# Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. An additional grant of patent rights # can be found in the PATENTS file in the same directory. import logging import os import sys from argparse import Namespace from dataclasses import dataclass, field from typing import Optional, OrderedDict from fairseq.data.multi_corpus_dataset import MultiCorpusDataset from omegaconf import MISSING, II, OmegaConf from fairseq.data import BinarizedAudioDataset, FileAudioDataset, SubsampleDataset from fairseq.dataclass import FairseqDataclass, ChoiceEnum from fairseq.data.text_compressor import TextCompressionLevel from . import FairseqTask, register_task logger = logging.getLogger(__name__) @dataclass class AudioMaskingConfig: feature_encoder_spec: str = II("model.modalities.audio.feature_encoder_spec") mask_prob: float = II("model.modalities.audio.mask_prob") mask_prob_adjust: float = II("model.modalities.audio.mask_prob_adjust") mask_length: int = II("model.modalities.audio.mask_length") inverse_mask: bool = II("model.modalities.audio.inverse_mask") mask_dropout: float = II("model.modalities.audio.mask_dropout") clone_batch: int = II("model.clone_batch") expand_adjacent: bool = False non_overlapping: bool = False @dataclass class AudioPretrainingConfig(FairseqDataclass): data: str = field(default=MISSING, metadata={"help": "path to data directory"}) labels: Optional[str] = field( default=None, metadata={"help": "extension of the label file to load, used for fine-tuning"}, ) multi_corpus_keys: Optional[str] = field( default=None, metadata={"help": "Comma separated names for loading multi corpus datasets"}) multi_corpus_sampling_weights: Optional[str] = field( default=None, metadata={"help": "Comma separated string of sampling weights corresponding to the multi_corpus_keys"}) binarized_dataset: bool = field( default=False, metadata={ "help": "if true, loads binarized dataset (useful for very large datasets). " "See examples/wav2vec/scripts/binarize_manifest.sh" }, ) sample_rate: int = field( default=16_000, metadata={ "help": "target sample rate. audio files will be up/down sampled to this rate" }, ) normalize: bool = field( default=False, metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, ) enable_padding: bool = field( default=False, metadata={"help": "pad shorter samples instead of cropping"} ) max_sample_size: Optional[int] = field( default=None, metadata={"help": "max sample size to crop to for batching"} ) min_sample_size: Optional[int] = field( default=None, metadata={"help": "min sample size to skip small examples"} ) num_batch_buckets: int = field( default=0, metadata={"help": "number of buckets"}, ) tpu: bool = II("common.tpu") text_compression_level: ChoiceEnum([x.name for x in TextCompressionLevel]) = field( default="none", metadata={ "help": "compression level for texts (e.g. audio filenames, " "target texts): none/low/high (default: none). " }, ) rebuild_batches: bool = True precompute_mask_config: Optional[AudioMaskingConfig] = None post_save_script: Optional[str] = None subsample: float = 1 seed: int = II("common.seed") @register_task("audio_pretraining", dataclass=AudioPretrainingConfig) class AudioPretrainingTask(FairseqTask): """ """ cfg: AudioPretrainingConfig @classmethod def setup_task(cls, cfg: AudioPretrainingConfig, **kwargs): """Setup the task (e.g., load dictionaries). Args: cfg (AudioPretrainingConfig): configuration of this task """ return cls(cfg) def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): data_path = self.cfg.data task_cfg = task_cfg or self.cfg # upgrade old task if isinstance(task_cfg, Namespace): if not hasattr(task_cfg, "autoregressive"): task_cfg.autoregressive = not task_cfg.criterion == "ctc" text_compression_level = getattr( TextCompressionLevel, str(self.cfg.text_compression_level) ) compute_mask = getattr(task_cfg, "precompute_mask_config", None) is