From d19f48e17478be273584853568ac101c994c37e5 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 08 四月 2024 18:51:53 +0800
Subject: [PATCH] Dev gzf exp (#1593)
---
funasr/models/llm_asr_nar/model.py | 59 +++++++++++++++++++++++++++++++++++++++++++++++++++--------
1 files changed, 51 insertions(+), 8 deletions(-)
diff --git a/funasr/models/llm_asr_nar/model.py b/funasr/models/llm_asr_nar/model.py
index 30537cf..994259a 100644
--- a/funasr/models/llm_asr_nar/model.py
+++ b/funasr/models/llm_asr_nar/model.py
@@ -366,7 +366,7 @@
decoder_conf: dict = None,
ctc: str = None,
ctc_conf: dict = None,
- ctc_weight: float = 0.5,
+ ctc_weight: float = 0.0,
llm: str = None,
llm_conf: dict = None,
adaptor: str = None,
@@ -473,6 +473,15 @@
self.length_normalized_loss = length_normalized_loss
self.beam_search = None
+ if ctc_weight > 0.0:
+ if ctc_conf is None:
+ ctc_conf = {}
+
+ ctc = CTC(
+ odim=vocab_size, encoder_output_size=adaptor_conf["encoder_dim"], **ctc_conf
+ )
+ self.ctc_weight = ctc_weight
+ self.ctc = ctc
def forward(
self,
@@ -502,9 +511,23 @@
speech_lengths = speech_lengths[:, 0]
batch_size = speech.shape[0]
-
+
+ stats = {}
# audio encoder
- encoder_out, encoder_out_lens, loss_pre = self.encode(speech, speech_lengths, audio_mask=audio_mask)
+ outs = self.encode(speech, speech_lengths, audio_mask=audio_mask)
+ enc, enc_lens = outs[0], outs[1]
+ encoder_out, encoder_out_lens, loss_pre = outs[2], outs[3], outs[4]
+
+
+ # decoder: CTC branch
+
+ if self.ctc_weight != 0.0:
+ loss_ctc, cer_ctc = self._calc_ctc_loss(
+ enc, enc_lens, text, text_lengths
+ )
+
+ # Collect CTC branch stats
+ stats["loss_ctc"] = torch.clone(loss_ctc.detach()) if loss_ctc is not None else None
# adaptor
encoder_out = self.adaptor(encoder_out)
@@ -536,17 +559,19 @@
# labels_ids[1:] -> [prompt, input, target, eos] -> [-1, input, target, eos];
model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids)
loss_llm = model_outputs.loss
+ stats["loss_llm"] = torch.clone(loss_llm.detach())
+ if self.ctc_weight > 0.0:
+ loss_llm = self.ctc_weight * loss_ctc + loss_llm
loss = loss_llm + loss_pre * self.predictor_weight
- stats = {}
+
with torch.no_grad():
preds = torch.argmax(model_outputs.logits, -1)
acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
stats["acc"] = acc_att
-
stats["loss_pre"] = torch.clone(loss_pre.detach())
- stats["loss_llm"] = torch.clone(loss_llm.detach())
stats["loss"] = torch.clone(loss.detach())
+ stats["batch_size"] = batch_size
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
@@ -576,7 +601,24 @@
if audio_token_lengths is not None:
loss_pre = self.criterion_pre(audio_token_lengths.type_as(pre_token_length), pre_token_length)
- return pre_acoustic_embeds, pre_token_length, loss_pre
+ return enc, enc_lens, pre_acoustic_embeds, pre_token_length, loss_pre
+
+ 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
def inference(self,
data_in,
@@ -648,7 +690,8 @@
else:
inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
- inputs_embeds = torch.cat((inputs_embeds[None, :, :], encoder_out, pad[None, :, :]), dim=1) # [prompt, audio]
+ # inputs_embeds = torch.cat((inputs_embeds[None, :, :], encoder_out, pad[None, :, :]), dim=1) # [prompt, audio, pad]
+ inputs_embeds = torch.cat((inputs_embeds[None, :, :], encoder_out), dim=1) # [prompt, audio]
attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(kwargs["device"])
# model_outputs = self.llm.generate(
--
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