650 lines
26 KiB
Python
Executable File
650 lines
26 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace 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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# limitations under the License.
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"""
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Fine-tuning the library models for sequence to sequence speech recognition.
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"""
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# You can also adapt this script on your own sequence to sequence speech
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# recognition task. Pointers for this are left as comments.
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import logging
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import os
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import sys
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import warnings
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional, Union
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import datasets
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import evaluate
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import torch
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from datasets import DatasetDict, load_dataset
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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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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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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, send_example_telemetry
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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("4.41.0.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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@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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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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feature_extractor_name: Optional[str] = field(
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default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
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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 to store the pretrained models downloaded from huggingface.co"},
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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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=None,
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metadata={
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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)
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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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freeze_encoder: bool = field(
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default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
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)
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forced_decoder_ids: List[List[int]] = field(
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default=None,
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metadata={"help": "Deprecated. Please use the `language` and `task` arguments instead."},
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)
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suppress_tokens: List[int] = field(
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default=None,
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metadata={
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"help": (
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"Deprecated. The use of `suppress_tokens` should not be required for the majority of fine-tuning examples."
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"Should you need to use `suppress_tokens`, please manually update them in the fine-tuning script directly."
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)
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},
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)
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apply_spec_augment: bool = field(
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default=False,
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metadata={
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"help": "Whether to apply *SpecAugment* data augmentation to the input features. This is currently only relevant for Wav2Vec2, HuBERT, WavLM and Whisper models."
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},
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: str = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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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 evaluation 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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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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max_duration_in_seconds: float = field(
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default=20.0,
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metadata={
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"help": (
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"Truncate 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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train_split_name: str = field(
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default="train",
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metadata={
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"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
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},
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)
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eval_split_name: str = field(
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default="test",
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metadata={
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"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
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},
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)
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do_lower_case: bool = field(
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default=True,
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metadata={"help": "Whether the target text should be lower cased."},
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)
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language: str = field(
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default=None,
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metadata={
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"help": (
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"Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
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"only. For English speech recognition, it should be set to `None`."
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)
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},
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)
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task: str = field(
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default="transcribe",
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metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
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)
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@dataclass
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class DataCollatorSpeechSeq2SeqWithPadding:
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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 ([`WhisperProcessor`])
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The processor used for processing the data.
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decoder_start_token_id (`int`)
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The begin-of-sentence of the decoder.
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forward_attention_mask (`bool`)
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Whether to return attention_mask.
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"""
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processor: Any
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decoder_start_token_id: int
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forward_attention_mask: bool
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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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model_input_name = self.processor.model_input_names[0]
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input_features = [{model_input_name: feature[model_input_name]} for feature in features]
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label_features = [{"input_ids": feature["labels"]} for feature in features]
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batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
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if self.forward_attention_mask:
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batch["attention_mask"] = torch.LongTensor([feature["attention_mask"] for feature in features])
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labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
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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 (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
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labels = labels[:, 1:]
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batch["labels"] = labels
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return batch
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def main():
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# 1. Parse input arguments
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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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if model_args.use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
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FutureWarning,
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)
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if model_args.token is not None:
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raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
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model_args.token = model_args.use_auth_token
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/PyTorch versions.
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send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args)
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# 2. 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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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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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: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
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)
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logger.info(f"Training/evaluation parameters {training_args}")
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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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# 3. Detecting last checkpoint and eventually continue from 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 and training_args.resume_from_checkpoint is 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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# Set seed before initializing model.
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set_seed(training_args.seed)
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# 4. Load dataset
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raw_datasets = DatasetDict()
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if training_args.do_train:
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raw_datasets["train"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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split=data_args.train_split_name,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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if training_args.do_eval:
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raw_datasets["eval"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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split=data_args.eval_split_name,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
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raise ValueError(
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f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
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"Make sure to set `--audio_column_name` to the correct audio column - one of "
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f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
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)
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if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
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raise ValueError(
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f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
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"Make sure to set `--text_column_name` to the correct text column - one of "
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f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
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)
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# 5. Load pretrained model, tokenizer, and feature extractor
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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config = AutoConfig.from_pretrained(
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model_args.config_name if model_args.config_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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# SpecAugment for whisper models
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if getattr(config, "model_type", None) == "whisper":
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config.update({"apply_spec_augment": model_args.apply_spec_augment})
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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model_args.model_name_or_path,
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config=config,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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if model.config.decoder_start_token_id is None:
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raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
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if model_args.freeze_feature_encoder:
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model.freeze_feature_encoder()
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if model_args.freeze_encoder:
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model.freeze_encoder()
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model.model.encoder.gradient_checkpointing = False
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if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
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# We only need to set the language and task ids in a multilingual setting
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tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
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model.generation_config.update(
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**{
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"language": data_args.language,
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"task": data_args.task,
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}
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)
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elif data_args.language is not None:
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raise ValueError(
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"Setting language token for an English-only checkpoint is not permitted. The language argument should "
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"only be set for multilingual checkpoints."
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)
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# TODO (Sanchit): deprecate these arguments in v4.41
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if model_args.forced_decoder_ids is not None:
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logger.warning(
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"The use of `forced_decoder_ids` is deprecated and will be removed in v4.41."
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"Please use the `language` and `task` arguments instead"
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)
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model.generation_config.forced_decoder_ids = model_args.forced_decoder_ids
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if model_args.suppress_tokens is not None:
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logger.warning(
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"The use of `suppress_tokens` is deprecated and will be removed in v4.41."
