#!/usr/bin/env python3
import argparse
import logging
import sys
from itertools import permutations
from pathlib import Path
from typing import Any, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from tqdm import trange
from typeguard import check_argument_types
from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainMSE
from espnet2.enh.loss.criterions.time_domain import SISNRLoss
from espnet2.enh.loss.wrappers.pit_solver import PITSolver
from espnet2.fileio.npy_scp import NpyScpWriter
from espnet2.fileio.sound_scp import SoundScpWriter
from espnet2.tasks.diar import DiarizationTask
from espnet2.tasks.enh_s2t import EnhS2TTask
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.utils import config_argparse
from espnet2.utils.types import (
humanfriendly_parse_size_or_none,
int_or_none,
str2bool,
str2triple_str,
str_or_none,
)
from espnet.utils.cli_utils import get_commandline_args
[docs]class DiarizeSpeech:
"""DiarizeSpeech class
Examples:
>>> import soundfile
>>> diarization = DiarizeSpeech("diar_config.yaml", "diar.pth")
>>> audio, rate = soundfile.read("speech.wav")
>>> diarization(audio)
[(spk_id, start, end), (spk_id2, start2, end2)]
"""
def __init__(
self,
train_config: Union[Path, str] = None,
model_file: Union[Path, str] = None,
segment_size: Optional[float] = None,
hop_size: Optional[float] = None,
normalize_segment_scale: bool = False,
show_progressbar: bool = False,
normalize_output_wav: bool = False,
num_spk: Optional[int] = None,
device: str = "cpu",
dtype: str = "float32",
enh_s2t_task: bool = False,
multiply_diar_result: bool = False,
):
assert check_argument_types()
task = DiarizationTask if not enh_s2t_task else EnhS2TTask
# 1. Build Diar model
diar_model, diar_train_args = task.build_model_from_file(
train_config, model_file, device
)
if enh_s2t_task:
diar_model.inherite_attributes(
inherite_s2t_attrs=[
"decoder",
"attractor",
],
inherite_enh_attrs=[
"mask_module",
],
)
diar_model.to(dtype=getattr(torch, dtype)).eval()
self.device = device
self.dtype = dtype
self.diar_train_args = diar_train_args
self.diar_model = diar_model
# only used when processing long speech, i.e.
# segment_size is not None and hop_size is not None
self.segment_size = segment_size
self.hop_size = hop_size
self.normalize_segment_scale = normalize_segment_scale
self.normalize_output_wav = normalize_output_wav
self.show_progressbar = show_progressbar
# not specifying "num_spk" in inference config file
# will enable speaker number prediction during inference
self.num_spk = num_spk
# multiply_diar_result corresponds to the "Post-processing"
# in https://arxiv.org/pdf/2203.17068.pdf
self.multiply_diar_result = multiply_diar_result
self.enh_s2t_task = enh_s2t_task
self.segmenting_diar = segment_size is not None and not enh_s2t_task
self.segmenting_enh_diar = (
segment_size is not None and hop_size is not None and enh_s2t_task
)
if self.segmenting_diar:
logging.info("Perform segment-wise speaker diarization")
logging.info("Segment length = {} sec".format(segment_size))
elif self.segmenting_enh_diar:
logging.info("Perform segment-wise speech separation and diarization")
logging.info(
"Segment length = {} sec, hop length = {} sec".format(
segment_size, hop_size
)
)
else:
logging.info("Perform direct speaker diarization on the input")
@torch.no_grad()
def __call__(
self, speech: Union[torch.Tensor, np.ndarray], fs: int = 8000
) -> List[torch.Tensor]:
"""Inference
Args:
speech: Input speech data (Batch, Nsamples [, Channels])
fs: sample rate
Returns:
[speaker_info1, speaker_info2, ...]
