split training in two, one per dataset

old_run.sh

@@ -0,0 +1,37 @@
+python run_speech_recognition_ctc.py \
+	--dataset_name="mozilla-foundation/common_voice_8_0,marinone94/nst_sv" \
+	--model_name_or_path="KBLab/wav2vec2-large-voxrex" \
+	--dataset_config_name="sv-SE,distant_channel" \
+	--train_split_name="train+validation,train" \
+	--eval_split_name="test,None" \
+	--output_dir="./" \
+	--overwrite_output_dir \
+	--num_train_epochs="3" \
+	--per_device_train_batch_size="32" \
+	--per_device_eval_batch_size="32" \
+	--gradient_accumulation_steps="4" \
+	--learning_rate="7.5e-4" \
+	--warmup_ratio="0.0" \
+	--length_column_name="input_length" \
+	--evaluation_strategy="steps" \
+	--save_strategy="steps" \
+	--eval_steps="100" \
+	--save_steps="100" \
+	--text_column_name="sentence" \
+	--chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – \
+	--logging_steps="20" \
+	--layerdrop="0.0" \
+	--activation_dropout="0.1" \
+	--save_total_limit="2" \
+	--freeze_feature_encoder \
+	--feat_proj_dropout="0.0" \
+	--mask_time_prob="0.75" \
+	--mask_time_length="10" \
+	--mask_feature_prob="0.25" \
+	--mask_feature_length="64" \
+	--gradient_checkpointing \
+	--use_auth_token \
+	--fp16 \
+	--group_by_length \
+	--do_train --do_eval \
+	--push_to_hub

