From a7ab8bd688d21e45f194dd9d87cb060d2cbc21bd Mon Sep 17 00:00:00 2001
From: Lizerui9926 <110582652+Lizerui9926@users.noreply.github.com>
Date: 星期二, 14 三月 2023 16:45:30 +0800
Subject: [PATCH] Merge pull request #230 from alibaba-damo-academy/dev_wjm
---
funasr/modules/eend_ola/encoder.py | 16
funasr/tasks/diar.py | 329 +++++++++++++++++
funasr/bin/eend_ola_inference.py | 413 ++++++++++++++++++++++
funasr/models/frontend/eend_ola_feature.py | 51 ++
funasr/models/e2e_diar_eend_ola.py | 242 +++++++++++++
5 files changed, 1,031 insertions(+), 20 deletions(-)
diff --git a/funasr/bin/eend_ola_inference.py b/funasr/bin/eend_ola_inference.py
new file mode 100755
index 0000000..d65895f
--- /dev/null
+++ b/funasr/bin/eend_ola_inference.py
@@ -0,0 +1,413 @@
+#!/usr/bin/env python3
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+import argparse
+import logging
+import os
+import sys
+from pathlib import Path
+from typing import Any
+from typing import List
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+
+import numpy as np
+import torch
+from typeguard import check_argument_types
+
+from funasr.models.frontend.wav_frontend import WavFrontendMel23
+from funasr.tasks.diar import EENDOLADiarTask
+from funasr.torch_utils.device_funcs import to_device
+from funasr.utils import config_argparse
+from funasr.utils.cli_utils import get_commandline_args
+from funasr.utils.types import str2bool
+from funasr.utils.types import str2triple_str
+from funasr.utils.types import str_or_none
+
+
+class Speech2Diarization:
+ """Speech2Diarlization class
+
+ Examples:
+ >>> import soundfile
+ >>> import numpy as np
+ >>> speech2diar = Speech2Diarization("diar_sond_config.yml", "diar_sond.pth")
+ >>> profile = np.load("profiles.npy")
+ >>> audio, rate = soundfile.read("speech.wav")
+ >>> speech2diar(audio, profile)
+ {"spk1": [(int, int), ...], ...}
+
+ """
+
+ def __init__(
+ self,
+ diar_train_config: Union[Path, str] = None,
+ diar_model_file: Union[Path, str] = None,
+ device: str = "cpu",
+ dtype: str = "float32",
+ ):
+ assert check_argument_types()
+
+ # 1. Build Diarization model
+ diar_model, diar_train_args = EENDOLADiarTask.build_model_from_file(
+ config_file=diar_train_config,
+ model_file=diar_model_file,
+ device=device
+ )
+ frontend = None
+ if diar_train_args.frontend is not None and diar_train_args.frontend_conf is not None:
+ frontend = WavFrontendMel23(**diar_train_args.frontend_conf)
+
+ # set up seed for eda
+ np.random.seed(diar_train_args.seed)
+ torch.manual_seed(diar_train_args.seed)
+ torch.cuda.manual_seed(diar_train_args.seed)
+ os.environ['PYTORCH_SEED'] = str(diar_train_args.seed)
+ logging.info("diar_model: {}".format(diar_model))
+ logging.info("diar_train_args: {}".format(diar_train_args))
+ diar_model.to(dtype=getattr(torch, dtype)).eval()
+
+ self.diar_model = diar_model
+ self.diar_train_args = diar_train_args
+ self.device = device
+ self.dtype = dtype
+ self.frontend = frontend
+
+ @torch.no_grad()
+ def __call__(
+ self,
+ speech: Union[torch.Tensor, np.ndarray],
+ speech_lengths: Union[torch.Tensor, np.ndarray] = None
+ ):
+ """Inference
+
+ Args:
+ speech: Input speech data
+ Returns:
+ diarization results
+
+ """
+ assert check_argument_types()
+ # Input as audio signal
+ if isinstance(speech, np.ndarray):
+ speech = torch.tensor(speech)
+
+ if self.frontend is not None:
+ feats, feats_len = self.frontend.forward(speech, speech_lengths)
+ feats = to_device(feats, device=self.device)
+ feats_len = feats_len.int()
+ self.diar_model.frontend = None
+ else:
+ feats = speech
+ feats_len = speech_lengths
+ batch = {"speech": feats, "speech_lengths": feats_len}
+ batch = to_device(batch, device=self.device)
+ results = self.diar_model.estimate_sequential(**batch)
+
+ return results
+
+ @staticmethod
+ def from_pretrained(
+ model_tag: Optional[str] = None,
+ **kwargs: Optional[Any],
+ ):
+ """Build Speech2Diarization 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:
+ Speech2Diarization: Speech2Diarization 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 Speech2Diarization(**kwargs)
+
+
+def inference_modelscope(
+ diar_train_config: str,
+ diar_model_file: str,
