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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils
from fairseq.data.fairseq_dataset import FairseqDataset
from fairseq.data.audio.audio_utils import (
parse_path,
read_from_stored_zip,
)
import io
logger = logging.getLogger(__name__)
def load_audio(manifest_path, max_keep, min_keep):
n_long, n_short = 0, 0
names, inds, sizes = [], [], []
with open(manifest_path) as f:
root = f.readline().strip()
for ind, line in enumerate(f):
items = line.strip().split("\t")
assert len(items) == 2, line
sz = int(items[1])
if min_keep is not None and sz < min_keep:
n_short += 1
elif max_keep is not None and sz > max_keep:
n_long += 1
else:
names.append(items[0])
inds.append(ind)
sizes.append(sz)
tot = ind + 1
logger.info(
(
f"max_keep={max_keep}, min_keep={min_keep}, "
f"loaded {len(names)}, skipped {n_short} short and {n_long} long, "
f"longest-loaded={max(sizes)}, shortest-loaded={min(sizes)}"
)
)
return root, names, inds, tot, sizes
def load_label(label_path, inds, tot):
with open(label_path) as f:
labels = [line.rstrip() for line in f]
assert (
len(labels) == tot
), f"number of labels does not match ({len(labels)} != {tot})"
labels = [labels[i] for i in inds]
return labels
def load_label_offset(label_path, inds, tot):
with open(label_path) as f:
code_lengths = [len(line.encode("utf-8")) for line in f]
assert (
len(code_lengths) == tot
), f"number of labels does not match ({len(code_lengths)} != {tot})"
offsets = list(itertools.accumulate([0] + code_lengths))
offsets = [(offsets[i], offsets[i + 1]) for i in inds]
return offsets
def verify_label_lengths(
audio_sizes,
audio_rate,
label_path,
label_rate,
inds,
tot,
tol=0.1, # tolerance in seconds
):
if label_rate < 0:
logger.info(f"{label_path} is sequence label. skipped")
return
with open(label_path) as f:
lengths = [len(line.rstrip().split()) for line in f]
assert len(lengths) == tot
lengths = [lengths[i] for i in inds]
num_invalid = 0
for i, ind in enumerate(inds):
dur_from_audio = audio_sizes[i] / audio_rate
dur_from_label = lengths[i] / label_rate
if abs(dur_from_audio - dur_from_label) > tol:
logger.warning(
(
f"audio and label duration differ too much "
f"(|{dur_from_audio} - {dur_from_label}| > {tol}) "
f"in line {ind+1} of {label_path}. Check if `label_rate` "
f"is correctly set (currently {label_rate}). "
f"num. of samples = {audio_sizes[i]}; "
f"label length = {lengths[i]}"
)
)
num_invalid += 1
if num_invalid > 0:
logger.warning(
f"total {num_invalid} (audio, label) pairs with mismatched lengths"
)
class HubertDataset(FairseqDataset):
def __init__(
self,
manifest_path: str,
sample_rate: float,
label_paths: List[str],
label_rates: Union[List[float], float], # -1 for sequence labels
pad_list: List[str],
eos_list: List[str],
label_processors: Optional[List[Any]] = None,
max_keep_sample_size: Optional[int] = None,
min_keep_sample_size: Optional[int] = None,
max_sample_size: Optional[int] = None,
shuffle: bool = True,
pad_audio: bool = False,
normalize: bool = False,
store_labels: bool = True,
random_crop: bool = False,
single_target: bool = False,
):
self.audio_root, self.audio_names, inds, tot, self.sizes = load_audio(
manifest_path, max_keep_sample_size, min_keep_sample_size
)
self.sample_rate = sample_rate
self.shuffle = shuffle
self.random_crop = random_crop
self.num_labels = len(label_paths)
self.pad_list = pad_list
self.eos_list = eos_list
self.label_processors = label_processors
self.single_target = single_target
self.label_rates = (
[label_rates for _ in range(len(label_paths))]
if isinstance(label_rates, float)
else label_rates
)
self.store_labels = store_labels
if store_labels:
self.label_list = [load_label(p, inds, tot) for p in label_paths]
else:
self.label_paths = label_paths
self.label_offsets_list = [
load_label_offset(p, inds, tot) for p in label_paths
]
assert label_processors is None or len(label_processors) == self.num_labels
for label_path, label_rate in zip(label_paths, self.label_rates):
verify_label_lengths(
self.sizes, sample_rate, label_path, label_rate, inds, tot
)
self.max_sample_size = (
max_sample_size if max_sample_size is not None else sys.maxsize
)
self.pad_audio = pad_audio
self.normalize = normalize
logger.info(
f"pad_audio={pad_audio}, random_crop={random_crop}, "
f"normalize={normalize}, max_sample_size={self.max_sample_size}"
)
def get_audio(self, index):
import soundfile as sf
wav_path = os.path.join(self.audio_root, self.audio_names[index])
_path, slice_ptr = parse_path(wav_path)
if len(slice_ptr) == 0:
wav, cur_sample_rate = sf.read(_path)
else:
assert _path.endswith(".zip")
data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1])
f = io.BytesIO(data)
wav, cur_sample_rate = sf.read(f)
wav = torch.from_numpy(wav).float()
wav = self.postprocess(wav, cur_sample_rate)
return wav
def get_label(self, index, label_idx):
if self.store_labels:
label = self.label_list[label_idx][index]
else:
with open(self.label_paths[label_idx]) as f:
offset_s, offset_e = self.label_offsets_list[label_idx][index]
f.seek(offset_s)
label = f.read(offset_e - offset_s)
if self.label_processors is not None:
label = self.label_processors[label_idx](label)
return label
def get_labels(self, index):
return [self.get_label(index, i) for i in range(self.num_labels)]
def __getitem__(self, index):
wav = self.get_audio(index)
labels = self.get_labels(index)
return {"id": index, "source": wav, "label_list": labels}
def __len__(self):
return len(self.sizes)
def crop_to_max_size(self, wav, target_size):
size = len(wav)
diff = size - target_size
if diff <= 0:
return wav, 0
start, end = 0, target_size
if self.random_crop:
start = np.random.randint(0, diff + 1)
end = size - diff + start
return wav[start:end], start
def collater(self, samples):
# target = max(sizes) -> random_crop not used
# target = max_sample_size -> random_crop used for long
samples = [s for s in samples if s["source"] is not None]
if len(samples) == 0:
return {}
audios = [s["source"] for s in samples]
audio_sizes = [len(s) for s in audios]
if self.pad_audio:
audio_size = min(max(audio_sizes), self.max_sample_size)
else:
audio_size = min(min(audio_sizes), self.max_sample_size)
collated_audios, padding_mask, audio_starts = self.collater_audio(
audios, audio_size
)
targets_by_label = [
[s["label_list"][i] for s in samples] for i in range(self.num_labels)
]
targets_list, lengths_list, ntokens_list = self.collater_label(
targets_by_label, audio_size, audio_starts
)
net_input = {"source": collated_audios, "padding_mask": padding_mask}
batch = {
"id": torch.LongTensor([s["id"] for s in samples]),
"net_input": net_input,
}
if self.single_target:
batch["target_lengths"] = lengths_list[0]
batch["ntokens"] = ntokens_list[0]
batch["target"] = targets_list[0]
else:
batch["target_lengths_list"] = lengths_list
batch["ntokens_list"] = ntokens_list
batch["target_list"] = targets_list
return batch
def collater_audio(self, audios, audio_size):
collated_audios = audios[0].new_zeros(len(audios), audio_size)
padding_mask = (
torch.BoolTensor(collated_audios.shape).fill_(False)
# if self.pad_audio else None
)
audio_starts = [0 for _ in audios]
for i, audio in enumerate(audios):
diff = len(audio) - audio_size
if diff == 0:
collated_audios[i] = audio
elif diff < 0:
assert self.pad_audio
collated_audios[i] = torch.cat([audio, audio.new_full((-diff,), 0.0)])
padding_mask[i, diff:] = True
else:
collated_audios[i], audio_starts[i] = self.crop_to_max_size(
audio, audio_size
)
return collated_audios, padding_mask, audio_starts
def collater_frm_label(self, targets, audio_size, audio_starts, label_rate, pad):
assert label_rate > 0
s2f = label_rate / self.sample_rate
frm_starts = [int(round(s * s2f)) for s in audio_starts]
frm_size = int(round(audio_size * s2f))
if not self.pad_audio:
rem_size = [len(t) - s for t, s in zip(targets, frm_starts)]
frm_size = min(frm_size, *rem_size)
targets = [t[s : s + frm_size] for t, s in zip(targets, frm_starts)]
logger.debug(f"audio_starts={audio_starts}")
logger.debug(f"frame_starts={frm_starts}")
logger.debug(f"frame_size={frm_size}")
lengths = torch.LongTensor([len(t) for t in targets])
ntokens = lengths.sum().item()
targets = data_utils.collate_tokens(targets, pad_idx=pad, left_pad=False)
return targets, lengths, ntokens
def collater_seq_label(self, targets, pad):
lengths = torch.LongTensor([len(t) for t in targets])
ntokens = lengths.sum().item()
targets = data_utils.collate_tokens(targets, pad_idx=pad, left_pad=False)
return targets, lengths, ntokens
def collater_label(self, targets_by_label, audio_size, audio_starts):
targets_list, lengths_list, ntokens_list = [], [], []
itr = zip(targets_by_label, self.label_rates, self.pad_list)
for targets, label_rate, pad in itr:
if label_rate == -1.0:
targets, lengths, ntokens = self.collater_seq_label(targets, pad)
else:
targets, lengths, ntokens = self.collater_frm_label(
targets, audio_size, audio_starts, label_rate, pad
)
targets_list.append(targets)
lengths_list.append(lengths)
ntokens_list.append(ntokens)
return targets_list, lengths_list, ntokens_list
def num_tokens(self, index):
return self.size(index)
def size(self, index):
if self.pad_audio:
return self.sizes[index]
return min(self.sizes[index], self.max_sample_size)
def ordered_indices(self):
if self.shuffle:
order = [np.random.permutation(len(self))]
else:
order = [np.arange(len(self))]
order.append(self.sizes)
return np.lexsort(order)[::-1]
def postprocess(self, wav, cur_sample_rate):
if wav.dim() == 2:
wav = wav.mean(-1)
assert wav.dim() == 1, wav.dim()
if cur_sample_rate != self.sample_rate:
raise Exception(f"sr {cur_sample_rate} != {self.sample_rate}")
if self.normalize:
with torch.no_grad():
wav = F.layer_norm(wav, wav.shape)
return wav
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