old_run_asr_ctc.py

@@ -0,0 +1,911 @@
+#!/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 datetime
+import functools
+import json
+import logging
+import os
+import re
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union
+
+import datasets
+import numpy as np
+import pandas as pd
+import torch
+import wandb
+from datasets import DatasetDict, concatenate_datasets, load_dataset, load_metric
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForCTC,
+    AutoProcessor,
+    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("4.16.0.dev0")
+
+require_version("datasets>=1.13.3", "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"}
+    )
+    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 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(
+        metadata={
+            "help": "The name of the dataset to use (via the datasets library)."
+            " To use multiple datasets, specify them separated by a comma."
+            " e.g.: 'mozilla-foundation/common_voice_7_0,marinone94/nst_sv'"
+        }
+    )
+    dataset_config_name: str = field(
+        default=None, metadata={
+            "help": "The configuration name of the dataset to use (via the datasets library)."
+            " To use multiple datasets, specify them separated by a comma."
+            " e.g.: 'sv-SE,sv'"    
+        }
+    )
+    train_split_name: str = field(
+        default="train+validation",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train+validation'"
+            " To use multiple datasets, specify them separated by a comma."
+            " e.g.: 'train+validation,all'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'test'"
+            " To use multiple datasets, specify them separated by a comma."
+            " e.g.: 'test,None'"
+        },
+    )
+    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'"},
+    )
+    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."},
+    )
+    eval_metrics: List[str] = list_field(
+        default=["wer"],
+        metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        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."
+        },
+    )
+    dataset_seed: Optional[int] = field(
+        default=None,
+        metadata={"help": "Seed for shuffling training data"},
+    )
+
+
+@dataclass
+class DataCollatorCTCWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor (:class:`~transformers.AutoProcessor`)
+            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: AutoProcessor
+    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,
+    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, vocab):
+        all_text = " ".join(batch)
+        return list(set(list(set(all_text)) + vocab))
+
+    batch_size = 10000
+    vocab = []
+    for i in range(0, datasets["train"].num_rows, 10000):
+        batch = datasets["train"].select(range(i, min(datasets["train"].num_rows, i+batch_size)))
+        vocab = extract_all_chars(batch["target_text"], vocab)
+    for i in range(0, datasets["eval"].num_rows, 10000):
+        batch = datasets["eval"].select(range(i, min(datasets["eval"].num_rows, i+batch_size)))
+        vocab = extract_all_chars(batch["target_text"], vocab)
+
+    vocab_dict = {v: k for k, v in enumerate(sorted(vocab))}
+
+    # 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, 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()
+
+    # TODO: Replace with check of wandb env vars
+    try:
+        repo_name = os.getcwd().split("/")[-1]
+        run_name = f"{datetime.datetime.utcnow()}".replace(" ", "T")
+        os.environ["WANDB_PROJECT"] = repo_name
+        wandb.login()
+        training_args.report_to = ["wandb"]
+        training_args.run_name = run_name
+        # wandb.init()
+    except:
+        pass
+
+    # 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()
+
+    def common_cols(columns_a, columns_b):
+        col_a = set(columns_a)
+        col_b = set(columns_b)
+        return [col for col in col_a if col in col_b]
+
+    if training_args.do_train:
+
+        # Multiple datasets might need to be loaded from HF
+        # It assumes they all follow the common voice format
+        for (dataset_name, dataset_config_name, train_split_name) in zip(
+            data_args.dataset_name.split(","),
+            data_args.dataset_config_name.split(","),
+            data_args.train_split_name.split(","),
+        ):
+
+                
+            if train_split_name != "None":
+                if "train" not in raw_datasets:
+                    raw_datasets["train"] = load_dataset(
+                        dataset_name,
+                        dataset_config_name,
+                        split=train_split_name,
+                        use_auth_token=data_args.use_auth_token,
+                    )
+                    min_columns_train = raw_datasets["train"].column_names
+                    dataset_sampling_rate = raw_datasets["train"].features[data_args.audio_column_name].sampling_rate
+                    print(f"Dataset sampling rate: {dataset_sampling_rate}")
+                else:
+                    new_dataset = DatasetDict()
+                    new_dataset["train"] = load_dataset(
+                        dataset_name,
+                        dataset_config_name,
+                        split=train_split_name,
+                        use_auth_token=data_args.use_auth_token,
+                    )
+                    new_dataset_sampling_rate = next(iter(new_dataset.values())).features[data_args.audio_column_name].sampling_rate
+                    if new_dataset_sampling_rate != dataset_sampling_rate:
+                        print(f"New dataset sampling rate casted from {dataset_sampling_rate} to {dataset_sampling_rate}")
+                        new_dataset["train"] = new_dataset["train"].cast_column(
+                            data_args.audio_column_name, datasets.features.Audio(sampling_rate=dataset_sampling_rate)
+                        )
+                    raw_datasets["train"] = concatenate_datasets(
+                        [
+                            raw_datasets["train"],
+                            new_dataset["train"]
+                        ]
+                    )
+                    min_columns_train = common_cols(min_columns_train, new_dataset.column_names)
+            else:
+                logging.warning(f"{dataset_name} {dataset_config_name} train not loaded as split is {train_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.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(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))
+        other_columns_train = [col for col in raw_datasets["train"].column_names if col not in min_columns_train]
+        raw_datasets["train"].remove_columns(other_columns_train)
+
+    # pd_train_head = raw_datasets["train"].select(range(10)).to_pandas()
+    # pd_train_tail = raw_datasets["train"].select(range(raw_datasets["train"].num_rows-10, raw_datasets["train"].num_rows)).to_pandas()
+    # pd_train = pd.concat([pd_train_head, pd_train_tail])
+    # print(pd_train["audio"])
+
+    if training_args.do_eval:
+        # Multiple datasets might need to be loaded from HF
+        # It assumes they all follow the common voice format
+        for (dataset_name, dataset_config_name, eval_split_name) in zip(
+            data_args.dataset_name.split(","),
+            data_args.dataset_config_name.split(","),
+            data_args.eval_split_name.split(","),
+        ):
+
+            if eval_split_name != "None":
+                if "eval" not in raw_datasets:
+                    raw_datasets["eval"] = load_dataset(
+                        dataset_name,
+                        dataset_config_name,
+                        split=eval_split_name,
+                        use_auth_token=data_args.use_auth_token,
+                    )
+                    min_columns_eval = raw_datasets["eval"].column_names
+                    dataset_sampling_rate = raw_datasets["eval"].features[data_args.audio_column_name].sampling_rate
