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whisper-medium-switchboard-5k / run_speech_recognition_whisper.py

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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 OpenAI Whisper models for speech recognition.
	"""
	# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
	# flake8: noqa: E501
	import logging
	import os
	import re

	import torchaudio
	import whisper
	import sys
	from dataclasses import dataclass, field

	from typing import Optional, Dict, Union, List

	import numpy as np
	import torch

	import datasets
	from datasets import DatasetDict, load_dataset
	import transformers
	from torch import nn
	from transformers import (
	HfArgumentParser,
	Seq2SeqTrainingArguments,
	set_seed,
	Seq2SeqTrainer,
	)
	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 whisper.normalizers.english import EnglishTextNormalizer

	# 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>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")

	logger = logging.getLogger(__name__)


	@dataclass
	class ModelArguments:
	"""
	Arguments pertaining to which model/tokenizer we are going to fine-tune from.
	"""
	model_name_or_path: Optional[str] = field(
	default=None,
	metadata={"help": "Path to pretrained model or model identifier from OpenAI Whisper NGC."}
	)
	cache_dir: Optional[str] = field(
	default=None,
	metadata={"help": "Where to store the pretrained models downloaded from huggingface.co or OpenAI Whisper NGC."},
	)
	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)."
	},
	)
	manifest_path: str = field(
	default="data",
	metadata={
	"help": "Manifest path."
	},
	)
	tokenizer_path: str = field(
	default="tokenizers",
	metadata={
	"help": "Tokenizer path."
	},
	)
	freeze_encoder: bool = field(
	default=False,
	metadata={"help": "Freeze the acoustic encoder of the model. Recommend when fine-tuning on small datasets."}
	)
	num_beams: int = field(
	default=1,
	metadata={"help": "Number of beams for evaluation."},
	)
	length_penalty: float = field(
	default=1.0,
	metadata={"help": "Length penalty for evaluation."},
	)
	use_adam8bit: bool = field(
	default=False,
	metadata={"help": "Whether to use bitsandbytes 8bit AdamW optimiser."}
	)
	dropout_rate: float = field(
	default=0.0,
	metadata={"help": "The dropout ratio for all dropout layers (default=0)."}
	)


	@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)."},
	)
	dataset_cache_dir: Optional[str] = field(
	default=None, metadata={"help": "Path to cache directory for saving and loading datasets"}
	)
	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."
	},
	)
	max_predict_samples: Optional[int] = field(
	default=None,
	metadata={
	"help": "For debugging purposes or quicker training, truncate the number of test 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 training 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"}
	)
	max_eval_duration_in_seconds: float = field(
	default=None,
	metadata={
	"help": "Truncate eval/test audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
	},
	)
	max_target_length: Optional[int] = field(
	default=128,
	metadata={
	"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
	"than this will be truncated, sequences shorter will be padded."
	},
	)
	min_target_length: Optional[int] = field(
	default=0,
	metadata={
	"help": "The minimum total sequence length for target text after tokenization. Sequences shorter "
	"than this will be filtered."
	},
	)
	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="validation",
	metadata={
	"help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'validation'"
	},
	)
	test_split_name: str = field(
	default="test",
	metadata={"help": "The name of the test data set split to use (via the datasets library). Defaults to 'test'"},
	)
	do_lower_case: bool = field(
	default=True,
	metadata={"help": "Whether the target text should be lower cased."},
	)
	wandb_project: str = field(
	default="speech-recognition-whisper",
	metadata={"help": "The name of the wandb project."},
	)
	ignore_verifications: bool = field(
	default=False,
	metadata={
	"help": "Ignore the verifications of the downloaded/processed dataset information in `load_dataset` (checksums/size/splits/...)."
	}
	)
	torchaudio_resampler: bool = field(
	default=False,
	metadata={
	"help": "Whether to use torchaudio to resample. If `False` (default) will use the default datataset backed."
	}
	)


	def write_wandb_pred(pred_str, label_str, prefix="eval"):
	# convert str data to a wandb compatible format
	str_data = [[label_str[i], pred_str[i]] for i in range(len(pred_str))]
	# we'll log all predictions for the last epoch
	wandb.log(
	{
	f"{prefix}/predictions": wandb.Table(
	columns=["label_str", "pred_str"], data=str_data
	)
	},
	)


