update ola

funasr/models/e2e_diar_eend_ola.py

@@ -1,38 +1,24 @@
 # Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved.
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 
-import logging
-import torch
 from contextlib import contextmanager
 from distutils.version import LooseVersion
-from funasr.layers.abs_normalize import AbsNormalize
-from funasr.losses.label_smoothing_loss import (
-    LabelSmoothingLoss,  # noqa: H301
-)
-from funasr.models.ctc import CTC
-from funasr.models.decoder.abs_decoder import AbsDecoder
-from funasr.models.encoder.abs_encoder import AbsEncoder
-from funasr.models.frontend.abs_frontend import AbsFrontend
-from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
-from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
-from funasr.models.specaug.abs_specaug import AbsSpecAug
-from funasr.modules.add_sos_eos import add_sos_eos
-from funasr.modules.e2e_asr_common import ErrorCalculator
+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.modules.eend_ola.encoder import TransformerEncoder
 from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
 from funasr.modules.eend_ola.utils.power import generate_mapping_dict
-from funasr.modules.nets_utils import th_accuracy
 from funasr.torch_utils.device_funcs import force_gatherable
 from funasr.train.abs_espnet_model import AbsESPnetModel
-from typeguard import check_argument_types
-from typing import Dict
-from typing import List
-from typing import Optional
-from typing import Tuple
-from typing import Union
 
 if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
-    from torch.cuda.amp import autocast
+    pass
 else:
     # Nothing to do if torch<1.6.0
     @contextmanager
@@ -47,6 +33,7 @@ class DiarEENDOLAModel(AbsESPnetModel):
             self,
             encoder: TransformerEncoder,
             eda: EncoderDecoderAttractor,
+            n_units: int = 256,
             max_n_speaker: int = 8,
             attractor_loss_weight: float = 1.0,
             mapping_dict=None,
@@ -62,6 +49,9 @@ class DiarEENDOLAModel(AbsESPnetModel):
         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(
             self,
@@ -163,233 +153,65 @@ class DiarEENDOLAModel(AbsESPnetModel):
         loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
         return loss, stats, weight
 
-    def collect_feats(
-            self,
-            speech: torch.Tensor,
-            speech_lengths: torch.Tensor,
-            text: torch.Tensor,
-            text_lengths: torch.Tensor,
-    ) -> Dict[str, torch.Tensor]:
-        if self.extract_feats_in_collect_stats:
-            feats, feats_lengths = self._extract_feats(speech, speech_lengths)
+    def estimate_sequential(self,
+                            speech: torch.Tensor,
+                            speech_lengths: torch.Tensor,
+                            n_speakers: int,
+                            shuffle: bool,
+                            threshold: float,
+                            **kwargs):
+        speech = [s[:s_len] for s, s_len in zip(speech, speech_lengths)]
+        emb = self.forward_core(speech)  # list, [(T1, C1), ..., (T1, C1)]
+        if shuffle:
+            orders = [np.arange(e.shape[0]) for e in emb]
+            for order in orders:
+                np.random.shuffle(order)
+            # e[order]: shuffle后的embeddings, list, [(T1, C1), ..., (T1, C1)]  每个sample的T维度已进行随机顺序交换
+            # attractors, list, hts(论文里的as), [(max_n_speakers, n_units), ..., (max_n_speakers, n_units)]
+            # probs, list, [(max_n_speakers, ), ..., (max_n_speakers, ]
+            attractors, probs = self.eda.estimate(
+                [e[torch.from_numpy(order).to(torch.long).to(xs[0].device)] for e, order in zip(emb, orders)])
         else:
-            # Generate dummy stats if extract_feats_in_collect_stats is False
-            logging.warning(
-                "Generating dummy stats for feats and feats_lengths, "
-                "because encoder_conf.extract_feats_in_collect_stats is "
-                f"{self.extract_feats_in_collect_stats}"
-            )
-            feats, feats_lengths = speech, speech_lengths
-        return {"feats": feats, "feats_lengths": feats_lengths}
-
-    def encode(
-            self, speech: torch.Tensor, speech_lengths: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Frontend + Encoder. Note that this method is used by asr_inference.py
-
-        Args:
-            speech: (Batch, Length, ...)
