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mesolitica
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unsupervised-malay-youtube-speaker-diarization

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The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 153, in compute
                  compute_split_names_from_info_response(
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 125, in compute_split_names_from_info_response
                  config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config)
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Unsupervised malay speakers from youtube videos

10492 unique speakers with at least 75 hours of voice activities. Steps to reproduce at https://github.com/huseinzol05/malaya-speech/blob/master/data/youtube/process-youtube.ipynb

how-to

Download and extract processed-youtube.tar.gz, each processed videos saved as pickle, {video_name}.pkl.
Each pickle file got,

[{'wav_data': '/home/husein/ssd2/processed-youtube-v2/"Abam_peluk_saya_lama_atas_pentas_akhir_MLM"-_Ali_Puteh_menangis_imbau_saat_manis_dengan_arwah_abang-_MdgGr7VD7w/0.mp3',
  'timestamp': datetime.datetime(2023, 3, 2, 18, 45, 45, 778042),
  'asr_model': ('kenapa tak mahu bangun kau abang',
   [0.5325799628135358],
   [309, 9, 399, 633, 108, 252]),
  'classification_model': (array([ 3.02432757e-03, -3.64390127e-02,  2.93319039e-02, -2.84599233e-02,
          -5.04244901e-02,  6.03185333e-02,  7.04260264e-03,  7.36895157e-03,
           2.41034012e-02, -3.31214964e-02, -1.61228217e-02, -1.92081463e-02,
          -1.77928973e-02,  1.05488757e-02,  5.11314301e-03,  2.08497643e-02,
           2.80407351e-02, -1.34683009e-02,  1.10213496e-02, -5.76948654e-03,
           2.11171638e-02, -3.10498872e-03,  1.60899870e-02, -2.22061612e-02,
          -3.09270490e-02,  1.03673469e-02,  2.29822248e-02,  5.44358939e-02,
          -9.44061391e-03,  3.24469656e-02, -1.40673192e-02,  6.55731931e-03,
           1.94134321e-02,  2.31755860e-02, -8.62774719e-03, -3.72681394e-03,
          -3.17485556e-02, -1.12474747e-02,  1.65595114e-02,  2.31244415e-02,
           3.28784771e-02,  8.52510054e-03, -6.41896739e-04,  3.13562714e-03,
          -3.15982029e-02,  1.72785181e-03,  1.58039071e-02, -9.93900001e-03,
           2.03248486e-02, -2.98949536e-02,  3.53759155e-02,  3.06809470e-02,
          -3.68881435e-03, -3.98267582e-02, -2.07101982e-02,  2.51877047e-02,
          -2.51530181e-03,  1.06034977e-02,  1.24978041e-02,  2.35916697e-03,
           1.31300613e-02, -1.62451845e-02, -2.09861826e-02,  3.17490734e-02,
          -1.18532358e-02,  4.25735563e-02,  4.17908467e-02,  1.21251179e-03,
          -3.85571155e-03, -9.50544327e-03, -7.37808086e-03,  2.63940021e-02,
           1.09219365e-02,  3.05683501e-02, -4.08848785e-02, -1.71920974e-02,
          -1.46033484e-02, -3.29053291e-05,  3.84788848e-02, -7.86552951e-03,
           1.01251132e-03,  2.72140447e-02,  2.52339337e-02,  3.39004360e-02,
          -1.38184745e-02,  2.60320995e-02, -1.01425601e-02, -1.16012329e-02,
           4.30319924e-03, -1.01203052e-02, -4.66396799e-03, -2.64480542e-02,
           3.44322808e-02, -4.64622118e-03,  1.06053520e-02,  1.37923108e-02,
          -2.05409434e-03, -1.19995829e-02,  2.10450366e-02, -2.87155900e-03,
          -1.39515549e-02, -1.51185887e-02,  2.29053162e-02, -1.78178120e-02,
           1.95855577e-03,  2.37271357e-02,  2.80657201e-03, -6.08753460e-03,
          -2.01220363e-02,  3.22612897e-02,  1.82474777e-02,  5.31493872e-02,
          -7.08705634e-02,  2.76431069e-03,  1.03597697e-02, -3.53837833e-02,
           1.38167264e-02, -5.91275143e-03,  1.84398554e-02,  6.05177172e-02,
           1.14565976e-02,  1.56977493e-02, -1.82731878e-02, -4.58574407e-02,
          -1.08330613e-02, -1.16500622e-02, -1.19803764e-04,  6.48374185e-02,
          -1.21538760e-03, -5.41793741e-02,  1.38867721e-02,  3.52845751e-02,
          -2.08288375e-02,  1.03750750e-02, -2.17110049e-02,  2.29265504e-02,
          -1.21381739e-02, -1.47071329e-03, -4.36875001e-02, -2.25690063e-02,
          -4.16939743e-02, -8.39853752e-03, -2.06098761e-02,  2.30504461e-02,
           3.48615423e-02, -4.18495797e-02, -2.41985917e-03, -3.18994140e-03,
           1.22078639e-02, -9.50168632e-03, -1.97298196e-03,  1.30731370e-02,
           2.07234323e-02,  1.08521534e-02,  2.30542179e-02, -2.54045837e-02,
           1.45645533e-02, -1.08493539e-02, -1.30415503e-02,  3.29123251e-02,
           3.46204527e-02,  2.58748885e-04, -1.28235819e-03, -1.32823242e-02,
           5.47284493e-03, -2.62062326e-02,  2.31803600e-02, -2.04505119e-02,
           2.32407395e-02,  2.12946888e-02, -1.28869051e-02, -6.81399694e-03,
           5.68802692e-02,  4.31004271e-04,  1.67261921e-02,  2.93559525e-02,
           1.32581135e-02, -9.03073605e-03, -9.38207190e-03,  1.74718127e-02,
           1.72506981e-02,  5.02267219e-02, -1.32851647e-02,  5.07321544e-02,
          -1.87530685e-02,  4.18599546e-02,  1.50075918e-02, -2.61102356e-02,
          -1.59594957e-02,  1.36823149e-03, -9.64679196e-03,  1.71130225e-02],
         dtype=float32),
   'speaker 0')}]

