950 lines
38 KiB
Python
950 lines
38 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2022 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 pretrained speech model on the XTREME-S benchmark tasks"""
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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 collections import OrderedDict, defaultdict
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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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AutoModelForAudioClassification,
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AutoModelForCTC,
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AutoModelForSpeechSeq2Seq,
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AutoProcessor,
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AutoTokenizer,
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HfArgumentParser,
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Seq2SeqTrainer,
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Seq2SeqTrainingArguments,
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Trainer,
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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("4.18.0.dev0")
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require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/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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TASK_TO_TARGET_COLUMN_NAME = {
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"fleurs-asr": "transcription",
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"fleurs-lang_id": "lang_id",
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"mls": "transcription",
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"voxpopuli": "transcription",
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"covost2": "translation",
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"minds14": "intent_class",
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"babel": "transcription",
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}
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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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tokenizer_name_or_path: Optional[str] = field(
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default=None,
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metadata={"help": "Path to pretrained tokenizer or tokenizer 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={
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"help": "Where do you want to store the pretrained models and datasets 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, 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, 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, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
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)
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feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
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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": (
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"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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)
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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": (
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"Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan"
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" to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature"
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" bins will be masked along the time axis."
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)
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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_zero_infinity: bool = field(
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default=False,
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metadata={"help": "Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`."},
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)
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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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default="google/xtreme_s",
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metadata={"help": "The name of the dataset to use (via the datasets library). Defaults to 'google/xtreme_s'"},
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)
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task: str = field(
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default=None,
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metadata={
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"help": (
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"The task name of the benchmark to use (via the datasets library). Should be on of: "
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"'fleurs-asr', 'mls', 'voxpopuli', 'covost2', 'minds14', 'fleurs-lang_id', 'babel'."
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)
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},
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)
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language: str = field(
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default="all",
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metadata={"help": "The language id as defined in the datasets config name or `all` for all languages."},
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)
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language_group: str = field(
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default=None,
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metadata={
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"help": (
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"The language group to select a subset of languages to train on. "
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"This option is only used the 'fleurs-asr' task. Should be one of: "
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"'western_european_we', 'eastern_european_ee', 'central_asia_middle_north_african_cmn', "
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"'sub_saharan_african_ssa', 'south_asian_sa', 'south_east_asian_sea', 'chinese_japanase_korean_cjk'."
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)
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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 dataset 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": (
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"The name of the evaluation dataset split to use (via the datasets library). Defaults to 'validation'"
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)
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},
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)
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predict_split_name: str = field(
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default="test",
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metadata={
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"help": "The name of the prediction dataset split to use (via the datasets library). Defaults to 'test'"
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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={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
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)
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target_column_name: str = field(
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default=None,
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metadata={
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"help": (
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"The name of the dataset column containing the target data (transcription/translation/label). If None,"
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" the name will be inferred from the task. Defaults to None."
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)
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"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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)
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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": (
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"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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)
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max_predict_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of prediction examples to this "
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"value if set."
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)
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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=', ? . ! - ; : " “ % ‘ ” <20>'.split(" "),
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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: float = field(
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default=30.0,
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metadata={
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"help": (
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"Filter audio files that are longer than `max_duration_in_seconds` seconds to"
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" 'max_duration_in_seconds`"
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)
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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, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
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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": (
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"Whether to only do data preprocessing and skip training. This is especially useful when data"
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" preprocessing errors out in distributed training due to timeout. In this case, one should run the"
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" preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
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" can consequently be loaded in distributed training"
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)
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},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": (
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"If :obj:`True`, will use the token generated when running"
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":obj:`huggingface-cli login` as HTTP bearer authorization for remote files."
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)
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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": (
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"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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)
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per_lang_metrics: bool = field(
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default=True,
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metadata={
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"help": (
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"If `True`, compute the test metrics separately for each language, and average the results. "
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"If `False` compute the average test metrics in a single pass for all languages at once."