"""
assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.as_tensor(speech)
assert speech.dim() > 1, speech.size()
batch_size = speech.size(0)
speech = speech.to(getattr(torch, self.dtype))
# lengths: (B,)
lengths = speech.new_full(
[batch_size], dtype=torch.long, fill_value=speech.size(1)
)
# a. To device
speech = to_device(speech, device=self.device)
lengths = to_device(lengths, device=self.device)
if self.segmenting_diar and lengths[0] > self.segment_size * fs:
# Segment-wise speaker diarization
# Note that the segments are processed independently for now
# i.e., no speaker tracing is performed
num_segments = int(np.ceil(speech.size(1) / (self.segment_size * fs)))
t = T = int(self.segment_size * fs)
pad_shape = speech[:, :T].shape
diarized_wavs = []
range_ = trange if self.show_progressbar else range
for i in range_(num_segments):
st = int(i * self.segment_size * fs)
en = st + T
if en >= lengths[0]:
# en - st < T (last segment)
en = lengths[0]
speech_seg = speech.new_zeros(pad_shape)
t = en - st
speech_seg[:, :t] = speech[:, st:en]
else:
t = T
speech_seg = speech[:, st:en] # B x T [x C]
lengths_seg = speech.new_full(
[batch_size], dtype=torch.long, fill_value=T
)
# b. Diarization Forward
encoder_out, encoder_out_lens = self.encode(
speech_seg,
lengths_seg,
)
spk_prediction, _ = self.decode(encoder_out, encoder_out_lens)
# List[torch.Tensor(B, T, num_spks)]
diarized_wavs.append(spk_prediction)
# Determine maximum estimated number of speakers among the segments
max_len = max([x.size(2) for x in diarized_wavs])
# pad tensors in diarized_wavs with "float('-inf')" to have same size
diarized_wavs = [
torch.nn.functional.pad(
x, (0, max_len - x.size(2)), "constant", float("-inf")
)
for x in diarized_wavs
]
spk_prediction = torch.cat(diarized_wavs, dim=1)
waves = None
else:
# b. Diarization Forward
encoder_out, encoder_out_lens = self.encode(speech, lengths)
spk_prediction, num_spk = self.decode(encoder_out, encoder_out_lens)
if self.enh_s2t_task:
# Segment-wise speech separation
# Note that this is done after diarization using the whole sequence
if self.segmenting_enh_diar and lengths[0] > self.segment_size * fs:
overlap_length = int(
np.round(fs * (self.segment_size - self.hop_size))
)
num_segments = int(
np.ceil(
(speech.size(1) - overlap_length) / (self.hop_size * fs)
)
)
t = T = int(self.segment_size * fs)
pad_shape = speech[:, :T].shape
enh_waves = []
range_ = trange if self.show_progressbar else range
for i in range_(num_segments):
st = int(i * self.hop_size * fs)
en = st + T
if en >= lengths[0]:
# en - st < T (last segment)
en = lengths[0]
speech_seg = speech.new_zeros(pad_shape)
t = en - st
speech_seg[:, :t] = speech[:, st:en]
else:
t = T
speech_seg = speech[:, st:en] # B x T [x C]
lengths_seg = speech.new_full(
[batch_size], dtype=torch.long, fill_value=T
)
# Separation Forward
_, _, processed_wav = self.diar_model.encode_diar(
speech_seg, lengths_seg, num_spk
)
if self.normalize_segment_scale:
# normalize the scale to match the input mixture scale
mix_energy = torch.sqrt(
torch.mean(
speech_seg[:, :t].pow(2), dim=1, keepdim=True
)
)
enh_energy = torch.sqrt(
torch.mean(
sum(processed_wav)[:, :t].pow(2),
dim=1,
keepdim=True,
)
)
processed_wav = [
w * (mix_energy / enh_energy) for w in processed_wav
