#!/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 import warnings from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Union import datasets import evaluate import torch from datasets import DatasetDict, load_dataset import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, HfArgumentParser, Seq2SeqTrainer, Seq2SeqTrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.36.0.dev0") require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/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)."}, ) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) use_auth_token: bool = field( default=None, metadata={ "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option" "should only be set to `True` for repositories you trust and in which you have read the code, as it will " "execute code present on the Hub on your local machine." ) }, ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) freeze_encoder: bool = field( default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."} ) forced_decoder_ids: List[List[int]] = field( default=None, metadata={ "help": ( "A list of pairs of integers which indicates a mapping from generation indices to token indices " "that will be forced before sampling. For example, [[0, 123]] means the first generated token " "will always be a token of index 123." ) }, ) suppress_tokens: List[int] = field( default=None, metadata={"help": "A list of tokens that will be suppressed at generation."} ) apply_spec_augment: bool = field( default=False, metadata={ "help": "Whether to apply *SpecAugment* data augmentation to the input features. This is currently only relevant for Wav2Vec2, HuBERT, WavLM and Whisper models." }, ) @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)."} ) 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."}, ) language: str = field( default=None, metadata={ "help": ( "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning " "only. For English speech recognition, it should be set to `None`." ) }, ) task: str = field( default="transcribe", metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."}, ) @dataclass class DataCollatorSpeechSeq2SeqWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor ([`WhisperProcessor`]) The processor used for processing the data. decoder_start_token_id (`int`) The begin-of-sentence of the decoder. forward_attention_mask (`bool`) Whether to return attention_mask. """ processor: Any decoder_start_token_id: int forward_attention_mask: bool 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 lengths and need # different padding methods model_input_name = self.processor.model_input_names[0] input_features = [{model_input_name: feature[model_input_name]} 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") if self.forward_attention_mask: batch["attention_mask"] = torch.LongTensor([feature["attention_mask"] for feature in features]) 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() if model_args.use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.", FutureWarning, ) if model_args.token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") model_args.token = model_args.use_auth_token # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args) # 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: {training_args.parallel_mode.value == 'distributed'}, 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 eventually 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, cache_dir=model_args.cache_dir, token=model_args.token, ) 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, cache_dir=model_args.cache_dir, token=model_args.token, ) 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, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens}) # SpecAugment for whisper models if getattr(config, "model_type", None) == "whisper": config.update({"apply_spec_augment": model_args.apply_spec_augment}) 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, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) 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, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) model = AutoModelForSpeechSeq2Seq.from_pretrained( model_args.model_name_or_path, config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) 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() if model_args.freeze_encoder: model.freeze_encoder() model.model.encoder.gradient_checkpointing = False if data_args.language is not None: # We only need to set the task id when the language is specified (i.e. in a multilingual setting) tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task) # 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") 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()