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)
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},
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)
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@dataclass
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class SpeechDataCollatorWithPadding:
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processor: AutoProcessor
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decoder_start_token_id: Optional[int] = None
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padding: Union[bool, str] = "longest"
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pad_labels: Optional[int] = True
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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 lengths 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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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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if self.pad_labels:
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label_features = [{"input_ids": feature["labels"]} for feature in features]
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labels_batch = self.processor.pad(
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labels=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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# if bos token is appended in previous tokenization step,
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# cut bos token here as it's append later anyways
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if (
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self.decoder_start_token_id is not None
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and (labels[:, 0] == self.decoder_start_token_id).all().cpu().item()
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):
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labels = labels[:, 1:]
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batch["labels"] = labels
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else:
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batch["labels"] = torch.tensor([feature["labels"] for feature in features])
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return batch
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def create_vocabulary_from_data(
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datasets: DatasetDict,
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word_delimiter_token: Optional[str] = None,
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unk_token: Optional[str] = None,
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pad_token: Optional[str] = None,
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):
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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 = (
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(set(vocabs["train"]["vocab"][0]) if "train" in vocabs else set())
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| (set(vocabs["eval"]["vocab"][0]) if "eval" in vocabs else set())
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| (set(vocabs["predict"]["vocab"][0]) if "predict" in vocabs else set())
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)
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vocab_dict = {v: k for k, v in enumerate(sorted(vocab_set))}
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# replace white space with delimiter token
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if word_delimiter_token is not None:
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vocab_dict[word_delimiter_token] = vocab_dict[" "]
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del vocab_dict[" "]
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# add unk and pad token
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if unk_token is not None:
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vocab_dict[unk_token] = len(vocab_dict)
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if pad_token is not None:
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vocab_dict[pad_token] = 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, Seq2SeqTrainingArguments))
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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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task_name = data_args.task
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lang_id = data_args.language
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if task_name is None:
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raise ValueError(
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"Set --task should be set to '<xtreme_s_task>' (e.g. 'fleurs-asr', 'mls', 'covost2', 'minds14') "
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)
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if lang_id is None:
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raise ValueError(
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"Set --language should be set to the language id of the sub dataset "
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"config to be used (e.g. 'pl', 'en.tr', 'fr-FR') or 'all'"
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" for multi-lingual fine-tuning."
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)
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if data_args.language_group is not None:
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if data_args.task != "fleurs-asr":
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raise ValueError("--language_group should only be used with --task=fleurs-asr")
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if data_args.language != "all":
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raise ValueError("--language_group should only be used with --language=all")
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if data_args.target_column_name is None:
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target_column_name = TASK_TO_TARGET_COLUMN_NAME[task_name]
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else:
|
||
target_column_name = data_args.target_column_name
|
||
|
||
# here we differentiate between tasks with text as the target and classification tasks
|
||
is_text_target = target_column_name in ("transcription", "translation")
|
||
|
||
config_name = ".".join([task_name.split("-")[0], lang_id])
|
||
|
||
if training_args.do_train:
|
||
raw_datasets["train"] = load_dataset(
|
||
data_args.dataset_name,
|
||
config_name,
|
||
split=data_args.train_split_name,
|
||
token=data_args.use_auth_token,
|
||
cache_dir=model_args.cache_dir,
|
||
)
|
||
|
||
if data_args.audio_column_name not in raw_datasets["train"].column_names:
|
||
raise ValueError(
|
||
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'."
|
||
" Make sure to set `--audio_column_name` to the correct audio column - one of"
|
||
f" {', '.join(raw_datasets['train'].column_names)}."
