def backtrace(trace: np.ndarray):
Traceback (most recent call last):
File "c:\users\t_care\appdata\local\programs\python\python38\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\t_care\appdata\local\programs\python\python38\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\Scripts\whisper.exe\__main__.py", line 7, in <module>
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\whisper\transcribe.py", line 433, in cli
model = load_model(model_name, device=device, download_root=model_dir)
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\whisper\__init__.py", line 144, in load_model
checkpoint = torch.load(fp, map_location=device)
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 809, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 1172, in _load
result = unpickler.load()
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 1142, in persistent_load
typed_storage = load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 1116, in load_tensor
wrap_storage=restore_location(storage, location),
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 1083, in restore_location
return default_restore_location(storage, map_location)
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 217, in default_restore_location
result = fn(storage, location)
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 182, in _cuda_deserialize
device = validate_cuda_device(location)
File "c:\users\t_care\appdata\local\programs\python\python38\lib\site-packages\torch\serialization.py", line 166, in validate_cuda_device
raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
PS C:\Users\T_Care\Desktop\whisper_dia\whisper-diarization> python diarize.py -a "sample.mp3" --no-stem
C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: otobuf is an invalid version and will not be supported in a future release
warnings.warn(
[NeMo W 2023-07-03 10:47:18 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-07-03 10:47:18 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2023-07-03 10:47:36 msdd_models:1092] Loading pretrained diar_msdd_telephonic model from NGC
[NeMo I 2023-07-03 10:47:36 cloud:58] Found existing object C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\diar_msdd_telephonic\3c3697a0a46f945574fa407149975a13\diar_msdd_telephonic.nemo.
[NeMo I 2023-07-03 10:47:36 cloud:64] Re-using file from: C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\diar_msdd_telephonic\3c3697a0a46f945574fa407149975a13\diar_msdd_telephonic.nemo
[NeMo I 2023-07-03 10:47:36 common:913] Instantiating model from pre-trained checkpoint
[NeMo W 2023-07-03 10:47:36 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: null
emb_dir: null
sample_rate: 16000
num_spks: 2
soft_label_thres: 0.5
labels: null
batch_size: 15
emb_batch_size: 0
shuffle: true
[NeMo W 2023-07-03 10:47:36 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: null
emb_dir: null
sample_rate: 16000
num_spks: 2
soft_label_thres: 0.5
labels: null
batch_size: 15
emb_batch_size: 0
shuffle: false
[NeMo W 2023-07-03 10:47:36 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: null
emb_dir: null
sample_rate: 16000
num_spks: 2
soft_label_thres: 0.5
labels: null
batch_size: 15
emb_batch_size: 0
shuffle: false
seq_eval_mode: false
[NeMo I 2023-07-03 10:47:36 features:287] PADDING: 16
[NeMo I 2023-07-03 10:47:37 features:287] PADDING: 16
[NeMo I 2023-07-03 10:47:38 save_restore_connector:247] Model EncDecDiarLabelModel was successfully restored from C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\diar_msdd_telephonic\3c3697a0a46f945574fa407149975a13\diar_msdd_telephonic.nemo.
[NeMo I 2023-07-03 10:47:38 features:287] PADDING: 16
[NeMo I 2023-07-03 10:47:38 clustering_diarizer:127] Loading pretrained vad_multilingual_marblenet model from NGC
[NeMo I 2023-07-03 10:47:38 cloud:58] Found existing object C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\vad_multilingual_marblenet\670f425c7f186060b7a7268ba6dfacb2\vad_multilingual_marblenet.nemo.
