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#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition""" | |
import functools | |
import json | |
import logging | |
from optparse import Option | |
import os | |
import re | |
import sys | |
import warnings | |
from dataclasses import dataclass, field | |
from typing import Dict, List, Optional, Union | |
import datasets | |
import numpy as np | |
import torch | |
from datasets import DatasetDict, load_dataset, load_metric | |
import transformers | |
from transformers import ( | |
AutoConfig, | |
AutoFeatureExtractor, | |
AutoModelForCTC, | |
AutoProcessor, | |
AutoTokenizer, | |
HfArgumentParser, | |
Trainer, | |
TrainingArguments, | |
Wav2Vec2Processor, | |
set_seed, | |
) | |
from transformers.trainer_pt_utils import get_parameter_names | |
from transformers.trainer_utils import get_last_checkpoint, is_main_process | |
from transformers.utils import check_min_version | |
from transformers.utils.versions import require_version | |
import wandb | |
from transformers import TrainingArguments, Trainer | |
wandb.init(project="wav2vec2_bn_xlsr", entity="arijitx") | |
# Will error if the minimal version of Transformers is not installed. Remove at your own risks. | |
check_min_version("4.16.0.dev0") | |
require_version("datasets>=1.13.3", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") | |
logger = logging.getLogger(__name__) | |
def list_field(default=None, metadata=None): | |
return field(default_factory=lambda: default, metadata=metadata) | |
@dataclass | |
class ModelArguments: | |
""" | |
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. | |
""" | |
model_name_or_path: str = field( | |
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} | |
) | |
tokenizer_name_or_path: Optional[str] = field( | |
default=None, | |
metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"}, | |
) | |
cache_dir: Optional[str] = field( | |
default=None, | |
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, | |
) | |
freeze_feature_encoder: bool = field( | |
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} | |
) | |
attention_dropout: float = field( | |
default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."} | |
) | |
activation_dropout: float = field( | |
default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} | |
) | |
feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."}) | |
hidden_dropout: float = field( | |
default=0.0, | |
metadata={ | |
"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler." | |
}, | |
) | |
final_dropout: float = field( | |
default=0.0, | |
metadata={"help": "The dropout probability for the final projection layer."}, | |
) | |
mask_time_prob: float = field( | |
default=0.05, | |
metadata={ | |
"help": "Probability of each feature vector along the time axis to be chosen as the start of the vector" | |
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature" | |
"vectors will be masked along the time axis." | |
}, | |
) | |
mask_time_length: int = field( | |
default=10, | |
metadata={"help": "Length of vector span to mask along the time axis."}, | |
) | |
mask_feature_prob: float = field( | |
default=0.0, | |
metadata={ | |
"help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector" | |
"span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis." | |
}, | |
) | |
mask_feature_length: int = field( | |
default=10, | |
metadata={"help": "Length of vector span to mask along the feature axis."}, | |
) | |
layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."}) | |
ctc_loss_reduction: Optional[str] = field( | |
default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."} | |
) | |
@dataclass | |
class DataTrainingArguments: | |
""" | |
Arguments pertaining to what data we are going to input our model for training and eval. | |
Using `HfArgumentParser` we can turn this class | |
into argparse arguments to be able to specify them on | |
the command line. | |
""" | |
dataset_name: str = field( | |
metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} | |
) | |
dataset_config_name: str = field( | |
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} | |
) | |
train_split_name: str = field( | |
default="train+validation", | |
metadata={ | |
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" | |
}, | |
) | |
eval_split_name: str = field( | |
default="test", | |
metadata={ | |
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" | |
}, | |
) | |
audio_column_name: str = field( | |
default="audio", | |
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, | |
) | |
text_column_name: str = field( | |
default="text", | |
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"}, | |
) | |
overwrite_cache: bool = field( | |
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} | |
) | |
preprocessing_num_workers: Optional[int] = field( | |
default=None, | |
metadata={"help": "The number of processes to use for the preprocessing."}, | |
) | |
max_train_samples: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": "For debugging purposes or quicker training, truncate the number of training examples to this " | |
