def __init__(
self,
batch_size: int,
key_file: str,
drop_last: bool = False,
utt2category_file: str = None,
):
assert check_argument_types()
assert batch_size > 0
self.batch_size = batch_size
self.key_file = key_file
self.drop_last = drop_last
# utt2shape:
# uttA <anything is o.k>
# uttB <anything is o.k>
utt2any = read_2column_text(key_file)
if len(utt2any) == 0:
logging.warning(f"{key_file} is empty")
# In this case the, the first column in only used
keys = list(utt2any)
if len(keys) == 0:
raise RuntimeError(f"0 lines found: {key_file}")
category2utt = {}
if utt2category_file is not None:
utt2category = read_2column_text(utt2category_file)
if set(utt2category) != set(keys):
raise RuntimeError(
f"keys are mismatched between {utt2category_file} != {key_file}"
)
for k, v in utt2category.items():
category2utt.setdefault(v, []).append(k)
else:
category2utt["default_category"] = keys
self.batch_list = []
for d, v in category2utt.items():
category_keys = v
# Apply max(, 1) to avoid 0-batches
N = max(len(category_keys) // batch_size, 1)
if not self.drop_last:
# Split keys evenly as possible as. Note that If N != 1,
# the these batches always have size of batch_size at minimum.
cur_batch_list = [
category_keys[i * len(keys) // N:(i + 1) * len(keys) // N]
for i in range(N)
]
else:
cur_batch_list = [
tuple(category_keys[i * batch_size:(i + 1) * batch_size])
for i in range(N)
]
self.batch_list.extend(cur_batch_list)
def test_read_2column_text(tmp_path: Path):
p = tmp_path / "dummy.scp"
with p.open("w") as f:
f.write("abc /some/path/a.wav\n")
f.write("def /some/path/b.wav\n")
d = read_2column_text(p)
assert d == {"abc": "/some/path/a.wav", "def": "/some/path/b.wav"}
def __init__(self,
batch_size: int,
key_file: str,
drop_last: bool = False):
assert check_argument_types()
assert batch_size > 0
self.batch_size = batch_size
self.key_file = key_file
self.drop_last = drop_last
# utt2shape:
# uttA <anything is o.k>
# uttB <anything is o.k>
utt2any = read_2column_text(key_file)
if len(utt2any) == 0:
logging.warning(f"{key_file} is empty")
# In this case the, the first column in only used
keys = list(utt2any)
if len(keys) == 0:
raise RuntimeError(f"0 lines found: {key_file}")
# Apply max(, 1) to avoid 0-batches
N = max(len(keys) // batch_size, 1)
if not self.drop_last:
# Split keys evenly as possible as. Note that If N != 1,
# the these batches always have size of batch_size at minimum.
self.batch_list = [
keys[i * len(keys) // N:(i + 1) * len(keys) // N]
for i in range(N)
]
else:
self.batch_list = [
tuple(keys[i * batch_size:(i + 1) * batch_size])
for i in range(N)
]
def __init__(
self,
fname,
dtype=np.int16,
always_2d: bool = False,
normalize: bool = False,
):
assert check_argument_types()
self.fname = fname
self.dtype = dtype
self.always_2d = always_2d
self.normalize = normalize
self.data = read_2column_text(fname)
def __init__(
self,
batch_size: int,
shape_files: Union[Tuple[str, ...], List[str]],
fold_lengths: Sequence[int],
min_batch_size: int = 1,
sort_in_batch: str = "descending",
sort_batch: str = "ascending",
drop_last: bool = False,
utt2category_file: str = None,
):
assert check_argument_types()
assert batch_size > 0
if sort_batch != "ascending" and sort_batch != "descending":
raise ValueError(
f"sort_batch must be ascending or descending: {sort_batch}")
if sort_in_batch != "descending" and sort_in_batch != "ascending":
raise ValueError(
f"sort_in_batch must be ascending or descending: {sort_in_batch}"
)
