up

create_model.py

@@ -0,0 +1,30 @@
+from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer
+import torch
+
+
+encoder_id = "facebook/wav2vec2-xls-r-300m"
+decoder_id = "facebook/bart-large"
+
+model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True)
+model.config.encoder.feat_proj_dropout = 0.0
+model.config.encoder.final_dropout = 0.0
+model.config.encoder.mask_time_prob = 0.1
+model.config.decoder_start_token_id = model.decoder.config.bos_token_id
+model.config.pad_token_id = model.decoder.config.pad_token_id
+model.config.eos_token_id = model.decoder.config.eos_token_id
+model.config.max_length = 40
+model.config.num_beams = 1
+model.config.encoder.layerdrop = 0.0
+model.config.use_cache = False
+model.config.processor_class = "Wav2Vec2Processor"
+
+# check if generation works
+out = model.generate(torch.ones((1, 2000)))
+
+model.save_pretrained("./")
+
+feature_etxractor = AutoFeatureExtractor.from_pretrained(encoder_id)
+feature_etxractor.save_pretrained("./")
+tokenizer = AutoTokenizer.from_pretrained(decoder_id)
+tokenizer.save_pretrained("./")
+

run.sh

@@ -0,0 +1,33 @@
+#!/usr/bin/env bash
+CUDA_VISIBLE_DEVICES=1 python run_xtreme_s.py \
+    --model_name_or_path="./" \
+    --task="covost2" \
+    --language="fr.en" \
+    --eval_split_name="test" \
+    --output_dir="./" \
+    --overwrite_output_dir \
+    --num_train_epochs="3" \
+    --per_device_train_batch_size="4" \
+    --per_device_eval_batch_size="2" \
+    --gradient_accumulation_steps="2" \
+    --generation_max_length="40" \
+    --generation_num_beams="1" \
+    --learning_rate="3e-4" \
+    --warmup_steps="500" \
+    --evaluation_strategy="steps" \
+    --max_duration_in_seconds="20" \
+    --save_steps="500" \
+    --eval_steps="500" \
+    --logging_steps="1" \
+    --freeze_feature_encoder \
+    --gradient_checkpointing \
+    --fp16 \
+    --max_train_samples="10" \
+    --group_by_length \
+    --do_train \
+    --do_eval \
+    --metric_for_best_model="bleu" \
+    --greater_is_better=True \
+    --load_best_model_at_end \
+    --push_to_hub \
+    --use_auth_token

run_xtreme_s.py

@@ -0,0 +1,885 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. 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
+
+""" Fine-tuning a 🤗 Transformers pretrained speech model on the XTREME-S benchmark tasks"""
+
+import json
+import logging
+import os
+import re
+import sys
+from collections import OrderedDict, defaultdict
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union
+
+import datasets
+import numpy as np
+import torch
+from datasets import DatasetDict, load_dataset, load_metric
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForAudioClassification,
+    AutoModelForCTC,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    Trainer,
+    set_seed,
+)
+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
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.18.0.dev0")
+
+require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+
+logger = logging.getLogger(__name__)
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+TASK_TO_TARGET_COLUMN_NAME = {
+    "fleurs-asr": "transcription",
+    "fleurs-lang_id": "lang_id",
+    "mls": "transcription",
+    "voxpopuli": "transcription",
+    "covost2": "translation",
+    "minds14": "intent_class",
+    "babel": "transcription",
+}
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    tokenizer_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Where do you want to store the pretrained models and datasets downloaded from " "huggingface.co"
+        },
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    attention_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
+    )
+    activation_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
+    )
+    feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
+    hidden_dropout: float = field(
+        default=0.0,
+        metadata={
+            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
+        },
+    )
+    final_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout probability for the final projection layer."},
+    )
+    mask_time_prob: float = field(
+        default=0.05,
+        metadata={
+            "help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
+            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
+            "vectors will be masked along the time axis."
+        },
+    )
+    mask_time_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the time axis."},
+    )
+    mask_feature_prob: float = field(
+        default=0.0,
+        metadata={
+            "help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
+            "span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
+        },
+    )
+    mask_feature_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the feature axis."},
+    )
+    layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
+    ctc_loss_reduction: Optional[str] = field(
+        default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: str = field(
+        default="google/xtreme_s",
+        metadata={"help": "The name of the dataset to use (via the datasets library). Defaults to 'google/xtreme_s'"},
+    )
+    task: str = field(
+        default=None,
+        metadata={
+            "help": "The task name of the benchmark to use (via the datasets library). Should be on of: "
+            "'fleurs-asr', 'mls', 'voxpopuli', 'covost2', 'minds14', 'fleurs-lang_id', 'babel'."