"value if set." | |
}, | |
) | |
max_eval_samples: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": "For debugging purposes or quicker training, truncate the number of validation examples to this " | |
"value if set." | |
}, | |
) | |
chars_to_ignore: Optional[List[str]] = list_field( | |
default=None, | |
metadata={"help": "A list of characters to remove from the transcripts."}, | |
) | |
eval_metrics: List[str] = list_field( | |
default=["wer"], | |
metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"}, | |
) | |
max_duration_in_seconds: float = field( | |
default=20.0, | |
metadata={ | |
"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`" | |
}, | |
) | |
min_duration_in_seconds: float = field( | |
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"} | |
) | |
preprocessing_only: bool = field( | |
default=False, | |
metadata={ | |
"help": "Whether to only do data preprocessing and skip training. " | |
"This is especially useful when data preprocessing errors out in distributed training due to timeout. " | |
"In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` " | |
"so that the cached datasets can consequently be loaded in distributed training" | |
}, | |
) | |
use_auth_token: bool = field( | |
default=False, | |
metadata={ | |
"help": "If :obj:`True`, will use the token generated when running" | |
":obj:`transformers-cli login` as HTTP bearer authorization for remote files." | |
}, | |
) | |
unk_token: str = field( | |
default="[UNK]", | |
metadata={"help": "The unk token for the tokenizer"}, | |
) | |
pad_token: str = field( | |
default="[PAD]", | |
metadata={"help": "The padding token for the tokenizer"}, | |
) | |
word_delimiter_token: str = field( | |
default="|", | |
metadata={"help": "The word delimiter token for the tokenizer"}, | |
) | |
phoneme_language: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "The target language that should be used be" | |
" passed to the tokenizer for tokenization. Note that" | |
" this is only relevant if the model classifies the" | |
" input audio to a sequence of phoneme sequences." | |
}, | |
) | |
@dataclass | |
class DataCollatorCTCWithPadding: | |
""" | |
Data collator that will dynamically pad the inputs received. | |
Args: | |
processor (:class:`~transformers.AutoProcessor`) | |
The processor used for proccessing the data. | |
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): | |
Select a strategy to pad the returned sequences (according to the model's padding side and padding index) | |
among: | |
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single | |
sequence if provided). | |
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the | |
maximum acceptable input length for the model if that argument is not provided. | |
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of | |
different lengths). | |
max_length (:obj:`int`, `optional`): | |
Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). | |
max_length_labels (:obj:`int`, `optional`): | |
Maximum length of the ``labels`` returned list and optionally padding length (see above). | |
pad_to_multiple_of (:obj:`int`, `optional`): | |
If set will pad the sequence to a multiple of the provided value. | |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= | |
7.5 (Volta). | |
""" | |
processor: AutoProcessor | |
padding: Union[bool, str] = "longest" | |
pad_to_multiple_of: Optional[int] = None | |
pad_to_multiple_of_labels: Optional[int] = None | |
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: | |
# split inputs and labels since they have to be of different lenghts and need | |
# different padding methods | |
input_features = [{"input_values": feature["input_values"]} for feature in features] | |
label_features = [{"input_ids": feature["labels"]} for feature in features] | |
batch = self.processor.pad( | |
input_features, | |
padding=self.padding, | |
pad_to_multiple_of=self.pad_to_multiple_of, | |
return_tensors="pt", | |
) | |
with self.processor.as_target_processor(): | |
labels_batch = self.processor.pad( | |
label_features, | |
padding=self.padding, | |
pad_to_multiple_of=self.pad_to_multiple_of_labels, | |
return_tensors="pt", | |
) | |
# replace padding with -100 to ignore loss correctly | |
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) | |
batch["labels"] = labels | |
return batch | |
def create_vocabulary_from_data( | |
datasets: DatasetDict, | |
word_delimiter_token: Optional[str] = None, | |
unk_token: Optional[str] = None, | |
pad_token: Optional[str] = None, | |
): | |
# Given training and test labels create vocabulary | |
def extract_all_chars(batch): | |
all_text = " ".join(batch["target_text"]) | |
vocab = list(set(all_text)) | |
return {"vocab": [vocab], "all_text": [all_text]} | |
vocabs = datasets.map( | |
extract_all_chars, | |
batched=True, | |
batch_size=-1, | |
keep_in_memory=True, | |
remove_columns=datasets["train"].column_names, | |
) | |
# take union of all unique characters in each dataset | |
vocab_set = functools.reduce( | |
lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values() | |