+                    print(f"Dataset sampling rate: {dataset_sampling_rate}")                       
+                else:
+                    new_dataset = DatasetDict()
+                    new_dataset["eval"] = load_dataset(
+                        dataset_name,
+                        dataset_config_name,
+                        split=train_split_name,
+                        use_auth_token=data_args.use_auth_token,
+                    )
+                    new_dataset_sampling_rate = new_dataset["eval"].features[data_args.audio_column_name].sampling_rate
+                    if new_dataset_sampling_rate != dataset_sampling_rate:
+                        print(f"New dataset sampling rate casted from {dataset_sampling_rate} to {dataset_sampling_rate}")
+                        new_dataset["eval"] = new_dataset["eval"].cast_column(
+                            data_args.audio_column_name, datasets.features.Audio(sampling_rate=dataset_sampling_rate)
+                        )
+                    raw_datasets["eval"] = concatenate_datasets(
+                        [
+                            raw_datasets["eval"],
+                            new_dataset["eval"]
+                        ]
+                    )
+                    min_columns_eval = common_cols(min_columns_eval, new_dataset.column_names)
+            else:
+                logging.warning(f"{dataset_name} {dataset_config_name} eval not loaded as split is {eval_split_name}")
+
+        if data_args.max_eval_samples is not None:
+            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+        other_columns_eval = [col for col in raw_datasets["eval"].column_names if col not in min_columns_eval]
+        raw_datasets["eval"].remove_columns(other_columns_eval)
+
+    # pd_eval_head = raw_datasets["eval"].select(range(10)).to_pandas()
+    # pd_eval_tail = raw_datasets["eval"].select(range(raw_datasets["eval"].num_rows-10, raw_datasets["eval"].num_rows)).to_pandas()
+    # pd_eval = pd.concat([pd_eval_head, pd_eval_tail])
+    # print(pd_eval["audio"])
+
+    # 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
+    )
+    text_column_name = data_args.text_column_name
+
+    def is_text_valid(text):
+        for token in text.split():
+            if len(token) > 1:
+                return True
+        return False
+
+    def remove_special_characters(batch):
+        if chars_to_ignore_regex is not None:
+            batch["target_text"] = \
+                re.sub(chars_to_ignore_regex, "", batch[text_column_name]) \
+                    .replace("\\\\Punkt", "") \
+                    .replace("\\\\Komma", "") \
+                    .replace("è", "e") \
+                    .replace("é", "e") \
+                    .replace("î", "i") \
+                    .replace("ü", "u") \
+                    .replace("ÿ", "y") \
+                    .replace("ô", "o") \
+                    .replace("\\", "") \
+                    .replace("/", "") \
+                    .replace("|", "") \
+                    .lower() + " "
+        else:
+            batch["target_text"] = batch[text_column_name] \
+                .replace("\\\\Punkt", "") \
+                .replace("\\\\Komma", "") \
+                .replace("è", "e") \
+                .replace("é", "e") \
+                .replace("î", "i") \
+                .replace("ü", "u") \
+                .replace("ÿ", "y") \
+                .replace("ô", "o") \
+                .replace("\\", "") \
+                .replace("/", "") \
+                .replace("|", "") \
+                .lower() + " "
+        return batch
+
+    num_workers = data_args.preprocessing_num_workers
+    with training_args.main_process_first(desc="dataset map special characters removal"):
+        raw_datasets = raw_datasets.map(
+            remove_special_characters,
+            remove_columns=[text_column_name],
+            desc="remove special characters from datasets",
+        )
+
+        raw_datasets = raw_datasets.filter(
+            is_text_valid,
+            num_proc=num_workers,
+            input_columns=["target_text"],
+            desc="remove single words, single chars and 'W O R D S'",
+        )
+
+    # 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
+    # load config
+    config = AutoConfig.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+    )
+
+    # 4. Next, 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`
+        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,
+        }
+
+    # 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
+    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
+    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,
+            "pad_token_id": tokenizer.pad_token_id,
+            "vocab_size": len(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,
+        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
+
+    # `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["input_length"] = len(batch["input_values"])
+
+        # encode targets
+        additional_kwargs = {}
+        if phoneme_language is not None:
+            additional_kwargs["phonemizer_lang"] = phoneme_language
+
+        batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map preprocessing"):
+        vectorized_datasets = DatasetDict()
+        vectorized_datasets["train"] = raw_datasets["train"].map(
+            prepare_dataset,
+            remove_columns=raw_datasets["train"].column_names,
+            num_proc=num_workers,
+            desc="preprocess datasets",
+        )
+        vectorized_datasets["eval"] = raw_datasets["eval"].map(
+            prepare_dataset,
+            remove_columns=raw_datasets["eval"].column_names,
+            num_proc=num_workers,
+            desc="preprocess datasets",
+        )
+
+        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 = vectorized_datasets.filter(
+            is_audio_in_length_range,
+            num_proc=num_workers,
+            input_columns=["input_length"],
+        )
+
+    # 7. 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 evaluation metrics during training, *i.e.* word error rate, character error rate
+    eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
+
+    # If dataset_seed is set, shuffle train
+    if data_args.dataset_seed is not None:
+        vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(seed=data_args.dataset_seed)
+    
+    # TODO: Log sample of datasets in the right way (see wandb docs)
+    pd_train = vectorized_datasets["train"].select(range(10)).to_pandas()
+    pd_eval = vectorized_datasets["eval"].select(range(10)).to_pandas()
+    # wandb.log({"train_sample": pd_train})
+    # wandb.log({"eval_sample": pd_eval})
+
+    # 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_metrics(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)
+
+        metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
+
+        return metrics
+
+    # 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)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    try:
+        processor = AutoProcessor.from_pretrained(training_args.output_dir)
+    except (OSError, KeyError):
+        warnings.warn(
+            "Loading a processor from a feature extractor config that does not"
+            " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
+            " attribute to your `preprocessor_config.json` file to suppress this warning: "
+            " `'processor_class': 'Wav2Vec2Processor'`",
+            FutureWarning,
+        )
+        processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
+
+    # 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=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
+    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
+    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: {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)
+    else:
+        trainer.create_model_card(**kwargs)
+        
+    return results
+
+
+if __name__ == "__main__":
+    main()