+        },
+    )
+    language: str = field(
+        default="all",
+        metadata={"help": "The language id as defined in the datasets config name or `all` for all languages."},
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training dataset split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="validation",
+        metadata={
+            "help": "The name of the evaluation dataset split to use (via the datasets library). "
+            "Defaults to 'validation'"
+        },
+    )
+    predict_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the prediction dataset split to use (via the datasets library). " "Defaults to 'test'"
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    target_column_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the dataset column containing the target data "
+            "(transcription/translation/label). If None, the name will be inferred from the task. Defaults to None."
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    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 validation 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 prediction examples to this "
+            "value if set."
+        },
+    )
+    chars_to_ignore: Optional[List[str]] = list_field(
+        default=', ? . ! - ; : " “ % ‘ ” �'.split(" "),
+        metadata={"help": "A list of characters to remove from the transcripts."},
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={
+            "help": "Filter 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"}
+    )
+    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"
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "If :obj:`True`, will use the token generated when running"
+            ":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
+        },
+    )
+    unk_token: str = field(
+        default="[UNK]",
+        metadata={"help": "The unk token for the tokenizer"},
+    )
+    pad_token: str = field(
+        default="[PAD]",
+        metadata={"help": "The padding token for the tokenizer"},
+    )
+    word_delimiter_token: str = field(
+        default="|",
+        metadata={"help": "The word delimiter token for the tokenizer"},
+    )
+    phoneme_language: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The target language that should be used be"
+            " passed to the tokenizer for tokenization. Note that"
+            " this is only relevant if the model classifies the"
+            " input audio to a sequence of phoneme sequences."
+        },
+    )
+    per_lang_metrics: bool = field(
+        default=True,
+        metadata={
+            "help": "If `True`, compute the test metrics separately for each language, and average the results. "
+            "If `False` compute the average test metrics in a single pass for all languages at once."
+        },
+    )
+
+
+@dataclass
+class SpeechDataCollatorWithPadding:
+
+    processor: AutoProcessor
+    decoder_start_token_id: Optional[int] = None
+    padding: Union[bool, str] = "longest"
+    pad_labels: Optional[int] = True
+    pad_to_multiple_of: Optional[int] = None
+    pad_to_multiple_of_labels: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lenghts and need
+        # different padding methods
+        input_features = [{"input_values": feature["input_values"]} for feature in features]
+
+        batch = self.processor.pad(
+            input_features,
+            padding=self.padding,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+        if self.pad_labels:
+            label_features = [{"input_ids": feature["labels"]} for feature in features]
+            with self.processor.as_target_processor():
+                labels_batch = self.processor.pad(
+                    label_features,
+                    padding=self.padding,
+                    pad_to_multiple_of=self.pad_to_multiple_of_labels,
+                    return_tensors="pt",
+                )
+
+            # replace padding with -100 to ignore loss correctly
+            labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+            # if bos token is appended in previous tokenization step,
+            # cut bos token here as it's append later anyways
+            if (
+                self.decoder_start_token_id is not None
+                and (labels[:, 0] == self.decoder_start_token_id).all().cpu().item()
+            ):
+                labels = labels[:, 1:]
+
+            batch["labels"] = labels
+        else:
+            batch["labels"] = torch.tensor([feature["labels"] for feature in features])
+
+        return batch
+
+
+def create_vocabulary_from_data(
+    datasets: DatasetDict,
+    word_delimiter_token: Optional[str] = None,
+    unk_token: Optional[str] = None,
+    pad_token: Optional[str] = None,
+):
+    # Given training and test labels create vocabulary
+    def extract_all_chars(batch):
+        all_text = " ".join(batch["target_text"])
+        vocab = list(set(all_text))
+        return {"vocab": [vocab], "all_text": [all_text]}
+
+    vocabs = datasets.map(
+        extract_all_chars,
+        batched=True,
+        batch_size=-1,
+        keep_in_memory=True,
+        remove_columns=datasets["train"].column_names,
+    )
+
+    # take union of all unique characters in each dataset
+    vocab_set = (
+        (set(vocabs["train"]["vocab"][0]) if "train" in vocabs else set())
+        | (set(vocabs["eval"]["vocab"][0]) if "eval" in vocabs else set())
+        | (set(vocabs["predict"]["vocab"][0]) if "predict" in vocabs else set())
+    )
+
+    vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
+
+    # replace white space with delimiter token
+    if word_delimiter_token is not None:
+        vocab_dict[word_delimiter_token] = vocab_dict[" "]
+        del vocab_dict[" "]
+
+    # add unk and pad token
+    if unk_token is not None:
+        vocab_dict[unk_token] = len(vocab_dict)
+
+    if pad_token is not None:
+        vocab_dict[pad_token] = len(vocab_dict)
+
+    return vocab_dict
+
+
+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()
+
+    # 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)
+
+    # 1. First, let's load the dataset
+    raw_datasets = DatasetDict()
+    task_name = data_args.task
+    lang_id = data_args.language
+
+    if task_name is None:
+        raise ValueError(
+            "Set --task should be set to '<xtreme_s_task>' " "(e.g. 'fleurs-asr', 'mls', 'covost2', 'minds14') "
+        )
+    if lang_id is None:
+        raise ValueError(
+            "Set --language should be set to the language id of the sub dataset "
+            "config to be used (e.g. 'pl', 'en.tr', 'fr-FR') or 'all'"
+            " for multi-lingual fine-tuning."
