[ASR] Add official ASR CTC example to `examples/pytorch/speech-recognition` (#13620)

* up

* rename

* add asr example

* add auto feature extractor

* some more fixes

* correct layerdrop

* correct for multi-gpu dist

* clean up

* refactor

* refactor

* more fixes

* more fixes

* clean-up

* finish

* up

* Apply suggestions from code review

* fix isort

* update

* up

* add note

* apply surajs suggestions

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* isort

* small change

* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* add hubert

* Update examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

examples/pytorch/speech-recognition/README.md

+<!---
+Copyright 2021 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.
+-->
+
+# Automatic Speech Recognition examples
+
+
+## Connectionist Temporal Classification without Language Model (CTC w/o LM)
+
+The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/transformers/master/model_doc/auto.html?highlight=automodelforctc#automodelforctc) for automatic speech 
+recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
+
+Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/master/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/master/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html), have shown to require only 
+very little annotated data to yield good performance on automatic speech recognition datasets.
+
+In the script [`run_speech_recognition_ctc`], we first create a vocabulary from all unique characters of both the training data and evaluation data. Then, we preprocesses the speech recognition dataset, which includes correct resampling, normalization and padding. Finally, the pretrained speech recognition model is fine-tuned on the annotated speech recognition datasets using CTC loss.
+
+---
+**NOTE**
+
+It is currently not recommended to make use of `--preprocessing_num_workers`.
+If however, you wish to use multi-processing for data preprocessing by setting `--preprocessing_num_workers` > 1, 
+please make sure to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
+
+```bash
+OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
+```
+
+If the environment variable is not set, the training script might hang, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239
+
+---
+
+### Single-GPU
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
+
+```bash
+python run_speech_recognition_ctc.py \
+	--dataset_name="common_voice" \
+	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+	--dataset_config_name="tr" \
+	--output_dir="./wav2vec2-common_voice-tr-demo" \
+	--overwrite_output_dir \
+	--num_train_epochs="15" \
+	--per_device_train_batch_size="16" \
+	--gradient_accumulation_steps="2" \
+	--learning_rate="3e-4" \
+	--warmup_steps="500" \
+	--evaluation_strategy="steps" \
+	--audio_column_name="path" \
+	--text_column_name="sentence" \
+	--save_steps="400" \
+	--eval_steps="100" \
+	--layerdrop="0.0" \
+	--save_total_limit="3" \
+	--freeze_feature_extractor \
+	--gradient_checkpointing \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--fp16 \
+	--group_by_length \
+	--push_to_hub \
+	--do_train --do_eval 
+```
+
+On a single V100 GPU, this script should run in *ca.* 1 hour 20 minutes and yield a CTC loss of **0.39** and word error rate
+of **0.35**.
+
+### Multi-GPU
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
+
+```bash
+python -m torch.distributed.launch \
+	--nproc_per_node 8 run_speech_recognition_ctc.py \
+	--dataset_name="common_voice" \
+	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+	--dataset_config_name="tr" \
+	--output_dir="./wav2vec2-common_voice-tr-demo-dist" \
+	--preprocessing_num_workers="16" \
+	--overwrite_output_dir \
+	--num_train_epochs="15" \
+	--per_device_train_batch_size="4" \
+	--learning_rate="3e-4" \
+	--warmup_steps="500" \
+	--evaluation_strategy="steps" \
+	--audio_column_name="path" \
+	--text_column_name="sentence" \
+	--save_steps="400" \
+	--eval_steps="100" \
+	--logging_steps="1" \
+	--layerdrop="0.0" \
+	--save_total_limit="3" \
+	--freeze_feature_extractor \
+	--gradient_checkpointing \
+	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
+	--fp16 \
+	--group_by_length \
+	--push_to_hub \
+	--do_train --do_eval
+```
+
+On 8 V100 GPUs, this script should run in *ca.* 18 minutes and yield a CTC loss of **0.39** and word error rate
+of **0.36**.
+
+### Examples
+
+In the following a couple of demonstration fine-tuning runs are listed.
+It has been verified that the script works for the following datasets:
+
+- [Common Voice](https://huggingface.co/datasets/common_voice)
+- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
+
+| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs |
+|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------|
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) | 0.088 | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist) |
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)  | 0.36     | 8 GPU V100   |  18min                 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo-dist)      |  
+| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.35 | 1 GPU V100   |  1h20min                      | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo)  | 