run.sh

@@ -1,16 +1,16 @@
 python run_speech_recognition_ctc.py \
-	--dataset_name="mozilla-foundation/common_voice_8_0,marinone94/nst_sv" \
+	--dataset_name="marinone94/nst_sv" \
 	--model_name_or_path="KBLab/wav2vec2-large-voxrex" \
-	--dataset_config_name="sv-SE,distant_channel" \
-	--train_split_name="train+validation,train" \
-	--eval_split_name="test,None" \
+	--dataset_config_name="distant_channel" \
+	--train_split_name="train" \
+	--eval_split_name="test" \
 	--output_dir="./" \
 	--overwrite_output_dir \
 	--num_train_epochs="3" \
 	--per_device_train_batch_size="32" \
 	--per_device_eval_batch_size="32" \
 	--gradient_accumulation_steps="4" \
-	--learning_rate="7.5e-5" \
+	--learning_rate="7.5e-" \
 	--warmup_ratio="0.02" \
 	--length_column_name="input_length" \
 	--evaluation_strategy="steps" \
@@ -20,7 +20,43 @@ python run_speech_recognition_ctc.py \
 	--text_column_name="sentence" \
 	--chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – \
 	--logging_steps="20" \
-	--dataset_seed="42" \
+	--layerdrop="0.0" \
+	--activation_dropout="0.1" \
+	--save_total_limit="2" \
+	--freeze_feature_encoder \
+	--feat_proj_dropout="0.0" \
+	--mask_time_prob="0.75" \
+	--mask_time_length="10" \
+	--mask_feature_prob="0.25" \
+	--mask_feature_length="64" \
+	--gradient_checkpointing \
+	--use_auth_token \
+	--fp16 \
+	--group_by_length \
+	--do_train --do_eval \
+	--push_to_hub
+
+python run_speech_recognition_ctc.py \
+	--dataset_name="mozilla-foundation/common_voice_8_0" \
+	--model_name_or_path="KBLab/wav2vec2-large-voxrex" \
+	--dataset_config_name="distant_channel" \
+	--train_split_name="train+validation" \
+	--eval_split_name="test" \
+	--output_dir="./" \
+	--num_train_epochs="50" \
+	--per_device_train_batch_size="32" \
+	--per_device_eval_batch_size="32" \
+	--gradient_accumulation_steps="4" \
+	--learning_rate="7.5e-5" \
+	--warmup_steps="2000" \
+	--length_column_name="input_length" \
+	--evaluation_strategy="steps" \
+	--save_strategy="steps" \
+	--eval_steps="500" \
+	--save_steps="500" \
+	--text_column_name="sentence" \
+	--chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – \
+	--logging_steps="100" \
 	--layerdrop="0.0" \
 	--activation_dropout="0.1" \
 	--save_total_limit="2" \

