run_speech_recognition_whisper.py

#!/usr/bin/env python
# coding=utf-8
#
# 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 whisper
import sys
from dataclasses import dataclass, field
import tempfile

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."}
    )
    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'"},
    )
    wandb_project: str = field(
        default="speech-recognition-whisper",
        metadata={"help": "The name of the wandb project."},
    )


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

    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):
        checkpoint = torch.load(model_args.model_name_or_path)
        need_to_rewrite_checkpoint = any(k.startswith("decoder.blocks") and ".mlp.3" in k for k in checkpoint.keys())
        if need_to_rewrite_checkpoint:
            new_checkpoint = {}
            for k, v in checkpoint.items():
                if k.startswith("decoder.blocks") and "mlp" in k.split("."):
                    if int(k.split(".mlp.")[-1].split(".")[0]) in [2, 4]:
                        continue
                    elif int(k.split(".mlp.")[-1].split(".")[0]) == 3:
                        k = k.replace(".mlp.3", ".mlp.2")

                new_checkpoint[k] = v

            with tempfile.TemporaryDirectory() as tmp:
                file = os.path.join(tmp, "model.pt")
                torch.save(new_checkpoint, file)
                model = whisper.Whisper.load_trained(file)
        else:
            model = whisper.Whisper.load_trained(model_args.model_name_or_path)
        del checkpoint
    else:
        model = whisper.load_model(model_args.model_name_or_path, dropout_rate=model_args.dropout_rate)

    if training_args.do_train:
        # 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_1 = nn.Dropout(p=model_args.dropout_rate)
                fc2 = mlp_layer[2]
                dropout_2 = nn.Dropout(p=model_args.dropout_rate)
                model.decoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout_1, fc2, dropout_2)
        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]
            dropout1 = nn.Dropout(p=model_args.dropout_rate)
            fc2 = mlp_layer[2]
            dropout2 = nn.Dropout(p=model_args.dropout_rate)
            model.decoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout1, fc2, dropout2)

    # 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
    raw_datasets = raw_datasets.cast_column(
            data_args.audio_column_name, datasets.features.Audio(sampling_rate=sample_rate)
        )

    # 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

    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))


    def prepare_dataset(batch):
        # pre-process audio
        sample = batch[audio_column_name]

        # 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"])

        input_str = batch[text_column_name]
        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"],
        )

    # 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 = 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)

    # 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()