not None mask_args = {} if compute_mask: mask_args = task_cfg.precompute_mask_config if getattr(task_cfg, "binarized_dataset", False): self.datasets[split] = BinarizedAudioDataset( data_path, split=split, sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), max_sample_size=self.cfg.max_sample_size, min_sample_size=self.cfg.min_sample_size, pad=task_cfg.labels is not None or task_cfg.enable_padding, normalize=task_cfg.normalize, num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), compute_mask=compute_mask, **mask_args, ) else: if task_cfg.multi_corpus_keys is None: manifest_path = os.path.join(data_path, "{}.tsv".format(split)) self.datasets[split] = FileAudioDataset( manifest_path=manifest_path, sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), max_sample_size=self.cfg.max_sample_size, min_sample_size=self.cfg.min_sample_size, pad=task_cfg.labels is not None or task_cfg.enable_padding, normalize=task_cfg.normalize, num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), text_compression_level=text_compression_level, compute_mask=compute_mask, **mask_args, ) else: dataset_map = OrderedDict() self.dataset_map = {} multi_corpus_keys = [k.strip() for k in task_cfg.multi_corpus_keys.split(",")] corpus_idx_map = {k: idx for idx, k in enumerate(multi_corpus_keys)} data_keys = [k.split(":") for k in split.split(",")] multi_corpus_sampling_weights = [float(val.strip()) for val in task_cfg.multi_corpus_sampling_weights.split(",")] data_weights = [] for key, file_name in data_keys: k = key.strip() manifest_path = os.path.join(data_path, "{}.tsv".format(file_name.strip())) # TODO: Remove duplication of code from the if block above dataset_map[k] = FileAudioDataset( manifest_path=manifest_path, sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), max_sample_size=self.cfg.max_sample_size, min_sample_size=self.cfg.min_sample_size, pad=task_cfg.labels is not None or task_cfg.enable_padding, normalize=task_cfg.normalize, num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), text_compression_level=text_compression_level, compute_mask=compute_mask, corpus_key=corpus_idx_map[k], **mask_args, ) data_weights.append(multi_corpus_sampling_weights[corpus_idx_map[k]]) self.dataset_map[split] = dataset_map if len(dataset_map) == 1: self.datasets[split] = list(dataset_map.values())[0] else: self.datasets[split] = MultiCorpusDataset(dataset_map, distribution=data_weights, seed=0, sort_indices=True) if getattr(task_cfg, "subsample", 1) < 1: self.datasets[split] = SubsampleDataset( self.datasets[split], task_cfg.subsample, shuffle=True, seed=task_cfg.seed, ) if self.cfg.tpu and task_cfg.inferred_w2v_config.mask_channel_prob == 0.0: logger.info( "Pretraining on TPUs may suffer convergence " "issues when training with `mask_channel_prob` value of " "0. You may want to set this to a low value close to 0." ) def max_positions(self): """Maximum input length supported by the encoder.""" return sys.maxsize, sys.maxsize def build_model(self, model_cfg: FairseqDataclass, from_checkpoint=False): model = super().build_model(model_cfg, from_checkpoint) actualized_cfg = getattr(model, "cfg", None) if actualized_cfg is not None: # if "w2v_args" in actualized_cfg: if hasattr(actualized_cfg, "w2v_args"): model_cfg.w2v_args = actualized_cfg.w2v_args return model def post_save(self, cp_path, num_updates): if self.cfg.post_save_script is not None: logger.info(f"launching {self.cfg.post_save_script}") import os.path as osp from fairseq.file_io import PathManager eval_cp_path = osp.join( osp.dirname(cp_path), f"checkpoint_eval_{num_updates}.pt" ) print(cp_path, eval_cp_path, osp.dirname(cp_path)) assert PathManager.copy( cp_path, eval_cp_path, overwrite=True ), f"Failed to copy {cp_path} to {eval_cp_path}" import subprocess import shlex subprocess.call(shlex.split(f"{self.cfg.post_save_script} {eval_cp_path}"))