+        )
+
+    if data_args.target_column_name is None:
+        target_column_name = TASK_TO_TARGET_COLUMN_NAME[task_name]
+    else:
+        target_column_name = data_args.target_column_name
+
+    # here we differentiate between tasks with text as the target and classification tasks
+    is_text_target = target_column_name in ("transcription", "translation")
+
+    config_name = ".".join([task_name.split("-")[0], lang_id])
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            config_name,
+            split=data_args.train_split_name,
+            use_auth_token=data_args.use_auth_token,
+            cache_dir=model_args.cache_dir,
+        )
+
+        if data_args.audio_column_name not in raw_datasets["train"].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(raw_datasets['train'].column_names)}."
+            )
+
+        if target_column_name not in raw_datasets["train"].column_names:
+            raise ValueError(
+                f"--target_column_name {target_column_name} not found in dataset '{data_args.dataset_name}'. "
+                "Make sure to set `--target_column_name` to the correct text column - one of "
+                f"{', '.join(raw_datasets['train'].column_names)}."
+            )
+
+        if 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:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=data_args.use_auth_token,
+            cache_dir=model_args.cache_dir,
+        )
+
+        if 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:
+        raw_datasets["predict"] = load_dataset(
+            data_args.dataset_name,
+            config_name,
+            split=data_args.predict_split_name,
+            use_auth_token=data_args.use_auth_token,
+            cache_dir=model_args.cache_dir,
+        )
+
+        if data_args.max_predict_samples is not None:
+            raw_datasets["predict"] = raw_datasets["predict"].select(range(data_args.max_predict_samples))
+
+    if not is_text_target:
+        label_list = next(iter(raw_datasets.values())).features[target_column_name].names
+        lang_list = next(iter(raw_datasets.values())).features["lang_id"].names
+        num_labels = len(label_list)
+
+    # 2. We remove some special characters from the datasets
+    # that make training complicated and do not help in transcribing the speech
+    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
+    # that could be easily picked up by the model
+    chars_to_ignore_regex = (
+        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
+    )
+
+    def remove_special_characters(batch):
+        if chars_to_ignore_regex is not None:
+            batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[target_column_name]).lower() + " "
+        else:
+            batch["target_text"] = batch[target_column_name].lower() + " "
+        return batch
+
+    if is_text_target:
+        with training_args.main_process_first(desc="dataset map special characters removal"):
+            raw_datasets = raw_datasets.map(
+                remove_special_characters,
+                remove_columns=[target_column_name],
+                desc="remove special characters from datasets",
+            )
+
+        # save special tokens for tokenizer
+        word_delimiter_token = data_args.word_delimiter_token
+        unk_token = data_args.unk_token
+        pad_token = data_args.pad_token
+
+    # 3. Next, let's load the config as we might need it to create
+    # the tokenizer
+    config = AutoConfig.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+    )
+
+    if is_text_target:
+        # 4. (Optional, for ASR and translation) If no tokenizer file is defined,
+        # we create the vocabulary of the model by extracting all unique characters from
+        # the training and evaluation datasets
+        # We need to make sure that only first rank saves vocabulary
+        # make sure all processes wait until vocab is created
+        tokenizer_name_or_path = model_args.tokenizer_name_or_path
+        tokenizer_kwargs = {}
+        if tokenizer_name_or_path is None:
+            # save vocab in training output dir
+            tokenizer_name_or_path = training_args.output_dir
+
+            vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
+
+            with training_args.main_process_first():
+                if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
+                    os.remove(vocab_file)
+
+            with training_args.main_process_first(desc="dataset map vocabulary creation"):
+                if not os.path.isfile(vocab_file):
+                    os.makedirs(tokenizer_name_or_path, exist_ok=True)
+                    vocab_dict = create_vocabulary_from_data(
+                        raw_datasets,
+                        word_delimiter_token=word_delimiter_token,
+                        unk_token=unk_token,
+                        pad_token=pad_token,
+                    )
+
+                    # save vocab dict to be loaded into tokenizer
+                    with open(vocab_file, "w") as file:
+                        json.dump(vocab_dict, file)
+
+            # if tokenizer has just been created
+            # it is defined by `tokenizer_class` if present in config else by `model_type`
+            if not config.is_encoder_decoder:
+                tokenizer_kwargs = {
+                    "config": config if config.tokenizer_class is not None else None,
+                    "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
+                    "unk_token": unk_token,
+                    "pad_token": pad_token,
+                    "word_delimiter_token": word_delimiter_token,
+                }
+            else:
+                tokenizer_kwargs = {}
+
+    # 5. Now we can instantiate the feature extractor, tokenizer and model
+    # Note for distributed training, the .from_pretrained methods guarantee that only
+    # one local process can concurrently download model & vocab.