all mp3 files postprocessing using https://malaya-speech.readthedocs.io/en/latest/load-noise-reduction.html and https://malaya-speech.readthedocs.io/en/latest/load-speech-enhancement.html
wav_data is directory of the audio, prune the path to proper extracted directory.
asr_model is predicted using the best model that we have, conformer-medium, returned (text, probability, subwords), https://malaya-speech.readthedocs.io/en/latest/load-stt-transducer-model-pt.html
classification_model is predicted using NEMO TITANET Large speaker verification model, https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/titanet_large, with streaming speaker similarity, https://malaya-speech.readthedocs.io/en/latest/huggingface-repository.html

Group by similar speakers using pagerank method (scipy.sparse.linalg.gmres),

90% similar, from 10492 unique speakers become 6085 unique speakers, https://github.com/huseinzol05/malaya-speech/blob/master/data/youtube/mapping-youtube-speakers-90.json
85% similar, from 10492 unique speakers become 4312 unique speakers, https://github.com/huseinzol05/malaya-speech/blob/master/data/youtube/mapping-youtube-speakers-85.json
80% similar, from 10492 unique speakers become 2912 unique speakers, https://github.com/huseinzol05/malaya-speech/blob/master/data/youtube/mapping-youtube-speakers-80.json

Speaker name defined as,

import os
import pickle

pkl = 'filename.pkl'
with open(pkl, 'rb') as fopen:
  data = pickle.load(fopen)

filename = os.path.split(pkl)[1].replace('.pkl', '')
for result in data:
  speaker_name = f'{filename}-{speaker}'
  actual_speaker = unique_speakers[speaker_name]

Check example at https://github.com/huseinzol05/malaya-speech/blob/master/data/youtube/calculate-lengths-80.ipynb

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