_name} not found in dataset. "
|
||
|
|
"Make sure to set `--text_column_name` to the correct text column - one of "
|
||
|
|
f"{', '.join(raw_datasets['train'].column_names)}."
|
||
|
|
)
|
||
|
|
|
||
|
|
if data_args.max_train_samples is not None:
|
||
|
|
raw_datasets[train_split_name] = raw_datasets[train_split_name].select(
|
||
|
|
range(data_args.max_train_samples)
|
||
|
|
)
|
||
|
|
|
||
|
|
if data_args.wav_filesize_column_name is not None:
|
||
|
|
raw_datasets[train_split_name] = raw_datasets[train_split_name].sort(
|
||
|
|
data_args.wav_filesize_column_name, reverse=True)
|
||
|
|
|
||
|
|
if training_args.do_eval:
|
||
|
|
if raw_datasets[eval_split_name] is None:
|
||
|
|
raw_datasets[eval_split_name] = load_dataset(
|
||
|
|
data_args.dataset_name,
|
||
|
|
data_args.dataset_config_name,
|
||
|
|
split=data_args.eval_split_name,
|
||
|
|
use_auth_token=data_args.use_auth_token,
|
||
|
|
)
|
||
|
|
|
||
|
|
if data_args.max_eval_samples is not None:
|
||
|
|
raw_datasets[eval_split_name] = raw_datasets[eval_split_name].select(
|
||
|
|
range(data_args.max_eval_samples)
|
||
|
|
)
|
||
|
|
if data_args.wav_filesize_column_name is not None:
|
||
|
|
raw_datasets[eval_split_name] = raw_datasets[eval_split_name].sort(
|
||
|
|
data_args.wav_filesize_column_name, reverse=True)
|
||
|
|
|
||
|
|
# save special tokens for tokenizer
|
||
|
|
word_delimiter_token = data_args.word_delimiter_token
|
||
|
|
unk_token = data_args.unk_token
|
||
|
|
pad_token = data_args.pad_token
|
||
|
|
|
||
|
|
# 3. Next, let's load the config as we might need it to create
|
||
|
|
# the tokenizer
|
||
|
|
# load config
|
||
|
|
config = AutoConfig.from_pretrained(
|
||
|
|
model_args.model_name_or_path,
|
||
|
|
cache_dir=model_args.cache_dir,
|
||
|
|
use_auth_token=data_args.use_auth_token,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 4. Next, if no tokenizer file is defined,
|
||
|
|
# we create the vocabulary of the model by extracting all unique characters from
|
||
|
|
# the training and evaluation datasets
|
||
|
|
# We need to make sure that only first rank saves vocabulary
|
||
|
|
# make sure all processes wait until vocab is created
|
||
|
|
tokenizer_name_or_path = model_args.tokenizer_name_or_path
|
||
|
|
tokenizer_kwargs = {}
|
||
|
|
|
||
|
|
# 5. Now we can instantiate the feature extractor, tokenizer and model
|
||
|
|
# Note for distributed training, the .from_pretrained methods guarantee that only
|
||
|
|
# one local process can concurrently download model & vocab.
|
||
|
|
with open(os.path.join(tokenizer_name_or_path, "vocab.json"), "r") as fin:
|
||
|
|
print("loading tokenizer")
|
||
|
|
print(fin.read())
|
||
|
|
|
||
|
|
# load feature_extractor and tokenizer
|
||
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
||
|
|
tokenizer_name_or_path,
|
||
|
|
use_auth_token=data_args.use_auth_token,
|
||
|
|
**tokenizer_kwargs,
|
||
|
|
)
|
||
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
||
|
|
model_args.model_name_or_path,
|
||
|
|
cache_dir=model_args.cache_dir,
|
||
|
|
use_auth_token=data_args.use_auth_token,
|
||
|
|
)
|
||
|
|
|
||
|
|
# adapt config
|
||
|
|
config.update(
|
||
|
|
{
|
||
|
|
"feat_proj_dropout": model_args.feat_proj_dropout,
|
||
|
|
"attention_dropout": model_args.attention_dropout,
|
||
|
|
"hidden_dropout": model_args.hidden_dropout,
|
||
|
|
"final_dropout": model_args.final_dropout,
|
||
|
|
"mask_time_prob": model_args.mask_time_prob,
|
||
|
|
"mask_time_length": model_args.mask_time_length,
|
||
|
|
"mask_feature_prob": model_args.mask_feature_prob,
|
||
|
|
"mask_feature_length": model_args.mask_feature_length,
|
||
|
|
"gradient_checkpointing": training_args.gradient_checkpointing,
|
||
|
|
"layerdrop": model_args.layerdrop,
|
||
|
|
"ctc_loss_reduction": model_args.ctc_loss_reduction,
|
||
|
|
"pad_token_id": tokenizer.pad_token_id,
|
||
|
|
"vocab_size": len(tokenizer),