[NeMo I 2023-07-03 10:47:38 cloud:64] Re-using file from: C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\vad_multilingual_marblenet\670f425c7f186060b7a7268ba6dfacb2\vad_multilingual_marblenet.nemo
[NeMo I 2023-07-03 10:47:38 common:913] Instantiating model from pre-trained checkpoint
[NeMo W 2023-07-03 10:47:38 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: /manifests/ami_train_0.63.json,/manifests/freesound_background_train.json,/manifests/freesound_laughter_train.json,/manifests/fisher_2004_background.json,/manifests/fisher_2004_speech_sampled.json,/manifests/google_train_manifest.json,/manifests/icsi_all_0.63.json,/manifests/musan_freesound_train.json,/manifests/musan_music_train.json,/manifests/musan_soundbible_train.json,/manifests/mandarin_train_sample.json,/manifests/german_train_sample.json,/manifests/spanish_train_sample.json,/manifests/french_train_sample.json,/manifests/russian_train_sample.json
sample_rate: 16000
labels:
- background
- speech
batch_size: 256
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
tarred_shard_strategy: scatter
augmentor:
shift:
prob: 0.5
min_shift_ms: -10.0
max_shift_ms: 10.0
white_noise:
prob: 0.5
min_level: -90
max_level: -46
norm: true
noise:
prob: 0.5
manifest_path: /manifests/noise_0_1_musan_fs.json
min_snr_db: 0
max_snr_db: 30
max_gain_db: 300.0
norm: true
gain:
prob: 0.5
min_gain_dbfs: -10.0
max_gain_dbfs: 10.0
norm: true
num_workers: 16
pin_memory: true
[NeMo W 2023-07-03 10:47:38 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: /manifests/ami_dev_0.63.json,/manifests/freesound_background_dev.json,/manifests/freesound_laughter_dev.json,/manifests/ch120_moved_0.63.json,/manifests/fisher_2005_500_speech_sampled.json,/manifests/google_dev_manifest.json,/manifests/musan_music_dev.json,/manifests/mandarin_dev.json,/manifests/german_dev.json,/manifests/spanish_dev.json,/manifests/french_dev.json,/manifests/russian_dev.json
sample_rate: 16000
labels:
- background
- speech
batch_size: 256
shuffle: false
val_loss_idx: 0
num_workers: 16
pin_memory: true
[NeMo W 2023-07-03 10:47:38 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: null
sample_rate: 16000
labels:
- background
- speech
batch_size: 128
shuffle: false
test_loss_idx: 0
[NeMo I 2023-07-03 10:47:38 features:287] PADDING: 16
[NeMo I 2023-07-03 10:47:38 save_restore_connector:247] Model EncDecClassificationModel was successfully restored from C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\vad_multilingual_marblenet\670f425c7f186060b7a7268ba6dfacb2\vad_multilingual_marblenet.nemo.
[NeMo I 2023-07-03 10:47:38 msdd_models:864] Multiscale Weights: [1, 1, 1, 1, 1]
[NeMo I 2023-07-03 10:47:38 msdd_models:865] Clustering Parameters: {
"oracle_num_speakers": false,
"max_num_speakers": 8,
"enhanced_count_thres": 80,
"max_rp_threshold": 0.25,
"sparse_search_volume": 30,
"maj_vote_spk_count": false
}
[NeMo W 2023-07-03 10:47:38 clustering_diarizer:411] Deleting previous clustering diarizer outputs.
[NeMo I 2023-07-03 10:47:38 speaker_utils:93] Number of files to diarize: 1
[NeMo I 2023-07-03 10:47:38 clustering_diarizer:309] Split long audio file to avoid CUDA memory issue
splitting manifest: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 332.27it/s]
[NeMo I 2023-07-03 10:47:38 vad_utils:101] The prepared manifest file exists. Overwriting!
[NeMo I 2023-07-03 10:47:38 classification_models:263] Perform streaming frame-level VAD
[NeMo I 2023-07-03 10:47:38 collections:298] Filtered duration for loading collection is 0.000000.
[NeMo I 2023-07-03 10:47:38 collections:301] Dataset loaded with 1 items, total duration of 0.00 hours.