]
# List[torch.Tensor(num_spk, B, T)]
enh_waves.append(torch.stack(processed_wav, dim=0))
# c. Stitch the enhanced segments together
waves = enh_waves[0]
for i in range(1, num_segments):
# permutation between separated streams
# in last and current segments
perm = self.cal_permumation(
waves[:, :, -overlap_length:],
enh_waves[i][:, :, :overlap_length],
criterion="si_snr",
)
# repermute separated streams in current segment
for batch in range(batch_size):
enh_waves[i][:, batch] = enh_waves[i][perm[batch], batch]
if i == num_segments - 1:
enh_waves[i][:, :, t:] = 0
enh_waves_res_i = enh_waves[i][:, :, overlap_length:t]
else:
enh_waves_res_i = enh_waves[i][:, :, overlap_length:]
# overlap-and-add (average over the overlapped part)
waves[:, :, -overlap_length:] = (
waves[:, :, -overlap_length:]
+ enh_waves[i][:, :, :overlap_length]
) / 2
# concatenate the residual parts of the later segment
waves = torch.cat([waves, enh_waves_res_i], dim=2)
# ensure the stitched length is same as input
assert waves.size(2) == speech.size(1), (waves.shape, speech.shape)
waves = torch.unbind(waves, dim=0)
else:
# Separation Forward using the whole signal
_, _, waves = self.diar_model.encode_diar(speech, lengths, num_spk)
# multiply diarization result and separation result
# by calculating the correlation
if self.multiply_diar_result:
spk_prediction, interp_prediction, _ = self.permute_diar(
waves, spk_prediction
)
waves = [
waves[i] * interp_prediction[:, :, i] for i in range(num_spk)
]
if self.normalize_output_wav:
waves = [
(w / abs(w).max(dim=1, keepdim=True)[0] * 0.9).cpu().numpy()
for w in waves
] # list[(batch, sample)]
else:
waves = [w.cpu().numpy() for w in waves]
else:
waves = None
if self.num_spk is not None:
assert spk_prediction.size(2) == self.num_spk, (
spk_prediction.size(2),
self.num_spk,
)
assert spk_prediction.size(0) == batch_size, (
spk_prediction.size(0),
batch_size,
)
spk_prediction = spk_prediction.cpu().numpy()
spk_prediction = 1 / (1 + np.exp(-spk_prediction))
return waves, spk_prediction if self.enh_s2t_task else spk_prediction
[docs] @torch.no_grad()
def cal_permumation(self, ref_wavs, enh_wavs, criterion="si_snr"):
"""Calculate the permutation between seaprated streams in two adjacent segments.
Args:
ref_wavs (List[torch.Tensor]): [(Batch, Nsamples)]
enh_wavs (List[torch.Tensor]): [(Batch, Nsamples)]
criterion (str): one of ("si_snr", "mse", "corr)
Returns:
perm (torch.Tensor): permutation for enh_wavs (Batch, num_spk)
"""
criterion_class = {"si_snr": SISNRLoss, "mse": FrequencyDomainMSE}[criterion]
pit_solver = PITSolver(criterion=criterion_class())
_, _, others = pit_solver(ref_wavs, enh_wavs)
perm = others["perm"]
return perm
[docs] @staticmethod
def from_pretrained(
model_tag: Optional[str] = None,
**kwargs: Optional[Any],
):
"""Build DiarizeSpeech instance from the pretrained model.
Args:
model_tag (Optional[str]): Model tag of the pretrained models.
Currently, the tags of espnet_model_zoo are supported.
Returns:
DiarizeSpeech: DiarizeSpeech instance.
"""
if model_tag is not None:
try:
from espnet_model_zoo.downloader import ModelDownloader
except ImportError:
logging.error(
"`espnet_model_zoo` is not installed. "
"Please install via `pip install -U espnet_model_zoo`."
