From 73a7cf596bd1ebc3bd0c9674a21072f9eaf4cc57 Mon Sep 17 00:00:00 2001
From: dyyzhmm <dyyzhmm@163.com>
Date: 星期四, 16 三月 2023 15:24:56 +0800
Subject: [PATCH] Merge pull request #3 from alibaba-damo-academy/main
---
funasr/models/e2e_diar_eend_ola.py | 35 +++++++++++++++++++++++------------
1 files changed, 23 insertions(+), 12 deletions(-)
diff --git a/funasr/models/e2e_diar_eend_ola.py b/funasr/models/e2e_diar_eend_ola.py
index f589269..097b23a 100644
--- a/funasr/models/e2e_diar_eend_ola.py
+++ b/funasr/models/e2e_diar_eend_ola.py
@@ -52,15 +52,15 @@
super().__init__()
self.frontend = frontend
- self.encoder = encoder
- self.encoder_decoder_attractor = encoder_decoder_attractor
+ self.enc = encoder
+ self.eda = 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.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):
@@ -68,7 +68,7 @@
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 = self.enc(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
@@ -76,8 +76,8 @@
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)
+ logits = nn.utils.rnn.pack_padded_sequence(logits, ilens.cpu().to(torch.int64), 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]
@@ -112,7 +112,7 @@
text = text[:, : text_lengths.max()]
# 1. Encoder
- encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ encoder_out, encoder_out_lens = self.enc(speech, speech_lengths)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
@@ -190,18 +190,16 @@
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(
+ attractors, probs = self.eda.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, probs = self.eda.estimate(emb)
attractors_active = []
for p, att, e in zip(probs, attractors, emb):
if n_speakers and n_speakers >= 0:
@@ -233,10 +231,23 @@
pred[i] = pred[i - 1]
else:
pred[i] = 0
- pred = [self.reporter.inv_mapping_func(i, self.mapping_dict) for i in pred]
+ pred = [self.inv_mapping_func(i) 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
+
+ def inv_mapping_func(self, label):
+
+ if not isinstance(label, int):
+ label = int(label)
+ if label in self.mapping_dict['label2dec'].keys():
+ num = self.mapping_dict['label2dec'][label]
+ else:
+ num = -1
+ return num
+
+ def collect_feats(self, **batch: torch.Tensor) -> Dict[str, torch.Tensor]:
+ pass
\ No newline at end of file
--
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