|
||
|
|
"activation_dropout": model_args.activation_dropout,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
# create model
|
||
|
|
model = AutoModelForCTC.from_pretrained(
|
||
|
|
model_args.model_name_or_path,
|
||
|
|
cache_dir=model_args.cache_dir,
|
||
|
|
config=config,
|
||
|
|
use_auth_token=data_args.use_auth_token,
|
||
|
|
)
|
||
|
|
|
||
|
|
# freeze encoder
|
||
|
|
if model_args.freeze_feature_encoder:
|
||
|
|
model.freeze_feature_encoder()
|
||
|
|
|
||
|
|
# 6. Now we preprocess the datasets including loading the audio, resampling and normalization
|
||
|
|
# 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
|
||
|
|
|
||
|
|
# derive max & min input length for sample rate & max duration
|
||
|
|
audio_column_name = data_args.audio_column_name
|
||
|
|
num_workers = data_args.preprocessing_num_workers
|
||
|
|
|
||
|
|
# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
|
||
|
|
phoneme_language = data_args.phoneme_language
|
||
|
|
|
||
|
|
raw_datasets[train_split_name] = raw_datasets[train_split_name].map(
|
||
|
|
speech_file_to_array_fn,
|
||
|
|
num_proc=num_workers,
|
||
|
|
fn_kwargs={"dataset_path": data_args.dataset_path,
|
||
|
|
"audio_column_name": audio_column_name},
|
||
|
|
)
|
||
|
|
raw_datasets[eval_split_name] = raw_datasets[eval_split_name].map(
|
||
|
|
speech_file_to_array_fn,
|
||
|
|
num_proc=num_workers,
|
||
|
|
fn_kwargs={"dataset_path": data_args.dataset_path,
|
||
|
|
"audio_column_name": audio_column_name},
|
||
|
|
)
|
||
|
|
|
||
|
|
# Preprocessing the datasets.
|
||
|
|
# We need to read the audio files as arrays and tokenize the targets.
|
||
|
|
def prepare_dataset(batch):
|
||
|
|
# load audio
|
||
|
|
sample = batch[audio_column_name]
|
||
|
|
|
||
|
|
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
|
||
|
|
batch["input_values"] = inputs.input_values[0]
|
||
|
|
batch["input_length"] = len(batch["input_values"])
|
||
|
|
|
||
|
|
# encode targets
|
||
|
|
additional_kwargs = {}
|
||
|
|
if phoneme_language is not None:
|
||
|
|
additional_kwargs["phonemizer_lang"] = phoneme_language
|
||
|
|
|
||
|
|
batch["labels"] = tokenizer(batch[data_args.text_column_name],
|
||
|
|
**additional_kwargs).input_ids
|
||
|
|
return batch
|
||
|
|
|
||
|
|
print(f"Vectorizing")
|
||
|
|
|
||
|
|
with training_args.main_process_first(desc="dataset map preprocessing"):
|
||
|
|
vectorized_datasets = raw_datasets.map(
|
||
|
|
prepare_dataset,
|
||
|
|
remove_columns=next(iter(raw_datasets.values())).column_names,
|
||
|
|
num_proc=num_workers,
|
||
|
|
desc="preprocess datasets",
|
||
|
|
)
|
||
|
|
|
||
|
|
# 7. 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 evaluation metrics during training, *i.e.* word error rate, character error rate
|
||
|
|
eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
|
||
|
|
|
||
|
|
# 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] = tokenizer.pad_token_id
|
||
|
|
|
||
|
|
pred_str = tokenizer.batch_decode(pred_ids)
|
||
|
|
# we do not want to group tokens when computing the metrics
|
||
|
|
label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
|
||
|
|
|
||
|
|
metrics = {
|
||
|
|
k: v.compute(predictions=pred_str, references=label_str)
|
||
|
|
for k, v in eval_metrics.items()
|
||
|
|
}
|
||
|
|
|
||
|
|
return metrics
|
||
|
|
|
||
|
|
# Now save everything to be able to create a single processor later
|
||
|
|
if is_main_process(training_args.local_rank):
|
||
|
|
# save feature extractor, tokenizer and config
|
||
|
|
feature_extractor.save_pretrained(training_args.output_dir)
|
||
|
|
tokenizer.save_pretrained(training_args.output_dir)
|
||
|
|
config.save_pretrained(training_args.output_dir)
|
||
|
|
|
||
|
|
try:
|
||
|
|
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
||
|
|
except (OSError, KeyError):
|
||
|
|
warnings.warn(
|
||
|
|
"Loading a processor from a feature extractor config that does not"