run_speech_recognition_ctc.py

@@ -429,38 +429,45 @@ def main():
 
         # Multiple datasets might need to be loaded from HF
         # It assumes they all follow the common voice format
-        for (dataset_name, dataset_config_name, train_split_name) in zip(
-            data_args.dataset_name.split(","),
-            data_args.dataset_config_name.split(","),
-            data_args.train_split_name.split(","),
-        ):
+        # for (dataset_name, dataset_config_name, train_split_name) in zip(
+        #     data_args.dataset_name.split(","),
+        #     data_args.dataset_config_name.split(","),
+        #     data_args.train_split_name.split(","),
+        # ):
 
                 
-            if train_split_name != "None":
-                if "train" not in raw_datasets:
-                    raw_datasets["train"] = load_dataset(
-                        dataset_name,
-                        dataset_config_name,
-                        split=train_split_name,
-                        use_auth_token=data_args.use_auth_token,
-                    )
-                    min_columns_train = raw_datasets["train"].column_names
-                else:
-                    new_dataset = load_dataset(
-                        dataset_name,
-                        dataset_config_name,
-                        split=train_split_name,
-                        use_auth_token=data_args.use_auth_token,
-                    )
-                    raw_datasets["train"] = concatenate_datasets(
-                        [
-                            raw_datasets["train"],
-                            new_dataset
-                        ]
-                    )
-                    min_columns_train = common_cols(min_columns_train, new_dataset.column_names)
-            else:
-                logging.warning(f"{dataset_name} {dataset_config_name} train not loaded as split is {train_split_name}")
+        #     if train_split_name != "None":
+        #         if "train" not in raw_datasets:
+        #             raw_datasets["train"] = load_dataset(
+        #                 dataset_name,
+        #                 dataset_config_name,
+        #                 split=train_split_name,
+        #                 use_auth_token=data_args.use_auth_token,
+        #             )
+        #             min_columns_train = raw_datasets["train"].column_names
+        #         else:
+        #             new_dataset = load_dataset(
+        #                 dataset_name,
+        #                 dataset_config_name,
+        #                 split=train_split_name,
+        #                 use_auth_token=data_args.use_auth_token,
+        #             )
+        #             raw_datasets["train"] = concatenate_datasets(
+        #                 [
+        #                     raw_datasets["train"],
+        #                     new_dataset
+        #                 ]
+        #             )
+        #             min_columns_train = common_cols(min_columns_train, new_dataset.column_names)
+        #     else:
+        #         logging.warning(f"{dataset_name} {dataset_config_name} train not loaded as split is {train_split_name}")
+
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            use_auth_token=data_args.use_auth_token,
+        )
 
         if data_args.audio_column_name not in raw_datasets["train"].column_names:
             raise ValueError(
@@ -478,8 +485,8 @@ def main():
 
         if data_args.max_train_samples is not None:
             raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
-        other_columns_train = [col for col in raw_datasets["train"].column_names if col not in min_columns_train]
-        raw_datasets["train"].remove_columns(other_columns_train)
+        # other_columns_train = [col for col in raw_datasets["train"].column_names if col not in min_columns_train]
+        # raw_datasets["train"].remove_columns(other_columns_train)
 