-            speech_lengths: (Batch, )
-        """
-        with autocast(False):
-            # 1. Extract feats
-            feats, feats_lengths = self._extract_feats(speech, speech_lengths)
-
-            # 2. Data augmentation
-            if self.specaug is not None and self.training:
-                feats, feats_lengths = self.specaug(feats, feats_lengths)
-
-            # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
-            if self.normalize is not None:
-                feats, feats_lengths = self.normalize(feats, feats_lengths)
-
-        # Pre-encoder, e.g. used for raw input data
-        if self.preencoder is not None:
-            feats, feats_lengths = self.preencoder(feats, feats_lengths)
-
-        # 4. Forward encoder
-        # feats: (Batch, Length, Dim)
-        # -> encoder_out: (Batch, Length2, Dim2)
-        if self.encoder.interctc_use_conditioning:
-            encoder_out, encoder_out_lens, _ = self.encoder(
-                feats, feats_lengths, ctc=self.ctc
-            )
-        else:
-            encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
-        intermediate_outs = None
-        if isinstance(encoder_out, tuple):
-            intermediate_outs = encoder_out[1]
-            encoder_out = encoder_out[0]
-
-        # Post-encoder, e.g. NLU
-        if self.postencoder is not None:
-            encoder_out, encoder_out_lens = self.postencoder(
-                encoder_out, encoder_out_lens
-            )
-
-        assert encoder_out.size(0) == speech.size(0), (
-            encoder_out.size(),
-            speech.size(0),
-        )
-        assert encoder_out.size(1) <= encoder_out_lens.max(), (
-            encoder_out.size(),
-            encoder_out_lens.max(),
-        )
-
-        if intermediate_outs is not None:
-            return (encoder_out, intermediate_outs), encoder_out_lens
-
-        return encoder_out, encoder_out_lens
-
-    def _extract_feats(
-            self, speech: torch.Tensor, speech_lengths: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        assert speech_lengths.dim() == 1, speech_lengths.shape
-
-        # for data-parallel
-        speech = speech[:, : speech_lengths.max()]
-
-        if self.frontend is not None:
-            # Frontend
-            #  e.g. STFT and Feature extract
-            #       data_loader may send time-domain signal in this case
-            # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
-            feats, feats_lengths = self.frontend(speech, speech_lengths)
-        else:
-            # No frontend and no feature extract
-            feats, feats_lengths = speech, speech_lengths
-        return feats, feats_lengths
-
-    def nll(
-            self,
-            encoder_out: torch.Tensor,
-            encoder_out_lens: torch.Tensor,
-            ys_pad: torch.Tensor,
-            ys_pad_lens: torch.Tensor,
-    ) -> torch.Tensor:
-        """Compute negative log likelihood(nll) from transformer-decoder
-
-        Normally, this function is called in batchify_nll.
-
-        Args:
-            encoder_out: (Batch, Length, Dim)
-            encoder_out_lens: (Batch,)
-            ys_pad: (Batch, Length)
-            ys_pad_lens: (Batch,)
-        """
-        ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
-        ys_in_lens = ys_pad_lens + 1
-
-        # 1. Forward decoder
-        decoder_out, _ = self.decoder(
-            encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
-        )  # [batch, seqlen, dim]
-        batch_size = decoder_out.size(0)
-        decoder_num_class = decoder_out.size(2)
-        # nll: negative log-likelihood
-        nll = torch.nn.functional.cross_entropy(
-            decoder_out.view(-1, decoder_num_class),
-            ys_out_pad.view(-1),
-            ignore_index=self.ignore_id,
-            reduction="none",
-        )
-        nll = nll.view(batch_size, -1)
-        nll = nll.sum(dim=1)
-        assert nll.size(0) == batch_size
-        return nll
-
-    def batchify_nll(
-            self,
-            encoder_out: torch.Tensor,
-            encoder_out_lens: torch.Tensor,
-            ys_pad: torch.Tensor,
-            ys_pad_lens: torch.Tensor,
-            batch_size: int = 100,
-    ):
-        """Compute negative log likelihood(nll) from transformer-decoder
-
-        To avoid OOM, this fuction seperate the input into batches.
