# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """AudioDataset support. In order to handle a larger number of files without having to scan again the folders, we precompute some metadata (filename, sample rate, duration), and use that to efficiently sample audio segments. """ import argparse import copy from concurrent.futures import ThreadPoolExecutor, Future from dataclasses import dataclass, fields from contextlib import ExitStack from functools import lru_cache import gzip import json import logging import os from pathlib import Path import random import sys import typing as tp import torch import torch.nn.functional as F from .audio import audio_read, audio_info from .audio_utils import convert_audio from .zip import PathInZip try: import dora except ImportError: dora = None # type: ignore @dataclass(order=True) class BaseInfo: @classmethod def _dict2fields(cls, dictionary: dict): return { field.name: dictionary[field.name] for field in fields(cls) if field.name in dictionary } @classmethod def from_dict(cls, dictionary: dict): _dictionary = cls._dict2fields(dictionary) return cls(**_dictionary) def to_dict(self): return { field.name: self.__getattribute__(field.name) for field in fields(self) } @dataclass(order=True) class AudioMeta(BaseInfo): path: str duration: float sample_rate: int bpm: float # meter: int amplitude: tp.Optional[float] = None weight: tp.Optional[float] = None phr_start: tp.List[tp.Optional[float]] = None # info_path is used to load additional information about the audio file that is stored in zip files. info_path: tp.Optional[PathInZip] = None @classmethod def from_dict(cls, dictionary: dict): base = cls._dict2fields(dictionary) if 'info_path' in base and base['info_path'] is not None: base['info_path'] = PathInZip(base['info_path']) return cls(**base) def to_dict(self): d = super().to_dict() if d['info_path'] is not None: d['info_path'] = str(d['info_path']) return d @dataclass(order=True) class SegmentInfo(BaseInfo): meta: AudioMeta seek_time: float # The following values are given once the audio is processed, e.g. # at the target sample rate and target number of channels. n_frames: int # actual number of frames without padding total_frames: int # total number of frames, padding included sample_rate: int # actual sample rate channels: int # number of audio channels. DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a'] logger = logging.getLogger(__name__) def _get_audio_meta(file_path: str, minimal: bool = True) -> AudioMeta: """AudioMeta from a path to an audio file. Args: file_path (str): Resolved path of valid audio file. minimal (bool): Whether to only load the minimal set of metadata (takes longer if not). Returns: AudioMeta: Audio file path and its metadata. """ info = audio_info(file_path) amplitude: tp.Optional[float] = None if not minimal: wav, sr = audio_read(file_path) amplitude = wav.abs().max().item() # load json info json_file = file_path.replace('.wav', '.json') with open(json_file ,'r') as f: json_str = f.read() info_json = json.loads(json_str) if "phr_start" not in info_json.keys(): info_json["phr_start"] = None # return AudioMeta(file_path, info.duration, info.sample_rate, info_json["bpm"], info_json["meter"], amplitude, None, info_json["phr_start"]) return AudioMeta(file_path, info.duration, info.sample_rate, info_json["bpm"], amplitude, None, info_json["phr_start"]) def _resolve_audio_meta(m: AudioMeta, fast: bool = True) -> AudioMeta: """If Dora is available as a dependency, try to resolve potential relative paths in list of AudioMeta. This method is expected to be used when loading meta from file. Args: m (AudioMeta): Audio meta to resolve. fast (bool): If True, uses a really fast check for determining if a file is already absolute or not. Only valid on Linux/Mac. Returns: AudioMeta: Audio meta with resolved path. """ def is_abs(m): if fast: return str(m)[0] == '/' else: os.path.isabs(str(m)) if not dora: return m if not is_abs(m.path): m.path = dora.git_save.to_absolute_path(m.path) if m.info_path is not None and not is_abs(m.info_path.zip_path): m.info_path.zip_path = dora.git_save.to_absolute_path(m.path) return m def find_audio_files(path: tp.Union[Path, str], exts: tp.List[str] = DEFAULT_EXTS, resolve: bool = True, minimal: bool = True, progress: bool = False, workers: int = 0) -> tp.List[AudioMeta]: """Build a list of AudioMeta from a given path, collecting relevant audio files and fetching meta info. Args: path (str or Path): Path to folder containing audio files. exts (list of str): List of file extensions to consider for audio files. minimal (bool): Whether to only load the minimal set of metadata (takes longer if not). progress (bool): Whether to log progress on audio files collection. workers (int): number of parallel workers, if 0, use only the current thread. Returns: list of AudioMeta: List of audio file path and its metadata. """ audio_files = [] futures: tp.List[Future] = [] pool: tp.Optional[ThreadPoolExecutor] = None with ExitStack() as stack: if workers > 0: pool = ThreadPoolExecutor(workers) stack.enter_context(pool) if progress: print("Finding audio files...") for root, folders, files in os.walk(path, followlinks=True): for file in files: full_path = Path(root) / file if full_path.suffix.lower() in exts: audio_files.append(full_path) if pool is not None: futures.append(pool.submit(_get_audio_meta, str(audio_files[-1]), minimal)) if progress: print(format(len(audio_files), " 8d"), end='\r', file=sys.stderr) if progress: print("Getting audio metadata...") meta: tp.List[AudioMeta] = [] for idx, file_path in enumerate(audio_files): try: if pool is None: m = _get_audio_meta(str(file_path), minimal) else: m = futures[idx].result() if resolve: m = _resolve_audio_meta(m) except Exception as err: print("Error with", str(file_path), err, file=sys.stderr) continue meta.append(m) if progress: print(format((1 + idx) / len(audio_files), " 3.1%"), end='\r', file=sys.stderr) meta.sort() return meta def load_audio_meta(path: tp.Union[str, Path], resolve: bool = True, fast: bool = True) -> tp.List[AudioMeta]: """Load list of AudioMeta from an optionally compressed json file. Args: path (str or Path): Path to JSON file. resolve (bool): Whether to resolve the path from AudioMeta (default=True). fast (bool): activates some tricks to make things faster. Returns: list of AudioMeta: List of audio file path and its total duration. """ open_fn = gzip.open if str(path).lower().endswith('.gz') else open with open_fn(path, 'rb') as fp: # type: ignore lines = fp.readlines() meta = [] for line in lines: d = json.loads(line) m = AudioMeta.from_dict(d) if resolve: m = _resolve_audio_meta(m, fast=fast) meta.append(m) return meta def save_audio_meta(path: tp.Union[str, Path], meta: tp.List[AudioMeta]): """Save the audio metadata to the file pointer as json. Args: path (str or Path): Path to JSON file. metadata (list of BaseAudioMeta): List of audio meta to save. """ Path(path).parent.mkdir(exist_ok=True, parents=True) open_fn = gzip.open if str(path).lower().endswith('.gz') else open with open_fn(path, 'wb') as fp: # type: ignore for m in meta: json_str = json.dumps(m.to_dict()) + '\n' json_bytes = json_str.encode('utf-8') fp.write(json_bytes) class AudioDataset: """Base audio dataset. The dataset takes a list of AudioMeta and create a dataset composed of segments of audio and potentially additional information, by creating random segments from the list of audio files referenced in the metadata and applying minimal data pre-processing such as resampling, mixing of channels, padding, etc. If no segment_duration value is provided, the AudioDataset will return the full wav for each audio file. Otherwise, it will randomly sample audio files and create a segment of the specified duration, applying padding if required. By default, only the torch Tensor corresponding to the waveform is returned. Setting return_info=True allows to return a tuple containing the torch Tensor and additional metadata on the segment and the original audio meta. Note that you can call `start_epoch(epoch)` in order to get a deterministic "randomization" for `shuffle=True`. For a given epoch and dataset index, this will always return the same extract. You can get back some diversity by setting the `shuffle_seed` param. Args: meta (list of AudioMeta): List of audio files metadata. segment_duration (float, optional): Optional segment duration of audio to load. If not specified, the dataset will load the full audio segment from the file. shuffle (bool): Set to `True` to have the data reshuffled at every epoch. sample_rate (int): Target sample rate