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 0,
+ num_workers: int = 0,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ model_tag: Optional[str] = None,
+ allow_variable_data_keys: bool = True,
+ streaming: bool = False,
+ param_dict: Optional[dict] = None,
+ **kwargs,
+):
+ 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",
+ )
+ logging.info("param_dict: {}".format(param_dict))
+
+ if ngpu >= 1 and torch.cuda.is_available():
+ device = "cuda"
+ else:
+ device = "cpu"
+
+ # 1. Build speech2diar
+ speech2diar_kwargs = dict(
+ diar_train_config=diar_train_config,
+ diar_model_file=diar_model_file,
+ device=device,
+ dtype=dtype,
+ streaming=streaming,
+ )
+ logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs))
+ speech2diar = Speech2Diarization.from_pretrained(
+ model_tag=model_tag,
+ **speech2diar_kwargs,
+ )
+ speech2diar.diar_model.eval()
+
+ def output_results_str(results: dict, uttid: str):
+ rst = []
+ mid = uttid.rsplit("-", 1)[0]
+ for key in results:
+ results[key] = [(x[0] / 100, x[1] / 100) for x in results[key]]
+ template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>"
+ for spk, segs in results.items():
+ rst.extend([template.format(mid, st, ed, spk) for st, ed in segs])
+
+ return "\n".join(rst)
+
+ def _forward(
+ data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
+ raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = None,
+ output_dir_v2: Optional[str] = None,
+ param_dict: Optional[dict] = None,
+ ):
+ # 2. Build data-iterator
+ if data_path_and_name_and_type is None and raw_inputs is not None:
+ if isinstance(raw_inputs, torch.Tensor):
+ raw_inputs = raw_inputs.numpy()
+ data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
+ loader = EENDOLADiarTask.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=EENDOLADiarTask.build_preprocess_fn(speech2diar.diar_train_args, False),
+ collate_fn=EENDOLADiarTask.build_collate_fn(speech2diar.diar_train_args, False),
+ allow_variable_data_keys=allow_variable_data_keys,
+ inference=True,
+ )
+
+ # 3. Start for-loop
+ output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ if output_path is not None:
+ os.makedirs(output_path, exist_ok=True)
+ output_writer = open("{}/result.txt".format(output_path), "w")
+ result_list = []
+ 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[0] for k, v in batch.items() if not k.endswith("_lengths")}
+
+ results = speech2diar(**batch)
+ # Only supporting batch_size==1
+ key, value = keys[0], output_results_str(results, keys[0])
+ item = {"key": key, "value": value}
+ result_list.append(item)
+ if output_path is not None:
+ output_writer.write(value)
+ output_writer.flush()
+
+ if output_path is not None:
+ output_writer.close()
+
+ return result_list
+
+ return _forward
+
+
+def inference(
+ data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
+ diar_train_config: Optional[str],
+ diar_model_file: Optional[str],
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 0,
+ seed: int = 0,
+ num_workers: int = 1,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ model_tag: Optional[str] = None,
+ allow_variable_data_keys: bool = True,
+ streaming: bool = False,
+ smooth_size: int = 83,
+ dur_threshold: int = 10,
+ out_format: str = "vad",
+ **kwargs,
+):
+ inference_pipeline = inference_modelscope(
+ diar_train_config=diar_train_config,
+ diar_model_file=diar_model_file,
+ output_dir=output_dir,
+ batch_size=batch_size,
+ dtype=dtype,
+ ngpu=ngpu,
+ seed=seed,
+ num_workers=num_workers,
+ log_level=log_level,
+ key_file=key_file,
+ model_tag=model_tag,
+ allow_variable_data_keys=allow_variable_data_keys,
+ streaming=streaming,
+ smooth_size=smooth_size,
+ dur_threshold=dur_threshold,
+ out_format=out_format,
+ **kwargs,
+ )
+
+ return inference_pipeline(data_path_and_name_and_type, raw_inputs=None)
+
+
+def get_parser():
+ parser = config_argparse.ArgumentParser(
+ description="Speaker verification/x-vector extraction",
+ 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=False)
+ parser.add_argument(
+ "--ngpu",
+ type=int,
+ default=0,
+ help="The number of gpus. 0 indicates CPU mode",
+ )
+ parser.add_argument(
+ "--gpuid_list",
+ type=str,
+ default="",
