From 8a8d60d5786510ec7b1dd4f622e848de8a15f8a8 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期一, 15 一月 2024 16:36:51 +0800
Subject: [PATCH] replace NULL for onnxruntime/src
---
funasr/bin/inference.py | 16 +++++++++-------
1 files changed, 9 insertions(+), 7 deletions(-)
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index 7d9c1b9..cefee55 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -115,7 +115,7 @@
vad_model = kwargs.get("vad_model", None)
vad_kwargs = kwargs.get("vad_model_revision", None)
if vad_model is not None:
- print("build vad model")
+ logging.info("Building VAD model.")
vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
vad_model, vad_kwargs = self.build_model(**vad_kwargs)
@@ -123,6 +123,7 @@
punc_model = kwargs.get("punc_model", None)
punc_kwargs = kwargs.get("punc_model_revision", None)
if punc_model is not None:
+ logging.info("Building punc model.")
punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
punc_model, punc_kwargs = self.build_model(**punc_kwargs)
@@ -130,6 +131,7 @@
spk_model = kwargs.get("spk_model", None)
spk_kwargs = kwargs.get("spk_model_revision", None)
if spk_model is not None:
+ logging.info("Building SPK model.")
spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
spk_model, spk_kwargs = self.build_model(**spk_kwargs)
self.cb_model = ClusterBackend()
@@ -173,7 +175,7 @@
# build tokenizer
tokenizer = kwargs.get("tokenizer", None)
if tokenizer is not None:
- tokenizer_class = tables.tokenizer_classes.get(tokenizer.lower())
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer)
tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
kwargs["tokenizer"] = tokenizer
kwargs["token_list"] = tokenizer.token_list
@@ -184,13 +186,13 @@
# build frontend
frontend = kwargs.get("frontend", None)
if frontend is not None:
- frontend_class = tables.frontend_classes.get(frontend.lower())
+ frontend_class = tables.frontend_classes.get(frontend)
frontend = frontend_class(**kwargs["frontend_conf"])
kwargs["frontend"] = frontend
kwargs["input_size"] = frontend.output_size()
# build model
- model_class = tables.model_classes.get(kwargs["model"].lower())
+ model_class = tables.model_classes.get(kwargs["model"])
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
model.eval()
model.to(device)
@@ -243,7 +245,7 @@
time1 = time.perf_counter()
with torch.no_grad():
- results, meta_data = model.generate(**batch, **kwargs)
+ results, meta_data = model.inference(**batch, **kwargs)
time2 = time.perf_counter()
asr_result_list.extend(results)
@@ -403,7 +405,7 @@
spk_embedding = result['spk_embedding']
labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
del result['spk_embedding']
- sv_output = postprocess(all_segments, None, labels, spk_embedding)
+ sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
if self.spk_mode == 'vad_segment':
sentence_list = []
for res, vadsegment in zip(restored_data, vadsegments):
@@ -441,7 +443,7 @@
# build frontend
frontend = kwargs.get("frontend", None)
if frontend is not None:
- frontend_class = tables.frontend_classes.get(frontend.lower())
+ frontend_class = tables.frontend_classes.get(frontend)
frontend = frontend_class(**kwargs["frontend_conf"])
self.frontend = frontend
--
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