of the loaded audio samples. channels (int): Target number of channels of the loaded audio samples. sample_on_duration (bool): Set to `True` to sample segments with probability dependent on audio file duration. This is only used if `segment_duration` is provided. sample_on_weight (bool): Set to `True` to sample segments using the `weight` entry of `AudioMeta`. If `sample_on_duration` is also True, the actual weight will be the product of the file duration and file weight. This is only used if `segment_duration` is provided. min_segment_ratio (float): Minimum segment ratio to use when the audio file is shorter than the desired segment. max_read_retry (int): Maximum number of retries to sample an audio segment from the dataset. return_info (bool): Whether to return the wav only or return wav along with segment info and metadata. min_audio_duration (float, optional): Minimum audio file duration, in seconds, if provided audio shorter than this will be filtered out. max_audio_duration (float, optional): Maximal audio file duration in seconds, if provided audio longer than this will be filtered out. shuffle_seed (int): can be used to further randomize load_wav (bool): if False, skip loading the wav but returns a tensor of 0 with the expected segment_duration (which must be provided if load_wav is False). permutation_on_files (bool): only if `sample_on_weight` and `sample_on_duration` are False. Will ensure a permutation on files when going through the dataset. In that case the epoch number must be provided in order for the model to continue the permutation across epochs. In that case, it is assumed that `num_samples = total_batch_size * num_updates_per_epoch`, with `total_batch_size` the overall batch size accounting for all gpus. """ def __init__(self, meta: tp.List[AudioMeta], segment_duration: tp.Optional[float] = None, shuffle: bool = True, num_samples: int = 10_000, sample_rate: int = 48_000, channels: int = 2, pad: bool = True, sample_on_duration: bool = True, sample_on_weight: bool = True, min_segment_ratio: float = 1, max_read_retry: int = 10, return_info: bool = False, min_audio_duration: tp.Optional[float] = None, max_audio_duration: tp.Optional[float] = None, shuffle_seed: int = 0, load_wav: bool = True, permutation_on_files: bool = False, ): assert len(meta) > 0, "No audio meta provided to AudioDataset. Please check loading of audio meta." assert segment_duration is None or segment_duration > 0 assert segment_duration is None or min_segment_ratio >= 0 self.segment_duration = segment_duration self.min_segment_ratio = min_segment_ratio self.max_audio_duration = max_audio_duration self.min_audio_duration = min_audio_duration if self.min_audio_duration is not None and self.max_audio_duration is not None: assert self.min_audio_duration <= self.max_audio_duration self.meta: tp.List[AudioMeta] = self._filter_duration(meta) assert len(self.meta) # Fail fast if all data has been filtered. self.total_duration = sum(d.duration for d in self.meta) if segment_duration is None: num_samples = len(self.meta) self.num_samples = num_samples self.shuffle = shuffle self.sample_rate = sample_rate self.channels = channels self.pad = pad self.sample_on_weight = sample_on_weight self.sample_on_duration = sample_on_duration self.sampling_probabilities = self._get_sampling_probabilities() self.max_read_retry = max_read_retry self.return_info = return_info self.shuffle_seed = shuffle_seed self.current_epoch: tp.Optional[int] = None self.load_wav = load_wav if not load_wav: assert segment_duration is not None self.permutation_on_files = permutation_on_files if permutation_on_files: assert not self.sample_on_duration assert not self.sample_on_weight assert self.shuffle def start_epoch(self, epoch: int): self.current_epoch = epoch def __len__(self): return self.num_samples def _get_sampling_probabilities(self, normalized: bool = True): """Return the sampling probabilities for each file inside `self.meta`.""" scores: tp.List[float] = [] for file_meta in self.meta: score = 1. if self.sample_on_weight and file_meta.weight is not None: score *= file_meta.weight if self.sample_on_duration: score *= file_meta.duration scores.append(score) probabilities = torch.tensor(scores) if normalized: probabilities /= probabilities.sum() return probabilities @staticmethod @lru_cache(16) def _get_file_permutation(num_files: int, permutation_index: int, base_seed: int): # Used to keep the most recent files permutation in memory implicitely. # will work unless someone is using a lot of Datasets in parallel. rng = torch.Generator() rng.manual_seed(base_seed + permutation_index) return torch.randperm(num_files, generator=rng) def sample_file(self, index: int, rng: torch.Generator) -> AudioMeta: """Sample a given file from `self.meta`. Can be overridden in subclasses. This is only called if `segment_duration` is not None. You must use the provided random number generator `rng` for reproducibility. You can further make use of the index accessed. """ if self.permutation_on_files: assert self.current_epoch is not None total_index = self.current_epoch * len(self) + index permutation_index = total_index // len(self.meta) relative_index = total_index % len(self.meta) permutation = AudioDataset._get_file_permutation( len(self.meta), permutation_index, self.shuffle_seed) file_index = permutation[relative_index] return self.meta[file_index] if not self.sample_on_weight and not self.sample_on_duration: file_index = int(torch.randint(len(self.sampling_probabilities), (1,), generator=rng).item()) else: file_index = int(torch.multinomial(self.sampling_probabilities, 1, generator=rng).item()) return self.meta[file_index] def _audio_read(self, path: str, seek_time: float = 0, duration: float = -1): # Override this method in subclass if needed. if self.load_wav: return audio_read(path, seek_time, duration, pad=False) else: assert self.segment_duration is not None n_frames = int(self.sample_rate * self.segment_duration) return torch.zeros(self.channels, n_frames), self.sample_rate def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentInfo]]: if self.segment_duration is None: file_meta = self.meta[index] out, sr = audio_read(file_meta.path) out = convert_audio(out, sr, self.sample_rate, self.channels) n_frames = out.shape[-1] segment_info = SegmentInfo(file_meta, seek_time=0., n_frames=n_frames, total_frames=n_frames, sample_rate=self.sample_rate, channels=out.shape[0]) else: rng = torch.Generator() if self.shuffle: # We use index, plus extra randomness, either totally random if we don't know the epoch. # otherwise we make use of the epoch number and optional shuffle_seed. if self.current_epoch is None: rng.manual_seed(index + self.num_samples * random.randint(0, 2**24)) else: rng.manual_seed(index + self.num_samples * (self.current_epoch + self.shuffle_seed)) else: # We only use index rng.manual_seed(index) for retry in range(self.max_read_retry): file_meta = self.sample_file(index, rng) # We add some variance in the file position even if audio file is smaller than segment # without ending up with empty segments # sample with phrase if file_meta.phr_start is not None: # max_seek = max(0, len(file_meta.phr_start[:-1])) max_seek = max(0, len([start for start in file_meta.phr_start if start + self.segment_duration <= file_meta.duration])) # sample with time seek_time = file_meta.phr_start[int(torch.rand(1, generator=rng).item() * max_seek)] # choose from phrase else: max_seek = max(0, file_meta.duration - self.segment_duration * self.min_segment_ratio) seek_time = torch.rand(1, generator=rng).item() * max_seek # can be change to choose phrase start if file_meta.duration == self.segment_duration: seek_time = 0 # phr_dur = 60./file_meta.bpm * (file_meta.meter * 4.) # if meter=4 then 16 beats per phrase try: out, sr = audio_read(file_meta.path, seek_time, self.segment_duration, pad=False) # out, sr = audio_read(file_meta.path, seek_time, phr_dur, pad=False) # use phrase trunk as input out = convert_audio(out, sr, self.sample_rate, self.channels) n_frames = out.shape[-1] target_frames = int(self.segment_duration * self.sample_rate) if self.pad: out = F.pad(out, (0, target_frames - n_frames)) segment_info = SegmentInfo(file_meta, seek_time, n_frames=n_frames, total_frames=target_frames, sample_rate=self.sample_rate, channels=out.shape[0]) except Exception as exc: logger.warning("Error opening file %s: %r", file_meta.path, exc) if retry == self.max_read_retry - 1: raise else: break if self.return_info: # Returns the wav and additional information on the wave segment return out, segment_info else: return out def