examples/pytorch/speech-recognition/requirements.txt

+datasets >= 1.12.0
+torch >= 1.5
+torchaudio

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 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 CTC model for automatic speech recognition"""
+
+import functools
+import json
+import logging
+import os
+import re
+import sys
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+import torch
+import torchaudio
+from datasets import DatasetDict, load_dataset, load_metric
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForCTC,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    Wav2Vec2Processor,
+    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("4.11.0.dev0")
+
+# TODO(Patrick) Bump up as soon as audio features are merged
+require_version("datasets>=1.12.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
+
+
+logger = logging.getLogger(__name__)
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@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"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    freeze_feature_extractor: Optional[bool] = field(
+        default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
+    )
+    attention_dropout: Optional[float] = field(
+        default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
+    )
+    activation_dropout: Optional[float] = field(
+        default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
+    )
+    feat_proj_dropout: Optional[float] = field(
+        default=0.0, metadata={"help": "The dropout ratio for the projected features."}
+    )
+    hidden_dropout: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
+        },
+    )
+    final_dropout: Optional[float] = field(
+        default=0.0,
+        metadata={"help": "The dropout probability for the final projection layer."},
+    )
+    mask_time_prob: Optional[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. This is only relevant if ``apply_spec_augment is True``."
+        },
+    )
+    gradient_checkpointing: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
+        },
+    )
+    layerdrop: Optional[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(
+        metadata={"help": "The configuration 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)."}
+    )
+    train_split_name: Optional[str] = field(
+        default="train+validation",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: Optional[str] = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    audio_column_name: Optional[str] = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: Optional[str] = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    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."
+        },
+    )
+    chars_to_ignore: Optional[List[str]] = list_field(
+        default=None,
+        metadata={"help": "A list of characters to remove from the transcripts."},
+    )
+    max_duration_in_seconds: Optional[float] = field(
+        default=20.0,
+        metadata={
+            "help": "Truncate audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
+        },
+    )
+    min_duration_in_seconds: Optional[float] = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    only_data_preprocessing: Optional[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 `only_data_preprocessing=True` "
+            "so that the cached datasets can consequently be loaded in distributed training"
+        },
+    )
+
+
+@dataclass
+class DataCollatorCTCWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor (:class:`~transformers.Wav2Vec2Processor`)
+            The processor used for proccessing the data.
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        max_length (:obj:`int`, `optional`):
+            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
+        max_length_labels (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` returned list and optionally padding length (see above).
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
+            7.5 (Volta).
+    """
+
+    processor: Wav2Vec2Processor
+    padding: Union[bool, str] = "longest"
+    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]
+        label_features = [{"input_ids": feature["labels"]} 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",
+        )
+
+        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)
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def create_vocabulary_from_data(datasets: DatasetDict):
+    # 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 = functools.reduce(
+        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
+    )
+
+    vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
+
+    # replace white space with delimiter token
+    vocab_dict["|"] = vocab_dict[" "]
+    del vocab_dict[" "]
+
+    # add unk and pad token
+    vocab_dict["[UNK]"] = len(vocab_dict)
+    vocab_dict["[PAD]"] = 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, TrainingArguments))
+    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()
+    raw_datasets["train"] = load_dataset(
+        data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
+    )
+    raw_datasets["eval"] = load_dataset(
+        data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
+    )
+
+    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 data_args.text_column_name not in raw_datasets["train"].column_names:
+        raise ValueError(
+            f"--text_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(raw_datasets['train'].column_names)}."
+        )
+
+    # prepare dataset
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+
+    # 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[data_args.text_column_name]).lower() + " "
+        else:
+            batch["target_text"] = batch[data_args.text_column_name].lower() + " "
+        return batch
+
+    with training_args.main_process_first(desc="dataset map special characters removal"):
+        raw_datasets = raw_datasets.map(
+            remove_special_characters,
+            remove_columns=[data_args.text_column_name],
+            desc="remove special characters from datasets",
+        )
+
+    # 3. Next, 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
+
+    with training_args.main_process_first(desc="dataset map vocabulary creation"):
