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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 numpy as np
import os.path as op
import re
from tabulate import tabulate
from collections import Counter
def comp_purity(p_xy, axis):
max_p = p_xy.max(axis=axis)
marg_p = p_xy.sum(axis=axis)
indv_pur = max_p / marg_p
aggr_pur = max_p.sum()
return indv_pur, aggr_pur
def comp_entropy(p):
return (-p * np.log(p + 1e-8)).sum()
def comp_norm_mutual_info(p_xy):
p_x = p_xy.sum(axis=1, keepdims=True)
p_y = p_xy.sum(axis=0, keepdims=True)
pmi = np.log(p_xy / np.matmul(p_x, p_y) + 1e-8)
mi = (p_xy * pmi).sum()
h_x = comp_entropy(p_x)
h_y = comp_entropy(p_y)
return mi, mi / h_x, mi / h_y, h_x, h_y
def pad(labs, n):
if n == 0:
return np.array(labs)
return np.concatenate([[labs[0]] * n, labs, [labs[-1]] * n])
def comp_avg_seg_dur(labs_list):
n_frms = 0
n_segs = 0
for labs in labs_list:
labs = np.array(labs)
edges = np.zeros(len(labs)).astype(bool)
edges[0] = True
edges[1:] = labs[1:] != labs[:-1]
n_frms += len(edges)
n_segs += edges.astype(int).sum()
return n_frms / n_segs
def comp_joint_prob(uid2refs, uid2hyps):
"""
Args:
pad: padding for spliced-feature derived labels
"""
cnts = Counter()
skipped = []
abs_frmdiff = 0
for uid in uid2refs:
if uid not in uid2hyps:
skipped.append(uid)
continue
refs = uid2refs[uid]
hyps = uid2hyps[uid]
abs_frmdiff += abs(len(refs) - len(hyps))
min_len = min(len(refs), len(hyps))
refs = refs[:min_len]
hyps = hyps[:min_len]
cnts.update(zip(refs, hyps))
tot = sum(cnts.values())
ref_set = sorted({ref for ref, _ in cnts.keys()})
hyp_set = sorted({hyp for _, hyp in cnts.keys()})
ref2pid = dict(zip(ref_set, range(len(ref_set))))
hyp2lid = dict(zip(hyp_set, range(len(hyp_set))))
# print(hyp_set)
p_xy = np.zeros((len(ref2pid), len(hyp2lid)), dtype=float)
for (ref, hyp), cnt in cnts.items():
p_xy[ref2pid[ref], hyp2lid[hyp]] = cnt
p_xy /= p_xy.sum()
return p_xy, ref2pid, hyp2lid, tot, abs_frmdiff, skipped
def read_phn(tsv_path, rm_stress=True):
uid2phns = {}
with open(tsv_path) as f:
for line in f:
uid, phns = line.rstrip().split("\t")
phns = phns.split(",")
if rm_stress:
phns = [re.sub("[0-9]", "", phn) for phn in phns]
uid2phns[uid] = phns
return uid2phns
def read_lab(tsv_path, lab_path, pad_len=0, upsample=1):
"""
tsv is needed to retrieve the uids for the labels
"""
with open(tsv_path) as f:
f.readline()
uids = [op.splitext(op.basename(line.rstrip().split()[0]))[0] for line in f]
with open(lab_path) as f:
labs_list = [pad(line.rstrip().split(), pad_len).repeat(upsample) for line in f]
assert len(uids) == len(labs_list)
return dict(zip(uids, labs_list))
def main_lab_lab(
tsv_dir,
lab_dir,
lab_name,
lab_sets,
ref_dir,
ref_name,
pad_len=0,
upsample=1,
verbose=False,
):
# assume tsv_dir is the same for both the reference and the hypotheses
tsv_dir = lab_dir if tsv_dir is None else tsv_dir
uid2refs = {}
for s in lab_sets:
uid2refs.update(read_lab(f"{tsv_dir}/{s}.tsv", f"{ref_dir}/{s}.{ref_name}"))
uid2hyps = {}
for s in lab_sets:
uid2hyps.update(
read_lab(
f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample
)
)
_main(uid2refs, uid2hyps, verbose)
def main_phn_lab(
tsv_dir,
lab_dir,
lab_name,
lab_sets,
phn_dir,
phn_sets,
pad_len=0,
upsample=1,
verbose=False,
):
uid2refs = {}
for s in phn_sets:
uid2refs.update(read_phn(f"{phn_dir}/{s}.tsv"))
uid2hyps = {}
tsv_dir = lab_dir if tsv_dir is None else tsv_dir
for s in lab_sets:
uid2hyps.update(
read_lab(
f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample
)
)
_main(uid2refs, uid2hyps, verbose)
def _main(uid2refs, uid2hyps, verbose):
(p_xy, ref2pid, hyp2lid, tot, frmdiff, skipped) = comp_joint_prob(
uid2refs, uid2hyps
)
ref_pur_by_hyp, ref_pur = comp_purity(p_xy, axis=0)
hyp_pur_by_ref, hyp_pur = comp_purity(p_xy, axis=1)
(mi, mi_norm_by_ref, mi_norm_by_hyp, h_ref, h_hyp) = comp_norm_mutual_info(p_xy)
outputs = {
"ref pur": ref_pur,
"hyp pur": hyp_pur,
"H(ref)": h_ref,
"H(hyp)": h_hyp,
"MI": mi,
"MI/H(ref)": mi_norm_by_ref,
"ref segL": comp_avg_seg_dur(uid2refs.values()),
"hyp segL": comp_avg_seg_dur(uid2hyps.values()),
"p_xy shape": p_xy.shape,
"frm tot": tot,
"frm diff": frmdiff,
"utt tot": len(uid2refs),
"utt miss": len(skipped),
}
print(tabulate([outputs.values()], outputs.keys(), floatfmt=".4f"))
if __name__ == "__main__":
"""
compute quality of labels with respect to phone or another labels if set
"""
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("tsv_dir")
parser.add_argument("lab_dir")
parser.add_argument("lab_name")
parser.add_argument("--lab_sets", default=["valid"], type=str, nargs="+")
parser.add_argument(
"--phn_dir",
default="/checkpoint/wnhsu/data/librispeech/960h/fa/raw_phn/phone_frame_align_v1",
)
parser.add_argument(
"--phn_sets", default=["dev-clean", "dev-other"], type=str, nargs="+"
)
parser.add_argument("--pad_len", default=0, type=int, help="padding for hypotheses")
parser.add_argument(
"--upsample", default=1, type=int, help="upsample factor for hypotheses"
)
parser.add_argument("--ref_lab_dir", default="")
parser.add_argument("--ref_lab_name", default="")
parser.add_argument("--verbose", action="store_true")
args = parser.parse_args()
if args.ref_lab_dir and args.ref_lab_name:
main_lab_lab(
args.tsv_dir,
args.lab_dir,
args.lab_name,
args.lab_sets,
args.ref_lab_dir,
args.ref_lab_name,
args.pad_len,
args.upsample,
args.verbose,
)
else:
main_phn_lab(
args.tsv_dir,
args.lab_dir,
args.lab_name,
args.lab_sets,
args.phn_dir,
args.phn_sets,
args.pad_len,
args.upsample,
args.verbose,
)
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