From 82530ddf974a706df5a6a1e258d80c8dbc3f1d72 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 10 六月 2024 09:19:16 +0800
Subject: [PATCH] fix bug
---
funasr/models/llm_asr/model.py | 28 +++++++++++++++++++++-------
1 files changed, 21 insertions(+), 7 deletions(-)
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 5fde3ff..21072b0 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -556,7 +556,7 @@
return contents
- def data_load_speech(self, contents: dict, tokenizer, frontend, **kwargs):
+ def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs):
system = contents["system"]
user = contents["user"]
@@ -594,7 +594,10 @@
)
if sub_str.startswith("!"):
try:
+ time1 = time.perf_counter()
data_src = load_audio_text_image_video(sub_str[1:], fs=frontend.fs)
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
except Exception as e:
logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
@@ -604,6 +607,15 @@
frontend=frontend,
is_final=True,
) # speech: [b, T, d]
+
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = (
+ speech_lengths.sum().item()
+ * frontend.frame_shift
+ * frontend.lfr_n
+ / 1000
+ )
if kwargs.get("permute", True):
speech = speech.permute(0, 2, 1)
@@ -666,7 +678,7 @@
raise NotImplementedError("batch decoding is not implemented")
contents = self.data_template(data_in[0])
- output = self.data_load_speech(contents, tokenizer, frontend, **kwargs)
+ output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs)
batch = to_device(output, kwargs["device"])
# audio encoder
@@ -700,10 +712,10 @@
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]
@@ -733,7 +745,8 @@
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)
@@ -741,5 +754,6 @@
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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