+ help="The visible gpus",
+ )
+ 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(
+ "--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=False,
+ 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(
+ "--diar_train_config",
+ type=str,
+ help="diarization training configuration",
+ )
+ group.add_argument(
+ "--diar_model_file",
+ type=str,
+ help="diarization model parameter file",
+ )
+ group.add_argument(
+ "--dur_threshold",
+ type=int,
+ default=10,
+ help="The threshold for short segments in number frames"
+ )
+ parser.add_argument(
+ "--smooth_size",
+ type=int,
+ default=83,
+ help="The smoothing window length in number frames"
+ )
+ group.add_argument(
+ "--model_tag",
+ type=str,
+ help="Pretrained model tag. If specify this option, *_train_config and "
+ "*_file will be overwritten",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=1,
+ help="The batch size for inference",
+ )
+ parser.add_argument("--streaming", type=str2bool, default=False)
+
+ return parser
+
+
+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)
+ logging.info("args: {}".format(kwargs))
+ if args.output_dir is None:
+ jobid, n_gpu = 1, 1
+ gpuid = args.gpuid_list.split(",")[jobid - 1]
+ else:
+ jobid = int(args.output_dir.split(".")[-1])
+ n_gpu = len(args.gpuid_list.split(","))
+ gpuid = args.gpuid_list.split(",")[(jobid - 1) % n_gpu]
+ os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+ os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
+ results_list = inference(**kwargs)
+ for results in results_list:
+ print("{} {}".format(results["key"], results["value"]))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/funasr/models/e2e_diar_eend_ola.py b/funasr/models/e2e_diar_eend_ola.py
new file mode 100644
index 0000000..f589269
--- /dev/null
+++ b/funasr/models/e2e_diar_eend_ola.py
@@ -0,0 +1,242 @@
+# Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved.
+# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+from typing import Dict
+from typing import Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+from typeguard import check_argument_types
+
+from funasr.models.frontend.wav_frontend import WavFrontendMel23
+from funasr.modules.eend_ola.encoder import EENDOLATransformerEncoder
+from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
+from funasr.modules.eend_ola.utils.power import generate_mapping_dict
+from funasr.torch_utils.device_funcs import force_gatherable
+from funasr.train.abs_espnet_model import AbsESPnetModel
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+ pass
+else:
+ # Nothing to do if torch<1.6.0
+ @contextmanager
+ def autocast(enabled=True):
+ yield
+
+
+def pad_attractor(att, max_n_speakers):
+ C, D = att.shape
+ if C < max_n_speakers:
+ att = torch.cat([att, torch.zeros(max_n_speakers - C, D).to(torch.float32).to(att.device)], dim=0)
+ return att
+
+
+class DiarEENDOLAModel(AbsESPnetModel):
+ """EEND-OLA diarization model"""
+
+ def __init__(
+ self,
+ frontend: WavFrontendMel23,
+ encoder: EENDOLATransformerEncoder,
+ encoder_decoder_attractor: EncoderDecoderAttractor,
+ n_units: int = 256,
+ max_n_speaker: int = 8,
+ attractor_loss_weight: float = 1.0,
+ mapping_dict=None,
+ **kwargs,
+ ):
+ assert check_argument_types()
+
+ super().__init__()
+ self.frontend = frontend
+ self.encoder = encoder
+ self.encoder_decoder_attractor = encoder_decoder_attractor
+ self.attractor_loss_weight = attractor_loss_weight
+ self.max_n_speaker = max_n_speaker
+ if mapping_dict is None:
+ mapping_dict = generate_mapping_dict(max_speaker_num=self.max_n_speaker)
+ self.mapping_dict = mapping_dict
+ # PostNet
+ self.PostNet = nn.LSTM(self.max_n_speaker, n_units, 1, batch_first=True)
+ self.output_layer = nn.Linear(n_units, mapping_dict['oov'] + 1)
+
+ def forward_encoder(self, xs, ilens):
+ xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1)
+ pad_shape = xs.shape
+ xs_mask = [torch.ones(ilen).to(xs.device) for ilen in ilens]
+ xs_mask = torch.nn.utils.rnn.pad_sequence(xs_mask, batch_first=True, padding_value=0).unsqueeze(-2)
+ emb = self.encoder(xs, xs_mask)
+ emb = torch.split(emb.view(pad_shape[0], pad_shape[1], -1), 1, dim=0)
+ emb = [e[0][:ilen] for e, ilen in zip(emb, ilens)]
