From 229efa6250927485a3e1018548630b6348a24d1c Mon Sep 17 00:00:00 2001 From: speech_asr Date: Mon, 13 Mar 2023 16:25:53 +0800 Subject: [PATCH] update ola --- funasr/models/e2e_diar_eend_ola.py | 56 ++++++++++++++++++++---------- 1 file changed, 38 insertions(+), 18 deletions(-) diff --git a/funasr/models/e2e_diar_eend_ola.py b/funasr/models/e2e_diar_eend_ola.py index 5c1c9ce4e..2960b23ca 100644 --- a/funasr/models/e2e_diar_eend_ola.py +++ b/funasr/models/e2e_diar_eend_ola.py @@ -26,6 +26,13 @@ else: 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): """CTC-attention hybrid Encoder-Decoder model""" @@ -53,6 +60,26 @@ class DiarEENDOLAModel(AbsESPnetModel): 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, @@ -156,51 +183,45 @@ class DiarEENDOLAModel(AbsESPnetModel): def estimate_sequential(self, speech: torch.Tensor, speech_lengths: torch.Tensor, - n_speakers: int, - shuffle: bool, - threshold: float, + n_speakers: int = None, + shuffle: bool = True, + threshold: float = 0.5, **kwargs): speech = [s[:s_len] for s, s_len in zip(speech, speech_lengths)] - emb = self.forward_core(speech) # list, [(T1, C1), ..., (T1, C1)] + 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) - # 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)]) + [e[torch.from_numpy(order).to(torch.long).to(speech[0].device)] for e, order in zip(emb, orders)]) else: 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 + 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] # 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 + 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 th has to be given.') - raw_n_speakers = [att.shape[0] for att in attractors_active] # [C1, C2, ..., CB] + 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)] - # 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) + 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) # (T, ) + 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: @@ -208,7 +229,6 @@ class DiarEENDOLAModel(AbsESPnetModel): 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(