+        vocab_dict = create_vocabulary_from_data(raw_datasets)
+
+        vocab_file = os.path.join(training_args.output_dir, "vocab.json")
+
+        # save vocab dict to be loaded into tokenizer
+        os.makedirs(training_args.output_dir, exist_ok=True)
+        if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
+            os.remove(vocab_file)
+
+        if not os.path.isfile(vocab_file):
+            with open(vocab_file, "w") as vocab_file:
+                json.dump(vocab_dict, vocab_file)
+
+    # 4. Now we can instantiate the configuration, 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 config
+    config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
+
+    # load feature_extractor, tokenizer and create processor
+    tokenizer = AutoTokenizer.from_pretrained(
+        training_args.output_dir,
+        tokenizer_type=config.model_type,
+        unk_token="[UNK]",
+        pad_token="[PAD]",
+        word_delimiter_token="|",
+    )
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir
+    )
+    processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
+
+    # adapt config
+    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,
+            "gradient_checkpointing": model_args.gradient_checkpointing,
+            "layerdrop": model_args.layerdrop,
+            "ctc_loss_reduction": model_args.ctc_loss_reduction,
+            "pad_token_id": processor.tokenizer.pad_token_id,
+            "vocab_size": len(processor.tokenizer),
+            "activation_dropout": model_args.activation_dropout,
+        }
+    )
+
+    # create model
+    model = AutoModelForCTC.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, config=config
+    )
+
+    # freeze encoder
+    if model_args.freeze_feature_extractor:
+        model.freeze_feature_extractor()
+
+    # 5. Now we preprocess the datasets which includes loading the audio, resampling and padding
+
+    # The following code should be cleaned up as soon as
+    # https://github.com/huggingface/datasets/pull/2324 is merged
+
+    # Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+
+    # derive max & min input length for sample rate & max duration
+    max_input_length = data_args.max_duration_in_seconds * processor.feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * processor.feature_extractor.sampling_rate
+
+    resampler = None
+    if raw_datasets["train"][data_args.audio_column_name][0].split(".")[-1] == "mp3":
+        # TODO(PVP) - remove hard-coded 48_000 after audio feature is merged
+        resampler = torchaudio.transforms.Resample(48_000, processor.feature_extractor.sampling_rate)
+
+    # Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    def prepare_dataset(batch):
+        # load audio
+        speech_array, sampling_rate = torchaudio.load(batch[data_args.audio_column_name])
+        speech_array = speech_array.squeeze()
+
+        # if necessary resample audio
+        if resampler is not None:
+            # TODO(PVP) - remove hard-coded 48_000 after audio feature is merged
+            speech_array = resampler(speech_array)
+            sampling_rate = resampler.new_freq
+
+        speech_array = speech_array.numpy()
+
+        batch["input_values"] = processor(
+            speech_array, sampling_rate=sampling_rate, truncate=True, max_length=max_input_length
+        ).input_values[0]
+
+        # Setup the processor for targets
+        with processor.as_target_processor():
+            batch["labels"] = processor(batch["target_text"]).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map preprocessing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=raw_datasets["train"].column_names,
+            num_proc=data_args.preprocessing_num_workers,
+            desc="preprocess datasets",
+        )
+
+        if min_input_length > 0.0:
+            # filter data that is shorter than min_input_length
+            vectorized_datasets = vectorized_datasets.filter(
+                lambda data: len(data["input_values"]) > min_input_length,
+                num_proc=data_args.preprocessing_num_workers,
+            )
+
+    # 6. Next, we can prepare the training.
+    # Let's use word error rate (WER) as our evaluation metric,
+    # instantiate a data collator and the trainer
+
+    # Define Metric during training
+    wer_metric = load_metric("wer")
+
+    if data_args.only_data_preprocessing:
+        logger.info("Data preprocessing finished.")
+        return
+
+    def compute_metrics(pred):
+        pred_logits = pred.predictions
+        pred_ids = np.argmax(pred_logits, axis=-1)
+
+        pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
+
+        pred_str = processor.batch_decode(pred_ids)
+        # we do not want to group tokens when computing the metrics
+        label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
+
+        wer = wer_metric.compute(predictions=pred_str, references=label_str)
+
+        return {"wer": wer}
+
+    # Instantiate custom data collator
+    data_collator = DataCollatorCTCWithPadding(processor=processor)
+
+    # Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        data_collator=data_collator,
+        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,
+        tokenizer=processor.feature_extractor,
+    )
+
+    # 7. 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
+
+        # Save the feature_extractor and the tokenizer
+        if is_main_process(training_args.local_rank):
+            processor.save_pretrained(training_args.output_dir)
+
+        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
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+        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"]))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Write model card and (optionally) push to hub
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "speech-recognition",
+        "tags": ["automatic-speech-recognition", data_args.dataset_name],
+        "dataset_args": f"Config: {data_args.dataset_config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
+        "dataset": f"{data_args.dataset_name.upper()} - {data_args.dataset_config_name.upper()}",
+    }
+    if "common_voice" in data_args.dataset_name:
+        kwargs["language"] = data_args.dataset_config_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