+ return emb
+
+ def forward_post_net(self, logits, ilens):
+ maxlen = torch.max(ilens).to(torch.int).item()
+ logits = nn.utils.rnn.pad_sequence(logits, batch_first=True, padding_value=-1)
+ logits = nn.utils.rnn.pack_padded_sequence(logits, ilens, batch_first=True, enforce_sorted=False)
+ outputs, (_, _) = self.PostNet(logits)
+ outputs = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True, padding_value=-1, total_length=maxlen)[0]
+ outputs = [output[:ilens[i].to(torch.int).item()] for i, output in enumerate(outputs)]
+ outputs = [self.output_layer(output) for output in outputs]
+ return outputs
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ """Frontend + Encoder + Decoder + Calc loss
+
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ text: (Batch, Length)
+ text_lengths: (Batch,)
+ """
+ assert text_lengths.dim() == 1, text_lengths.shape
+ # Check that batch_size is unified
+ assert (
+ speech.shape[0]
+ == speech_lengths.shape[0]
+ == text.shape[0]
+ == text_lengths.shape[0]
+ ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
+ batch_size = speech.shape[0]
+
+ # for data-parallel
+ text = text[:, : text_lengths.max()]
+
+ # 1. Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+
+ loss_att, acc_att, cer_att, wer_att = None, None, None, None
+ loss_ctc, cer_ctc = None, None
+ stats = dict()
+
+ # 1. CTC branch
+ if self.ctc_weight != 0.0:
+ loss_ctc, cer_ctc = self._calc_ctc_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # Collect CTC branch stats
+ stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
+ stats["cer_ctc"] = cer_ctc
+
+ # Intermediate CTC (optional)
+ loss_interctc = 0.0
+ if self.interctc_weight != 0.0 and intermediate_outs is not None:
+ for layer_idx, intermediate_out in intermediate_outs:
+ # we assume intermediate_out has the same length & padding
+ # as those of encoder_out
+ loss_ic, cer_ic = self._calc_ctc_loss(
+ intermediate_out, encoder_out_lens, text, text_lengths
+ )
+ loss_interctc = loss_interctc + loss_ic
+
+ # Collect Intermedaite CTC stats
+ stats["loss_interctc_layer{}".format(layer_idx)] = (
+ loss_ic.detach() if loss_ic is not None else None
+ )
+ stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
+
+ loss_interctc = loss_interctc / len(intermediate_outs)
+
+ # calculate whole encoder loss
+ loss_ctc = (
+ 1 - self.interctc_weight
+ ) * loss_ctc + self.interctc_weight * loss_interctc
+
+ # 2b. Attention decoder branch
+ if self.ctc_weight != 1.0:
+ loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # 3. CTC-Att loss definition
+ if self.ctc_weight == 0.0:
+ loss = loss_att
+ elif self.ctc_weight == 1.0:
+ loss = loss_ctc
+ else:
+ loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
+
+ # Collect Attn branch stats
+ stats["loss_att"] = loss_att.detach() if loss_att is not None else None
+ stats["acc"] = acc_att
+ stats["cer"] = cer_att
+ stats["wer"] = wer_att
+
+ # Collect total loss stats
+ stats["loss"] = torch.clone(loss.detach())
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+ return loss, stats, weight
+
+ def estimate_sequential(self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ n_speakers: int = None,
+ shuffle: bool = True,
+ threshold: float = 0.5,
+ **kwargs):
+ if self.frontend is not None:
+ speech = self.frontend(speech)
+ speech = [s[:s_len] for s, s_len in zip(speech, speech_lengths)]
+ emb = self.forward_encoder(speech, speech_lengths)
+ if shuffle:
+ orders = [np.arange(e.shape[0]) for e in emb]
+ for order in orders:
+ np.random.shuffle(order)
+ attractors, probs = self.encoder_decoder_attractor.estimate(
+ [e[torch.from_numpy(order).to(torch.long).to(speech[0].device)] for e, order in zip(emb, orders)])
+ else:
+ attractors, probs = self.encoder_decoder_attractor.estimate(emb)
+ attractors_active = []
+ for p, att, e in zip(probs, attractors, emb):
+ if n_speakers and n_speakers >= 0:
+ att = att[:n_speakers, ]
+ attractors_active.append(att)
+ elif threshold is not None:
+ silence = torch.nonzero(p < threshold)[0]
+ n_spk = silence[0] if silence.size else None
+ att = att[:n_spk, ]
+ attractors_active.append(att)
+ else:
+ NotImplementedError('n_speakers or threshold has to be given.')