|
||
)
|
||
|
||
if target_column_name not in raw_datasets["train"].column_names:
|
||
raise ValueError(
|
||
f"--target_column_name {target_column_name} not found in dataset '{data_args.dataset_name}'. "
|
||
"Make sure to set `--target_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"] = raw_datasets["train"].select(range(data_args.max_train_samples))
|
||
|
||
if training_args.do_eval:
|
||
raw_datasets["eval"] = load_dataset(
|
||
data_args.dataset_name,
|
||
config_name,
|
||
split=data_args.eval_split_name,
|
||
token=data_args.use_auth_token,
|
||
cache_dir=model_args.cache_dir,
|
||
)
|
||
|
||
if data_args.max_eval_samples is not None:
|
||
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
||
|
||
if training_args.do_predict:
|
||
raw_datasets["predict"] = load_dataset(
|
||
data_args.dataset_name,
|
||
config_name,
|
||
split=data_args.predict_split_name,
|
||
token=data_args.use_auth_token,
|
||
cache_dir=model_args.cache_dir,
|
||
)
|
||
|
||
if data_args.max_predict_samples is not None:
|
||
raw_datasets["predict"] = raw_datasets["predict"].select(range(data_args.max_predict_samples))
|
||
|
||
lang_list = next(iter(raw_datasets.values())).features["lang_id"].names
|
||
if not is_text_target:
|
||
label_list = next(iter(raw_datasets.values())).features[target_column_name].names
|
||
num_labels = len(label_list)
|
||
|
||
num_workers = data_args.preprocessing_num_workers
|
||
|
||
lang_group = data_args.language_group
|
||
if lang_group is not None:
|
||
with training_args.main_process_first(desc="language group filter"):
|
||
lang_group_id = next(iter(raw_datasets.values())).features["lang_group_id"].str2int(lang_group)
|
||
raw_datasets = raw_datasets.filter(
|
||
lambda lang_group: lang_group == lang_group_id,
|
||
num_proc=num_workers,
|
||
input_columns=["lang_group_id"],
|
||
)
|
||
|
||
# 2. We remove some special characters from the datasets
|
||
# that make training complicated and do not help in transcribing the speech
|
||
# E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
|
||
# that could be easily picked up by the model
|
||
chars_to_ignore_regex = (
|
||
f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
|
||
)
|
||
|
||
def remove_special_characters(batch):
|
||
if chars_to_ignore_regex is not None:
|
||
batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[target_column_name]).lower() + " "
|
||
else:
|
||
batch["target_text"] = batch[target_column_name].lower() + " "
|
||
return batch
|
||
|
||
if is_text_target:
|
||
with training_args.main_process_first(desc="dataset map special characters removal"):
|
||
raw_datasets = raw_datasets.map(
|
||
remove_special_characters,
|
||
remove_columns=[target_column_name],
|
||
desc="remove special characters from datasets",
|
||
)
|
||
|
||
# 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
|
||
config = AutoConfig.from_pretrained(
|
||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, token=data_args.use_auth_token
|
||
)
|
||
|
||
if is_text_target:
|
||
# 4. (Optional, for ASR and translation) 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 = {}
|
||
if tokenizer_name_or_path is None:
|
||
# save vocab in training output dir
|
||
tokenizer_name_or_path = training_args.output_dir
|
||
|
||
vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
|
||
|
||
with training_args.main_process_first():
|
||
if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
|
||
os.remove(vocab_file)
|
||
|
||
with training_args.main_process_first(desc="dataset map vocabulary creation"):
|
||
if not os.path.isfile(vocab_file):
|
||
os.makedirs(tokenizer_name_or_path, exist_ok=True)
|
||
vocab_dict = create_vocabulary_from_data(
|
||
raw_datasets,
|
||
word_delimiter_token=word_delimiter_token,
|
||
unk_token=unk_token,
|
||
pad_token=pad_token,
|
||
)
|
||
|
||
# save vocab dict to be loaded into tokenizer
|
||
with open(vocab_file, "w") as file:
|
||
json.dump(vocab_dict, file)
|
||
|
||
# if tokenizer has just been created
|
||
# it is defined by `tokenizer_class` if present in config else by `model_type`
|
||
if not config.is_encoder_decoder:
|
||
tokenizer_kwargs = {
|
||
"config": config if config.tokenizer_class is not None else None,
|
||
"tokenizer_type": config.model_type if config.tokenizer_class is None else None,
|
||
"unk_token": unk_token,
|
||
"pad_token": pad_token,
|
||
"word_delimiter_token": word_delimiter_token,
|
||
}
|
||
else:
|
||
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.