)
raise
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return DiarizeSpeech(**kwargs)
[docs] def permute_diar(self, waves, spk_prediction):
# Permute the diarization result using the correlation
# between wav and spk_prediction
# FIXME(YushiUeda): batch_size > 1 is not considered
num_spk = len(waves)
permute_list = [np.array(p) for p in permutations(range(num_spk))]
corr_list = []
interp_prediction = F.interpolate(
torch.sigmoid(spk_prediction).transpose(1, 2),
size=waves[0].size(1),
mode="linear",
).transpose(1, 2)
for p in permute_list:
diar_perm = interp_prediction[:, :, p]
corr_perm = [0]
for q in range(num_spk):
corr_perm += np.corrcoef(
torch.squeeze(abs(waves[q])).cpu().numpy(),
torch.squeeze(diar_perm[:, :, q]).cpu().numpy(),
)[0, 1]
corr_list.append(corr_perm)
max_corr, max_idx = torch.max(torch.from_numpy(np.array(corr_list)), dim=0)
return (
spk_prediction[:, :, permute_list[max_idx]],
interp_prediction[:, :, permute_list[max_idx]],
permute_list[max_idx],
)
[docs] def encode(self, speech, lengths):
if self.enh_s2t_task:
encoder_out, encoder_out_lens, _ = self.diar_model.encode_diar(
speech, lengths, self.num_spk
)
else:
bottleneck_feats = bottleneck_feats_lengths = None
encoder_out, encoder_out_lens = self.diar_model.encode(
speech, lengths, bottleneck_feats, bottleneck_feats_lengths
)
return encoder_out, encoder_out_lens
[docs] def decode(self, encoder_out, encoder_out_lens):
# SA-EEND
if self.diar_model.attractor is None:
assert self.num_spk is not None, 'Argument "num_spk" must be specified'
spk_prediction = self.diar_model.decoder(encoder_out, encoder_out_lens)
num_spk = self.num_spk
# EEND-EDA
else:
# if num_spk is specified, use that number
if self.num_spk is not None:
attractor, att_prob = self.diar_model.attractor(
encoder_out,
encoder_out_lens,
to_device(
torch.zeros(
encoder_out.size(0),
self.num_spk + 1,
encoder_out.size(2),
),
device=self.device,
),
)
spk_prediction = torch.bmm(
encoder_out,
attractor[:, : self.num_spk, :].permute(0, 2, 1),
)
num_spk = self.num_spk
# else find the first att_prob[i] < 0
else:
max_num_spk = 15 # upper bound number for estimation
attractor, att_prob = self.diar_model.attractor(
encoder_out,
encoder_out_lens,
to_device(
torch.zeros(
encoder_out.size(0),
max_num_spk + 1,
encoder_out.size(2),
),
device=self.device,
),
)
att_prob = torch.squeeze(att_prob)
for num_spk in range(len(att_prob)):
if att_prob[num_spk].item() < 0:
break
spk_prediction = torch.bmm(
encoder_out, attractor[:, :num_spk, :].permute(0, 2, 1)
)
return spk_prediction, num_spk
[docs]def inference(
output_dir: str,
batch_size: int,
dtype: str,
fs: int,
ngpu: int,
seed: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
train_config: Optional[str],
model_file: Optional[str],
model_tag: Optional[str],
allow_variable_data_keys: bool,
segment_size: Optional[float],
hop_size: Optional[float],
normalize_segment_scale: bool,
show_progressbar: bool,
num_spk: Optional[int],
normalize_output_wav: bool,
multiply_diar_result: bool,
enh_s2t_task: bool,
):
assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build separate_speech
diarize_speech_kwargs = dict(
train_config=train_config,
model_file=model_file,
segment_size=segment_size,
hop_size=hop_size,
normalize_segment_scale=normalize_segment_scale,
show_progressbar=show_progressbar,
normalize_output_wav=normalize_output_wav,
num_spk=num_spk,
device=device,
dtype=dtype,
multiply_diar_result=multiply_diar_result,
enh_s2t_task=enh_s2t_task,
)
diarize_speech = DiarizeSpeech.from_pretrained(
model_tag=model_tag,
**diarize_speech_kwargs,
)
# 3. Build data-iterator
loader = DiarizationTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=DiarizationTask.build_preprocess_fn(
diarize_speech.diar_train_args, False
),
collate_fn=DiarizationTask.build_collate_fn(
diarize_speech.diar_train_args, False
),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 4. Start for-loop
writer = NpyScpWriter(f"{output_dir}/predictions", f"{output_dir}/diarize.scp")
if enh_s2t_task:
wav_writers = []
if diarize_speech.num_spk is not None:
for i in range(diarize_speech.num_spk):
wav_writers.append(
SoundScpWriter(
f"{output_dir}/wavs/{i + 1}", f"{output_dir}/spk{i + 1}.scp"
)
)
else:
for i in range(diarize_speech.diar_model.mask_module.max_num_spk):
wav_writers.append(
SoundScpWriter(
f"{output_dir}/wavs/{i + 1}", f"{output_dir}/spk{i + 1}.scp"
)
)
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
if enh_s2t_task:
waves, spk_predictions = diarize_speech(**batch)
for b in range(batch_size):
writer[keys[b]] = spk_predictions[b]
for spk, w in enumerate(waves):
wav_writers[spk][keys[b]] = fs, w[b]
else:
spk_predictions = diarize_speech(**batch)
for b in range(batch_size):
writer[keys[b]] = spk_predictions[b]
if enh_s2t_task:
for w in wav_writers:
w.close()
writer.close()
[docs]def get_parser():
parser = config_argparse.ArgumentParser(
description="Speaker Diarization inference",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
