From 0281b70896bd8069a9d26dad1ef2b5090ae5fc4a Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期四, 25 一月 2024 10:20:31 +0800
Subject: [PATCH] update umap import

---
 funasr/auto/auto_model.py |   23 ++++++++++++-----------
 1 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 0538f66..5f89dd6 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -6,6 +6,7 @@
 import string
 import logging
 import os.path
+import numpy as np
 from tqdm import tqdm
 from omegaconf import DictConfig, OmegaConf, ListConfig
 
@@ -96,7 +97,7 @@
         vad_kwargs = kwargs.get("vad_model_revision", None)
         if vad_model is not None:
             logging.info("Building VAD model.")
-            vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
+            vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs, "device": kwargs["device"]}
             vad_model, vad_kwargs = self.build_model(**vad_kwargs)
 
         # if punc_model is not None, build punc model else None
@@ -104,7 +105,7 @@
         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_kwargs = {"model": punc_model, "model_revision": punc_kwargs, "device": kwargs["device"]}
             punc_model, punc_kwargs = self.build_model(**punc_kwargs)
 
         # if spk_model is not None, build spk model else None
@@ -112,9 +113,9 @@
         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_kwargs = {"model": spk_model, "model_revision": spk_kwargs, "device": kwargs["device"]}
             spk_model, spk_kwargs = self.build_model(**spk_kwargs)
-            self.cb_model = ClusterBackend()
+            self.cb_model = ClusterBackend().to(kwargs["device"])
             spk_mode = kwargs.get("spk_mode", 'punc_segment')
             if spk_mode not in ["default", "vad_segment", "punc_segment"]:
                 logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
@@ -145,7 +146,7 @@
         set_all_random_seed(kwargs.get("seed", 0))
         
         device = kwargs.get("device", "cuda")
-        if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
+        if not torch.cuda.is_available() or kwargs.get("ngpu", 0) == 0:
             device = "cpu"
             kwargs["batch_size"] = 1
         kwargs["device"] = device
@@ -334,7 +335,7 @@
                     for _b in range(len(speech_j)):
                         vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0,
                                         sorted_data[beg_idx:end_idx][_b][0][1]/1000.0,
-                                        speech_j[_b]]]
+                                        np.array(speech_j[_b])]]
                         segments = sv_chunk(vad_segments)
                         all_segments.extend(segments)
                         speech_b = [i[2] for i in segments]
@@ -376,7 +377,7 @@
                             result[k] = restored_data[j][k]
                         else:
                             result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
-                    elif k == 'text':
+                    elif k == 'raw_text':
                         if k not in result:
                             result[k] = restored_data[j][k]
                         else:
@@ -391,13 +392,13 @@
             if self.punc_model is not None:
                 self.punc_kwargs.update(cfg)
                 punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
-                result["text_with_punc"] = punc_res[0]["text"]
+                result["text"] = punc_res[0]["text"]
                      
             # speaker embedding cluster after resorted
             if self.spk_model is not None:
                 all_segments = sorted(all_segments, key=lambda x: x[0])
                 spk_embedding = result['spk_embedding']
-                labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
+                labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num)
                 del result['spk_embedding']
                 sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
                 if self.spk_mode == 'vad_segment':
@@ -405,12 +406,12 @@
                     for res, vadsegment in zip(restored_data, vadsegments):
                         sentence_list.append({"start": vadsegment[0],\
                                                 "end": vadsegment[1],
-                                                "sentence": res['text'],
+                                                "sentence": res['raw_text'],
                                                 "timestamp": res['timestamp']})
                 else: # punc_segment
                     sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
                                                         result['timestamp'], \
-                                                        result['text'])
+                                                        result['raw_text'])
                 distribute_spk(sentence_list, sv_output)
                 result['sentence_info'] = sentence_list
                     

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