def scoring(
output_dir: str,
dtype: str,
log_level: Union[int, str],
key_file: str,
ref_scp: List[str],
inf_scp: List[str],
ref_channel: int,
metrics: List[str],
frame_size: int = 512,
frame_hop: int = 256,
):
assert check_argument_types()
for metric in metrics:
assert metric in (
"STOI",
"ESTOI",
"SNR",
"SI_SNR",
"SDR",
"SAR",
"SIR",
"framewise-SNR",
), metric
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
assert len(ref_scp) == len(inf_scp), ref_scp
num_spk = len(ref_scp)
keys = [
line.rstrip().split(maxsplit=1)[0]
for line in open(key_file, encoding="utf-8")
]
ref_readers = [
SoundScpReader(f, dtype=dtype, normalize=True) for f in ref_scp
]
inf_readers = [
SoundScpReader(f, dtype=dtype, normalize=True) for f in inf_scp
]
# get sample rate
fs, _ = ref_readers[0][keys[0]]
# check keys
for inf_reader, ref_reader in zip(inf_readers, ref_readers):
assert inf_reader.keys() == ref_reader.keys()
stft = STFTEncoder(n_fft=frame_size, hop_length=frame_hop)
do_bss_eval = "SDR" in metrics or "SAR" in metrics or "SIR" in metrics
with DatadirWriter(output_dir) as writer:
for key in keys:
ref_audios = [ref_reader[key][1] for ref_reader in ref_readers]
inf_audios = [inf_reader[key][1] for inf_reader in inf_readers]
ref = np.array(ref_audios)
inf = np.array(inf_audios)
if ref.ndim > inf.ndim:
# multi-channel reference and single-channel output
ref = ref[..., ref_channel]
assert ref.shape == inf.shape, (ref.shape, inf.shape)
elif ref.ndim < inf.ndim:
# single-channel reference and multi-channel output
raise ValueError("Reference must be multi-channel when the "
"network output is multi-channel.")
elif ref.ndim == inf.ndim == 3:
# multi-channel reference and output
ref = ref[..., ref_channel]
inf = inf[..., ref_channel]
if do_bss_eval or num_spk > 1:
sdr, sir, sar, perm = bss_eval_sources(
ref, inf, compute_permutation=True)
else:
perm = [0]
ilens = torch.LongTensor([ref.shape[1]])
# (num_spk, T, F)
ref_spec, flens = stft(torch.from_numpy(ref), ilens)
inf_spec, _ = stft(torch.from_numpy(inf), ilens)
for i in range(num_spk):
p = int(perm[i])
for metric in metrics:
name = f"{metric}_spk{i + 1}"
if metric == "STOI":
writer[name][key] = str(
stoi(ref[i], inf[p], fs_sig=fs, extended=False))
elif metric == "ESTOI":
writer[name][key] = str(
stoi(ref[i], inf[p], fs_sig=fs, extended=True))
elif metric == "SNR":
si_snr_score = -float(
ESPnetEnhancementModel.snr_loss(
torch.from_numpy(ref[i][None, ...]),
torch.from_numpy(inf[p][None, ...]),
))
writer[name][key] = str(si_snr_score)
elif metric == "SI_SNR":
si_snr_score = -float(
ESPnetEnhancementModel.si_snr_loss(
torch.from_numpy(ref[i][None, ...]),
torch.from_numpy(inf[p][None, ...]),
))
writer[name][key] = str(si_snr_score)
elif metric == "SDR":
writer[name][key] = str(sdr[i])
elif metric == "SAR":
writer[name][key] = str(sar[i])
elif metric == "SIR":
writer[name][key] = str(sir[i])
elif metric == "framewise-SNR":
framewise_snr = -ESPnetEnhancementModel.snr_loss(
ref_spec[i].abs(), inf_spec[i].abs())
writer[name][key] = " ".join(
map(str, framewise_snr.tolist()))
else:
raise ValueError("Unsupported metric: %s" % metric)
# save permutation assigned script file
writer[f"wav_spk{i + 1}"][key] = inf_readers[
perm[i]].data[key]
def scoring(
output_dir: str,
dtype: str,
log_level: Union[int, str],
key_file: str,
ref_scp: List[str],
inf_scp: List[str],
ref_channel: int,
):
assert check_argument_types()
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
assert len(ref_scp) == len(inf_scp), ref_scp
num_spk = len(ref_scp)
keys = [
line.rstrip().split(maxsplit=1)[0]
for line in open(key_file, encoding="utf-8")
]
ref_readers = [
SoundScpReader(f, dtype=dtype, normalize=True) for f in ref_scp
]
inf_readers = [
SoundScpReader(f, dtype=dtype, normalize=True) for f in inf_scp
]
# get sample rate
sample_rate, _ = ref_readers[0][keys[0]]
# check keys
for inf_reader, ref_reader in zip(inf_readers, ref_readers):
assert inf_reader.keys() == ref_reader.keys()
with DatadirWriter(output_dir) as writer:
for key in keys:
ref_audios = [ref_reader[key][1] for ref_reader in ref_readers]
inf_audios = [inf_reader[key][1] for inf_reader in inf_readers]
ref = np.array(ref_audios)
inf = np.array(inf_audios)
if ref.ndim > inf.ndim:
# multi-channel reference and single-channel output
ref = ref[..., ref_channel]
assert ref.shape == inf.shape, (ref.shape, inf.shape)
elif ref.ndim < inf.ndim:
# single-channel reference and multi-channel output
raise ValueError("Reference must be multi-channel when the \
network output is multi-channel.")
elif ref.ndim == inf.ndim == 3:
# multi-channel reference and output
ref = ref[..., ref_channel]
inf = inf[..., ref_channel]
sdr, sir, sar, perm = bss_eval_sources(ref,
inf,
compute_permutation=True)
for i in range(num_spk):
stoi_score = stoi(ref[i],
inf[int(perm[i])],
fs_sig=sample_rate)
si_snr_score = -float(
ESPnetEnhancementModel.si_snr_loss(
torch.from_numpy(ref[i][None, ...]),
torch.from_numpy(inf[int(perm[i])][None, ...]),
))
writer[f"STOI_spk{i + 1}"][key] = str(stoi_score)
writer[f"SI_SNR_spk{i + 1}"][key] = str(si_snr_score)
writer[f"SDR_spk{i + 1}"][key] = str(sdr[i])
writer[f"SAR_spk{i + 1}"][key] = str(sar[i])
writer[f"SIR_spk{i + 1}"][key] = str(sir[i])
# save permutation assigned script file
writer[f"wav_spk{i + 1}"][key] = inf_readers[perm[i]].data[key]
