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from dataclasses import dataclass, field |
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from typing import Optional |
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from transformers import Seq2SeqTrainingArguments |
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@dataclass |
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class ModelArguments: |
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""" |
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. |
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""" |
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model_name_or_path: str = field( |
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} |
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) |
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config_name: Optional[str] = field( |
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} |
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) |
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feature_extractor_name: Optional[str] = field( |
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default=None, metadata={"help": "Pretrained feature extractor name or path if not the same as model_name"} |
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) |
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description_tokenizer_name: Optional[str] = field( |
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default=None, metadata={"help": "Pretrained description tokenizer name or path if not the same as model_name"} |
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) |
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prompt_tokenizer_name: Optional[str] = field( |
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default=None, |
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metadata={"help": "Pretrained prompt tokenizer name or path if not the same as description_tokenizer_name"}, |
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) |
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cache_dir: Optional[str] = field( |
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default=None, |
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metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, |
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) |
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use_fast_tokenizer: bool = field( |
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default=True, |
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, |
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) |
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model_revision: str = field( |
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default="main", |
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, |
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) |
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pad_token_id: int = field( |
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default=None, |
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metadata={"help": "If specified, change the model pad token id."}, |
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) |
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decoder_start_token_id: int = field( |
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default=None, |
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metadata={"help": "If specified, change the model decoder start token id."}, |
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) |
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freeze_text_encoder: bool = field( |
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default=False, |
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metadata={"help": "Whether to freeze the text encoder."}, |
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) |
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do_sample: bool = field( |
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default=True, |
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metadata={"help": "Whether to do sampling or greedy decoding."}, |
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) |
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temperature: float = field( |
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default=1.0, |
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metadata={"help": "Temperature if sampling."}, |
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) |
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max_length: int = field( |
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default=2580, |
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metadata={"help": "Generation max length."}, |
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) |
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bandwidth: float = field( |
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default=6, |
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metadata={"help": "Audio encoder bandwidth."}, |
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) |
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asr_model_name_or_path: str = field( |
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default="distil-whisper/distil-large-v2", |
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metadata={ |
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"help": "Used to compute WER during evaluation. Path to pretrained model or model identifier from huggingface.co/models" |
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}, |
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) |
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clap_model_name_or_path: str = field( |
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default="laion/larger_clap_music_and_speech", |
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metadata={ |
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"help": "Used to compute audio similarity during evaluation. Path to pretrained model or model identifier from huggingface.co/models" |
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}, |
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) |
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@dataclass |
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class DataTrainingArguments: |
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""" |
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Arguments pertaining to what data we are going to input our model for training and eval. |
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Using `HfArgumentParser` we can turn this class |
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into argparse arguments to be able to specify them on |
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the command line. |
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""" |
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train_dataset_name: str = field( |
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default=None, |
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metadata={ |
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"help": "The name of the training dataset to use (via the datasets library). Load and combine " |
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"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine " |
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" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`." |
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}, |
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) |
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train_dataset_config_name: Optional[str] = field( |
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default=None, |
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metadata={ |
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"help": "The configuration name of the training dataset to use (via the datasets library). Load and combine " |
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"multiple datasets by separating dataset configs by a '+' symbol." |
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}, |
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) |
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train_split_name: str = field( |
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default="train", |
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metadata={ |
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"help": ("The name of the training data set split to use (via the datasets library). Defaults to 'train'") |
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}, |
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) |
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train_dataset_samples: str = field( |
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default=None, |
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metadata={ |
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"help": "Number of samples in the training data. Load and combine " |
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"multiple datasets by separating dataset samples by a '+' symbol." |
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}, |
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) |
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train_metadata_dataset_name: str = field( |
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default=None, |
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metadata={ |
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"help": "The name of the metadata training dataset to use (via the datasets library). Load and combine " |
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"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine " |
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" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`." |
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}, |
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) |
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eval_dataset_name: str = field( |
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default=None, |
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metadata={ |
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"help": "The name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset name if unspecified." |
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}, |
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) |
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eval_dataset_config_name: Optional[str] = field( |
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default=None, |
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metadata={ |
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"help": "The configuration name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset config name if unspecified" |
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}, |
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) |
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eval_split_name: str = field( |
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default="test", |
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metadata={ |
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"help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'test'" |
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}, |
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) |
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eval_metadata_dataset_name: str = field( |
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default=None, |
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metadata={ |
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"help": "The name of the metadata training dataset to use (via the datasets library). Load and combine " |
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"multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine " |
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" librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`." |
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}, |
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) |
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target_audio_column_name: str = field( |
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default="audio", |
