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#!/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 CTC model for automatic speech recognition in streaming mode"""
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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 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 IterableDatasetDict, interleave_datasets, load_dataset, load_metric
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from torch.utils.data import IterableDataset
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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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AutoProcessor,
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AutoTokenizer,
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HfArgumentParser,
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Trainer,
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TrainerCallback,
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TrainingArguments,
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Wav2Vec2Processor,
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set_seed,
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)
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from transformers.trainer_pt_utils import IterableDatasetShard
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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 risk.
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check_min_version("4.17.0.dev0")
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require_version("datasets>=1.18.2", "To fix: pip install 'datasets>=1.18.2'")
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logger = logging.getLogger(__name__)
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def list_field(default=None, metadata=None):
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return field(default_factory=lambda: default, metadata=metadata)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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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={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
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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": "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", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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Using `HfArgumentParser` we can turn this class
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into argparse arguments to be able to specify them on
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the command line.
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"""
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dataset_name: str = field(
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metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: str = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_split_name: str = field(
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default="train+validation",
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metadata={
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"help": "The name of the training data set split to use (via the datasets library). Defaults to "
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"'train+validation'"
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},
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)
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eval_split_name: str = field(
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default="test",
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metadata={
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"help": "The name of the training data set split to use (via the datasets library). Defaults to '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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text_column_name: str = field(
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default="text",
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metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
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"value if set."
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},
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)
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shuffle_buffer_size: Optional[int] = field(
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default=500,
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metadata={
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"help": "The number of streamed examples to download before shuffling them. The large the buffer, "
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"the closer it is to real offline shuffling."
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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={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
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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={"help": "Filter audio files that are longer than `max_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": "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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use_auth_token: bool = field(
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default=False,
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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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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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@dataclass
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class DataCollatorCTCWithPadding:
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"""
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Data collator that will dynamically pad the inputs received.
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Args:
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processor (:class:`~transformers.AutoProcessor`)
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The processor used for proccessing the data.
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padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
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among:
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* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
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maximum acceptable input length for the model if that argument is not provided.
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* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
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different lengths).
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max_length (:obj:`int`, `optional`):
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Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
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max_length_labels (:obj:`int`, `optional`):
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Maximum length of the ``labels`` returned list and optionally padding length (see above).
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pad_to_multiple_of (:obj:`int`, `optional`):
