From 2a8d041806df41fa3719505d1b3379bbbd369574 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 08 六月 2024 21:35:21 +0800
Subject: [PATCH] fix bug
---
funasr/models/llm_asr/model.py | 13 ++++++++-----
1 files changed, 8 insertions(+), 5 deletions(-)
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 697f78d..5fde3ff 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -19,6 +19,7 @@
from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
from funasr.train_utils.device_funcs import to_device
+import traceback
@tables.register("model_classes", "LLMASR")
@@ -489,6 +490,7 @@
fbank_fake_len = fbank_fake_lens[batch_idx].item()
fbank_beg_idx = fbank_beg[batch_idx, 0].item()
min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
+
try:
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
batch_idx, :min_len, :
@@ -496,10 +498,10 @@
except Exception as e:
logging.error(f"{str(e)}, {traceback.format_exc()}")
logging.info(
- f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}"
+ f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens[batch_idx].item()}"
)
fbank_fake_len = encoder_out_lens[batch_idx].item()
- min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
+ min_len = min(fbank_fake_len, min_len)
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
batch_idx, :min_len, :
]
@@ -692,6 +694,7 @@
batch_idx, :min_len, :
]
+ label = contents["assistant"][0]
if not kwargs.get("tearchforing", False):
generated_ids = self.llm.generate(
@@ -704,7 +707,7 @@
response = tokenizer.batch_decode(
generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
)[0]
- label = contents["assistant"][0]
+
loss = None
else:
@@ -715,13 +718,13 @@
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
)
- preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1]]
+ preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :]
response = tokenizer.batch_decode(
preds,
add_special_tokens=False,
skip_special_tokens=kwargs.get("skip_special_tokens", True),
)[0]
- loss = model_outputs.loss
+ loss = model_outputs.loss.item()
ibest_writer = None
if kwargs.get("output_dir") is not None:
--
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