From 83cc9f5c3e2524b5ac5fd9646fd869670e8aeb81 Mon Sep 17 00:00:00 2001
From: "paul.best" <paul.best@sms-cluster.lis-lab.fr>
Date: Wed, 12 Jun 2024 12:52:54 +0200
Subject: [PATCH] fixes
---
src/data/audio_datamodule.py | 16 +++++++++++++---
src/models/pesto.py | 4 ++--
2 files changed, 15 insertions(+), 5 deletions(-)
diff --git a/src/data/audio_datamodule.py b/src/data/audio_datamodule.py
index 77c0297..d0b077d 100644
--- a/src/data/audio_datamodule.py
+++ b/src/data/audio_datamodule.py
@@ -59,6 +59,8 @@ class AudioDataModule(LightningDataModule):
bins_per_semitone: int = 1,
n_bins: int = 84,
center_bins: bool = False,
+ min_samples: int = 0,
+ downsample: float = 1,
batch_size: int = 256,
num_workers: int = 0,
pin_memory: bool = False,
@@ -93,7 +95,7 @@ class AudioDataModule(LightningDataModule):
self.fold = fold
self.n_folds = n_folds
-
+ self.min_samples, self.downsample = min_samples, downsample
# HCQT
self.hcqt_sr = None
self.hcqt_kernels = None
@@ -178,6 +180,7 @@ class AudioDataModule(LightningDataModule):
def load_data(self, audio_files: Path, annot_files: Path | None = None) -> torch.utils.data.Dataset:
cache_cqt = self.build_cqt_filename(audio_files)
if cache_cqt.exists():
+ print('loading cached CQT', cache_cqt)
inputs = np.load(cache_cqt, mmap_mode=self.mmap_mode)
cache_annot = cache_cqt.with_suffix(".csv")
annotations = np.loadtxt(cache_annot, dtype=np.float32) if cache_annot.exists() else None
@@ -217,15 +220,22 @@ class AudioDataModule(LightningDataModule):
annot_files = []
annot_list = None
- log.info("Precomputing HCQT...")
+ log.info(f"Precomputing HCQT for {len(audio_files)} files")
pbar = tqdm(itertools.zip_longest(audio_files, annot_files, fillvalue=None),
total=len(audio_files),
leave=False)
for fname, annot in pbar:
fname = fname.strip()
pbar.set_description(fname)
+ downsample = self.downsample[fname.split('/')[0]] if str(type(self.downsample)) == "<class 'omegaconf.dictconfig.DictConfig'>" else self.downsample
+ if torchaudio.info(data_dir / fname).num_frames < self.min_samples:
+ print(f'{fname} is too small :/')
+ continue
+ if torchaudio.info(data_dir / fname).sample_rate / downsample / 2 < self.hcqt_kwargs['fmax']:
+ print(f'{fname} has a too small sampling rate for the given fmax :/')
+ continue
x, sr = torchaudio.load(data_dir / fname)
- out = self.hcqt(x.mean(dim=0), sr) # convert to mono and compute HCQT
+ out = self.hcqt(x.mean(dim=0), sr/downsample) # convert to mono and compute HCQT
if annot is not None:
annot = annot.strip()
diff --git a/src/models/pesto.py b/src/models/pesto.py
index 638c543..bcd3830 100644
--- a/src/models/pesto.py
+++ b/src/models/pesto.py
@@ -171,14 +171,14 @@ class PESTO(LightningModule):
def estimate_shift(self) -> None:
r"""Estimate the shift to predict absolute pitches from relative activations"""
# 0. Define labels
- labels = torch.arange(60, 72)
+ labels = torch.arange(60, 96, 2)
# 1. Generate synthetic audio and convert it to HCQT
sr = 16000
dm = self.trainer.datamodule
batch = []
for p in labels:
- audio = generate_synth_data(p, sr=sr)
+ audio = generate_synth_data(p, sr=sr, num_harmonics=2)
hcqt = dm.hcqt(audio, sr)
batch.append(hcqt[0])
--
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