|
||
|
||
# load feature_extractor and tokenizer
|
||
if is_text_target:
|
||
tokenizer = AutoTokenizer.from_pretrained(
|
||
tokenizer_name_or_path,
|
||
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, token=data_args.use_auth_token
|
||
)
|
||
|
||
# adapt config
|
||
# (speech translation requires pre-configured seq2seq models)
|
||
if task_name != "covost2":
|
||
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_zero_infinity": model_args.ctc_zero_infinity,
|
||
"ctc_loss_reduction": model_args.ctc_loss_reduction,
|
||
"activation_dropout": model_args.activation_dropout,
|
||
}
|
||
)
|
||
if training_args.do_train:
|
||
if is_text_target:
|
||
config.pad_token_id = tokenizer.pad_token_id
|
||
config.vocab_size = len(tokenizer)
|
||
else:
|
||
label_to_id = {v: i for i, v in enumerate(label_list)}
|
||
config.label2id = label_to_id
|
||
config.id2label = {id: label for label, id in label_to_id.items()}
|
||
config.num_labels = num_labels
|
||
|
||
# create model
|
||
if target_column_name == "transcription":
|
||
model = AutoModelForCTC.from_pretrained(
|
||
model_args.model_name_or_path,
|
||
cache_dir=model_args.cache_dir,
|
||
config=config,
|
||
token=data_args.use_auth_token,
|
||
)
|
||
elif config.is_encoder_decoder:
|
||
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
||
model_args.model_name_or_path,
|
||
cache_dir=model_args.cache_dir,
|
||
config=config,
|
||
token=data_args.use_auth_token,
|
||
)
|
||
if model.config.decoder_start_token_id is None:
|
||
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
|
||
else:
|
||
model = AutoModelForAudioClassification.from_pretrained(
|
||
model_args.model_name_or_path,
|
||
cache_dir=model_args.cache_dir,
|
||
config=config,
|
||
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
|
||
dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
|
||
if dataset_sampling_rate != feature_extractor.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 * feature_extractor.sampling_rate
|
||
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
||
audio_column_name = data_args.audio_column_name
|
||
|
||
# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
|
||
phoneme_language = data_args.phoneme_language
|
||
|
||
# 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["length"] = len(batch["input_values"])
|
||
|
||
# encode targets
|
||
additional_kwargs = {}
|
||
if phoneme_language is not None:
|
||
additional_kwargs["phonemizer_lang"] = phoneme_language
|
||
|
||
if is_text_target:
|
||
batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
|
||
else:
|
||
batch["labels"] = batch[target_column_name]
|
||
|
||
batch["lang"] = batch["lang_id"]
|
||
|
||
return batch
|
||
|
||
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",
|
||
)
|
||
|
||
if training_args.do_train:
|
||
|
||
def is_audio_in_length_range(length):
|
||
return length > min_input_length and length < max_input_length
|
||
|
||
# filter data that is shorter than min_input_length
|
||
vectorized_datasets["train"] = vectorized_datasets["train"].filter(
|
||
is_audio_in_length_range,
|
||
num_proc=num_workers,
|
||
input_columns=["length"],
|
||
)