	def to_pad_to_mel(array):
	"""Static function which:
	1. Pads/trims a list of audio arrays to a max length of 30s
	2. Computes log-mel filter coefficients from padded/trimmed audio sequences
	Inputs:
	array: list of audio arrays
	Returns:
	input_ids: torch.tensor of log-mel filter bank coefficients
	"""
	padded_input = whisper.pad_or_trim(np.asarray(array, dtype=np.float32))
	input_ids = whisper.log_mel_spectrogram(padded_input)
	return input_ids


	def to_mel_to_pad(array):
	"""Static function which:
	1. Computes log-mel filter coefficients from padded/trimmed audio sequences
	2. Pads/trims a list of audio arrays to a max length of 30s
	Inputs:
	array: list of audio arrays
	Returns:
	input_ids: torch.tensor of log-mel filter bank coefficients
	"""
	mels = whisper.log_mel_spectrogram(np.asarray(array, dtype=np.float32))
	input_ids = whisper.pad_or_trim(mels, 3000)
	return input_ids


	@dataclass
	class WhisperDataCollatorWithPadding:
	"""
	Data collator that dynamically pads the audio inputs received. An EOS token is appended to the labels sequences.
	They are then dynamically padded to max length.
	Args:
	eos_token_id (`int`)
	The end-of-sentence token for the Whisper tokenizer. Ensure to set for sequences to terminate before
	generation max length.
	"""

	eos_token_id: int
	time_stamp_token_id: int

	def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
	"""
	Since Whisper models don't have a HF processor defined (feature extractor + tokenizer), we'll pad by hand...
	"""
	# split inputs and labels since they have to be of different lengths
	# and need different padding methods
	input_ids = [feature["input_ids"] for feature in features]
	labels = [feature["labels"] for feature in features]

	# first, pad the audio inputs to max_len
	input_ids = torch.concat([to_pad_to_mel(input_val)[None, :] for input_val in input_ids])

	# next, append the eos token to our sequence of labels
	labels = [lab + [self.eos_token_id] for lab in labels]
	# finally, pad the target labels to max_len
	label_lengths = [len(lab) for lab in labels]
	max_label_len = max(label_lengths)
	labels = [np.pad(lab, (0, max_label_len - lab_len), 'constant', constant_values=-100) for lab, lab_len in zip(labels, label_lengths)]

	batch = {"labels": labels}
	batch = {k: torch.tensor(np.array(v), requires_grad=False) for k, v in batch.items()}

	batch["input_ids"] = input_ids

	return batch


	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, 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()

	# Set wandb project ID before instantiating the Trainer
	os.environ["WANDB_PROJECT"] = data_args.wandb_project
	report_to_wandb = "wandb" in training_args.report_to

	sample_rate = 16_000

	# 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)

	# load the model
	if os.path.isfile(model_args.model_name_or_path):
	model = whisper.Whisper.load_trained(model_args.model_name_or_path)
	else:
	model = whisper.load_model(model_args.model_name_or_path, dropout_rate=model_args.dropout_rate)

	# set the dropout for the MLP layers -> we do this here as the MLP layers are written as a 'sequential'
	# so changing the modelling code gives mis-matches in the state-dict

	if not model_args.freeze_encoder:
	# only apply dropout when training the encoder
	for block_idx in range(len(model.encoder.blocks)):
	mlp_layer = model.encoder.blocks[block_idx].mlp
	# going very verbose to explain what we're doing here!
	fc1 = mlp_layer[0]
	act_fn = mlp_layer[1]
	dropout = nn.Dropout(p=model_args.dropout_rate)
	fc2 = mlp_layer[2]
	model.encoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout, fc2, dropout)

	"""for block_idx in range(len(model.decoder.blocks)):
	mlp_layer = model.decoder.blocks[block_idx].mlp
	fc1 = mlp_layer[0]
	act_fn = mlp_layer[1]
	dropout = nn.Dropout(p=model_args.dropout_rate)
	fc2 = mlp_layer[2]
	model.decoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout, fc2, dropout)"""

	# load the tokenizer
	whisper_tok = whisper.tokenizer.get_tokenizer(False, task="transcribe", language="en")
	tokenizer = whisper_tok.tokenizer
	tokenizer.pad_token = tokenizer.eos_token

	# 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=data_args.dataset_cache_dir,
	use_auth_token=True if model_args.use_auth_token else None,
	)