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If set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
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7.5 (Volta).
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"""
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processor: AutoProcessor
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padding: Union[bool, str] = "longest"
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max_length: Optional[int] = None
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pad_to_multiple_of: Optional[int] = None
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pad_to_multiple_of_labels: Optional[int] = None
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def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
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# split inputs and labels since they have to be of different lenghts and need
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# different padding methods
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input_features = []
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label_features = []
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for feature in features:
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if self.max_length and feature["input_values"].shape[-1] > self.max_length:
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continue
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input_features.append({"input_values": feature["input_values"]})
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label_features.append({"input_ids": feature["labels"]})
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batch = self.processor.pad(
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input_features,
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padding=self.padding,
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pad_to_multiple_of=self.pad_to_multiple_of,
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return_tensors="pt",
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)
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with self.processor.as_target_processor():
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labels_batch = self.processor.pad(
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label_features,
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padding=self.padding,
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pad_to_multiple_of=self.pad_to_multiple_of_labels,
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return_tensors="pt",
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)
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# replace padding with -100 to ignore loss correctly
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labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
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batch["labels"] = labels
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return batch
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
# 1. First, let's load the dataset
|
|
|
|
|
raw_datasets = IterableDatasetDict()
|
|
|
|
|
raw_column_names = {}
|
|
|
|
|
|
|
|
|
|
def load_streaming_dataset(split, sampling_rate, **kwargs):
|
|
|
|
|
if "+" in split:
|
|
|
|
|
dataset_splits = [load_dataset(split=split_name, **kwargs) for split_name in split.split("+")]
|
|
|
|
|
# `features` and `cast_column` won't be available after interleaving, so we'll use them here
|
|
|
|
|
features = dataset_splits[0].features
|
|
|
|
|
# make sure that the dataset decodes audio with a correct sampling rate
|
|
|
|
|
dataset_splits = [
|
|
|
|
|
dataset.cast_column(data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate))
|
|
|
|
|
for dataset in dataset_splits
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
interleaved_dataset = interleave_datasets(dataset_splits)
|
|
|
|
|
return interleaved_dataset, features
|
|
|
|
|
else:
|
|
|
|
|
dataset = load_dataset(split=split, **kwargs)
|
|
|
|
|
features = dataset.features
|
|
|
|
|
# make sure that the dataset decodes audio with a correct sampling rate
|
|
|
|
|
dataset = dataset.cast_column(
|
|
|
|
|
data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)
|
|
|
|
|
)
|
|
|
|
|
return dataset, features
|
|
|
|
|
|
|
|
|
|
# `datasets` takes care of automatically loading and resampling the audio,
|
|
|
|
|
# so we just need to set the correct target sampling rate and normalize the input
|
|
|
|
|
# via the `feature_extractor`
|
|
|
|
|
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
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
if training_args.do_train:
|
|
|
|
|
raw_datasets["train"], train_features = load_streaming_dataset(
|
|
|
|
|
path=data_args.dataset_name,
|
|
|
|
|
name=data_args.dataset_config_name,
|
|
|
|
|
split=data_args.train_split_name,
|
|
|
|
|
use_auth_token=data_args.use_auth_token,
|
|
|
|
|
streaming=True,
|
|
|
|
|
sampling_rate=feature_extractor.sampling_rate,
|
|
|
|
|
)
|
|
|
|
|
raw_column_names["train"] = list(train_features.keys())
|
|
|
|
|
|
|
|
|
|
if data_args.audio_column_name not in raw_column_names["train"]:
|
|
|
|
|
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_column_names['train'])}."
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
if data_args.text_column_name not in raw_column_names["train"]:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
|
|
|
|
|
"Make sure to set `--text_column_name` to the correct text column - one of "
|
|
|
|
|
f"{', '.join(raw_column_names['train'])}."
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
if data_args.max_train_samples is not None:
|
|
|
|
|
raw_datasets["train"] = raw_datasets["train"].take(range(data_args.max_train_samples))
|
|
|
|
|
|
|
|
|
|
if training_args.do_eval:
|
|
|
|
|
raw_datasets["eval"], eval_features = load_streaming_dataset(
|
|
|
|
|
path=data_args.dataset_name,
|
|
|
|
|
name=data_args.dataset_config_name,
|
|
|
|
|
split=data_args.eval_split_name,
|
|
|
|
|
use_auth_token=data_args.use_auth_token,
|
|
|
|
|
streaming=True,
|
|
|
|
|
sampling_rate=feature_extractor.sampling_rate,
|
|
|
|
|
)
|
|
|
|
|
raw_column_names["eval"] = list(eval_features.keys())
|
|
|
|
|
|
|
|
|
|
if data_args.max_eval_samples is not None:
|
|
|
|
|
raw_datasets["eval"] = raw_datasets["eval"].take(range(data_args.max_eval_samples))
|
|
|
|
|
|
|
|
|
|
# 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
|
|
|
|
|
)
|
|
|
|
|
text_column_name = data_args.text_column_name
|
|
|
|
|
|
|
|
|
|
def remove_special_characters(batch):
|
|
|
|
|
if chars_to_ignore_regex is not None:
|
|
|
|
|
batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
|
|
|
|
|
else:
|
|
|
|
|
batch["target_text"] = batch[text_column_name].lower() + " "
|
|
|
|
|
return batch
|
|
|
|
|
|
|
|
|
|
with training_args.main_process_first(desc="dataset map special characters removal"):
|
|
|
|
|
for split, dataset in raw_datasets.items():
|
|
|
|
|
raw_datasets[split] = dataset.map(
|
|
|
|
|
remove_special_characters,
|
|
|
|
|
).remove_columns([text_column_name])
|
|
|
|
|
|
|
|
|
|
# 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, use_auth_token=data_args.use_auth_token
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 4. Now we can instantiate the tokenizer and model