+ raw_n_speakers = [att.shape[0] for att in attractors_active]
+ attractors = [
+ pad_attractor(att, self.max_n_speaker) if att.shape[0] <= self.max_n_speaker else att[:self.max_n_speaker]
+ for att in attractors_active]
+ ys = [torch.matmul(e, att.permute(1, 0)) for e, att in zip(emb, attractors)]
+ logits = self.forward_post_net(ys, speech_lengths)
+ ys = [self.recover_y_from_powerlabel(logit, raw_n_speaker) for logit, raw_n_speaker in
+ zip(logits, raw_n_speakers)]
+
+ return ys, emb, attractors, raw_n_speakers
+
+ def recover_y_from_powerlabel(self, logit, n_speaker):
+ pred = torch.argmax(torch.softmax(logit, dim=-1), dim=-1)
+ oov_index = torch.where(pred == self.mapping_dict['oov'])[0]
+ for i in oov_index:
+ if i > 0:
+ pred[i] = pred[i - 1]
+ else:
+ pred[i] = 0
+ pred = [self.reporter.inv_mapping_func(i, self.mapping_dict) for i in pred]
+ decisions = [bin(num)[2:].zfill(self.max_n_speaker)[::-1] for num in pred]
+ decisions = torch.from_numpy(
+ np.stack([np.array([int(i) for i in dec]) for dec in decisions], axis=0)).to(logit.device).to(
+ torch.float32)
+ decisions = decisions[:, :n_speaker]
+ return decisions
diff --git a/funasr/models/frontend/eend_ola_feature.py b/funasr/models/frontend/eend_ola_feature.py
new file mode 100644
index 0000000..e15b71c
--- /dev/null
+++ b/funasr/models/frontend/eend_ola_feature.py
@@ -0,0 +1,51 @@
+# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita)
+# Licensed under the MIT license.
+#
+# This module is for computing audio features
+
+import librosa
+import numpy as np
+
+
+def transform(Y, dtype=np.float32):
+ Y = np.abs(Y)
+ n_fft = 2 * (Y.shape[1] - 1)
+ sr = 8000
+ n_mels = 23
+ mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
+ Y = np.dot(Y ** 2, mel_basis.T)
+ Y = np.log10(np.maximum(Y, 1e-10))
+ mean = np.mean(Y, axis=0)
+ Y = Y - mean
+ return Y.astype(dtype)
+
+
+def subsample(Y, T, subsampling=1):
+ Y_ss = Y[::subsampling]
+ T_ss = T[::subsampling]
+ return Y_ss, T_ss
+
+
+def splice(Y, context_size=0):
+ Y_pad = np.pad(
+ Y,
+ [(context_size, context_size), (0, 0)],
+ 'constant')
+ Y_spliced = np.lib.stride_tricks.as_strided(
+ np.ascontiguousarray(Y_pad),
+ (Y.shape[0], Y.shape[1] * (2 * context_size + 1)),
+ (Y.itemsize * Y.shape[1], Y.itemsize), writeable=False)
+ return Y_spliced
+
+
+def stft(
+ data,
+ frame_size=1024,
+ frame_shift=256):
+ fft_size = 1 << (frame_size - 1).bit_length()
+ if len(data) % frame_shift == 0:
+ return librosa.stft(data, n_fft=fft_size, win_length=frame_size,
+ hop_length=frame_shift).T[:-1]
+ else:
+ return librosa.stft(data, n_fft=fft_size, win_length=frame_size,
+ hop_length=frame_shift).T
\ No newline at end of file
diff --git a/funasr/modules/eend_ola/encoder.py b/funasr/modules/eend_ola/encoder.py
index 17d11ac..4999031 100644
--- a/funasr/modules/eend_ola/encoder.py
+++ b/funasr/modules/eend_ola/encoder.py
@@ -1,5 +1,5 @@
import math
-import numpy as np
+
import torch
import torch.nn.functional as F
from torch import nn
@@ -81,10 +81,16 @@
return self.dropout(x)
-class TransformerEncoder(nn.Module):
- def __init__(self, idim, n_layers, n_units,
- e_units=2048, h=8, dropout_rate=0.1, use_pos_emb=False):
- super(TransformerEncoder, self).__init__()
+class EENDOLATransformerEncoder(nn.Module):
+ def __init__(self,
+ idim: int,
+ n_layers: int,
+ n_units: int,
+ e_units: int = 2048,
+ h: int = 8,
+ dropout_rate: float = 0.1,
+ use_pos_emb: bool = False):
+ super(EENDOLATransformerEncoder, self).__init__()