examples/pytorch/test_examples.py

@@ -39,6 +39,7 @@ SRC_DIRS = [
         "summarization",
         "translation",
         "image-classification",
+        "speech-recognition",
     ]
 ]
 sys.path.extend(SRC_DIRS)
@@ -52,6 +53,7 @@ if SRC_DIRS is not None:
     import run_mlm
     import run_ner
     import run_qa as run_squad
+    import run_speech_recognition_ctc
     import run_summarization
     import run_swag
     import run_translation
@@ -374,3 +376,37 @@ class ExamplesTests(TestCasePlus):
             run_image_classification.main()
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_accuracy"], 0.8)
+
+    def test_run_speech_recognition_ctc(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_speech_recognition_ctc.py
+            --output_dir {tmp_dir}
+            --model_name_or_path hf-internal-testing/tiny-random-wav2vec2
+            --dataset_name patrickvonplaten/librispeech_asr_dummy
+            --dataset_config_name clean
+            --train_split_name validation
+            --eval_split_name validation
+            --audio_column_name file
+            --do_train
+            --do_eval
+            --learning_rate 1e-4
+            --per_device_train_batch_size 2
+            --per_device_eval_batch_size 1
+            --remove_unused_columns False
+            --overwrite_output_dir True
+            --preprocessing_num_workers 16
+            --max_steps 10
+            --seed 42
+        """.split()
+
+        if is_cuda_and_apex_available():
+            testargs.append("--fp16")
+
+        with patch.object(sys, "argv", testargs):
+            run_speech_recognition_ctc.main()
+            result = get_results(tmp_dir)
+            self.assertLess(result["eval_loss"], result["train_loss"])

src/transformers/models/hubert/configuration_hubert.py

@@ -54,10 +54,12 @@ class HubertConfig(PretrainedConfig):
         hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string,
             :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
-        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
-            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+        hidden_dropout(:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout(:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention probabilities.
+        final_dropout (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probabilitiy for the final projection layer of :class:`Wav2Vec2ForCTC`.
         initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
@@ -66,8 +68,8 @@ class HubertConfig(PretrainedConfig):
             The norm to be applied to 1D convolutional layers in feature extractor. One of :obj:`"group"` for group
             normalization of only the first 1D convolutional layer or :obj:`"layer"` for layer normalization of all 1D
             convolutional layers.
-        feat_extract_dropout (:obj:`float`, `optional`, defaults to 0.0):
-            The dropout probabilitiy for all 1D convolutional layers in feature extractor.
+        feat_proj_dropout (:obj:`float`, `optional`, defaults to 0.0):
+            The dropout probability for output of the feature extractor.
         feat_extract_activation (:obj:`str, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the 1D convolutional layers of the feature
             extractor. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
@@ -147,7 +149,7 @@ class HubertConfig(PretrainedConfig):
         hidden_dropout=0.1,
         activation_dropout=0.1,
         attention_dropout=0.1,
-        feat_proj_dropout=0.1,
+        feat_proj_dropout=0.0,
         final_dropout=0.1,
         layerdrop=0.1,
         initializer_range=0.02,

src/transformers/models/wav2vec2/configuration_wav2vec2.py

@@ -54,10 +54,12 @@ class Wav2Vec2Config(PretrainedConfig):
         hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the encoder and pooler. If string,
             :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
-        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
-            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+        hidden_dropout (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
             The dropout ratio for the attention probabilities.
+        final_dropout (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probability for the final projection layer of :class:`Wav2Vec2ForCTC`.
         initializer_range (:obj:`float`, `optional`, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
@@ -66,8 +68,8 @@ class Wav2Vec2Config(PretrainedConfig):
             The norm to be applied to 1D convolutional layers in feature extractor. One of :obj:`"group"` for group
             normalization of only the first 1D convolutional layer or :obj:`"layer"` for layer normalization of all 1D
             convolutional layers.
-        feat_extract_dropout (:obj:`float`, `optional`, defaults to 0.0):
-            The dropout probabilitiy for all 1D convolutional layers in feature extractor.
+        feat_proj_dropout (:obj:`float`, `optional`, defaults to 0.0):
+            The dropout probability for output of the feature extractor.
         feat_extract_activation (:obj:`str, `optional`, defaults to :obj:`"gelu"`):
             The non-linear activation function (function or string) in the 1D convolutional layers of the feature
             extractor. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
@@ -165,7 +167,7 @@ class Wav2Vec2Config(PretrainedConfig):
         hidden_dropout=0.1,
         activation_dropout=0.1,
         attention_dropout=0.1,
-        feat_proj_dropout=0.1,
+        feat_proj_dropout=0.0,
         feat_quantizer_dropout=0.0,
         final_dropout=0.1,
         layerdrop=0.1,