From a459f7f97d0ad4980aa715661c4de82fcb5ca785 Mon Sep 17 00:00:00 2001 From: Anton Lozhkov Date: Mon, 7 Feb 2022 18:35:37 +0300 Subject: [PATCH] Add ASR CTC streaming example (#15309) * Single-epoch run * Apply suggestions from code review Co-authored-by: Patrick von Platen * Infinite dataset * Trainer fix + distributed benchmark * Benchmark fix * unused import * interleaved splits * interleaved splits * has_length util * Move to research projects * Leftover Sized checks * Bump min version * Unused import * Revert trainer changes Co-authored-by: Patrick von Platen --- examples/pytorch/speech-recognition/README.md | 57 ++ .../speech-recognition/requirements.txt | 2 +- .../run_speech_recognition_ctc.py | 5 +- .../run_speech_recognition_seq2seq.py | 2 +- .../run_speech_recognition_ctc_streaming.py | 659 ++++++++++++++++++ 5 files changed, 721 insertions(+), 4 deletions(-) create mode 100644 examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py diff --git a/examples/pytorch/speech-recognition/README.md b/examples/pytorch/speech-recognition/README.md index 7db7811ba2..a462cf3385 100644 --- a/examples/pytorch/speech-recognition/README.md +++ b/examples/pytorch/speech-recognition/README.md @@ -127,6 +127,62 @@ python -m torch.distributed.launch \ 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**. + +### Multi GPU CTC with Dataset Streaming + +The following command shows how to use [Dataset Streaming mode](https://huggingface.co/docs/datasets/dataset_streaming.html) +to fine-tune [XLS-R](https://huggingface.co/transformers/master/model_doc/xls_r.html) +on [Common Voice](https://huggingface.co/datasets/common_voice) using 4 GPUs in half-precision. + +Streaming mode imposes several constraints on training: +1. We need to construct a tokenizer beforehand and define it via `--tokenizer_name_or_path`. +2. `--num_train_epochs` has to be replaced by `--max_steps`. Similarly, all other epoch-based arguments have to be +replaced by step-based ones. +3. Full dataset shuffling on each epoch is not possible, since we don't have the whole dataset available at once. +However, the `--shuffle_buffer_size` argument controls how many examples we can pre-download before shuffling them. + + +```bash +**python -m torch.distributed.launch \ + --nproc_per_node 4 run_speech_recognition_ctc_streaming.py \ + --dataset_name="common_voice" \ + --model_name_or_path="facebook/wav2vec2-xls-r-300m" \ + --tokenizer_name_or_path="anton-l/wav2vec2-tokenizer-turkish" \ + --dataset_config_name="tr" \ + --train_split_name="train+validation" \ + --eval_split_name="test" \ + --output_dir="wav2vec2-xls-r-common_voice-tr-ft" \ + --overwrite_output_dir \ + --max_steps="5000" \ + --per_device_train_batch_size="8" \ + --gradient_accumulation_steps="2" \ + --learning_rate="5e-4" \ + --warmup_steps="500" \ + --evaluation_strategy="steps" \ + --text_column_name="sentence" \ + --save_steps="500" \ + --eval_steps="500" \ + --logging_steps="1" \ + --layerdrop="0.0" \ + --eval_metrics wer cer \ + --save_total_limit="1" \ + --mask_time_prob="0.3" \ + --mask_time_length="10" \ + --mask_feature_prob="0.1" \ + --mask_feature_length="64" \ + --freeze_feature_encoder \ + --chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \ + --max_duration_in_seconds="20" \ + --shuffle_buffer_size="500" \ + --fp16 \ + --push_to_hub \ + --do_train --do_eval \ + --gradient_checkpointing** +``` + +On 4 V100 GPUs, this script should run in *ca.* 3h 31min and yield a CTC loss of **0.35** and word error rate +of **0.29**. + ### Examples CTC The following tables present a couple of example runs on the most popular speech-recognition datasets. @@ -175,6 +231,7 @@ they can serve as a baseline to improve upon. | [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) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo/blob/main/run.sh) | | [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.31 | - | 8 GPU V100 | 1h05 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft/blob/main/run.sh) | | [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) | 0.21 | - | 2 GPU Titan 24 GB RAM | 15h10 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xls-r-1b-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-1b-common_voice-tr-ft/blob/main/run.sh) | +| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` in streaming mode | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.29 | - | 4 GPU V100 | 3h31 | [here](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream) | [run.sh](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream/blob/main/run.sh) | #### Multilingual Librispeech CTC diff --git a/examples/pytorch/speech-recognition/requirements.txt b/examples/pytorch/speech-recognition/requirements.txt index b8c475e40a..219959a4b2 100644 --- a/examples/pytorch/speech-recognition/requirements.txt +++ b/examples/pytorch/speech-recognition/requirements.txt @@ -1,4 +1,4 @@ -datasets >= 1.13.3 +datasets >= 1.18.0 torch >= 1.5 torchaudio librosa diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py b/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py index eea78f58ae..b09b0b33f2 100755 --- a/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py +++ b/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py @@ -51,7 +51,7 @@ 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("4.17.0.dev0") -require_version("datasets>=1.13.3", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") +require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt") logger = logging.getLogger(__name__) @@ -146,7 +146,8 @@ class DataTrainingArguments: 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'" + "help": "The name of the training data set split to use (via the datasets library). Defaults to " + "'train+validation'" }, ) eval_split_name: str = field( diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py index 40bc9aeb9a..ee5530caeb 100755 --- a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py +++ b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py @@ -49,7 +49,7 @@ 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("4.17.0.dev0") -require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt") +require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt") logger = logging.getLogger(__name__) diff --git a/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py b/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py new file mode 100644 index 0000000000..9e69178088 --- /dev/null +++ b/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py @@ -0,0 +1,659 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and + +""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition in streaming mode""" + +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 torch +from datasets import IterableDatasetDict, interleave_datasets, load_dataset, load_metric +from torch.utils.data import IterableDataset + +import transformers +from transformers import ( + AutoConfig, + AutoFeatureExtractor, + AutoModelForCTC, + AutoProcessor, + AutoTokenizer, + HfArgumentParser, + Trainer, + TrainerCallback, + TrainingArguments, + Wav2Vec2Processor, + set_seed, +) +from transformers.trainer_pt_utils import IterableDatasetShard +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 risk. +check_min_version("4.17.0.dev0") + +require_version("datasets>=1.18.2", "To fix: pip install 'datasets>=1.18.2'") + + +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 configuration name of the dataset to use (via the datasets library)."} + ) + dataset_config_name: str = field( + default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} + ) + 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'" + }, + ) + 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'" + }, + ) + 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." + }, + ) + shuffle_buffer_size: Optional[int] = field( + default=500, + metadata={ + "help": "The number of streamed examples to download before shuffling them. The large the buffer, " + "the closer it is to real offline shuffling." + }, + ) + 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."}, + ) + 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." + }, + ) + 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." + }, + ) + + +@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" + max_length: Optional[int] = None + 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 = [] + label_features = [] + for feature in features: + if self.max_length and feature["input_values"].shape[-1] > self.max_length: + continue + input_features.append({"input_values": feature["input_values"]}) + label_features.append({"input_ids": feature["labels"]}) + + 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 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 = IterableDatasetDict() + raw_column_names = {} + + def load_streaming_dataset(split, sampling_rate, **kwargs): + if "+" in split: + dataset_splits = [load_dataset(split=split_name, **kwargs) for split_name in split.split("+")] + # `features` and `cast_column` won't be available after interleaving, so we'll use them here + features = dataset_splits[0].features + # make sure that the dataset decodes audio with a correct sampling rate + dataset_splits = [ + dataset.cast_column(data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)) + for dataset in dataset_splits + ] + + interleaved_dataset = interleave_datasets(dataset_splits) + return interleaved_dataset, features + else: + dataset = load_dataset(split=split, **kwargs) + features = dataset.features + # make sure that the dataset decodes audio with a correct sampling rate + dataset = dataset.cast_column( + data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate) + ) + return dataset, features + + # `datasets` takes care of automatically loading and resampling the audio, + # so we just need to set the correct target sampling rate and normalize the input + # via the `feature_extractor` + 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 + ) + + if training_args.do_train: + raw_datasets["train"], train_features = load_streaming_dataset( + path=data_args.dataset_name, + name=data_args.dataset_config_name, + split=data_args.train_split_name, + use_auth_token=data_args.use_auth_token, + streaming=True, + sampling_rate=feature_extractor.sampling_rate, + ) + raw_column_names["train"] = list(train_features.keys()) + + if data_args.audio_column_name not in raw_column_names["train"]: + 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_column_names['train'])}." + ) + + if data_args.text_column_name not in raw_column_names["train"]: + 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_column_names['train'])}." + ) + + if data_args.max_train_samples is not None: + raw_datasets["train"] = raw_datasets["train"].take(range(data_args.max_train_samples)) + + if training_args.do_eval: + raw_datasets["eval"], eval_features = load_streaming_dataset( + path=data_args.dataset_name, + name=data_args.dataset_config_name, + split=data_args.eval_split_name, + use_auth_token=data_args.use_auth_token, + streaming=True, + sampling_rate=feature_extractor.sampling_rate, + ) + raw_column_names["eval"] = list(eval_features.keys()) + + if data_args.max_eval_samples is not None: + raw_datasets["eval"] = raw_datasets["eval"].take(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 + ) + text_column_name = data_args.text_column_name + + 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]).lower() + " " + else: + batch["target_text"] = batch[text_column_name].lower() + " " + return batch + + with training_args.main_process_first(desc="dataset map special characters removal"): + for split, dataset in raw_datasets.items(): + raw_datasets[split] = dataset.map( + remove_special_characters, + ).remove_columns([text_column_name]) + + # 3. Next, let's load the config as we might need it to create + # the tokenizer + config = AutoConfig.from_pretrained( + model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token + ) + + # 4. Now we can instantiate the tokenizer and model + # Note for distributed training, the .from_pretrained methods guarantee that only + # one local process can concurrently download model & vocab. + + tokenizer_name_or_path = model_args.tokenizer_name_or_path + if tokenizer_name_or_path is None: + raise ValueError( + "Tokenizer has to be created before training in streaming mode. Please specify --tokenizer_name_or_path" + ) + # load feature_extractor and tokenizer + tokenizer = AutoTokenizer.from_pretrained( + tokenizer_name_or_path, + config=config, + 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() + + # 5. Now we preprocess the datasets including loading the audio, resampling and normalization + 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 + + vectorized_datasets = IterableDatasetDict() + with training_args.main_process_first(desc="dataset map preprocessing"): + for split, dataset in raw_datasets.items(): + vectorized_datasets[split] = ( + dataset.map(prepare_dataset) + .remove_columns(raw_column_names[split] + ["target_text"]) + .with_format("torch") + ) + if split == "train": + vectorized_datasets[split] = vectorized_datasets[split].shuffle( + buffer_size=data_args.shuffle_buffer_size, + seed=training_args.seed, + ) + + # 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 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} + + 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 + max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate + data_collator = DataCollatorCTCWithPadding(processor=processor, max_length=max_input_length) + + # trainer callback to reinitialize and reshuffle the streamable datasets at the beginning of each epoch + class ShuffleCallback(TrainerCallback): + def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs): + if isinstance(train_dataloader.dataset, IterableDatasetShard): + pass # set_epoch() is handled by the Trainer + elif isinstance(train_dataloader.dataset, IterableDataset): + train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1) + + # 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, + callbacks=[ShuffleCallback()], + ) + + # 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 + + train_result = trainer.train(resume_from_checkpoint=checkpoint) + trainer.save_model() + + metrics = train_result.metrics + if data_args.max_train_samples: + metrics["train_samples"] = data_args.max_train_samples + + 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() + if data_args.max_eval_samples: + metrics["eval_samples"] = data_args.max_eval_samples + + 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()