#!/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 string

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

# 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."}
    )
    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)."}
    )


class SuppressBlank:
    def __init__(self, tokenizer, sample_begin: int = 1):
        self.tokenizer = tokenizer
        self.sample_begin = sample_begin

    def __call__(self, input_ids, scores):
        tokens = input_ids
        logits = scores
        if tokens.shape[1] == self.sample_begin:
            logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
        return logits


class SuppressTokens:
    def __init__(self, suppress_tokens):
        self.suppress_tokens = list(suppress_tokens)

    def __call__(self, input_ids, scores):
        logits = scores
        logits[:, self.suppress_tokens] = -np.inf
        return logits


class ApplyTimestampRules:
    def __init__(
        self, tokenizer, sample_begin: int = 1, max_initial_timestamp_index: Optional[int] = None
    ):
        self.tokenizer = tokenizer
        self.sample_begin = sample_begin
        self.max_initial_timestamp_index = max_initial_timestamp_index

    def __call__(self, input_ids, scores):
        tokens = input_ids
        logits = scores
        # suppress <|notimestamps|> which is handled by without_timestamps
        if self.tokenizer.no_timestamps is not None:
            logits[:, self.tokenizer.no_timestamps] = -np.inf

        # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
        for k in range(tokens.shape[0]):
            seq = [t for t in tokens[k, self.sample_begin :].tolist()]
            last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
            penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin

            if last_was_timestamp:
                if penultimate_was_timestamp:  # has to be non-timestamp
                    logits[k, self.tokenizer.timestamp_begin :] = -np.inf
                else:  # cannot be normal text tokens
                    logits[k, : self.tokenizer.eot] = -np.inf

        # apply the `max_initial_timestamp` option
        if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None:
            last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
            logits[:, last_allowed + 1 :] = -np.inf

        # if sum of probability over timestamps is above any other token, sample timestamp
        logprobs = torch.nn.functional.log_softmax(logits.float(), dim=-1)
        for k in range(tokens.shape[0]):
            timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
            max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
            if timestamp_logprob > max_text_token_logprob:
                logits[k, : self.tokenizer.timestamp_begin] = -np.inf
        return logits


@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 transform(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


@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

    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([transform(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 
    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
    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")
    decoding_options = whisper.decoding.DecodingOptions(task="transcribe", language="en")
    task = whisper.decoding.DecodingTask(model, decoding_options)
    suppress_tokens = task._get_suppress_tokens()

    logits_processors = [SuppressBlank(whisper_tok), SuppressTokens(suppress_tokens), ApplyTimestampRules(whisper_tok)]
    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")

    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)

        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)

        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
    whisper_data_collator = WhisperDataCollatorWithPadding(eos_token_id=tokenizer.eos_token_id)

    # 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", logits_processor=logits_processors)
        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, logits_processor=logits_processors)
            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()