	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=data_args.dataset_cache_dir,
	use_auth_token=True if model_args.use_auth_token else None,
	)

	if training_args.do_predict:
	test_split = data_args.test_split_name.split("+")
	for split in test_split:
	raw_datasets[split] = load_dataset(
	data_args.dataset_name,
	data_args.dataset_config_name,
	split=split,
	cache_dir=data_args.dataset_cache_dir,
	use_auth_token=True if model_args.use_auth_token else None,
	)

	if not training_args.do_train and not training_args.do_eval and not training_args.do_predict:
	raise ValueError(
	"Cannot not train, not do evaluation and not do prediction. At least one of "
	"training, evaluation or prediction has to be done."
	)

	# if not training, there is no need to run multiple epochs
	if not training_args.do_train:
	training_args.num_train_epochs = 1

	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)}."
	)

	# 6. Resample speech dataset ALWAYS
	if data_args.torchaudio_resampler:
	# TODO: remove hardcoding of orig sr
	resampler = torchaudio.transforms.Resample(8_000, sample_rate)
	else:
	raw_datasets = raw_datasets.cast_column(
	data_args.audio_column_name, datasets.features.Audio(sampling_rate=sample_rate)
	)
	resampler = None

	# 7. Preprocessing the datasets.
	# We need to read the audio files as arrays and tokenize the targets.
	max_input_length = int(data_args.max_duration_in_seconds * sample_rate)
	min_input_length = min(int(data_args.min_duration_in_seconds * sample_rate), 1)
	max_eval_input_length = int(data_args.max_eval_duration_in_seconds * sample_rate) if data_args.max_eval_duration_in_seconds else None
	max_target_length = data_args.max_target_length
	min_target_length = data_args.min_target_length
	audio_column_name = data_args.audio_column_name
	num_workers = data_args.preprocessing_num_workers
	text_column_name = data_args.text_column_name
	do_lower_case = data_args.do_lower_case
	dataset_name = data_args.dataset_name

	# Define tokens to ignore/replace
	tedlium_contractions = [" 's", " 't", " 're", " 've", " 'm", " 'll", " 'd", " 'clock", " 'all"]
	gigaspeech_punctuation = {" <comma>": ",", " <period>": ".", " <questionmark>": "?", " <exclamationpoint>": "!"}
	gigaspeech_disfluencies = ["<other>", "<sil>"]
	swb_disfluencies = ["[noise]", "[laughter]", "[silence]", "[vocalized-noise]", "<a_aside>", "<b_aside>", "<e_aside>",
	"[laughter-", "_1", "[laugh]", "[sigh]", "[cough]", "[mn]", "[breath]", "[lipsmack]",
	"[sneeze]", "[skip]", "[pause]", "(%hesitation)", "(%HESITATION)"]
	swb_punctuations = ["{", "}", "[", "]-", "]", "((", "))", "(", ")"]
	earnings_disfluencies = ["<noise>", "<crosstalk>", "<affirmative>", "<inaudible>", "inaudible", "<laugh>", "<silence>"]
	ignore_segments = ["ignore_time_segment_in_scoring", "<noise>", "<music>", "[noise]", "[laughter]", "[silence]",
	"[vocalized-noise]", "<crosstalk>", "<affirmative>", "<inaudible>", "<laugh>", ""]
	ignore_segments = ignore_segments + gigaspeech_disfluencies + swb_disfluencies + earnings_disfluencies

	if training_args.do_train and 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 and data_args.max_eval_samples is not None:
	raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))

	if training_args.do_predict and data_args.max_predict_samples is not None:
	for split in test_split:
	raw_datasets[split] = raw_datasets[split].select(range(data_args.max_predict_samples))

	# filter data where the targets are ignored in scoring
	def is_target_labels(input_str):
	return input_str.lower() not in ignore_segments

	raw_datasets = raw_datasets.filter(
	is_target_labels,
	num_proc=num_workers,
	input_columns=[text_column_name],
	desc="filtering data where the targets are ignored in scoring",
	)

	def prepare_dataset(batch):
	# pre-process audio
	try:
	sample = batch[audio_column_name]
	except ValueError:
	# E22: some samples are empty (no audio). Reading the empty audio array will trigger
	# a soundfile ValueError. For now, we'll manually set these arrays to a zero array.
	# They will be filtered in the subsequent filtering stage and so are
	# explicitly ignored during training.
	sample = {"array": np.array([0.]), "sampling_rate": sample_rate}

	if resampler is not None:
	speech_tensor = torch.FloatTensor(sample["array"])
	speech_tensor = speech_tensor.squeeze()
	speech_tensor = resampler(speech_tensor)
	sample["array"] = speech_tensor.numpy()
	sample["sampling_rate"] = resampler.new_freq

