/
wsj0_mix.py
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/
wsj0_mix.py
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import torch
from torch.utils import data
import json
import os
import numpy as np
import soundfile as sf
def make_dataloaders(
train_dir,
valid_dir,
n_src=2,
sample_rate=8000,
segment=4.0,
batch_size=4,
num_workers=None,
**kwargs,
):
num_workers = num_workers if num_workers else batch_size
train_set = Wsj0mixDataset(train_dir, n_src=n_src, sample_rate=sample_rate, segment=segment)
val_set = Wsj0mixDataset(valid_dir, n_src=n_src, sample_rate=sample_rate, segment=segment)
train_loader = data.DataLoader(
train_set, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True
)
val_loader = data.DataLoader(
val_set, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True
)
return train_loader, val_loader
class Wsj0mixDataset(data.Dataset):
"""Dataset class for the wsj0-mix source separation dataset.
Args:
json_dir (str): The path to the directory containing the json files.
sample_rate (int, optional): The sampling rate of the wav files.
segment (float, optional): Length of the segments used for training,
in seconds. If None, use full utterances (e.g. for test).
n_src (int, optional): Number of sources in the training targets.
References
"Deep clustering: Discriminative embeddings for segmentation and
separation", Hershey et al. 2015.
"""
dataset_name = "wsj0-mix"
def __init__(self, json_dir, n_src=2, sample_rate=8000, segment=4.0):
super().__init__()
# Task setting
self.json_dir = json_dir
self.sample_rate = sample_rate
if segment is None:
self.seg_len = None
else:
self.seg_len = int(segment * sample_rate)
self.n_src = n_src
self.like_test = self.seg_len is None
# Load json files
mix_json = os.path.join(json_dir, "mix.json")
sources_json = [
os.path.join(json_dir, source + ".json") for source in [f"s{n+1}" for n in range(n_src)]
]
with open(mix_json, "r") as f:
mix_infos = json.load(f)
sources_infos = []
for src_json in sources_json:
with open(src_json, "r") as f:
sources_infos.append(json.load(f))
# Filter out short utterances only when segment is specified
orig_len = len(mix_infos)
drop_utt, drop_len = 0, 0
if not self.like_test:
for i in range(len(mix_infos) - 1, -1, -1): # Go backward
if mix_infos[i][1] < self.seg_len:
drop_utt += 1
drop_len += mix_infos[i][1]
del mix_infos[i]
for src_inf in sources_infos:
del src_inf[i]
print(
"Drop {} utts({:.2f} h) from {} (shorter than {} samples)".format(
drop_utt, drop_len / sample_rate / 36000, orig_len, self.seg_len
)
)
self.mix = mix_infos
self.sources = sources_infos
def __len__(self):
return len(self.mix)
def __getitem__(self, idx):
"""Gets a mixture/sources pair.
Returns:
mixture, vstack([source_arrays])
"""
# Random start
if self.mix[idx][1] == self.seg_len or self.like_test:
rand_start = 0
else:
rand_start = np.random.randint(0, self.mix[idx][1] - self.seg_len)
if self.like_test:
stop = None
else:
stop = rand_start + self.seg_len
# Load mixture
x, _ = sf.read(self.mix[idx][0], start=rand_start, stop=stop, dtype="float32")
seg_len = torch.as_tensor([len(x)])
# Load sources
source_arrays = []
for src in self.sources:
if src[idx] is None:
# Target is filled with zeros if n_src > default_nsrc
s = np.zeros((seg_len,))
else:
s, _ = sf.read(src[idx][0], start=rand_start, stop=stop, dtype="float32")
source_arrays.append(s)
sources = torch.from_numpy(np.vstack(source_arrays))
return torch.from_numpy(x), sources
def get_infos(self):
"""Get dataset infos (for publishing models).
Returns:
dict, dataset infos with keys `dataset`, `task` and `licences`.
"""
infos = dict()
infos["dataset"] = self.dataset_name
infos["task"] = "sep_clean"
infos["licenses"] = [wsj0_license]
return infos
wsj0_license = dict(
title="CSR-I (WSJ0) Complete",
title_link="https://catalog.ldc.upenn.edu/LDC93S6A",
author="LDC",
author_link="https://www.ldc.upenn.edu/",
license="LDC User Agreement for Non-Members",
license_link="https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf",
non_commercial=True,
)