[NeMo I 2023-07-03 10:47:38 collections:303] # 1 files loaded accounting to # 1 labels
vad: 0%| | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
File "diarize.py", line 112, in <module>
msdd_model.diarize()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\msdd_models.py", line 1180, in diarize
self.clustering_embedding.prepare_cluster_embs_infer()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\msdd_models.py", line 699, in prepare_cluster_embs_infer
self.emb_sess_test_dict, self.emb_seq_test, self.clus_test_label_dict, _ = self.run_clustering_diarizer(
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\msdd_models.py", line 866, in run_clustering_diarizer
scores = self.clus_diar_model.diarize(batch_size=self.cfg_diar_infer.batch_size)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\clustering_diarizer.py", line 437, in diarize
self._perform_speech_activity_detection()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\clustering_diarizer.py", line 325, in _perform_speech_activity_detection
self._run_vad(manifest_vad_input)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\clustering_diarizer.py", line 218, in _run_vad
for i, test_batch in enumerate(
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\tqdm\std.py", line 1178, in __iter__
for obj in iterable:
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 441, in __iter__
return self._get_iterator()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 388, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 1042, in __init__
w.start()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class 'nemo.collections.common.parts.preprocessing.collections.SpeechLabelEntity'>: attribute lookup SpeechLabelEntity on nemo.collections.common.parts.preprocessing.collections failed
PS C:\Users\T_Care\Desktop\whisper_dia\whisper-diarization> C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\pkg_resources\__init__.py:123: PkgResourcesDeprecationWarning: otobuf is an invalid version and will not be supported in a future release
warnings.warn(
[NeMo W 2023-07-03 10:47:42 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-07-03 10:47:42 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo I 2023-07-03 10:47:59 msdd_models:1092] Loading pretrained diar_msdd_telephonic model from NGC
[NeMo I 2023-07-03 10:47:59 cloud:58] Found existing object C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\diar_msdd_telephonic\3c3697a0a46f945574fa407149975a13\diar_msdd_telephonic.nemo.
[NeMo I 2023-07-03 10:47:59 cloud:64] Re-using file from: C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\diar_msdd_telephonic\3c3697a0a46f945574fa407149975a13\diar_msdd_telephonic.nemo
[NeMo I 2023-07-03 10:47:59 common:913] Instantiating model from pre-trained checkpoint
[NeMo W 2023-07-03 10:48:00 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: null
emb_dir: null
sample_rate: 16000
num_spks: 2
soft_label_thres: 0.5
labels: null
batch_size: 15
emb_batch_size: 0
shuffle: true
[NeMo W 2023-07-03 10:48:00 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: null
emb_dir: null
sample_rate: 16000
num_spks: 2
soft_label_thres: 0.5
labels: null
batch_size: 15
emb_batch_size: 0
shuffle: false
[NeMo W 2023-07-03 10:48:00 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: null
emb_dir: null
sample_rate: 16000
num_spks: 2
soft_label_thres: 0.5
labels: null
batch_size: 15
emb_batch_size: 0
shuffle: false
seq_eval_mode: false
[NeMo I 2023-07-03 10:48:00 features:287] PADDING: 16
[NeMo I 2023-07-03 10:48:00 features:287] PADDING: 16
[NeMo I 2023-07-03 10:48:02 save_restore_connector:247] Model EncDecDiarLabelModel was successfully restored from C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\diar_msdd_telephonic\3c3697a0a46f945574fa407149975a13\diar_msdd_telephonic.nemo.
[NeMo I 2023-07-03 10:48:02 features:287] PADDING: 16
[NeMo I 2023-07-03 10:48:02 clustering_diarizer:127] Loading pretrained vad_multilingual_marblenet model from NGC
[NeMo I 2023-07-03 10:48:02 cloud:58] Found existing object C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\vad_multilingual_marblenet\670f425c7f186060b7a7268ba6dfacb2\vad_multilingual_marblenet.nemo.
[NeMo I 2023-07-03 10:48:02 cloud:64] Re-using file from: C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\vad_multilingual_marblenet\670f425c7f186060b7a7268ba6dfacb2\vad_multilingual_marblenet.nemo
[NeMo I 2023-07-03 10:48:02 common:913] Instantiating model from pre-trained checkpoint
[NeMo W 2023-07-03 10:48:02 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: /manifests/ami_train_0.63.json,/manifests/freesound_background_train.json,/manifests/freesound_laughter_train.json,/manifests/fisher_2004_background.json,/manifests/fisher_2004_speech_sampled.json,/manifests/google_train_manifest.json,/manifests/icsi_all_0.63.json,/manifests/musan_freesound_train.json,/manifests/musan_music_train.json,/manifests/musan_soundbible_train.json,/manifests/mandarin_train_sample.json,/manifests/german_train_sample.json,/manifests/spanish_train_sample.json,/manifests/french_train_sample.json,/manifests/russian_train_sample.json