	# For training Whisper we perform the audio preprocessing in the WhisperDataCollator
	# => we only need to supply it with the raw audio values
	batch["input_ids"] = sample["array"]
	batch["input_lengths"] = len(batch["input_ids"])

	# 'Error correction' of targets
	input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]

	# LibriSpeech ASR
	if dataset_name == "librispeech_asr":
	pass # no error correction necessary

	# VoxPopuli
	if dataset_name == "google/xtreme_s":
	pass # no error correction necessary

	# Common Voice 9
	if dataset_name == "mozilla-foundation/common_voice_9_0":
	if input_str.startswith('"') and input_str.endswith('"'):
	# we can remove trailing quotation marks as they do not affect the transcription
	input_str = input_str[1:-1]
	# replace double quotation marks with single
	input_str = input_str.replace('""', '"')

	# TED-LIUM (Release 3)
	if dataset_name == "LIUM/tedlium":
	# delete the <unk> token from the text
	input_str = input_str.replace("<unk>", "")
	# replace spaced apostrophes with un-spaced (it 's -> it's)
	for contraction in tedlium_contractions:
	input_str = input_str.replace(contraction, contraction[1:])

	# GigaSpeech
	if dataset_name == "speechcolab/gigaspeech":
	for disfluency in gigaspeech_disfluencies:
	input_str = input_str.replace(disfluency, "")
	# convert spelled out punctuation to symbolic form
	for punctuation, replacement in gigaspeech_punctuation.items():
	input_str = input_str.replace(punctuation, replacement)

	# SWB: hide the path to the private HF dataset
	if "switchboard" in dataset_name:
	# In one conversation people speak some German phrases that are tagged as
	# <german (( ja wohl )) > -- we remove these
	input_str = re.sub("<[^>]*>", "", input_str)

	# Remove junk tokens
	for disfluency in swb_disfluencies:
	input_str = input_str.replace(disfluency, "")

	# normalise acronyms (Fisher: u_.c_.l_.a., SWBD: u c l a)
	input_str = input_str.replace("_.", " ")

	# Replace partially pronounced words (square brackets + hyphen): westmin[ster]- to westmin- or -[go]ing to -ing
	# Replace anomalous words (square brackets + backslack): [lemguini/linguini] to linguini
	# Replace the combo of the two: [lem[guini]-/linguini] to lem-
	# Example: we [ah/are] -[go]ing to westmin[ster]- for [lem[guini]-/linguini]
	# Target: we ah -ing to westmin- for lem-
	# Treat anomalous words first then destroy the content of all square brackets (partially pronounced words)

	# First treat partially pronounced anomalous words by removing correct word: [lem[guini]-/linguini] to [lem[guini]-
	input_str = re.sub(r"\-\/.*?\]", "-", input_str)

	# Now replace anomalous words with their correct transcriptions: [lemguini/linguini] to linguini
	split_str = input_str.split("/")
	if len(split_str) > 1:
	input_str = " ".join(
	[" ".join([" ".join(i.split(" ")[:-1]) for i in split_str])] + [split_str[-1].split(" ")[-1]])

	# Remove the trailing brackets on the start/end of words
	processed_str = []
	for word in input_str.split():
	if word[0] == "[":
	processed_str.append(word[1:])
	elif word[-1] == "]":
	processed_str.append(word[:-1])
	else:
	processed_str.append(word)

	# Stick the processed words back together
	input_str = " ".join(processed_str)

	# Now we can remove all words in square brackets: -[go]ing to -ing
	input_str = re.sub(r"\-\[(.*?)\]", "-", input_str)

	# westmin[ster]- to westmin-
	input_str = re.sub(r"\[(.*?)\]\-", "-", input_str)

	# tech[n]ology to tech-ology
	input_str = re.sub(r"\[(.*?)\]", "-", input_str)

	# partially pronounced words are now done!
	# remove erroneous punctuations (curly braces, trailing square brackets, etc.)
	for punctuation in swb_punctuations:
	input_str = input_str.replace(punctuation, "")

