wav2vec2-2-gpt2-no-adapter-regularisation / run_speech_recognition_seq2seq.py

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	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2021 The HuggingFace 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
	# limitations under the License.
	"""
	Fine-tuning the library models for sequence to sequence speech recognition.
	"""
	# You can also adapt this script on your own sequence to sequence speech
	# recognition task. Pointers for this are left as comments.

	import logging
	import os
	import sys
	from dataclasses import dataclass, field
	from typing import Any, Dict, List, Optional, Union

	import datasets
	import torch
	from datasets import DatasetDict, load_dataset, load_metric

	import bitsandbytes as bnb
	import transformers
	from transformers import (
	AutoConfig,
	AutoFeatureExtractor,
	AutoModelForSpeechSeq2Seq,
	AutoProcessor,
	AutoTokenizer,
	HfArgumentParser,
	Seq2SeqTrainer,
	Seq2SeqTrainingArguments,
	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
	from transformers.optimization import Adafactor


	# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
	check_min_version("4.17.0.dev0")

	require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")

	logger = logging.getLogger(__name__)


	@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"}
	)
	config_name: Optional[str] = field(
	default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
	)
	tokenizer_name: Optional[str] = field(
	default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
	)
	feature_extractor_name: Optional[str] = field(
	default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
	)
	cache_dir: Optional[str] = field(
	default=None,
	metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
	)
	use_fast_tokenizer: bool = field(
	default=True,
	metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
	)
	model_revision: str = field(
	default="main",
	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
	)
	use_auth_token: bool = field(
	default=False,
	metadata={
	"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
	"with private models)."
	},
	)
	freeze_feature_encoder: bool = field(
	default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
	)


	@dataclass
	class DataTrainingArguments:
	"""
	Arguments pertaining to what data we are going to input our model for training and eval.
	"""

	dataset_name: str = field(
	default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
	)
	dataset_config_name: Optional[str] = field(
	default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
	)
	text_column: Optional[str] = field(
	default=None,
	metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
	)
	overwrite_cache: bool = field(
	default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
	)
	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 evaluation examples to this "
	"value if set."
	},
	)
	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'"},
	)
	max_duration_in_seconds: float = field(
	default=20.0,
	metadata={
	"help": "Truncate 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"
	},
	)
	train_split_name: str = field(
	default="train",
	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'"
	},
	)
	do_lower_case: bool = field(
	default=True,
	metadata={"help": "Whether the target text should be lower cased."},
	)


	@dataclass
	class DataCollatorSpeechSeq2SeqWithPadding:
	"""
	Data collator that will dynamically pad the inputs received.
	Args:
	processor ([`Wav2Vec2Processor`])
	The processor used for proccessing the data.
	decoder_start_token_id (`int`)
	The begin-of-sentence of the decoder.
	"""

	processor: Any
	decoder_start_token_id: int

	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.feature_extractor.pad(input_features, return_tensors="pt")

	labels_batch = self.processor.tokenizer.pad(label_features, 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)

	# if bos token is appended in previous tokenization step,
	# cut bos token here as it's append later anyways
	if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
	labels = labels[:, 1:]

	batch["labels"] = labels

	return batch


	def main():
	# 1. Parse input arguments
	# 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, Seq2SeqTrainingArguments))

	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()

	# 2. 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)],
	)
	log_level = training_args.get_process_log_level()
	logger.setLevel(log_level)
	datasets.utils.logging.set_verbosity(log_level)
	transformers.utils.logging.set_verbosity(log_level)
	transformers.utils.logging.enable_default_handler()
	transformers.utils.logging.enable_explicit_format()

	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}"
	)
	logger.info(f"Training/evaluation parameters {training_args}")

	# 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)

	# 3. Detecting last checkpoint and eventualy continue from 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 and training_args.resume_from_checkpoint is 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."
	)

	# Set seed before initializing model.
	set_seed(training_args.seed)

	# 4. Load dataset
	raw_datasets = DatasetDict()

	if training_args.do_train:
	raw_datasets["train"] = load_dataset(
	data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
	)

	if training_args.do_eval:
	raw_datasets["eval"] = load_dataset(
	data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
	)

	if data_args.audio_column_name not in next(iter(raw_datasets.values())).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(next(iter(raw_datasets.values())).column_names)}."
	)

	if data_args.text_column_name not in next(iter(raw_datasets.values())).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(next(iter(raw_datasets.values())).column_names)}."
	)

	# 5. Load pretrained model, tokenizer, and feature extractor
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	config = AutoConfig.from_pretrained(
	model_args.config_name if model_args.config_name else model_args.model_name_or_path,
	cache_dir=model_args.cache_dir,
	revision=model_args.model_revision,
	use_auth_token=True if model_args.use_auth_token else None,
	)

	feature_extractor = AutoFeatureExtractor.from_pretrained(
	model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
	cache_dir=model_args.cache_dir,
	revision=model_args.model_revision,
	use_auth_token=True if model_args.use_auth_token else None,
	)
	tokenizer = AutoTokenizer.from_pretrained(
	model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
	cache_dir=model_args.cache_dir,
	use_fast=model_args.use_fast_tokenizer,
	revision=model_args.model_revision,
	use_auth_token=True if model_args.use_auth_token else None,
	)
	model = AutoModelForSpeechSeq2Seq.from_pretrained(
	model_args.model_name_or_path,
	config=config,
	cache_dir=model_args.cache_dir,
	revision=model_args.model_revision,
	use_auth_token=True if model_args.use_auth_token else None,
	)

	if model.config.decoder_start_token_id is None:
	raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

	if model_args.freeze_feature_encoder:
	model.freeze_feature_encoder()

	# 6. Resample speech dataset if necassary
	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 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"])
	# process audio length
	batch[model_input_name] = inputs.input_values[0]
	batch["input_length"] = len(batch["input_values"])

	# 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 = load_metric("wer")

	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
	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
	)

	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,
	},
	]

	optimizer = bnb.optim.Adam8bit(
	params=optimizer_grouped_parameters,
	lr=training_args.learning_rate,
	betas=(training_args.adam_beta1, training_args.adam_beta2),
	eps=training_args.adam_epsilon,
	)

	"""optimizer = Adafactor(
	params=optimizer_grouped_parameters,
	lr=training_args.learning_rate,
	beta1=training_args.adam_beta1,
	eps=training_args.adam_epsilon,
	relative_step=False,
	)"""


	optimizers = (optimizer, None)


	#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,
	optimizers=optimizers,
	)

	# 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=model.config.max_length, num_beams=model.config.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": "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()