+
+    # load feature_extractor and tokenizer
+    if is_text_target:
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name_or_path,
+            use_auth_token=data_args.use_auth_token,
+            **tokenizer_kwargs,
+        )
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+    )
+
+    # adapt config
+    # (speech translation requires pre-configured seq2seq models)
+    if task_name != "covost2":
+        config.update(
+            {
+                "feat_proj_dropout": model_args.feat_proj_dropout,
+                "attention_dropout": model_args.attention_dropout,
+                "hidden_dropout": model_args.hidden_dropout,
+                "final_dropout": model_args.final_dropout,
+                "mask_time_prob": model_args.mask_time_prob,
+                "mask_time_length": model_args.mask_time_length,
+                "mask_feature_prob": model_args.mask_feature_prob,
+                "mask_feature_length": model_args.mask_feature_length,
+                "gradient_checkpointing": training_args.gradient_checkpointing,
+                "layerdrop": model_args.layerdrop,
+                "ctc_loss_reduction": model_args.ctc_loss_reduction,
+                "activation_dropout": model_args.activation_dropout,
+            }
+        )
+        if training_args.do_train:
+            if is_text_target:
+                config.pad_token_id = tokenizer.pad_token_id
+                config.vocab_size = len(tokenizer)
+            else:
+                label_to_id = {v: i for i, v in enumerate(label_list)}
+                config.label2id = label_to_id
+                config.id2label = {id: label for label, id in label_to_id.items()}
+                config.num_labels = num_labels
+    else:
+        config.encoder.update({"hidden_dropout": model_args.hidden_dropout})
+
+    # create model
+    if target_column_name == "transcription":
+        model = AutoModelForCTC.from_pretrained(
+            model_args.model_name_or_path,
+            cache_dir=model_args.cache_dir,
+            config=config,
+            use_auth_token=data_args.use_auth_token,
+        )
+    elif config.is_encoder_decoder:
+        model = AutoModelForSpeechSeq2Seq.from_pretrained(
+            model_args.model_name_or_path,
+            cache_dir=model_args.cache_dir,
+            config=config,
+            use_auth_token=data_args.use_auth_token,
+        )
+        if model.config.decoder_start_token_id is None:
+            raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+    else:
+        model = AutoModelForAudioClassification.from_pretrained(
+            model_args.model_name_or_path,
+            cache_dir=model_args.cache_dir,
+            config=config,
+            use_auth_token=data_args.use_auth_token,
+        )
+
+    # freeze encoder
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    # 6. Now we preprocess the datasets including loading the audio, resampling and normalization
+    # Thankfully, `datasets` takes care of automatically loading and resampling the audio,
+    # so that we just need to set the correct target sampling rate and normalize the input
+    # via the `feature_extractor`
+
+    # make sure that dataset decodes audio with correct sampling rate
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # derive max & min input length for sample rate & max duration
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+
+    # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
+    phoneme_language = data_args.phoneme_language
+
+    # Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    def prepare_dataset(batch):
+        # load audio
+        sample = batch[audio_column_name]
+
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        batch["input_values"] = inputs.input_values[0]
+        batch["length"] = len(batch["input_values"])
+
+        # encode targets
+        additional_kwargs = {}
+        if phoneme_language is not None:
+            additional_kwargs["phonemizer_lang"] = phoneme_language
+
+        if is_text_target:
+            batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
+        else:
+            batch["labels"] = batch[target_column_name]
+
+        batch["lang"] = batch["lang_id"]
+
+        return batch
+
+    with training_args.main_process_first(desc="dataset map preprocessing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=next(iter(raw_datasets.values())).column_names,
+            num_proc=num_workers,
+            desc="preprocess datasets",
+        )
+
+        if training_args.do_train:
+
+            def is_audio_in_length_range(length):
+                return length > min_input_length and length < max_input_length
+
+            # filter data that is shorter than min_input_length
+            vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+                is_audio_in_length_range,
+                num_proc=num_workers,
+                input_columns=["length"],
+            )
+
+    # 7. Next, we can prepare for the training step.