	# Earnings 22: still figuring out best segmenting method. Thus, dataset name subject to change
	if "earnings22" in dataset_name:
	# Remove the 100ms offset at the end of the sample
	sampling_rate = sample["sampling_rate"]
	offset = int(100 * (10 ** -3) * sampling_rate)
	batch["input_ids"] = sample["array"][:-offset]
	batch["input_lengths"] = len(batch["input_ids"])
	# Remove junk tokens
	for disfluency in earnings_disfluencies:
	input_str = input_str.replace(disfluency, "")

	# SPGISpeech
	if dataset_name == "kensho/spgispeech":
	pass # no error correction necessary

	# JIWER compliance (for WER/CER calc.)
	# remove multiple spaces
	input_str = re.sub(r"\s\s+", " ", input_str)
	# strip trailing spaces
	input_str = input_str.strip()

	# Finally, we tokenize the processed text
	batch["labels"] = tokenizer(input_str).input_ids
	return batch

	vectorized_datasets = raw_datasets.map(
	prepare_dataset,
	remove_columns=next(iter(raw_datasets.values())).column_names,
	num_proc=num_workers,
	desc="preprocess train dataset",
	)

	# filter training data with inputs longer than max_input_length
	def is_audio_in_length_range(input_length):
	return min_input_length < input_length < max_input_length

	if training_args.do_train:
	vectorized_datasets["train"] = vectorized_datasets["train"].filter(
	is_audio_in_length_range,
	num_proc=num_workers,
	input_columns=["input_lengths"],
	)

	if max_eval_input_length is not None:
	# filter training data with inputs longer than max_input_length
	def is_eval_audio_in_length_range(input_length):
	return min_input_length < input_length < max_eval_input_length

	if training_args.do_eval:
	vectorized_datasets["eval"] = vectorized_datasets["eval"].filter(
	is_eval_audio_in_length_range,
	num_proc=num_workers,
	input_columns=["input_lengths"],
	)

	if training_args.do_predict:
	for split in test_split:
	vectorized_datasets[split] = vectorized_datasets[split].filter(
	is_eval_audio_in_length_range,
	num_proc=num_workers,
	input_columns=["input_lengths"],
	)

	# filter training data with targets shorter than min_target_length or longer than max_target_length
	def is_labels_in_length_range(labels):
	return min_target_length < len(labels) < max_target_length

	if training_args.do_train:
	vectorized_datasets["train"] = vectorized_datasets["train"].filter(
	is_labels_in_length_range,
	num_proc=num_workers,
	input_columns=["labels"],
	)

	# filter data with targets empty sentences
	def is_labels_greater_than_min(labels):
	return len(labels) > 0

	vectorized_datasets = vectorized_datasets.filter(
	is_labels_greater_than_min,
	num_proc=num_workers,
	input_columns=["labels"],
	)

	# 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

	if model_args.freeze_encoder:
	model.freeze_encoder()
	logging.info("Model encoder has been frozen")

	# 8. Load Metric
	#metric_wer = evaluate.load("wer")
	#metric_cer = evaluate.load("cer")
	metric_wer = datasets.load_metric("wer")
	metric_cer = datasets.load_metric("cer")

	normalizer = EnglishTextNormalizer()

	def compute_metrics(pred):
	pred_ids = pred.predictions
	pred.label_ids[pred.label_ids == -100] = tokenizer.eos_token_id

	pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
	pred_str = [x.lstrip().strip() for x in pred_str]

	# 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_wer.compute(predictions=pred_str, references=label_str)
	cer = metric_cer.compute(predictions=pred_str, references=label_str)

	#normalized_pred_str = [normalizer(str(input_str)) for input_str in pred_str]
	#normalized_label_str = [normalizer(str(input_str)) for input_str in label_str]

	#wer_norm = metric_wer.compute(predictions=normalized_pred_str, references=normalized_label_str)
	#cer_norm = metric_cer.compute(predictions=normalized_pred_str, references=normalized_label_str)

	#return {"wer": wer, "cer": cer, "wer_norm": wer_norm, "cer_norm": cer_norm}
	return {"wer": wer, "cer": cer}

	def compute_metrics_and_predictions(pred):
	pred_ids = pred.predictions
	pred.label_ids[pred.label_ids == -100] = tokenizer.eos_token_id

	pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
	pred_str = [x.lstrip().strip() for x in pred_str]

	# 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_wer.compute(predictions=pred_str, references=label_str)
	cer = metric_cer.compute(predictions=pred_str, references=label_str)

