run_speech_recognition_seq2seq_streaming.py · sassyphil/whispertest at main

whispertest / run_speech_recognition_seq2seq_streaming.py

Duplicate from NbAiLab/whisper-large-v2-nob

9b42bf4 over 1 year ago

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	#!/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

	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)
	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

	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]

	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()