+    # Let's use the appropriate XTREME-S evaluation metric,
+    # instantiate a data collator and the trainer
+
+    # Define evaluation metrics during training, *i.e.* word error rate, character error rate
+    eval_metric = load_metric("xtreme_s", task_name)
+
+    # 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:
+        logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
+        return
+
+    def compute_asr_metric(pred):
+        pred_logits = pred.predictions
+        pred_ids = np.argmax(pred_logits, axis=-1)
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
+
+        metric = eval_metric.compute(predictions=pred_str, references=label_str)
+        return metric
+
+    def compute_classification_metric(pred):
+        pred_ids = np.argmax(pred.predictions, axis=1)
+        metric = eval_metric.compute(predictions=pred_ids, references=pred.label_ids)
+        return metric
+
+    # Now save everything to be able to create a single processor later
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        if is_text_target:
+            tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+    # wait until configs are saved in the main process before loading the processor
+    if training_args.local_rank != -1:
+        torch.distributed.barrier()
+
+    if is_text_target:
+        processor = AutoProcessor.from_pretrained(training_args.output_dir)
+    else:
+        processor = AutoFeatureExtractor.from_pretrained(training_args.output_dir)
+
+    # Instantiate custom data collator
+    data_collator = SpeechDataCollatorWithPadding(processor=processor, pad_labels=is_text_target)
+
+    # Initialize Trainer
+    if target_column_name == "translation":
+        trainer = Seq2SeqTrainer(
+            model=model,
+            data_collator=data_collator,
+            args=training_args,
+            compute_metrics=compute_asr_metric if training_args.predict_with_generate else None,
+            train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+            eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+            tokenizer=feature_extractor,
+        )
+    else:
+        trainer = Trainer(
+            model=model,
+            data_collator=data_collator,
+            args=training_args,
+            compute_metrics=compute_asr_metric if is_text_target else compute_classification_metric,
+            train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+            eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+            tokenizer=feature_extractor,
+        )
+
+    # 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 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()
+
+    # Evaluation on the test set
+    results = {}
+    if training_args.do_predict:
+        logger.info(f"*** Evaluating on the `{data_args.predict_split_name}` set ***")
+        if data_args.per_lang_metrics:
+            # separate the `test` dataset into language-specific subsets and compute metrics for each of them
+            metrics = {}
+            average_metrics = defaultdict(list)
+            for lang_id in range(len(lang_list)):
+                lang_name = lang_list[lang_id]
+                lang_dataset = vectorized_datasets["predict"].filter(lambda example: example["lang"] == lang_id)
+                lang_metrics = trainer.evaluate(lang_dataset)
+                for metric_name, value in lang_metrics.items():
+                    average_metrics[metric_name].append(value)
+                    if metric_name not in ["eval_runtime", "eval_samples_per_second", "eval_steps_per_second"]:
+                        metrics[f"{metric_name}_{lang_name}"] = value
+            for metric_name, value in average_metrics.items():
+                metrics[metric_name] = np.mean(value)
+        else:
+            metrics = trainer.evaluate(vectorized_datasets["predict"])
+        max_predict_samples = (
+            data_args.max_predict_samples
+            if data_args.max_predict_samples is not None
+            else len(vectorized_datasets["predict"])
+        )
+        metrics["predict_samples"] = min(max_predict_samples, len(vectorized_datasets["predict"]))
+
+        # make sure that the `predict` metrics end up in the log history for the model card
+        trainer.log(OrderedDict(sorted(metrics.items())))
+
+        trainer.log_metrics("predict", metrics)
+        trainer.save_metrics("predict", metrics)
+
+    # Write model card and (optionally) push to hub
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": task_name,
+        "tags": [task_name, data_args.dataset_name],
+        "dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}, Predict split: {data_args.predict_split_name}",
+        "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
+        "language": data_args.language,
+    }
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
+