collater(self, samples): """The collater function has to be provided to the dataloader if AudioDataset has return_info=True in order to properly collate the samples of a batch. """ if self.segment_duration is None and len(samples) > 1: assert self.pad, "Must allow padding when batching examples of different durations." # In this case the audio reaching the collater is of variable length as segment_duration=None. to_pad = self.segment_duration is None and self.pad if to_pad: max_len = max([wav.shape[-1] for wav, _ in samples]) def _pad_wav(wav): return F.pad(wav, (0, max_len - wav.shape[-1])) if self.return_info: if len(samples) > 0: assert len(samples[0]) == 2 assert isinstance(samples[0][0], torch.Tensor) assert isinstance(samples[0][1], SegmentInfo) wavs = [wav for wav, _ in samples] segment_infos = [copy.deepcopy(info) for _, info in samples] if to_pad: # Each wav could be of a different duration as they are not segmented. for i in range(len(samples)): # Determines the total length of the signal with padding, so we update here as we pad. segment_infos[i].total_frames = max_len wavs[i] = _pad_wav(wavs[i]) wav = torch.stack(wavs) return wav, segment_infos else: assert isinstance(samples[0], torch.Tensor) if to_pad: samples = [_pad_wav(s) for s in samples] return torch.stack(samples) def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]: """Filters out audio files with audio durations that will not allow to sample examples from them.""" orig_len = len(meta) # Filter data that is too short. if self.min_audio_duration is not None: meta = [m for m in meta if m.duration >= self.min_audio_duration] # Filter data that is too long. if self.max_audio_duration is not None: meta = [m for m in meta if m.duration <= self.max_audio_duration] filtered_len = len(meta) removed_percentage = 100*(1-float(filtered_len)/orig_len) msg = 'Removed %.2f percent of the data because it was too short or too long.' % removed_percentage if removed_percentage < 10: logging.debug(msg) else: logging.warning(msg) return meta @classmethod def from_meta(cls, root: tp.Union[str, Path], **kwargs): """Instantiate AudioDataset from a path to a directory containing a manifest as a jsonl file. Args: root (str or Path): Path to root folder containing audio files. kwargs: Additional keyword arguments for the AudioDataset. """ root = Path(root) if root.is_dir(): if (root / 'data.jsonl').exists(): root = root / 'data.jsonl' elif (root / 'data.jsonl.gz').exists(): root = root / 'data.jsonl.gz' else: raise ValueError("Don't know where to read metadata from in the dir. " "Expecting either a data.jsonl or data.jsonl.gz file but none found.") meta = load_audio_meta(root) return cls(meta, **kwargs) @classmethod def from_path(cls, root: tp.Union[str, Path], minimal_meta: bool = True, exts: tp.List[str] = DEFAULT_EXTS, **kwargs): """Instantiate AudioDataset from a path containing (possibly nested) audio files. Args: root (str or Path): Path to root folder containing audio files. minimal_meta (bool): Whether to only load minimal metadata or not. exts (list of str): Extensions for audio files. kwargs: Additional keyword arguments for the AudioDataset. """ root = Path(root) if root.is_file(): meta = load_audio_meta(root, resolve=True) else: meta = find_audio_files(root, exts, minimal=minimal_meta, resolve=True) return cls(meta, **kwargs) def main(): logging.basicConfig(stream=sys.stderr, level=logging.INFO) parser = argparse.ArgumentParser( prog='audio_dataset', description='Generate .jsonl files by scanning a folder.') parser.add_argument('root', help='Root folder with all the audio files') parser.add_argument('output_meta_file', help='Output file to store the metadata, ') parser.add_argument('--complete', action='store_false', dest='minimal', default=True, help='Retrieve all metadata, even the one that are expansive ' 'to compute (e.g. normalization).') parser.add_argument('--resolve', action='store_true', default=False, help='Resolve the paths to be absolute and with no symlinks.') parser.add_argument('--workers', default=10, type=int, help='Number of workers.') args = parser.parse_args() meta = find_audio_files(args.root, DEFAULT_EXTS, progress=True, resolve=args.resolve, minimal=args.minimal, workers=args.workers) save_audio_meta(args.output_meta_file, meta) if __name__ == '__main__': main()