	#normalized_pred_str = [normalizer(str(input_str)) for input_str in pred_str]
	#normalized_label_str = [normalizer(str(input_str)) for input_str in label_str]

	#wer_norm = metric_wer.compute(predictions=normalized_pred_str, references=normalized_label_str)
	#cer_norm = metric_cer.compute(predictions=normalized_pred_str, references=normalized_label_str)

	#return {"wer": wer, "cer": cer, "wer_norm": wer_norm, "cer_norm": cer_norm, "pred_str": pred_str, "label_str": label_str}
	return {"wer": wer, "cer": cer, "pred_str": pred_str, "label_str": label_str}

	class WhisperTrainer(Seq2SeqTrainer):
	def _save(self, output_dir: Optional[str] = None, state_dict=None):
	# If we are executing this function, we are the process zero, so we don't check for that.
	output_dir = output_dir if output_dir is not None else self.args.output_dir
	os.makedirs(output_dir, exist_ok=True)
	logger.info(f"Saving model checkpoint to {output_dir}")
	# Save a trained model and configuration using `save_pretrained()`.
	# They can then be reloaded using `from_pretrained()`
	self.model.save_to(save_path=os.path.join(output_dir, model_args.model_name_or_path + ".whisper"))
	# Good practice: save your training arguments together with the trained model
	torch.save(self.args, os.path.join(output_dir, "training_args.bin"))

	# Define data collator
	eos = tokenizer.eos_token_id
	t_stamp = tokenizer("<\|notimestamps\|>").input_ids[0]
	whisper_data_collator = WhisperDataCollatorWithPadding(eos_token_id=eos, time_stamp_token_id=t_stamp)

	# make sure model uses 50257 as BOS
	bos = tokenizer("<\|startoftranscript\|>").input_ids[0]
	model.config.decoder_start_token_id = bos

	# Initialize Trainer
	trainer = WhisperTrainer(
	model=model,
	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,
	data_collator=whisper_data_collator,
	)

	# 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 model_args.model_name_or_path is not None and 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()

	# Change decoding strategy for final eval/predict
	# if training_args.do_eval or training_args.do_predict:
	# trainer.model.num_beams = 2

	trainer.compute_metrics = compute_metrics_and_predictions

	results = {}
	if training_args.do_eval:
	if not training_args.do_train and report_to_wandb:
	# manually init wandb
	wandb.init(project=data_args.wandb_project, name=training_args.run_name)
	# Have to run this as a predict step, otherwise trainer will try to log the pred/label strings to wandb
	eval_results = trainer.predict(vectorized_datasets["eval"], metric_key_prefix="eval", num_beams=model_args.num_beams, length_penalty=model_args.length_penalty)
	metrics = eval_results.metrics
	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"]))
	pred_str = metrics.pop("eval_pred_str", None)
	label_str = metrics.pop("eval_label_str", None)

	trainer.log_metrics("eval", metrics)
	trainer.save_metrics("eval", metrics)

	if report_to_wandb:
	metrics = {os.path.join("eval", k[len("eval") + 1:]): v for k, v in metrics.items()}
	wandb.log(metrics)
	write_wandb_pred(pred_str, label_str, prefix="eval")

	if training_args.do_predict:
	if not training_args.do_train and not training_args.do_eval and report_to_wandb:
	# manually init wandb
	wandb.init(project=data_args.wandb_project, name=training_args.run_name)
	for split in test_split:
	predict_results = trainer.predict(
	vectorized_datasets[split], metric_key_prefix=split, num_beams=model_args.num_beams, length_penalty=model_args.length_penalty)
	metrics = predict_results.metrics
	max_predict_samples = (
	data_args.max_predict_samples if data_args.max_predict_samples is not None else len(vectorized_datasets[split])
	)
	metrics[f"{split}_samples"] = min(max_predict_samples, len(vectorized_datasets[split]))
	pred_str = metrics.pop(f"{split}_pred_str", None)
	label_str = metrics.pop(f"{split}_label_str", None)

	trainer.log_metrics(split, metrics)
	trainer.save_metrics(split, metrics)

	if report_to_wandb:
	metrics = {os.path.join(split, k[len(split)+1:]): v for k, v in metrics.items()}
	wandb.log(metrics)
	write_wandb_pred(pred_str, label_str, prefix=split)

	# 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],
	"dataset_args": (
	f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:"
	f" {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)

	return results


	if __name__ == "__main__":
	main()