self.lnorm_in = nn.LayerNorm(n_units)
self.n_layers = n_layers
self.dropout = nn.Dropout(dropout_rate)
diff --git a/funasr/tasks/diar.py b/funasr/tasks/diar.py
index e699dcc..ae7ee9b 100644
--- a/funasr/tasks/diar.py
+++ b/funasr/tasks/diar.py
@@ -20,19 +20,19 @@
from funasr.datasets.preprocessor import CommonPreprocessor
from funasr.layers.abs_normalize import AbsNormalize
from funasr.layers.global_mvn import GlobalMVN
-from funasr.layers.utterance_mvn import UtteranceMVN
from funasr.layers.label_aggregation import LabelAggregate
-from funasr.models.ctc import CTC
-from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
-from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
-from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
-from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
-from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
-from funasr.models.encoder.opennmt_encoders.ci_scorers import DotScorer, CosScorer
+from funasr.layers.utterance_mvn import UtteranceMVN
from funasr.models.e2e_diar_sond import DiarSondModel
+from funasr.models.e2e_diar_eend_ola import DiarEENDOLAModel
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.encoder.conformer_encoder import ConformerEncoder
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
+from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
+from funasr.models.encoder.opennmt_encoders.ci_scorers import DotScorer, CosScorer
+from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
+from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
+from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
+from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
from funasr.models.encoder.rnn_encoder import RNNEncoder
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
from funasr.models.encoder.transformer_encoder import TransformerEncoder
@@ -41,17 +41,13 @@
from funasr.models.frontend.fused import FusedFrontends
from funasr.models.frontend.s3prl import S3prlFrontend
from funasr.models.frontend.wav_frontend import WavFrontend
+from funasr.models.frontend.wav_frontend import WavFrontendMel23
from funasr.models.frontend.windowing import SlidingWindow
-from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
-from funasr.models.postencoder.hugging_face_transformers_postencoder import (
- HuggingFaceTransformersPostEncoder, # noqa: H301
-)
-from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
-from funasr.models.preencoder.linear import LinearProjection
-from funasr.models.preencoder.sinc import LightweightSincConvs
from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.models.specaug.specaug import SpecAug
from funasr.models.specaug.specaug import SpecAugLFR
+from funasr.modules.eend_ola.encoder import EENDOLATransformerEncoder
+from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
from funasr.tasks.abs_task import AbsTask
from funasr.torch_utils.initialize import initialize
from funasr.train.abs_espnet_model import AbsESPnetModel
@@ -70,6 +66,7 @@
s3prl=S3prlFrontend,
fused=FusedFrontends,
wav_frontend=WavFrontend,
+ wav_frontend_mel23=WavFrontendMel23,
),
type_check=AbsFrontend,
default="default",
@@ -107,6 +104,7 @@
"model",
classes=dict(
sond=DiarSondModel,
+ eend_ola=DiarEENDOLAModel,
),
type_check=AbsESPnetModel,
default="sond",
@@ -126,6 +124,7 @@
sanm_chunk_opt=SANMEncoderChunkOpt,
data2vec_encoder=Data2VecEncoder,
ecapa_tdnn=ECAPA_TDNN,
+ eend_ola_transformer=EENDOLATransformerEncoder,