-        Then call nll for each batch and combine and return results.
-        Args:
-            encoder_out: (Batch, Length, Dim)
-            encoder_out_lens: (Batch,)
-            ys_pad: (Batch, Length)
-            ys_pad_lens: (Batch,)
-            batch_size: int, samples each batch contain when computing nll,
-                        you may change this to avoid OOM or increase
-                        GPU memory usage
-        """
-        total_num = encoder_out.size(0)
-        if total_num <= batch_size:
-            nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
-        else:
-            nll = []
-            start_idx = 0
-            while True:
-                end_idx = min(start_idx + batch_size, total_num)
-                batch_encoder_out = encoder_out[start_idx:end_idx, :, :]
-                batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx]
-                batch_ys_pad = ys_pad[start_idx:end_idx, :]
-                batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx]
-                batch_nll = self.nll(
-                    batch_encoder_out,
-                    batch_encoder_out_lens,
-                    batch_ys_pad,
-                    batch_ys_pad_lens,
-                )
-                nll.append(batch_nll)
-                start_idx = end_idx
-                if start_idx == total_num:
-                    break
-            nll = torch.cat(nll)
-        assert nll.size(0) == total_num
-        return nll
-
-    def _calc_att_loss(
-            self,
-            encoder_out: torch.Tensor,
-            encoder_out_lens: torch.Tensor,
-            ys_pad: torch.Tensor,
-            ys_pad_lens: torch.Tensor,
-    ):
-        ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
-        ys_in_lens = ys_pad_lens + 1
-
-        # 1. Forward decoder
-        decoder_out, _ = self.decoder(
-            encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
-        )
-
-        # 2. Compute attention loss
-        loss_att = self.criterion_att(decoder_out, ys_out_pad)
-        acc_att = th_accuracy(
-            decoder_out.view(-1, self.vocab_size),
-            ys_out_pad,
-            ignore_label=self.ignore_id,
-        )
-
-        # Compute cer/wer using attention-decoder
-        if self.training or self.error_calculator is None:
-            cer_att, wer_att = None, None
-        else:
-            ys_hat = decoder_out.argmax(dim=-1)
-            cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
-
-        return loss_att, acc_att, cer_att, wer_att
-
-    def _calc_ctc_loss(
-            self,
-            encoder_out: torch.Tensor,
-            encoder_out_lens: torch.Tensor,
-            ys_pad: torch.Tensor,
-            ys_pad_lens: torch.Tensor,
-    ):
-        # Calc CTC loss
-        loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
-
-        # Calc CER using CTC
-        cer_ctc = None
-        if not self.training and self.error_calculator is not None:
-            ys_hat = self.ctc.argmax(encoder_out).data
-            cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
-        return loss_ctc, cer_ctc
+            attractors, probs = self.eda.estimate(emb)
+        attractors_active = []
+        for p, att, e in zip(probs, attractors, emb):
+            if n_speakers and n_speakers >= 0:  # 根据指定说话人数, 选择对应数量的ys
+                # TODO：在测试有不同数量speaker数的数据集时，考虑改成根据sample来确定具体的speaker数，而不是直接指定
+                # raise NotImplementedError
+                att = att[:n_speakers, ]
+                attractors_active.append(att)
+            elif threshold is not None:
+                silence = torch.nonzero(p < threshold)[0]  # 找到第一个输出概率小于阈值的索引, 作为结束, 且值刚好等于说话人数
+                n_spk = silence[0] if silence.size else None
+                att = att[:n_spk, ]
+                attractors_active.append(att)
+            else:
+                NotImplementedError('n_speakers or th has to be given.')
+        raw_n_speakers = [att.shape[0] for att in attractors_active]  # [C1, C2, ..., CB]
+        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)]
+        # ys_eda = [torch.sigmoid(y[:, :n_spk]) for y,n_spk in zip(ys, raw_n_speakers)]
+        logits = self.cal_postnet(ys, self.max_n_speaker)
+        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)  # (T, )
+        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]
+        # print(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