     # pd_train_head = raw_datasets["train"].select(range(10)).to_pandas()
     # pd_train_tail = raw_datasets["train"].select(range(raw_datasets["train"].num_rows-10, raw_datasets["train"].num_rows)).to_pandas()
@@ -489,42 +496,49 @@ def main():
     if training_args.do_eval:
         # Multiple datasets might need to be loaded from HF
         # It assumes they all follow the common voice format
-        for (dataset_name, dataset_config_name, eval_split_name) in zip(
-            data_args.dataset_name.split(","),
-            data_args.dataset_config_name.split(","),
-            data_args.eval_split_name.split(","),
-        ):
-
-            if eval_split_name != "None":
-                if "eval" not in raw_datasets:
-                    raw_datasets["eval"] = load_dataset(
-                        dataset_name,
-                        dataset_config_name,
-                        split=eval_split_name,
-                        use_auth_token=data_args.use_auth_token,
-                    )
-                    min_columns_eval = raw_datasets["eval"].column_names
-                else:
-                    new_dataset = load_dataset(
-                        dataset_name,
-                        dataset_config_name,
-                        split=eval_split_name,
-                        use_auth_token=data_args.use_auth_token,
-                    )
-                    raw_datasets["eval"] = concatenate_datasets(
-                        [
-                            raw_datasets["eval"],
-                            new_dataset
-                        ]
-                    )
-                    min_columns_eval = common_cols(min_columns_eval, new_dataset.column_names)
-            else:
-                logging.warning(f"{dataset_name} {dataset_config_name} eval not loaded as split is {eval_split_name}")
+        # for (dataset_name, dataset_config_name, eval_split_name) in zip(
+        #     data_args.dataset_name.split(","),
+        #     data_args.dataset_config_name.split(","),
+        #     data_args.eval_split_name.split(","),
+        # ):
+
+        #     if eval_split_name != "None":
+        #         if "eval" not in raw_datasets:
+        #             raw_datasets["eval"] = load_dataset(
+        #                 dataset_name,
+        #                 dataset_config_name,
+        #                 split=eval_split_name,
+        #                 use_auth_token=data_args.use_auth_token,
+        #             )
+        #             min_columns_eval = raw_datasets["eval"].column_names
+        #         else:
+        #             new_dataset = load_dataset(
+        #                 dataset_name,
+        #                 dataset_config_name,
+        #                 split=eval_split_name,
+        #                 use_auth_token=data_args.use_auth_token,
+        #             )
+        #             raw_datasets["eval"] = concatenate_datasets(
+        #                 [
+        #                     raw_datasets["eval"],
+        #                     new_dataset
+        #                 ]
+        #             )
+        #             min_columns_eval = common_cols(min_columns_eval, new_dataset.column_names)
+        #     else:
+        #         logging.warning(f"{dataset_name} {dataset_config_name} eval not loaded as split is {eval_split_name}")
+
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=data_args.use_auth_token,
+        )
 
         if data_args.max_eval_samples is not None:
             raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
-        other_columns_eval = [col for col in raw_datasets["eval"].column_names if col not in min_columns_eval]
-        raw_datasets["eval"].remove_columns(other_columns_eval)
+        # other_columns_eval = [col for col in raw_datasets["eval"].column_names if col not in min_columns_eval]
+        # raw_datasets["eval"].remove_columns(other_columns_eval)
 
     # pd_eval_head = raw_datasets["eval"].select(range(10)).to_pandas()
     # pd_eval_tail = raw_datasets["eval"].select(range(raw_datasets["eval"].num_rows-10, raw_datasets["eval"].num_rows)).to_pandas()