|
||
|
||
# 7. Next, we can prepare for the training step.
|
||
# Let's use the appropriate XTREME-S evaluation metric,
|
||
# instantiate a data collator and the trainer
|
||
|
||
# Define evaluation metrics during training, *i.e.* word error rate, character error rate
|
||
eval_metric = load_metric("xtreme_s", task_name)
|
||
|
||
# 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 asr_logits_argmax(logits, labels):
|
||
return logits.argmax(dim=-1)
|
||
|
||
def compute_asr_metric(pred):
|
||
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
|
||
|
||
pred_str = tokenizer.batch_decode(pred.predictions)
|
||
# we do not want to group tokens when computing the metrics
|
||
label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
|
||
|
||
metric = eval_metric.compute(predictions=pred_str, references=label_str)
|
||
return metric
|
||
|
||
def compute_classification_metric(pred):
|
||
pred_ids = np.argmax(pred.predictions, axis=1)
|
||
metric = eval_metric.compute(predictions=pred_ids, references=pred.label_ids)
|
||
return metric
|
||
|
||
# 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)
|
||
if is_text_target:
|
||
tokenizer.save_pretrained(training_args.output_dir)
|
||
config.save_pretrained(training_args.output_dir)
|
||
# wait until configs are saved in the main process before loading the processor
|
||
if training_args.local_rank != -1:
|
||
torch.distributed.barrier()
|
||
|
||
if is_text_target:
|
||
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
||
else:
|
||
processor = AutoFeatureExtractor.from_pretrained(training_args.output_dir)
|
||
|
||
# Instantiate custom data collator
|
||
data_collator = SpeechDataCollatorWithPadding(processor=processor, pad_labels=is_text_target)
|
||
|
||
# Initialize Trainer
|
||
if target_column_name == "translation":
|
||
trainer = Seq2SeqTrainer(
|
||
model=model,
|
||
data_collator=data_collator,
|
||
args=training_args,
|
||
preprocess_logits_for_metrics=asr_logits_argmax if training_args.predict_with_generate else None,
|
||
compute_metrics=compute_asr_metric if training_args.predict_with_generate else None,
|
||
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=feature_extractor,
|
||
)
|
||
else:
|
||
trainer = Trainer(
|
||
model=model,
|
||
data_collator=data_collator,
|
||
args=training_args,
|
||
preprocess_logits_for_metrics=asr_logits_argmax if is_text_target else None,
|
||
compute_metrics=compute_asr_metric if is_text_target else compute_classification_metric,
|
||
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=feature_extractor,
|
||
)
|
||
|
||
# 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"])
|
||
)
|
||
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 on the test set
|
||
results = {}
|
||
if training_args.do_predict:
|
||
logger.info(f"*** Evaluating on the `{data_args.predict_split_name}` set ***")
|
||
if data_args.per_lang_metrics:
|
||
# separate the `test` dataset into language-specific subsets and compute metrics for each of them
|
||
metrics = {}
|
||
average_metrics = defaultdict(list)
|
||
for lang_id in range(len(lang_list)):
|
||
lang_name = lang_list[lang_id]
|
||
with training_args.main_process_first(desc="per-language dataset filter"):
|
||
lang_dataset = vectorized_datasets["predict"].filter(
|
||
lambda lang: lang == lang_id,
|
||
num_proc=num_workers,
|
||
input_columns=["lang"],
|
||
)
|
||
lang_metrics = trainer.evaluate(lang_dataset)
|
||
redundant_metrics = ["eval_runtime", "eval_samples_per_second", "eval_steps_per_second", "eval_epoch"]
|
||
for metric_name, value in lang_metrics.items():
|
||
average_metrics[metric_name].append(value)
|
||
if metric_name not in redundant_metrics:
|
||
metrics[f"{metric_name}_{lang_name}"] = value
|
||
for metric_name, value in average_metrics.items():
|
||
metrics[metric_name] = np.mean(value)
|
||
else:
|
||
metrics = trainer.evaluate(vectorized_datasets["predict"])
|
||
max_predict_samples = (
|
||
data_args.max_predict_samples
|
||
if data_args.max_predict_samples is not None
|
||
else len(vectorized_datasets["predict"])
|
||
)
|
||
metrics["predict_samples"] = min(max_predict_samples, len(vectorized_datasets["predict"]))
|
||
|
||
# make sure that the `predict` metrics end up in the log history for the model card
|
||
trainer.log(OrderedDict(sorted(metrics.items())))
|
||
|
||
trainer.log_metrics("predict", metrics)
|
||
trainer.save_metrics("predict", metrics)
|
||
|
||
# Write model card and (optionally) push to hub
|
||
kwargs = {
|
||
"finetuned_from": model_args.model_name_or_path,
|
||
"tasks": task_name,
|
||
"tags": [task_name, data_args.dataset_name],
|
||
"dataset_args": (
|
||
f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:"
|
||
f" {data_args.eval_split_name}, Predict split: {data_args.predict_split_name}"
|
||
),
|
||
"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
|
||
"language": data_args.language,
|
||
}
|
||
|
||
if training_args.push_to_hub:
|
||
trainer.push_to_hub(**kwargs)
|
||
else:
|
||
trainer.create_model_card(**kwargs)
|
||
|
||
return results
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|