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MahmoudAshraf97 / whisper-diarization

Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
BSD 2-Clause "Simplified" License
3.28k stars 272 forks source link

error while running this command "python diarize.py -a "sample.mp3" --no-stem" #60

Closed saranchandr closed 1 year ago

saranchandr commented 1 year ago 
  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.
MahmoudAshraf97 commented 1 year ago

please upgrade to python 3.10 and disable multiprocessing in nemo config in helpers.py

labspicsprod commented 1 year ago

Hey, I am having the same issue, but I don't see how to disable multiprocessing in helpers.py ? Is there a line I should edit ?

Thanks in advance !