From 1d27a1507b7a98d3d957f984bbab7e14523181fb Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期日, 09 六月 2024 22:01:14 +0800
Subject: [PATCH] fix bug
---
funasr/models/llm_asr/model.py | 25 +++++++++++++++----------
1 files changed, 15 insertions(+), 10 deletions(-)
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 697f78d..aacbe45 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, :
]
@@ -692,19 +694,20 @@
batch_idx, :min_len, :
]
+ label = contents["assistant"][0]
if not kwargs.get("tearchforing", False):
generated_ids = self.llm.generate(
inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512)
)
- generated_ids = [
- output_ids[len(input_id) :]
- for input_id, output_ids in zip(input_ids, generated_ids)
- ]
+ # generated_ids = [
+ # output_ids[len(input_id) :]
+ # for input_id, output_ids in zip(input_ids, generated_ids)
+ # ]
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:
@@ -730,7 +733,8 @@
ibest_writer = self.writer[f"{0 + 1}best_recog"]
results = []
- result_i = {"key": key[0], "text": response, "label": label}
+ response_clean = re.sub("[^\w\s\u3000\u4e00-\u9fff]+", "", response)
+ result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label}
if loss is not None:
result_i["loss"] = loss
results.append(result_i)
@@ -738,5 +742,6 @@
if ibest_writer is not None:
ibest_writer["text"][key[0]] = response
ibest_writer["label"][key[0]] = label
+ ibest_writer["text_tn"][key[0]] = response_clean
return results, meta_data
--
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