# Note(kamo): Use '_' instead of '-' as separator.
# '-' is confusing if written in yaml.
parser.add_argument(
"--log_level",
type=lambda x: x.upper(),
default="INFO",
choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
help="The verbose level of logging",
)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument(
"--ngpu",
type=int,
default=0,
help="The number of gpus. 0 indicates CPU mode",
)
parser.add_argument("--seed", type=int, default=0, help="Random seed")
parser.add_argument(
"--dtype",
default="float32",
choices=["float16", "float32", "float64"],
help="Data type",
)
parser.add_argument(
"--fs",
type=humanfriendly_parse_size_or_none,
default=8000,
help="Sampling rate",
)
parser.add_argument(
"--num_workers",
type=int,
default=1,
help="The number of workers used for DataLoader",
)
group = parser.add_argument_group("Input data related")
group.add_argument(
"--data_path_and_name_and_type",
type=str2triple_str,
required=True,
action="append",
)
group.add_argument("--key_file", type=str_or_none)
group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
group = parser.add_argument_group("The model configuration related")
group.add_argument(
"--train_config",
type=str,
help="Diarization training configuration",
)
group.add_argument(
"--model_file",
type=str,
help="Diarization model parameter file",
)
group.add_argument(
"--model_tag",
type=str,
help="Pretrained model tag. If specify this option, train_config and "
"model_file will be overwritten",
)
group = parser.add_argument_group("Data loading related")
group.add_argument(
"--batch_size",
type=int,
default=1,
help="The batch size for inference",
)
group = parser.add_argument_group("Diarize speech related")
group.add_argument(
"--segment_size",
type=float,
default=None,
help="Segment length in seconds for segment-wise speaker diarization",
)
group.add_argument(
"--hop_size",
type=float,
default=None,
help="Hop length in seconds for segment-wise speech enhancement/separation",
)
group.add_argument(
"--show_progressbar",
type=str2bool,
default=False,
help="Whether to show a progress bar when performing segment-wise speaker "
"diarization",
)
group.add_argument(
"--num_spk",
type=int_or_none,
default=None,
help="Predetermined number of speakers for inference",
)
group = parser.add_argument_group("Enh + Diar related")
group.add_argument(
"--enh_s2t_task",
type=str2bool,
default=False,
help="enhancement and diarization joint model",
)
group.add_argument(
"--normalize_segment_scale",
type=str2bool,
default=False,
help="Whether to normalize the energy of the separated streams in each segment",
)
group.add_argument(
"--normalize_output_wav",
type=str2bool,
default=False,
help="Whether to normalize the predicted wav to [-1~1]",
)
group.add_argument(
"--multiply_diar_result",
type=str2bool,
default=False,
help="Whether to multiply diar results to separated waves",
)
return parser
[docs]def main(cmd=None):
print(get_commandline_args(), file=sys.stderr)
parser = get_parser()
args = parser.parse_args(cmd)
kwargs = vars(args)
kwargs.pop("config", None)
inference(**kwargs)
if __name__ == "__main__":
main()