sample_rate: 16000
labels:
- background
- speech
batch_size: 256
shuffle: true
is_tarred: false
tarred_audio_filepaths: null
tarred_shard_strategy: scatter
augmentor:
shift:
prob: 0.5
min_shift_ms: -10.0
max_shift_ms: 10.0
white_noise:
prob: 0.5
min_level: -90
max_level: -46
norm: true
noise:
prob: 0.5
manifest_path: /manifests/noise_0_1_musan_fs.json
min_snr_db: 0
max_snr_db: 30
max_gain_db: 300.0
norm: true
gain:
prob: 0.5
min_gain_dbfs: -10.0
max_gain_dbfs: 10.0
norm: true
num_workers: 16
pin_memory: true
[NeMo W 2023-07-03 10:48:02 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: /manifests/ami_dev_0.63.json,/manifests/freesound_background_dev.json,/manifests/freesound_laughter_dev.json,/manifests/ch120_moved_0.63.json,/manifests/fisher_2005_500_speech_sampled.json,/manifests/google_dev_manifest.json,/manifests/musan_music_dev.json,/manifests/mandarin_dev.json,/manifests/german_dev.json,/manifests/spanish_dev.json,/manifests/french_dev.json,/manifests/russian_dev.json
sample_rate: 16000
labels:
- background
- speech
batch_size: 256
shuffle: false
val_loss_idx: 0
num_workers: 16
pin_memory: true
[NeMo W 2023-07-03 10:48:02 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: null
sample_rate: 16000
labels:
- background
- speech
batch_size: 128
shuffle: false
test_loss_idx: 0
[NeMo I 2023-07-03 10:48:02 features:287] PADDING: 16
[NeMo I 2023-07-03 10:48:02 save_restore_connector:247] Model EncDecClassificationModel was successfully restored from C:\Users\T_Care\.cache\torch\NeMo\NeMo_1.17.0\vad_multilingual_marblenet\670f425c7f186060b7a7268ba6dfacb2\vad_multilingual_marblenet.nemo.
[NeMo I 2023-07-03 10:48:02 msdd_models:864] Multiscale Weights: [1, 1, 1, 1, 1]
[NeMo I 2023-07-03 10:48:02 msdd_models:865] Clustering Parameters: {
"oracle_num_speakers": false,
"max_num_speakers": 8,
"enhanced_count_thres": 80,
"max_rp_threshold": 0.25,
"sparse_search_volume": 30,
"maj_vote_spk_count": false
}
[NeMo W 2023-07-03 10:48:02 clustering_diarizer:411] Deleting previous clustering diarizer outputs.
[NeMo I 2023-07-03 10:48:02 speaker_utils:93] Number of files to diarize: 1
[NeMo I 2023-07-03 10:48:02 clustering_diarizer:309] Split long audio file to avoid CUDA memory issue
splitting manifest: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 335.06it/s]
[NeMo I 2023-07-03 10:48:02 vad_utils:101] The prepared manifest file exists. Overwriting!
[NeMo I 2023-07-03 10:48:02 classification_models:263] Perform streaming frame-level VAD
[NeMo I 2023-07-03 10:48:02 collections:298] Filtered duration for loading collection is 0.000000.
[NeMo I 2023-07-03 10:48:02 collections:301] Dataset loaded with 1 items, total duration of 0.00 hours.
[NeMo I 2023-07-03 10:48:02 collections:303] # 1 files loaded accounting to # 1 labels
vad: 0%| | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\T_Care\Desktop\whisper_dia\whisper-diarization\diarize.py", line 112, in <module>
msdd_model.diarize()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\msdd_models.py", line 1180, in diarize
self.clustering_embedding.prepare_cluster_embs_infer()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\msdd_models.py", line 699, in prepare_cluster_embs_infer
self.emb_sess_test_dict, self.emb_seq_test, self.clus_test_label_dict, _ = self.run_clustering_diarizer(
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\msdd_models.py", line 866, in run_clustering_diarizer
scores = self.clus_diar_model.diarize(batch_size=self.cfg_diar_infer.batch_size)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\clustering_diarizer.py", line 437, in diarize
self._perform_speech_activity_detection()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\clustering_diarizer.py", line 325, in _perform_speech_activity_detection
self._run_vad(manifest_vad_input)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\nemo\collections\asr\models\clustering_diarizer.py", line 218, in _run_vad
for i, test_batch in enumerate(
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\tqdm\std.py", line 1178, in __iter__
for obj in iterable:
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 441, in __iter__
return self._get_iterator()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 388, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\utils\data\dataloader.py", line 1042, in __init__
w.start()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\T_Care\AppData\Local\Programs\Python\Python38\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.