),
type_check=torch.nn.Module,
default="resnet34",
@@ -176,6 +175,15 @@
),
type_check=torch.nn.Module,
default="fsmn",
+)
+# encoder_decoder_attractor is used for EEND-OLA
+encoder_decoder_attractor_choices = ClassChoices(
+ "encoder_decoder_attractor",
+ classes=dict(
+ eda=EncoderDecoderAttractor,
+ ),
+ type_check=torch.nn.Module,
+ default="eda",
)
@@ -594,3 +602,294 @@
var_dict_torch_update.update(var_dict_torch_update_local)
return var_dict_torch_update
+
+
+class EENDOLADiarTask(AbsTask):
+ # If you need more than 1 optimizer, change this value
+ num_optimizers: int = 1
+
+ # Add variable objects configurations
+ class_choices_list = [
+ # --frontend and --frontend_conf
+ frontend_choices,
+ # --specaug and --specaug_conf
+ model_choices,
+ # --encoder and --encoder_conf
+ encoder_choices,
+ # --speaker_encoder and --speaker_encoder_conf
+ encoder_decoder_attractor_choices,
+ ]
+
+ # If you need to modify train() or eval() procedures, change Trainer class here
+ trainer = Trainer
+
+ @classmethod
+ def add_task_arguments(cls, parser: argparse.ArgumentParser):
+ group = parser.add_argument_group(description="Task related")
+
+ # NOTE(kamo): add_arguments(..., required=True) can't be used
+ # to provide --print_config mode. Instead of it, do as
+ # required = parser.get_default("required")
+ # required += ["token_list"]
+
+ group.add_argument(
+ "--token_list",
+ type=str_or_none,
+ default=None,
+ help="A text mapping int-id to token",
+ )
+ group.add_argument(
+ "--split_with_space",
+ type=str2bool,
+ default=True,
+ help="whether to split text using <space>",
+ )
+ group.add_argument(
+ "--seg_dict_file",
+ type=str,
+ default=None,
+ help="seg_dict_file for text processing",
+ )
+ group.add_argument(
+ "--init",
+ type=lambda x: str_or_none(x.lower()),
+ default=None,
+ help="The initialization method",
+ choices=[
+ "chainer",
+ "xavier_uniform",
+ "xavier_normal",
+ "kaiming_uniform",
+ "kaiming_normal",
+ None,
+ ],
+ )
+
+ group.add_argument(
+ "--input_size",
+ type=int_or_none,
+ default=None,
+ help="The number of input dimension of the feature",
+ )
+
+ group = parser.add_argument_group(description="Preprocess related")
+ group.add_argument(
+ "--use_preprocessor",
+ type=str2bool,
+ default=True,
+ help="Apply preprocessing to data or not",
+ )
+ group.add_argument(
+ "--token_type",
+ type=str,
+ default="char",
+ choices=["char"],
+ help="The text will be tokenized in the specified level token",
+ )
+ parser.add_argument(
+ "--speech_volume_normalize",
+ type=float_or_none,
+ default=None,
+ help="Scale the maximum amplitude to the given value.",
+ )
+ parser.add_argument(
+ "--rir_scp",
+ type=str_or_none,
+ default=None,
+ help="The file path of rir scp file.",
+ )
+ parser.add_argument(
+ "--rir_apply_prob",
+ type=float,
+ default=1.0,
+ help="THe probability for applying RIR convolution.",
+ )
+ parser.add_argument(
+ "--cmvn_file",
+ type=str_or_none,
+ default=None,
+ help="The file path of noise scp file.",
+ )
+ parser.add_argument(
+ "--noise_scp",
+ type=str_or_none,
+ default=None,
+ help="The file path of noise scp file.",
+ )
+ parser.add_argument(
+ "--noise_apply_prob",
+ type=float,
+ default=1.0,
+ help="The probability applying Noise adding.",
+ )
+ parser.add_argument(
+ "--noise_db_range",
+ type=str,
+ default="13_15",
+ help="The range of noise decibel level.",
+ )
+
+ for class_choices in cls.class_choices_list:
+ # Append --<name> and --<name>_conf.