self.batch_size = batch_size
self.shape_files = shape_files
self.sort_in_batch = sort_in_batch
self.sort_batch = sort_batch
self.drop_last = drop_last
# utt2shape: (Length, ...)
# uttA 100,...
# uttB 201,...
utt2shapes = [
load_num_sequence_text(s, loader_type="csv_int")
for s in shape_files
]
first_utt2shape = utt2shapes[0]
for s, d in zip(shape_files, utt2shapes):
if set(d) != set(first_utt2shape):
raise RuntimeError(
f"keys are mismatched between {s} != {shape_files[0]}")
# Sort samples in ascending order
# (shape order should be like (Length, Dim))
keys = sorted(first_utt2shape, key=lambda k: first_utt2shape[k][0])
if len(keys) == 0:
raise RuntimeError(f"0 lines found: {shape_files[0]}")
category2utt = {}
if utt2category_file is not None:
utt2category = read_2column_text(utt2category_file)
if set(utt2category) != set(first_utt2shape):
raise RuntimeError("keys are mismatched between "
f"{utt2category_file} != {shape_files[0]}")
for k in keys:
category2utt.setdefault(utt2category[k], []).append(k)
else:
category2utt["default_category"] = keys
self.batch_list = []
for d, v in category2utt.items():
category_keys = v
# Decide batch-sizes
start = 0
batch_sizes = []
while True:
k = category_keys[start]
factor = max(
int(d[k][0] / m) for d, m in zip(utt2shapes, fold_lengths))
bs = max(min_batch_size, int(batch_size / (1 + factor)))
if self.drop_last and start + bs > len(category_keys):
# This if-block avoids 0-batches
if len(self.batch_list) > 0:
break
bs = min(len(category_keys) - start, bs)
batch_sizes.append(bs)
start += bs
if start >= len(category_keys):
break
if len(batch_sizes) == 0:
# Maybe we can't reach here
raise RuntimeError("0 batches")
# If the last batch-size is smaller than minimum batch_size,
# the samples are redistributed to the other mini-batches
if len(batch_sizes) > 1 and batch_sizes[-1] < min_batch_size:
for i in range(batch_sizes.pop(-1)):
batch_sizes[-(i % len(batch_sizes)) - 2] += 1
if not self.drop_last:
# Bug check
assert sum(batch_sizes) == len(
category_keys
), f"{sum(batch_sizes)} != {len(category_keys)}"
# Set mini-batch
cur_batch_list = []
start = 0
for bs in batch_sizes:
assert len(category_keys) >= start + bs, "Bug"
minibatch_keys = category_keys[start:start + bs]
start += bs
if sort_in_batch == "descending":
minibatch_keys.reverse()
elif sort_in_batch == "ascending":
# Key are already sorted in ascending
pass
else:
raise ValueError("sort_in_batch must be ascending or "
f"descending: {sort_in_batch}")
cur_batch_list.append(tuple(minibatch_keys))
if sort_batch == "ascending":
pass
elif sort_batch == "descending":
cur_batch_list.reverse()
else:
raise ValueError(
f"sort_batch must be ascending or descending: {sort_batch}"
)
self.batch_list.extend(cur_batch_list)
def main():
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
logging.basicConfig(level=logging.INFO, format=logfmt)
logging.info(get_commandline_args())
parser = argparse.ArgumentParser(
description='Create waves list from "wav.scp"',
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("scp")
parser.add_argument("outdir")
parser.add_argument(
"--name",
default="wav",
help="Specify the prefix word of output file name "
'such as "wav.scp"',
)
parser.add_argument("--segments", default=None)
parser.add_argument(
"--fs",
type=humanfriendly_or_none,
default=None,
help="If the sampling rate specified, "
"Change the sampling rate.",
)
parser.add_argument("--audio-format", default="wav")
group = parser.add_mutually_exclusive_group()
group.add_argument("--ref-channels", default=None, type=str2int_tuple)
group.add_argument("--utt2ref-channels", default=None, type=str)
args = parser.parse_args()
out_num_samples = Path(args.outdir) / f"utt2num_samples"
if args.ref_channels is not None:
def utt2ref_channels(x) -> Tuple[int, ...]:
return args.ref_channels
elif args.utt2ref_channels is not None:
utt2ref_channels_dict = read_2column_text(args.utt2ref_channels)
def utt2ref_channels(x, d=utt2ref_channels_dict) -> Tuple[int, ...]:
chs_str = d[x]
return tuple(map(int, chs_str.split()))
else:
utt2ref_channels = None
Path(args.outdir).mkdir(parents=True, exist_ok=True)
out_wavscp = Path(args.outdir) / f"{args.name}.scp"
if args.segments is not None:
# Note: kaldiio supports only wav-pcm-int16le file.
loader = kaldiio.load_scp_sequential(args.scp, segments=args.segments)
if args.audio_format.endswith("ark"):
fark = open(Path(args.outdir) / f"data_{args.name}.ark", "wb")
fscp = out_wavscp.open("w")
else:
writer = SoundScpWriter(
args.outdir,
out_wavscp,
format=args.audio_format,
)
with out_num_samples.open("w") as fnum_samples:
for uttid, (rate, wave) in tqdm(loader):
# wave: (Time,) or (Time, Nmic)
if wave.ndim == 2 and utt2ref_channels is not None:
wave = wave[:, utt2ref_channels(uttid)]
if args.fs is not None and args.fs != rate:
# FIXME(kamo): To use sox?
wave = resampy.resample(wave.astype(np.float64),
rate,
args.fs,
axis=0)
wave = wave.astype(np.int16)
rate = args.fs
if args.audio_format.endswith("ark"):
if "flac" in args.audio_format:
suf = "flac"
elif "wav" in args.audio_format:
suf = "wav"
else:
raise RuntimeError("wav.ark or flac")
# NOTE(kamo): Using extended ark format style here.
# This format is incompatible with Kaldi
kaldiio.save_ark(
fark,
{uttid: (wave, rate)},
scp=fscp,
append=True,
write_function=f"soundfile_{suf}",
)
else:
writer[uttid] = rate, wave
fnum_samples.write(f"{uttid} {len(wave)}\n")
else:
if args.audio_format.endswith("ark"):
fark = open(Path(args.outdir) / f"data_{args.name}.ark", "wb")
else:
wavdir = Path(args.outdir) / f"data_{args.name}"
wavdir.mkdir(parents=True, exist_ok=True)
with Path(args.scp).open("r") as fscp, out_wavscp.open(
"w") as fout, out_num_samples.open("w") as fnum_samples:
for line in tqdm(fscp):
uttid, wavpath = line.strip().split(None, 1)
if wavpath.endswith("|"):
# Streaming input e.g. cat a.wav |
with kaldiio.open_like_kaldi(wavpath, "rb") as f:
with BytesIO(f.read()) as g:
wave, rate = soundfile.read(g, dtype=np.int16)
if wave.ndim == 2 and utt2ref_channels is not None:
wave = wave[:, utt2ref_channels(uttid)]
if args.fs is not None and args.fs != rate:
# FIXME(kamo): To use sox?
wave = resampy.resample(wave.astype(np.float64),
rate,
args.fs,
axis=0)
wave = wave.astype(np.int16)
rate = args.fs
if args.audio_format.endswith("ark"):
if "flac" in args.audio_format:
suf = "flac"
elif "wav" in args.audio_format:
suf = "wav"
else:
raise RuntimeError("wav.ark or flac")
# NOTE(kamo): Using extended ark format style here.
# This format is incompatible with Kaldi
kaldiio.save_ark(
fark,
{uttid: (wave, rate)},
scp=fout,
append=True,
write_function=f"soundfile_{suf}",
)
else:
owavpath = str(wavdir /
f"{uttid}.{args.audio_format}")
soundfile.write(owavpath, wave, rate)
fout.write(f"{uttid} {owavpath}\n")
else:
wave, rate = soundfile.read(wavpath, dtype=np.int16)
if wave.ndim == 2 and utt2ref_channels is not None:
wave = wave[:, utt2ref_channels(uttid)]
save_asis = False
elif args.audio_format.endswith("ark"):
save_asis = False
elif Path(wavpath).suffix == "." + args.audio_format and (
args.fs is None or args.fs == rate):
save_asis = True
else:
save_asis = False
if save_asis:
# Neither --segments nor --fs are specified and
# the line doesn't end with "|",
# i.e. not using unix-pipe,
# only in this case,
# just using the original file as is.
fout.write(f"{uttid} {wavpath}\n")
else:
if args.fs is not None and args.fs != rate:
# FIXME(kamo): To use sox?
wave = resampy.resample(wave.astype(np.float64),
rate,
args.fs,
axis=0)
wave = wave.astype(np.int16)
rate = args.fs
if args.audio_format.endswith("ark"):
if "flac" in args.audio_format:
suf = "flac"
elif "wav" in args.audio_format:
suf = "wav"
else:
raise RuntimeError("wav.ark or flac")
# NOTE(kamo): Using extended ark format style here.
# This format is not supported in Kaldi.
kaldiio.save_ark(
fark,
{uttid: (wave, rate)},
scp=fout,
append=True,
write_function=f"soundfile_{suf}",
)
else:
owavpath = str(wavdir /
f"{uttid}.{args.audio_format}")
soundfile.write(owavpath, wave, rate)
fout.write(f"{uttid} {owavpath}\n")
fnum_samples.write(f"{uttid} {len(wave)}\n")
def __init__(self, fname: Union[Path, str]):
assert check_argument_types()
self.fname = Path(fname)
self.data = read_2column_text(fname)
