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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import collections
import os
import subprocess
from collections import defaultdict
from contextlib import contextmanager
from datetime import datetime
from pathlib import Path
from typing import Dict, Iterable, List, TextIO, Tuple, Union
import k2
import kaldialign
import torch
import torch.distributed as dist
from torch.utils.tensorboard import SummaryWriter
Pathlike = Union[str, Path]
@contextmanager
def get_executor():
# We'll either return a process pool or a distributed worker pool.
# Note that this has to be a context manager because we might use multiple
# context manager ("with" clauses) inside, and this way everything will
# free up the resources at the right time.
try:
# If this is executed on the CLSP grid, we will try to use the
# Grid Engine to distribute the tasks.
# Other clusters can also benefit from that, provided a
# cluster-specific wrapper.
# (see https://github.com/pzelasko/plz for reference)
#
# The following must be installed:
# $ pip install dask distributed
# $ pip install git+https://github.com/pzelasko/plz
name = subprocess.check_output("hostname -f", shell=True, text=True)
if name.strip().endswith(".clsp.jhu.edu"):
import plz
from distributed import Client
with plz.setup_cluster() as cluster:
cluster.scale(80)
yield Client(cluster)
return
except Exception:
pass
# No need to return anything - compute_and_store_features
# will just instantiate the pool itself.
yield None
def str2bool(v):
"""Used in argparse.ArgumentParser.add_argument to indicate
that a type is a bool type and user can enter
- yes, true, t, y, 1, to represent True
- no, false, f, n, 0, to represent False
See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse # noqa
"""
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
def setup_logger(
log_filename: Pathlike, log_level: str = "info", use_console: bool = True
) -> None:
"""Setup log level.
Args:
log_filename:
The filename to save the log.
log_level:
The log level to use, e.g., "debug", "info", "warning", "error",
"critical"
"""
now = datetime.now()
date_time = now.strftime("%Y-%m-%d-%H-%M-%S")
if dist.is_available() and dist.is_initialized():
world_size = dist.get_world_size()
rank = dist.get_rank()
formatter = f"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] ({rank}/{world_size}) %(message)s" # noqa
log_filename = f"{log_filename}-{date_time}-{rank}"
else:
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
log_filename = f"{log_filename}-{date_time}"
os.makedirs(os.path.dirname(log_filename), exist_ok=True)
level = logging.ERROR
if log_level == "debug":
level = logging.DEBUG
elif log_level == "info":
level = logging.INFO
elif log_level == "warning":
level = logging.WARNING
elif log_level == "critical":
level = logging.CRITICAL
logging.basicConfig(
filename=log_filename, format=formatter, level=level, filemode="w"
)
if use_console:
console = logging.StreamHandler()
console.setLevel(level)
console.setFormatter(logging.Formatter(formatter))
logging.getLogger("").addHandler(console)
def get_env_info():
"""
TODO:
"""
return {
"k2-git-sha1": None,
"k2-version": None,
"lhotse-version": None,
"torch-version": None,
"icefall-sha1": None,
"icefall-version": None,
}
class AttributeDict(dict):
def __getattr__(self, key):
if key in self:
return self[key]
raise AttributeError(f"No such attribute '{key}'")
def __setattr__(self, key, value):
self[key] = value
def __delattr__(self, key):
if key in self:
del self[key]
return
raise AttributeError(f"No such attribute '{key}'")
def encode_supervisions(
supervisions: dict, subsampling_factor: int
) -> Tuple[torch.Tensor, List[str]]:
"""
Encodes Lhotse's ``batch["supervisions"]`` dict into a pair of torch Tensor,
and a list of transcription strings.
The supervision tensor has shape ``(batch_size, 3)``.
Its second dimension contains information about sequence index [0],
start frames [1] and num frames [2].
The batch items might become re-ordered during this operation -- the
returned tensor and list of strings are guaranteed to be consistent with
each other.
"""
supervision_segments = torch.stack(
(
supervisions["sequence_idx"],
supervisions["start_frame"] // subsampling_factor,
supervisions["num_frames"] // subsampling_factor,
),
1,
).to(torch.int32)
indices = torch.argsort(supervision_segments[:, 2], descending=True)
supervision_segments = supervision_segments[indices]
texts = supervisions["text"]
texts = [texts[idx] for idx in indices]
return supervision_segments, texts
def get_texts(
best_paths: k2.Fsa, return_ragged: bool = False
) -> Union[List[List[int]], k2.RaggedTensor]:
"""Extract the texts (as word IDs) from the best-path FSAs.