) | |
vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))} | |
# replace white space with delimiter token | |
if word_delimiter_token is not None: | |
vocab_dict[word_delimiter_token] = vocab_dict[" "] | |
del vocab_dict[" "] | |
# add unk and pad token | |
if unk_token is not None: | |
vocab_dict[unk_token] = len(vocab_dict) | |
if pad_token is not None: | |
vocab_dict[pad_token] = len(vocab_dict) | |
return vocab_dict | |
def main(): | |
# See all possible arguments in src/transformers/training_args.py | |
# or by passing the --help flag to this script. | |
# We now keep distinct sets of args, for a cleaner separation of concerns. | |
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) | |
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): | |
# If we pass only one argument to the script and it's the path to a json file, | |
# let's parse it to get our arguments. | |
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) | |
else: | |
model_args, data_args, training_args = parser.parse_args_into_dataclasses() | |
# Detecting last checkpoint. | |
last_checkpoint = None | |
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: | |
last_checkpoint = get_last_checkpoint(training_args.output_dir) | |
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: | |
raise ValueError( | |
f"Output directory ({training_args.output_dir}) already exists and is not empty. " | |
"Use --overwrite_output_dir to overcome." | |
) | |
elif last_checkpoint is not None: | |
logger.info( | |
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " | |
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." | |
) | |
# Setup logging | |
logging.basicConfig( | |
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | |
datefmt="%m/%d/%Y %H:%M:%S", | |
handlers=[logging.StreamHandler(sys.stdout)], | |
) | |
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) | |
# Log on each process the small summary: | |
logger.warning( | |
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" | |
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" | |
) | |
# Set the verbosity to info of the Transformers logger (on main process only): | |
if is_main_process(training_args.local_rank): | |
transformers.utils.logging.set_verbosity_info() | |
logger.info("Training/evaluation parameters %s", training_args) | |
# Set seed before initializing model. | |
set_seed(training_args.seed) | |
# 1. First, let's load the dataset | |
raw_datasets = DatasetDict() | |
if training_args.do_train: | |
raw_datasets["train"] = load_dataset( | |
data_args.dataset_name, | |
data_args.dataset_config_name, | |
split='train[:95%]', | |
use_auth_token=data_args.use_auth_token, | |
) | |
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 data_args.text_column_name not in raw_datasets["train"].column_names: | |
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_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, | |
data_args.dataset_config_name, | |
split='train[95%:]', | |
use_auth_token=data_args.use_auth_token, | |
) | |
if data_args.max_eval_samples is not None: | |
raw_datasets["eval"] = raw_datasets["eval"].select(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"): | |
raw_datasets = raw_datasets.map( | |
remove_special_characters, | |
remove_columns=[text_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 | |
# load config | |
config = AutoConfig.from_pretrained( | |
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token | |
) | |
# 4. Next, if no tokenizer file is defined, | |
# we create the vocabulary of the model by extracting all unique characters from | |
# the training and evaluation datasets | |
# We need to make sure that only first rank saves vocabulary | |
# make sure all processes wait until vocab is created | |
tokenizer_name_or_path = model_args.tokenizer_name_or_path | |
tokenizer_kwargs = {} | |
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` | |
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, | |
} | |
# 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 | |
tokenizer = AutoTokenizer.from_pretrained( | |
tokenizer_name_or_path, | |
use_auth_token=data_args.use_auth_token, | |
**tokenizer_kwargs, | |
) | |
feature_extractor = AutoFeatureExtractor.from_pretrained( | |
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token | |
) | |
# adapt config | |
config.update( | |
{ | |
"feat_proj_dropout": model_args.feat_proj_dropout, | |
"attention_dropout": model_args.attention_dropout, | |
"hidden_dropout": model_args.hidden_dropout, | |
"final_dropout": model_args.final_dropout, | |
"mask_time_prob": model_args.mask_time_prob, | |
"mask_time_length": model_args.mask_time_length, | |
"mask_feature_prob": model_args.mask_feature_prob, | |
"mask_feature_length": model_args.mask_feature_length, | |
"gradient_checkpointing": training_args.gradient_checkpointing, | |
"layerdrop": model_args.layerdrop, | |
"ctc_loss_reduction": model_args.ctc_loss_reduction, | |
"pad_token_id": tokenizer.pad_token_id, | |
"vocab_size": len(tokenizer), | |
"activation_dropout": model_args.activation_dropout, | |