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"Should you need `suppress_tokens`, please manually set them in the fine-tuning script."
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)
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model.generation_config.suppress_tokens = model_args.suppress_tokens
|
|
|
|
# 6. Resample speech dataset if necessary
|
|
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)
|
|
)
|
|
|
|
# 7. Preprocessing the datasets.
|
|
# We need to read the audio files as arrays and tokenize the targets.
|
|
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
|
|
num_workers = data_args.preprocessing_num_workers
|
|
text_column_name = data_args.text_column_name
|
|
model_input_name = feature_extractor.model_input_names[0]
|
|
do_lower_case = data_args.do_lower_case
|
|
# if SpecAugment is used for whisper models, return attention_mask to guide the mask along time axis
|
|
forward_attention_mask = (
|
|
getattr(config, "model_type", None) == "whisper"
|
|
and getattr(config, "apply_spec_augment", False)
|
|
and getattr(config, "mask_time_prob", 0) > 0
|
|
)
|
|
|
|
if data_args.max_train_samples is not None:
|
|
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
|
|
|
|
if data_args.max_eval_samples is not None:
|
|
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
|
|
|
def prepare_dataset(batch):
|
|
# process audio
|
|
sample = batch[audio_column_name]
|
|
inputs = feature_extractor(
|
|
sample["array"], sampling_rate=sample["sampling_rate"], return_attention_mask=forward_attention_mask
|
|
)
|
|
# process audio length
|
|
batch[model_input_name] = inputs.get(model_input_name)[0]
|
|
batch["input_length"] = len(sample["array"])
|
|
if forward_attention_mask:
|
|
batch["attention_mask"] = inputs.get("attention_mask")[0]
|
|
|
|
# process targets
|
|
input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
|
|
batch["labels"] = tokenizer(input_str).input_ids
|
|
return batch
|
|
|
|
with training_args.main_process_first(desc="dataset map pre-processing"):
|
|
vectorized_datasets = raw_datasets.map(
|
|
prepare_dataset,
|
|
remove_columns=next(iter(raw_datasets.values())).column_names,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
desc="preprocess train dataset",
|
|
)
|
|
|
|
# filter data that is shorter than min_input_length or longer than
|
|
# max_input_length
|
|
def is_audio_in_length_range(length):
|
|
return length > min_input_length and length < max_input_length
|
|
|
|
vectorized_datasets = vectorized_datasets.filter(
|
|
is_audio_in_length_range,
|
|
num_proc=num_workers,
|
|
input_columns=["input_length"],
|
|
)
|
|
|
|
# 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:
|
|
cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
|
|
logger.info(f"Data preprocessing finished. Files cached at {cache}.")
|
|
return
|
|
|
|
# 8. Load Metric
|
|
metric = evaluate.load("wer", cache_dir=model_args.cache_dir)
|
|
|
|
def compute_metrics(pred):
|
|
pred_ids = pred.predictions
|
|
|
|
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
|
|
|
|
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
|
|
# we do not want to group tokens when computing the metrics
|
|
label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
|
|
|
|
wer = metric.compute(predictions=pred_str, references=label_str)
|
|
|
|
return {"wer": wer}
|
|
|
|
# 9. Create a single speech processor
|
|
# make sure all processes wait until data is saved
|
|
with training_args.main_process_first():
|
|
# only the main process saves them
|
|
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)
|
|
|
|
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
|
|
|
# 10. Define data collator
|
|
data_collator = DataCollatorSpeechSeq2SeqWithPadding(
|
|
processor=processor,
|
|
decoder_start_token_id=model.config.decoder_start_token_id,
|
|
forward_attention_mask=forward_attention_mask,
|
|
)
|
|
|
|
# 11. Initialize Trainer
|
|
trainer = Seq2SeqTrainer(
|
|
model=model,
|
|
args=training_args,
|
|
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,
|
|
data_collator=data_collator,
|
|
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
|
|
)
|
|
|
|
# 12. Training
|
|
if training_args.do_train:
|
|
checkpoint = None
|
|
if training_args.resume_from_checkpoint is not None:
|
|
checkpoint = training_args.resume_from_checkpoint
|
|
elif last_checkpoint is not None:
|
|
checkpoint = last_checkpoint
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
|
trainer.save_model() # Saves the feature extractor too for easy upload
|
|
|
|
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()
|
|
|
|
# 13. Evaluation
|
|
results = {}
|
|
if training_args.do_eval:
|
|
logger.info("*** Evaluate ***")
|
|
metrics = trainer.evaluate(
|
|
metric_key_prefix="eval",
|
|
max_length=training_args.generation_max_length,
|
|
num_beams=training_args.generation_num_beams,
|
|
)
|
|
max_eval_samples = (
|
|
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
|
|
)
|
|
metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
|
|
|
|
trainer.log_metrics("eval", metrics)
|
|
trainer.save_metrics("eval", metrics)
|
|
|
|
# 14. Write Training Stats
|
|
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition"}
|
|
if data_args.dataset_name is not None:
|
|
kwargs["dataset_tags"] = data_args.dataset_name
|
|
if data_args.dataset_config_name is not None:
|
|
kwargs["dataset_args"] = data_args.dataset_config_name
|
|
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
|
else:
|
|
kwargs["dataset"] = data_args.dataset_name
|
|
|
|
if training_args.push_to_hub:
|
|
trainer.push_to_hub(**kwargs)
|
|
else:
|
|
trainer.create_model_card(**kwargs)
|
|
|
|
return results
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|