#!/usr/bin/env python # coding=utf-8 # Copyright 2022 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 with 🤗 Datasets' streaming mode. """ # You can also adapt this script for 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 os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["CUDA_VISIBLE_DEVICES"].replace("CUDA", "") import datasets import torch from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset from torch.utils.data import IterableDataset import evaluate import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, HfArgumentParser, Seq2SeqTrainer, Seq2SeqTrainingArguments, TrainerCallback, set_seed, ) from transformers.models.whisper.english_normalizer import BasicTextNormalizer from transformers.trainer_pt_utils import IterableDatasetShard 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("4.25.0.dev0") require_version("datasets>=1.18.2", "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)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-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."} ) 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."} ) model_index_name: str = field(default=None, metadata={"help": "Pretty name for the model card."}) @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)."}, ) 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"} ) 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=False, metadata={"help": "Whether the target text should be lower cased."}, ) do_remove_punctuation: bool = field( default=False, metadata={"help": "Whether the target text should be striped of punctuation."}, ) do_normalize_eval: bool = field( default=True, metadata={"help": "Whether to normalise the references and predictions in the eval WER calculation."}, ) 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."}, ) shuffle_buffer_size: Optional[int] = field( default=500, metadata={ "help": ( "The number of streamed examples to download before shuffling them. The large the buffer, " "the closer it is to real offline shuffling." ) }, ) streaming: bool = field( default=True, metadata={"help": "Whether to use streaming mode to load and pre-process the data."}, ) @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. """ 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 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") 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 load_maybe_streaming_dataset(dataset_name, dataset_config_name, split="train", streaming=True, **kwargs): """ Utility function to load a dataset in streaming mode. For datasets with multiple splits, each split is loaded individually and then splits combined by taking alternating examples from each (interleaving). """ if "+" in split: # load multiple splits separated by the `+` symbol with streaming mode dataset_splits = [ load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, **kwargs) for split_name in split.split("+") ] # interleave multiple splits to form one dataset interleaved_dataset = interleave_datasets(dataset_splits) return interleaved_dataset else: # load a single split *with* streaming mode dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=streaming, **kwargs) return dataset 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() # 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_streaming", 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: {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 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) logger.info(f"Last CHECKPOINT {last_checkpoint}") 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 = IterableDatasetDict() if data_args.streaming else DatasetDict() if training_args.do_train: raw_datasets["train"] = load_maybe_streaming_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=True if model_args.use_auth_token else None, streaming=data_args.streaming, ) if training_args.do_eval: raw_datasets["eval"] = load_maybe_streaming_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=True if model_args.use_auth_token else None, streaming=data_args.streaming, ) raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys()) if data_args.audio_column_name not in raw_datasets_features: 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_features)}." ) if data_args.text_column_name not in raw_datasets_features: 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_features)}." ) # 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, ) config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens}) if training_args.gradient_checkpointing: config.update({"use_cache": False}) 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() if model_args.freeze_encoder: model.freeze_encoder() 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 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 do_remove_punctuation = data_args.do_remove_punctuation normalizer = BasicTextNormalizer() # 'official' text normalizer from OpenAI if data_args.max_train_samples is not None: raw_datasets["train"] = ( raw_datasets["train"].take(data_args.max_train_samples) if data_args.streaming else 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"].take(data_args.max_eval_samples) if data_args.streaming else 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.get(model_input_name)[0] batch["input_length"] = len(sample["array"]) # process targets input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name] if do_remove_punctuation: input_str = normalizer(input_str).strip() 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=raw_datasets_features, ).with_format("torch") if training_args.do_train and data_args.streaming: # manually shuffle if streaming (done by the trainer for non-streaming) vectorized_datasets["train"] = vectorized_datasets["train"].shuffle( buffer_size=data_args.shuffle_buffer_size, seed=training_args.seed, ) # filter training data that is shorter than min_input_length or longer than # max_input_length def is_audio_in_length_range(length): return min_input_length < length < max_input_length if training_args.do_train: vectorized_datasets["train"] = vectorized_datasets["train"].filter( is_audio_in_length_range, input_columns=["input_length"], ) # 8. Load Metric metric = evaluate.load("wer") do_normalize_eval = data_args.do_normalize_eval 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) if do_normalize_eval: pred_str = [normalizer(pred) for pred in pred_str] label_str = [normalizer(label) for label in label_str] # filtering step to only evaluate the samples that correspond to non-zero references: pred_str = [pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0] label_str = [label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0] wer = 100 * 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, ) # 11. Configure Trainer # Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch # Only required for streaming: Trainer automatically shuffles non-streaming datasets class ShuffleCallback(TrainerCallback): def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs): if isinstance(train_dataloader.dataset, IterableDatasetShard): pass # set_epoch() is handled by the Trainer elif isinstance(train_dataloader.dataset, IterableDataset): train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1) # Initialize Trainer trainer = 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, callbacks=[ShuffleCallback()] if data_args.streaming 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 if data_args.max_train_samples: metrics["train_samples"] = data_args.max_train_samples 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, ) if data_args.max_eval_samples: metrics["eval_samples"] = data_args.max_eval_samples 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", "tags": "whisper-event", } 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"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: kwargs["dataset"] = data_args.dataset_name if "common_voice" in data_args.dataset_name: kwargs["language"] = data_args.dataset_config_name[:2] if model_args.model_index_name is not None: kwargs["model_name"] = model_args.model_index_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) return results if __name__ == "__main__": main()