From e9d2cfc3a134b00f4e98271fbee3838d1ccecbcc Mon Sep 17 00:00:00 2001
From: VirtuosoQ <2416050435@qq.com>
Date: 星期五, 26 四月 2024 14:59:30 +0800
Subject: [PATCH] FunASR java http client
---
funasr/models/seaco_paraformer/model.py | 19 ++++++++++++-------
1 files changed, 12 insertions(+), 7 deletions(-)
diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py
index 21b6aba..b28de94 100644
--- a/funasr/models/seaco_paraformer/model.py
+++ b/funasr/models/seaco_paraformer/model.py
@@ -97,7 +97,8 @@
smoothing=seaco_lsm_weight,
normalize_length=seaco_length_normalized_loss,
)
- self.train_decoder = kwargs.get("train_decoder", False)
+ self.train_decoder = kwargs.get("train_decoder", True)
+ self.seaco_weight = kwargs.get("seaco_weight", 0.01)
self.NO_BIAS = kwargs.get("NO_BIAS", 8377)
self.predictor_name = kwargs.get("predictor")
@@ -117,9 +118,10 @@
text: (Batch, Length)
text_lengths: (Batch,)
"""
- text_lengths = text_lengths.squeeze()
- speech_lengths = speech_lengths.squeeze()
- assert text_lengths.dim() == 1, text_lengths.shape
+ if len(text_lengths.size()) > 1:
+ text_lengths = text_lengths[:, 0]
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
# Check that batch_size is unified
assert (
speech.shape[0]
@@ -131,6 +133,8 @@
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
seaco_label_pad = kwargs.get("seaco_label_pad")
+ if len(hotword_lengths.size()) > 1:
+ hotword_lengths = hotword_lengths[:, 0]
batch_size = speech.shape[0]
# for data-parallel
@@ -153,14 +157,15 @@
seaco_label_pad,
)
if self.train_decoder:
- loss_att, acc_att = self._calc_att_loss(
+ loss_att, acc_att, _, _, _ = self._calc_att_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
- loss = loss_seaco + loss_att
+ loss = loss_seaco + loss_att * self.seaco_weight
stats["loss_att"] = torch.clone(loss_att.detach())
stats["acc_att"] = acc_att
else:
loss = loss_seaco
+
stats["loss_seaco"] = torch.clone(loss_seaco.detach())
stats["loss"] = torch.clone(loss.detach())
@@ -345,7 +350,7 @@
pre_acoustic_embeds, pre_token_length = predictor_outs[0], predictor_outs[1]
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
- return []
+ return ([],)
decoder_out = self._seaco_decode_with_ASF(encoder_out,
encoder_out_lens,
--
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