|
||
|
|
" include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
|
||
|
|
" attribute to your `preprocessor_config.json` file to suppress this warning: "
|
||
|
|
" `'processor_class': 'Wav2Vec2Processor'`",
|
||
|
|
FutureWarning,
|
||
|
|
)
|
||
|
|
processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
|
||
|
|
|
||
|
|
# Instantiate custom data collator
|
||
|
|
data_collator = DataCollatorCTCWithPadding(
|
||
|
|
processor=processor,
|
||
|
|
augmentator_fn=Augmentator(),
|
||
|
|
use_augmentations=data_args.use_augmentations
|
||
|
|
)
|
||
|
|
|
||
|
|
decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm])
|
||
|
|
decay_parameters = [name for name in decay_parameters if "bias" not in name]
|
||
|
|
optimizer_grouped_parameters = [
|
||
|
|
{
|
||
|
|
"params": [p for n, p in model.named_parameters() if n in decay_parameters],
|
||
|
|
"weight_decay": training_args.weight_decay,
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"params": [
|
||
|
|
p for n, p in model.named_parameters() if n not in decay_parameters
|
||
|
|
],
|
||
|
|
"weight_decay": 0.0,
|
||
|
|
},
|
||
|
|
]
|
||
|
|
trainer_kwargs = {}
|
||
|
|
if BNB_AVAILABLE:
|
||
|
|
optimizer = bnb.optim.Adam8bit(
|
||
|
|
params=optimizer_grouped_parameters,
|
||
|
|
betas=(training_args.adam_beta1, training_args.adam_beta2),
|
||
|
|
eps=training_args.adam_epsilon,
|
||
|
|
)
|
||
|
|
trainer_kwargs["optimizers"] = (optimizer, None)
|
||
|
|
|
||
|
|
samples_to_log = [
|
||
|
|
{
|
||
|
|
**vectorized_datasets[eval_split_name][i],
|
||
|
|
"references": raw_datasets[eval_split_name][i][data_args.text_column_name],
|
||
|
|
"audio": raw_datasets[eval_split_name][i][data_args.audio_column_name],
|
||
|
|
} for i in range(5)
|
||
|
|
]
|
||
|
|
|
||
|
|
trainer = Trainer(
|
||
|
|
model=model,
|
||
|
|
data_collator=data_collator,
|
||
|
|
args=training_args,
|
||
|
|
compute_metrics=compute_metrics,
|
||
|
|
train_dataset=vectorized_datasets[
|
||
|
|
train_split_name] if training_args.do_train else None,
|
||
|
|
eval_dataset=vectorized_datasets[
|
||
|
|
eval_split_name] if training_args.do_eval else None,
|
||
|
|
tokenizer=feature_extractor,
|
||
|
|
**trainer_kwargs,
|
||
|
|
callbacks=[PrintSamplesPredictionCallback(
|
||
|
|
processor=processor,
|
||
|
|
eval_dataset=samples_to_log)] if data_args.print_samples and training_args.do_eval else None,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 8. 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
|
||
|
|
|
||
|
|
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_split_name])
|
||
|
|
)
|
||
|
|
metrics["train_samples"] = min(
|
||
|
|
max_train_samples, len(vectorized_datasets[train_split_name])
|
||
|
|
)
|
||
|
|
|
||
|
|
trainer.log_metrics(train_split_name, metrics)
|
||
|
|
trainer.save_metrics(train_split_name, 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_split_name])
|
||
|
|
)
|
||
|
|
metrics["eval_samples"] = min(max_eval_samples,
|
||
|
|
len(vectorized_datasets[eval_split_name]))
|
||
|
|
|
||
|
|
trainer.log_metrics(eval_split_name, metrics)
|
||
|
|
trainer.save_metrics(eval_split_name, metrics)
|
||
|
|
|
||
|
|
# Write model card and (optionally) push to hub
|
||
|
|
config_name = (
|
||
|
|
data_args.dataset_config_name
|
||
|
|
if data_args.dataset_config_name is not None
|
||
|
|
else "na"
|
||
|
|
)
|
||
|
|
kwargs = {
|
||
|
|
"language": "he",
|
||
|
|
"finetuned_from": model_args.model_name_or_path,
|
||
|
|
"tasks": "speech-recognition",
|
||
|
|
"tags": ["automatic-speech-recognition", "robust-speech-event", "he"],
|
||
|
|
"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
|
||
|
|
}
|
||
|
|
|
||
|
|
if training_args.push_to_hub:
|
||
|
|
trainer.push_to_hub(**kwargs)
|
||
|
|
else:
|
||
|
|
trainer.create_model_card(**kwargs)
|
||
|
|
|
||
|
|
return results
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|