Delete demucs directory
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Anjok07
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demucs/wav.py
174
demucs/wav.py
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# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from collections import OrderedDict
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import hashlib
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import math
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import json
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from pathlib import Path
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import julius
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import torch as th
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from torch import distributed
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import torchaudio as ta
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from torch.nn import functional as F
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from .audio import convert_audio_channels
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from .compressed import get_musdb_tracks
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MIXTURE = "mixture"
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EXT = ".wav"
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def _track_metadata(track, sources):
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track_length = None
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track_samplerate = None
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for source in sources + [MIXTURE]:
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file = track / f"{source}{EXT}"
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info = ta.info(str(file))
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length = info.num_frames
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if track_length is None:
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track_length = length
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track_samplerate = info.sample_rate
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elif track_length != length:
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raise ValueError(
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f"Invalid length for file {file}: "
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f"expecting {track_length} but got {length}.")
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elif info.sample_rate != track_samplerate:
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raise ValueError(
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f"Invalid sample rate for file {file}: "
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f"expecting {track_samplerate} but got {info.sample_rate}.")
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if source == MIXTURE:
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wav, _ = ta.load(str(file))
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wav = wav.mean(0)
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mean = wav.mean().item()
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std = wav.std().item()
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return {"length": length, "mean": mean, "std": std, "samplerate": track_samplerate}
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def _build_metadata(path, sources):
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meta = {}
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path = Path(path)
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for file in path.iterdir():
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meta[file.name] = _track_metadata(file, sources)
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return meta
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class Wavset:
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def __init__(
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self,
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root, metadata, sources,
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length=None, stride=None, normalize=True,
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samplerate=44100, channels=2):
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"""
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Waveset (or mp3 set for that matter). Can be used to train
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with arbitrary sources. Each track should be one folder inside of `path`.
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The folder should contain files named `{source}.{ext}`.
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Files will be grouped according to `sources` (each source is a list of
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filenames).
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Sample rate and channels will be converted on the fly.
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`length` is the sample size to extract (in samples, not duration).
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`stride` is how many samples to move by between each example.
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"""
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self.root = Path(root)
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self.metadata = OrderedDict(metadata)
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self.length = length
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self.stride = stride or length
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self.normalize = normalize
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self.sources = sources
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self.channels = channels
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self.samplerate = samplerate
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self.num_examples = []
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for name, meta in self.metadata.items():
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track_length = int(self.samplerate * meta['length'] / meta['samplerate'])
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if length is None or track_length < length:
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examples = 1
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else:
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examples = int(math.ceil((track_length - self.length) / self.stride) + 1)
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self.num_examples.append(examples)
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def __len__(self):
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return sum(self.num_examples)
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def get_file(self, name, source):
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return self.root / name / f"{source}{EXT}"
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def __getitem__(self, index):
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for name, examples in zip(self.metadata, self.num_examples):
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if index >= examples:
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index -= examples
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continue
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meta = self.metadata[name]
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num_frames = -1
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offset = 0
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if self.length is not None:
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offset = int(math.ceil(
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meta['samplerate'] * self.stride * index / self.samplerate))
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num_frames = int(math.ceil(
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meta['samplerate'] * self.length / self.samplerate))
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wavs = []
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for source in self.sources:
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file = self.get_file(name, source)
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wav, _ = ta.load(str(file), frame_offset=offset, num_frames=num_frames)
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wav = convert_audio_channels(wav, self.channels)
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wavs.append(wav)
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example = th.stack(wavs)
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example = julius.resample_frac(example, meta['samplerate'], self.samplerate)
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if self.normalize:
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example = (example - meta['mean']) / meta['std']
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if self.length:
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example = example[..., :self.length]
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example = F.pad(example, (0, self.length - example.shape[-1]))
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return example
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def get_wav_datasets(args, samples, sources):
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sig = hashlib.sha1(str(args.wav).encode()).hexdigest()[:8]
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metadata_file = args.metadata / (sig + ".json")
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train_path = args.wav / "train"
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valid_path = args.wav / "valid"
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if not metadata_file.is_file() and args.rank == 0:
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train = _build_metadata(train_path, sources)
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valid = _build_metadata(valid_path, sources)
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json.dump([train, valid], open(metadata_file, "w"))
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if args.world_size > 1:
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distributed.barrier()
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train, valid = json.load(open(metadata_file))
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train_set = Wavset(train_path, train, sources,
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length=samples, stride=args.data_stride,
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samplerate=args.samplerate, channels=args.audio_channels,
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normalize=args.norm_wav)
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valid_set = Wavset(valid_path, valid, [MIXTURE] + sources,
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samplerate=args.samplerate, channels=args.audio_channels,
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normalize=args.norm_wav)
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return train_set, valid_set
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def get_musdb_wav_datasets(args, samples, sources):
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metadata_file = args.metadata / "musdb_wav.json"
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root = args.musdb / "train"
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if not metadata_file.is_file() and args.rank == 0:
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metadata = _build_metadata(root, sources)
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json.dump(metadata, open(metadata_file, "w"))
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if args.world_size > 1:
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distributed.barrier()
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metadata = json.load(open(metadata_file))
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train_tracks = get_musdb_tracks(args.musdb, is_wav=True, subsets=["train"], split="train")
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metadata_train = {name: meta for name, meta in metadata.items() if name in train_tracks}
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metadata_valid = {name: meta for name, meta in metadata.items() if name not in train_tracks}
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train_set = Wavset(root, metadata_train, sources,
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length=samples, stride=args.data_stride,
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samplerate=args.samplerate, channels=args.audio_channels,
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normalize=args.norm_wav)
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valid_set = Wavset(root, metadata_valid, [MIXTURE] + sources,
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samplerate=args.samplerate, channels=args.audio_channels,
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normalize=args.norm_wav)
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return train_set, valid_set
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