Args:
best_paths:
A k2.Fsa with best_paths.arcs.num_axes() == 3, i.e.
containing multiple FSAs, which is expected to be the result
of k2.shortest_path (otherwise the returned values won't
be meaningful).
return_ragged:
True to return a ragged tensor with two axes [utt][word_id].
False to return a list-of-list word IDs.
Returns:
Returns a list of lists of int, containing the label sequences we
decoded.
"""
if isinstance(best_paths.aux_labels, k2.RaggedTensor):
# remove 0's and -1's.
aux_labels = best_paths.aux_labels.remove_values_leq(0)
# TODO: change arcs.shape() to arcs.shape
aux_shape = best_paths.arcs.shape().compose(aux_labels.shape)
# remove the states and arcs axes.
aux_shape = aux_shape.remove_axis(1)
aux_shape = aux_shape.remove_axis(1)
aux_labels = k2.RaggedTensor(aux_shape, aux_labels.values)
else:
# remove axis corresponding to states.
aux_shape = best_paths.arcs.shape().remove_axis(1)
aux_labels = k2.RaggedTensor(aux_shape, best_paths.aux_labels)
# remove 0's and -1's.
aux_labels = aux_labels.remove_values_leq(0)
assert aux_labels.num_axes == 2
if return_ragged:
return aux_labels
else:
return aux_labels.tolist()
def store_transcripts(
filename: Pathlike, texts: Iterable[Tuple[str, str]]
) -> None:
"""Save predicted results and reference transcripts to a file.
Args:
filename:
File to save the results to.
texts:
An iterable of tuples. The first element is the reference transcript
while the second element is the predicted result.
Returns:
Return None.
"""
with open(filename, "w") as f:
for ref, hyp in texts:
print(f"ref={ref}", file=f)
print(f"hyp={hyp}", file=f)
def write_error_stats(
f: TextIO,
test_set_name: str,
results: List[Tuple[str, str]],
enable_log: bool = True,
) -> float:
"""Write statistics based on predicted results and reference transcripts.
It will write the following to the given file:
- WER
- number of insertions, deletions, substitutions, corrects and total
reference words. For example::
Errors: 23 insertions, 57 deletions, 212 substitutions, over 2606
reference words (2337 correct)
- The difference between the reference transcript and predicted results.
An instance is given below::
THE ASSOCIATION OF (EDISON->ADDISON) ILLUMINATING COMPANIES
The above example shows that the reference word is `EDISON`, but it is
predicted to `ADDISON` (a substitution error).
Another example is::
FOR THE FIRST DAY (SIR->*) I THINK
The reference word `SIR` is missing in the predicted
results (a deletion error).
results:
An iterable of tuples. The first element is the reference transcript
while the second element is the predicted result.
enable_log:
If True, also print detailed WER to the console.
Otherwise, it is written only to the given file.
Returns:
Return None.
"""
subs: Dict[Tuple[str, str], int] = defaultdict(int)
ins: Dict[str, int] = defaultdict(int)
dels: Dict[str, int] = defaultdict(int)
# `words` stores counts per word, as follows:
# corr, ref_sub, hyp_sub, ins, dels
words: Dict[str, List[int]] = defaultdict(lambda: [0, 0, 0, 0, 0])
num_corr = 0
ERR = "*"
for ref, hyp in results:
ali = kaldialign.align(ref, hyp, ERR)
for ref_word, hyp_word in ali:
if ref_word == ERR:
ins[hyp_word] += 1
words[hyp_word][3] += 1
elif hyp_word == ERR:
dels[ref_word] += 1
words[ref_word][4] += 1
elif hyp_word != ref_word:
subs[(ref_word, hyp_word)] += 1
words[ref_word][1] += 1
words[hyp_word][2] += 1
else:
words[ref_word][0] += 1
num_corr += 1
ref_len = sum([len(r) for r, _ in results])
sub_errs = sum(subs.values())
ins_errs = sum(ins.values())
del_errs = sum(dels.values())
tot_errs = sub_errs + ins_errs + del_errs
tot_err_rate = "%.2f" % (100.0 * tot_errs / ref_len)
if enable_log:
logging.info(
f"[{test_set_name}] %WER {tot_errs / ref_len:.2%} "
f"[{tot_errs} / {ref_len}, {ins_errs} ins, "
f"{del_errs} del, {sub_errs} sub ]"
)
print(f"%WER = {tot_err_rate}", file=f)