} | |
) | |
# create model | |
model = AutoModelForCTC.from_pretrained( | |
model_args.model_name_or_path, | |
cache_dir=model_args.cache_dir, | |
config=config, | |
use_auth_token=data_args.use_auth_token, | |
) | |
# freeze encoder | |
if model_args.freeze_feature_encoder: | |
model.freeze_feature_encoder() | |
# 6. Now we preprocess the datasets including loading the audio, resampling and normalization | |
# Thankfully, `datasets` takes care of automatically loading and resampling the audio, | |
# so that we just need to set the correct target sampling rate and normalize the input | |
# via the `feature_extractor` | |
# make sure that dataset decodes audio with correct sampling rate | |
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 | |
num_workers = data_args.preprocessing_num_workers | |
# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification | |
phoneme_language = data_args.phoneme_language | |
# 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 | |
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", | |
) | |
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 = vectorized_datasets.filter( | |
is_audio_in_length_range, | |
num_proc=num_workers, | |
input_columns=["input_length"], | |
) | |
# 7. Next, we can prepare the training. | |
# Let's use word error rate (WER) as our evaluation metric, | |
# instantiate a data collator and the trainer | |
# Define evaluation metrics during training, *i.e.* word error rate, character error rate | |
eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics} | |
# for large datasets it is advised to run the preprocessing on a | |
# single machine first with ``args.preprocessing_only`` since there will mostly likely | |
# be a timeout when running the script in distributed mode. | |
# In a second step ``args.preprocessing_only`` can then be set to `False` to load the | |
# cached dataset | |
if data_args.preprocessing_only: | |
logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}") | |
return | |
def compute_metrics(pred): | |
pred_logits = pred.predictions | |
pred_ids = np.argmax(pred_logits, axis=-1) | |
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id | |
pred_str = tokenizer.batch_decode(pred_ids) | |
# we do not want to group tokens when computing the metrics | |
label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False) | |
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} | |
return metrics | |
# Now save everything to be able to create a single processor later | |
if is_main_process(training_args.local_rank): | |
# save feature extractor, tokenizer and config | |
feature_extractor.save_pretrained(training_args.output_dir) | |
tokenizer.save_pretrained(training_args.output_dir) | |
config.save_pretrained(training_args.output_dir) | |
try: | |
processor = AutoProcessor.from_pretrained(training_args.output_dir) | |
except (OSError, KeyError): | |
warnings.warn( | |
"Loading a processor from a feature extractor config that does not" | |
" include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following " | |
" attribute to your `preprocessor_config.json` file to suppress this warning: " | |
" `'processor_class': 'Wav2Vec2Processor'`", | |
FutureWarning, | |
) | |
processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir) | |
# Instantiate custom data collator | |
data_collator = DataCollatorCTCWithPadding(processor=processor) | |
decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm]) | |
decay_parameters = [name for name in decay_parameters if "bias" not in name] | |
optimizer_grouped_parameters = [ | |
{ | |
"params": [p for n, p in model.named_parameters() if n in decay_parameters], | |
"weight_decay": training_args.weight_decay, | |
}, | |
{ | |
"params": [p for n, p in model.named_parameters() if n not in decay_parameters], | |
"weight_decay": 0.0, | |
}, | |
] | |
# 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=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 | |
results = {} | |
if training_args.do_eval: | |
logger.info("*** Evaluate ***") | |
metrics = trainer.evaluate() | |
max_eval_samples = ( | |
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"]) | |
) | |
metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"])) | |
trainer.log_metrics("eval", metrics) | |
trainer.save_metrics("eval", metrics) | |
# Write model card and (optionally) push to hub | |
config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na" | |
kwargs = { | |
"finetuned_from": model_args.model_name_or_path, | |
"tasks": "speech-recognition", | |
"tags": ["automatic-speech-recognition", data_args.dataset_name,"robust-speech-event","bn"], | |
"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}", | |
"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}", | |
} | |
if "common_voice" in data_args.dataset_name: | |
kwargs["language"] = config_name | |
if training_args.push_to_hub: | |
trainer.push_to_hub(**kwargs) | |
else: | |
trainer.create_model_card(**kwargs) | |
return results | |
if __name__ == "__main__": | |
main() |
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