|
|
|
|
|
# Note for distributed training, the .from_pretrained methods guarantee that only
|
|
|
|
|
# one local process can concurrently download model & vocab.
|
|
|
|
|
|
|
|
|
|
tokenizer_name_or_path = model_args.tokenizer_name_or_path
|
|
|
|
|
if tokenizer_name_or_path is None:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
"Tokenizer has to be created before training in streaming mode. Please specify --tokenizer_name_or_path"
|
|
|
|
|
)
|
|
|
|
|
# load feature_extractor and tokenizer
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
|
|
|
tokenizer_name_or_path,
|
|
|
|
|
config=config,
|
|
|
|
|
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()
|
|
|
|
|
|
|
|
|
|
# 5. Now we preprocess the datasets including loading the audio, resampling and normalization
|
|
|
|
|
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["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["target_text"], **additional_kwargs).input_ids
|
|
|
|
|
return batch
|
|
|
|
|
|
|
|
|
|
vectorized_datasets = IterableDatasetDict()
|
|
|
|
|
with training_args.main_process_first(desc="dataset map preprocessing"):
|
|
|
|
|
for split, dataset in raw_datasets.items():
|
|
|
|
|
vectorized_datasets[split] = (
|
|
|
|
|
dataset.map(prepare_dataset)
|
|
|
|
|
.remove_columns(raw_column_names[split] + ["target_text"])
|
|
|
|
|
.with_format("torch")
|
|
|
|
|
)
|
|
|
|
|
if split == "train":
|
|
|
|
|
vectorized_datasets[split] = vectorized_datasets[split].shuffle(
|
|
|
|
|
buffer_size=data_args.shuffle_buffer_size,
|
|
|
|
|
seed=training_args.seed,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 6. Next, we can prepare the training.
|
|
|
|
|
# Let's use word error rate (WER) as our evaluation metric,
|
|
|
|
|
# instantiate a data collator and the trainer
|
|
|
|
|
|
|
|
|
|
# Define 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}
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
|
|
|
|
data_collator = DataCollatorCTCWithPadding(processor=processor, max_length=max_input_length)
|
|
|
|
|
|
|
|
|
|
# trainer callback to reinitialize and reshuffle the streamable datasets at the beginning of each epoch
|
|
|
|
|
class ShuffleCallback(TrainerCallback):
|
|
|
|
|
def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
|
|
|
|
|
if isinstance(train_dataloader.dataset, IterableDatasetShard):
|
|
|
|
|
pass # set_epoch() is handled by the Trainer
|
|
|
|
|
elif isinstance(train_dataloader.dataset, IterableDataset):
|
|
|
|
|
train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
|
|
|
|
|
|
|
|
|
|
# Initialize Trainer
|
|
|
|
|
trainer = Trainer(
|
|
|
|
|
model=model,
|
|
|
|
|
data_collator=data_collator,
|
|
|
|
|
args=training_args,
|
|
|
|
|
compute_metrics=compute_metrics,
|
|
|
|
|
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
|
|
|
|
|
eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
|
|
|
|
|
tokenizer=processor,
|
|
|
|
|
callbacks=[ShuffleCallback()],
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 7. Finally, we can start training
|
|
|
|
|
|
|
|
|
|
# Training
|
|
|
|
|
if training_args.do_train:
|
|
|
|
|
|
|
|
|
|
# use last checkpoint if exist
|
|
|
|
|
if last_checkpoint is not None:
|
|
|
|
|
checkpoint = last_checkpoint
|
|
|
|
|
elif os.path.isdir(model_args.model_name_or_path):
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checkpoint = model_args.model_name_or_path
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else:
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checkpoint = None
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train_result = trainer.train(resume_from_checkpoint=checkpoint)
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trainer.save_model()
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metrics = train_result.metrics
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if data_args.max_train_samples:
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metrics["train_samples"] = data_args.max_train_samples
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trainer.log_metrics("train", metrics)
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trainer.save_metrics("train", metrics)
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trainer.save_state()
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# Evaluation
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results = {}
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if training_args.do_eval:
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logger.info("*** Evaluate ***")
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metrics = trainer.evaluate()
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if data_args.max_eval_samples:
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metrics["eval_samples"] = data_args.max_eval_samples
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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# Write model card and (optionally) push to hub
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config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
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kwargs = {
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"finetuned_from": model_args.model_name_or_path,
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"tasks": "speech-recognition",
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"tags": ["automatic-speech-recognition", data_args.dataset_name],
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"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
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"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
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}
|
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if "common_voice" in data_args.dataset_name:
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kwargs["language"] = config_name
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if training_args.push_to_hub:
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trainer.push_to_hub(**kwargs)
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else:
|
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trainer.create_model_card(**kwargs)
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return results
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if __name__ == "__main__":
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main()
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Anton Lozhkov