print(
f"Errors: {ins_errs} insertions, {del_errs} deletions, "
f"{sub_errs} substitutions, over {ref_len} reference "
f"words ({num_corr} correct)",
file=f,
)
print(
"Search below for sections starting with PER-UTT DETAILS:, "
"SUBSTITUTIONS:, DELETIONS:, INSERTIONS:, PER-WORD STATS:",
file=f,
)
print("", file=f)
print("PER-UTT DETAILS: corr or (ref->hyp) ", file=f)
for ref, hyp in results:
ali = kaldialign.align(ref, hyp, ERR)
combine_successive_errors = True
if combine_successive_errors:
ali = [[[x], [y]] for x, y in ali]
for i in range(len(ali) - 1):
if ali[i][0] != ali[i][1] and ali[i + 1][0] != ali[i + 1][1]:
ali[i + 1][0] = ali[i][0] + ali[i + 1][0]
ali[i + 1][1] = ali[i][1] + ali[i + 1][1]
ali[i] = [[], []]
ali = [
[
list(filter(lambda a: a != ERR, x)),
list(filter(lambda a: a != ERR, y)),
]
for x, y in ali
]
ali = list(filter(lambda x: x != [[], []], ali))
ali = [
[
ERR if x == [] else " ".join(x),
ERR if y == [] else " ".join(y),
]
for x, y in ali
]
print(
" ".join(
(
ref_word
if ref_word == hyp_word
else f"({ref_word}->{hyp_word})"
for ref_word, hyp_word in ali
)
),
file=f,
)
print("", file=f)
print("SUBSTITUTIONS: count ref -> hyp", file=f)
for count, (ref, hyp) in sorted(
[(v, k) for k, v in subs.items()], reverse=True
):
print(f"{count} {ref} -> {hyp}", file=f)
print("", file=f)
print("DELETIONS: count ref", file=f)
for count, ref in sorted([(v, k) for k, v in dels.items()], reverse=True):
print(f"{count} {ref}", file=f)
print("", file=f)
print("INSERTIONS: count hyp", file=f)
for count, hyp in sorted([(v, k) for k, v in ins.items()], reverse=True):
print(f"{count} {hyp}", file=f)
print("", file=f)
print(
"PER-WORD STATS: word corr tot_errs count_in_ref count_in_hyp", file=f
)
for _, word, counts in sorted(
[(sum(v[1:]), k, v) for k, v in words.items()], reverse=True
):
(corr, ref_sub, hyp_sub, ins, dels) = counts
tot_errs = ref_sub + hyp_sub + ins + dels
ref_count = corr + ref_sub + dels
hyp_count = corr + hyp_sub + ins
print(f"{word} {corr} {tot_errs} {ref_count} {hyp_count}", file=f)
return float(tot_err_rate)
class LossRecord(collections.defaultdict):
def __init__(self):
# Passing the type 'int' to the base-class constructor
# makes undefined items default to int() which is zero.
super(LossRecord, self).__init__(int)
def __add__(self, other: 'LossRecord') -> 'LossRecord':
ans = LossRecord()
for k, v in self.items():
ans[k] = v
for k, v in other.items():
ans[k] = ans[k] + v
return ans
def __mul__(self, alpha: float) -> 'LossRecord':
ans = LossRecord()
for k, v in self.items():
ans[k] = v * alpha
return ans
def __str__(self) -> str:
ans = ''
for k, v in self.norm_items():
norm_value = '%.4g' % v
ans += (str(k) + '=' + str(norm_value) + ', ')
frames = str(self['frames'])
ans += 'over ' + frames + ' frames.'
return ans
def norm_items(self) -> List[Tuple[str, float]]:
"""
Returns a list of pairs, like:
[('ctc_loss', 0.1), ('att_loss', 0.07)]
"""
num_frames = self['frames'] if 'frames' in self else 1
ans = []
for k, v in self.items():
if k != 'frames':
norm_value = float(v) / num_frames
ans.append((k, norm_value))
return ans
def reduce(self, device):
"""
Reduce using torch.distributed, which I believe ensures that
all processes get the total.
"""
keys = sorted(self.keys())
s = torch.tensor([float(self[k]) for k in keys],
device=device)
dist.all_reduce(s, op=dist.ReduceOp.SUM)
for k, v in zip(keys, s.cpu().tolist()):
self[k] = v
def write_summary(
self,
tb_writer: SummaryWriter,
prefix: str,
batch_idx: int,
) -> None:
"""Add logging information to a TensorBoard writer.
Args:
tb_writer: a TensorBoard writer
prefix: a prefix for the name of the loss, e.g. "train/valid_",
or "train/current_"
batch_idx: The current batch index, used as the x-axis of the plot.
"""
for k, v in self.norm_items():
tb_writer.add_scalar(prefix + k, v, batch_idx)