Spaces:
Running
Running
File size: 4,068 Bytes
7bc29af |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
# Copyright (c) Facebook, Inc. and its 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.
import gzip
import sys
from concurrent import futures
import musdb
import museval
import torch as th
import tqdm
from scipy.io import wavfile
from torch import distributed
from .audio import convert_audio
from .utils import apply_model
def evaluate(model,
musdb_path,
eval_folder,
workers=2,
device="cpu",
rank=0,
save=False,
shifts=0,
split=False,
overlap=0.25,
is_wav=False,
world_size=1):
"""
Evaluate model using museval. Run the model
on a single GPU, the bottleneck being the call to museval.
"""
output_dir = eval_folder / "results"
output_dir.mkdir(exist_ok=True, parents=True)
json_folder = eval_folder / "results/test"
json_folder.mkdir(exist_ok=True, parents=True)
# we load tracks from the original musdb set
test_set = musdb.DB(musdb_path, subsets=["test"], is_wav=is_wav)
src_rate = 44100 # hardcoded for now...
for p in model.parameters():
p.requires_grad = False
p.grad = None
pendings = []
with futures.ProcessPoolExecutor(workers or 1) as pool:
for index in tqdm.tqdm(range(rank, len(test_set), world_size), file=sys.stdout):
track = test_set.tracks[index]
out = json_folder / f"{track.name}.json.gz"
if out.exists():
continue
mix = th.from_numpy(track.audio).t().float()
ref = mix.mean(dim=0) # mono mixture
mix = (mix - ref.mean()) / ref.std()
mix = convert_audio(mix, src_rate, model.samplerate, model.audio_channels)
estimates = apply_model(model, mix.to(device),
shifts=shifts, split=split, overlap=overlap)
estimates = estimates * ref.std() + ref.mean()
estimates = estimates.transpose(1, 2)
references = th.stack(
[th.from_numpy(track.targets[name].audio).t() for name in model.sources])
references = convert_audio(references, src_rate,
model.samplerate, model.audio_channels)
references = references.transpose(1, 2).numpy()
estimates = estimates.cpu().numpy()
win = int(1. * model.samplerate)
hop = int(1. * model.samplerate)
if save:
folder = eval_folder / "wav/test" / track.name
folder.mkdir(exist_ok=True, parents=True)
for name, estimate in zip(model.sources, estimates):
wavfile.write(str(folder / (name + ".wav")), 44100, estimate)
if workers:
pendings.append((track.name, pool.submit(
museval.evaluate, references, estimates, win=win, hop=hop)))
else:
pendings.append((track.name, museval.evaluate(
references, estimates, win=win, hop=hop)))
del references, mix, estimates, track
for track_name, pending in tqdm.tqdm(pendings, file=sys.stdout):
if workers:
pending = pending.result()
sdr, isr, sir, sar = pending
track_store = museval.TrackStore(win=44100, hop=44100, track_name=track_name)
for idx, target in enumerate(model.sources):
values = {
"SDR": sdr[idx].tolist(),
"SIR": sir[idx].tolist(),
"ISR": isr[idx].tolist(),
"SAR": sar[idx].tolist()
}
track_store.add_target(target_name=target, values=values)
json_path = json_folder / f"{track_name}.json.gz"
gzip.open(json_path, "w").write(track_store.json.encode('utf-8'))
if world_size > 1:
distributed.barrier()
|