+ # e.g. --encoder and --encoder_conf
+ class_choices.add_arguments(group)
+
+ @classmethod
+ def build_collate_fn(
+ cls, args: argparse.Namespace, train: bool
+ ) -> Callable[
+ [Collection[Tuple[str, Dict[str, np.ndarray]]]],
+ Tuple[List[str], Dict[str, torch.Tensor]],
+ ]:
+ assert check_argument_types()
+ # NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
+ return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
+
+ @classmethod
+ def build_preprocess_fn(
+ cls, args: argparse.Namespace, train: bool
+ ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
+ assert check_argument_types()
+ if args.use_preprocessor:
+ retval = CommonPreprocessor(
+ train=train,
+ token_type=args.token_type,
+ token_list=args.token_list,
+ bpemodel=None,
+ non_linguistic_symbols=None,
+ text_cleaner=None,
+ g2p_type=None,
+ split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
+ seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
+ # NOTE(kamo): Check attribute existence for backward compatibility
+ rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
+ rir_apply_prob=args.rir_apply_prob
+ if hasattr(args, "rir_apply_prob")
+ else 1.0,
+ noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
+ noise_apply_prob=args.noise_apply_prob
+ if hasattr(args, "noise_apply_prob")
+ else 1.0,
+ noise_db_range=args.noise_db_range
+ if hasattr(args, "noise_db_range")
+ else "13_15",
+ speech_volume_normalize=args.speech_volume_normalize
+ if hasattr(args, "rir_scp")
+ else None,
+ )
+ else:
+ retval = None
+ assert check_return_type(retval)
+ return retval
+
+ @classmethod
+ def required_data_names(
+ cls, train: bool = True, inference: bool = False
+ ) -> Tuple[str, ...]:
+ if not inference:
+ retval = ("speech", "profile", "binary_labels")
+ else:
+ # Recognition mode
+ retval = ("speech")
+ return retval
+
+ @classmethod
+ def optional_data_names(
+ cls, train: bool = True, inference: bool = False
+ ) -> Tuple[str, ...]:
+ retval = ()
+ assert check_return_type(retval)
+ return retval
+
+ @classmethod
+ def build_model(cls, args: argparse.Namespace):
+ assert check_argument_types()
+
+ # 1. frontend
+ if args.input_size is None or args.frontend == "wav_frontend_mel23":
+ # Extract features in the model
+ frontend_class = frontend_choices.get_class(args.frontend)
+ if args.frontend == 'wav_frontend':
+ frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
+ else:
+ frontend = frontend_class(**args.frontend_conf)
+ input_size = frontend.output_size()
+ else:
+ # Give features from data-loader
+ args.frontend = None
+ args.frontend_conf = {}
+ frontend = None
+ input_size = args.input_size
+
+ # 2. Encoder
+ encoder_class = encoder_choices.get_class(args.encoder)
+ encoder = encoder_class(input_size=input_size, **args.encoder_conf)
+
+ # 3. EncoderDecoderAttractor
+ encoder_decoder_attractor_class = encoder_decoder_attractor_choices.get_class(args.encoder_decoder_attractor)
+ encoder_decoder_attractor = encoder_decoder_attractor_class(**args.encoder_decoder_attractor_conf)
+
+ # 9. Build model
+ model_class = model_choices.get_class(args.model)
+ model = model_class(
+ frontend=frontend,
+ encoder=encoder,
+ encoder_decoder_attractor=encoder_decoder_attractor,
+ **args.model_conf,
+ )
+
+ # 10. Initialize
+ if args.init is not None:
+ initialize(model, args.init)
+
+ assert check_return_type(model)
+ return model
+
+ # ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
+ @classmethod
+ def build_model_from_file(
+ cls,
+ config_file: Union[Path, str] = None,
+ model_file: Union[Path, str] = None,
+ cmvn_file: Union[Path, str] = None,
+ device: str = "cpu",
+ ):
+ """Build model from the files.
+
+ This method is used for inference or fine-tuning.
+
+ Args:
+ config_file: The yaml file saved when training.
+ model_file: The model file saved when training.
+ cmvn_file: The cmvn file for front-end
+ device: Device type, "cpu", "cuda", or "cuda:N".
+
+ """
+ assert check_argument_types()
+ if config_file is None:
+ assert model_file is not None, (
+ "The argument 'model_file' must be provided "
+ "if the argument 'config_file' is not specified."
+ )
+ config_file = Path(model_file).parent / "config.yaml"
+ else:
+ config_file = Path(config_file)
+
+ with config_file.open("r", encoding="utf-8") as f:
+ args = yaml.safe_load(f)
+ args = argparse.Namespace(**args)
+ model = cls.build_model(args)
+ if not isinstance(model, AbsESPnetModel):
+ raise RuntimeError(
+ f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
+ )
+ if model_file is not None:
+ if device == "cuda":
+ device = f"cuda:{torch.cuda.current_device()}"
+ checkpoint = torch.load(model_file, map_location=device)
+ if "state_dict" in checkpoint.keys():
+ model.load_state_dict(checkpoint["state_dict"])
+ else:
+ model.load_state_dict(checkpoint)
+ model.to(device)
+ return model, args
--
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