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metadata={"help": "The name of the dataset column containing the target audio data. Defaults to 'audio'"}, |
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) |
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description_column_name: str = field( |
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default=None, |
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metadata={"help": "The name of the dataset column containing the description text data. Defaults to 'None'."}, |
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) |
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prompt_column_name: str = field( |
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default=None, |
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metadata={"help": "The name of the dataset column containing the prompt text data. Defaults to 'None'."}, |
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) |
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overwrite_cache: bool = field( |
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default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} |
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) |
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preprocessing_num_workers: Optional[int] = field( |
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default=None, |
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metadata={"help": "The number of processes to use for the preprocessing."}, |
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) |
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max_train_samples: Optional[int] = field( |
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default=None, |
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metadata={ |
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"help": ( |
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"For debugging purposes or quicker training, truncate the number of training examples to this " |
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"value if set." |
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) |
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}, |
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) |
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max_eval_samples: Optional[int] = field( |
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default=None, |
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metadata={ |
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"help": ( |
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"For debugging purposes or quicker training, truncate the number of validation examples to this " |
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"value if set." |
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) |
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}, |
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) |
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max_duration_in_seconds: float = field( |
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default=35.0, |
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metadata={ |
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"help": ( |
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"Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`." |
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"Also, used to set maximum audio length if `pad_to_max_length=True`." |
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) |
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}, |
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) |
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min_duration_in_seconds: float = field( |
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default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"} |
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) |
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max_text_length: int = field( |
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default=500, metadata={"help": "If set, max description lengths in number of characters."} |
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) |
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max_prompt_token_length: int = field( |
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default=None, |
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metadata={ |
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"help": ( |
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"If set, filter samples with prompts that are longer than `max_prompt_token_length` tokens." |
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"Also, used to set maximum prompt token length if `pad_to_max_length=True`." |
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) |
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}, |
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) |
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max_description_token_length: int = field( |
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default=None, |
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metadata={ |
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"help": ( |
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"If set, filter samples with descriptions that are longer than `max_description_token_length` tokens." |
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"Also, used to set maximum desription token length if `pad_to_max_length=True`." |
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) |
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}, |
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) |
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pad_to_max_length: bool = field( |
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default=False, |
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metadata={ |
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"help": ( |
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"If `True`, pad audio, prompt and description to a maximum length set with respectively " |
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"`max_duration_in_seconds`, `max_prompt_token_length`, `max_description_token_length`." |
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) |
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}, |
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) |
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preprocessing_only: bool = field( |
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default=False, |
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metadata={ |
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"help": ( |
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"Whether to only do data preprocessing and skip training. This is especially useful when data" |
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" preprocessing errors out in distributed training due to timeout. In this case, one should run the" |
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" preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets" |
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" can consequently be loaded in distributed training." |
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" In this training script, `save_to_disk` must be set to the path in which the dataset should be saved. " |
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) |
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}, |
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) |
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token: str = field( |
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default=None, |
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metadata={ |
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"help": ( |
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " |
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)." |
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) |
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}, |
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) |
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use_auth_token: bool = field( |
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default=None, |
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metadata={ |
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." |
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}, |
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) |
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trust_remote_code: bool = field( |
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default=False, |
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metadata={ |
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"help": ( |
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option " |
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will " |
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"execute code present on the Hub on your local machine." |
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) |
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}, |
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) |
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add_audio_samples_to_wandb: bool = field( |
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default=False, |
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metadata={"help": "If set and if `wandb` in args.report_to, will add generated audio samples to wandb logs."}, |
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) |
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id_column_name: str = field(default=None, metadata={"help": "id column name."}) |
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wandb_project: str = field( |
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default="parler-speech", |
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metadata={"help": "The name of the wandb project."}, |
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) |
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save_to_disk: str = field( |
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default=None, |
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metadata={ |
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"help": "If set, will save the dataset to this path if this is an empyt folder. If not empty, will load the datasets from it." |
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}, |
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) |
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temporary_save_to_disk: str = field(default=None, metadata={"help": "Temporarily save audio labels here."}) |
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pad_to_multiple_of: Optional[int] = field( |
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default=2, |
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metadata={"help": ("Pad to multiple of for tokenizers.")}, |
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) |
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@dataclass |
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class ParlerTTSTrainingArguments(Seq2SeqTrainingArguments): |
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dtype: Optional[str] = field( |
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default="float32", |
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metadata={ |
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"help": ( |
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"The data type (dtype) in which to run training. One of `float32` (full-precision), " |
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"`float16` or `bfloat16` (both half-precision)." |
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) |
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}, |
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) |
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audio_encoder_per_device_batch_size: int = field( |
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default=8, |
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metadata={"help": ("Specify the batch size of the audio encoding pre-processing steps.")}, |
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) |
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