JSONDecodeError: Extra data: line 1 column 126 (char 125)
Hello, I am trying to train a custom model using the provided code. I've compiled my custom Cyrillic  manifests according to the instruction with each line being:
{"audio_filepath": "/path/to/wav/file", "duration": float, "text": "transcript_in_cyrillic"}

However, when I setup the data i get the following error. I've double checked for any invisible characters and what not but my custom manifest is exactly like the one in the example. 

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JSONDecodeError                           Traceback (most recent call last)
/nfs/home/create_manifest.ipynb Cell 14' in <cell line: 13>()
      2 trainer = pl.Trainer(devices=1, accelerator='gpu', max_epochs=50)
      3 #trainer = pl.Trainer(devices=1, accelerator='gpu', max_epochs=2)
      4 
      5 #import copy
   (...)
     11 
     12 # Point to the data we'll use for fine-tuning as the training set
---> 13 quartznet_transfer_mon.setup_training_data(train_data_config=params['model']['train_ds'])
     15 # Point to the new validation data for fine-tuning
     16 quartznet_transfer_mon.setup_validation_data(val_data_config=params['model']['validation_ds'])

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py:464, in EncDecCTCModel.setup_training_data(self, train_data_config)
    461 # preserve config
    462 self._update_dataset_config(dataset_name='train', config=train_data_config)
--> 464 self._train_dl = self._setup_dataloader_from_config(config=train_data_config)
    466 # Need to set this because if using an IterableDataset, the length of the dataloader is the total number
    467 # of samples rather than the number of batches, and this messes up the tqdm progress bar.
    468 # So we set the number of steps manually (to the correct number) to fix this.
    469 if 'is_tarred' in train_data_config and train_data_config['is_tarred']:
    470     # We also need to check if limit_train_batches is already set.
    471     # If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
    472     # and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py:426, in EncDecCTCModel._setup_dataloader_from_config(self, config)
    423         logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}")
    424         return None
--> 426     dataset = audio_to_text_dataset.get_char_dataset(config=config, augmentor=augmentor)
    428 if hasattr(dataset, 'collate_fn'):
    429     collate_fn = dataset.collate_fn

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text_dataset.py:86, in get_char_dataset(config, augmentor)
     83 if 'labels' not in config:
     84     logging.warning(f"dataset does not have explicitly defined labels")
---> 86 dataset = audio_to_text.AudioToCharDataset(
     87     manifest_filepath=config['manifest_filepath'],
     88     labels=config.get('labels', None),
     89     sample_rate=config['sample_rate'],
     90     int_values=config.get('int_values', False),
     91     augmentor=augmentor,
     92     max_duration=config.get('max_duration', None),
     93     min_duration=config.get('min_duration', None),
     94     max_utts=config.get('max_utts', 0),
     95     blank_index=config.get('blank_index', -1),
     96     unk_index=config.get('unk_index', -1),
     97     normalize=config.get('normalize_transcripts', False),
     98     trim=config.get('trim_silence', False),
     99     parser=config.get('parser', 'en'),
    100     return_sample_id=config.get('return_sample_id', False),
    101 )
    102 return dataset

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py:388, in AudioToCharDataset.__init__(self, manifest_filepath, labels, sample_rate, int_values, augmentor, max_duration, min_duration, max_utts, blank_index, unk_index, normalize, trim, bos_id, eos_id, pad_id, parser, return_sample_id)
    382 self.labels = labels
    384 parser = parsers.make_parser(
    385     labels=labels, name=parser, unk_id=unk_index, blank_id=blank_index, do_normalize=normalize
    386 )
--> 388 super().__init__(
    389     manifest_filepath=manifest_filepath,
    390     parser=parser,
    391     sample_rate=sample_rate,
    392     int_values=int_values,
    393     augmentor=augmentor,
    394     max_duration=max_duration,
    395     min_duration=min_duration,
    396     max_utts=max_utts,
    397     trim=trim,
    398     bos_id=bos_id,
    399     eos_id=eos_id,
    400     pad_id=pad_id,
    401     return_sample_id=return_sample_id,
    402 )

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py:274, in _AudioTextDataset.__init__(self, manifest_filepath, parser, sample_rate, int_values, augmentor, max_duration, min_duration, max_utts, trim, bos_id, eos_id, pad_id, return_sample_id)
    271 if type(manifest_filepath) == str:
    272     manifest_filepath = manifest_filepath.split(",")
--> 274 self.manifest_processor = ASRManifestProcessor(
    275     manifest_filepath=manifest_filepath,
    276     parser=parser,
    277     max_duration=max_duration,
    278     min_duration=min_duration,
    279     max_utts=max_utts,
    280     bos_id=bos_id,
    281     eos_id=eos_id,
    282     pad_id=pad_id,
    283 )
    284 self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor)
    285 self.trim = trim

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/asr/data/audio_to_text.py:135, in ASRManifestProcessor.__init__(self, manifest_filepath, parser, max_duration, min_duration, max_utts, bos_id, eos_id, pad_id, index_by_file_id)
    121 def __init__(
    122     self,
    123     manifest_filepath: str,
   (...)
    131     index_by_file_id: bool = False,
    132 ):
    133     self.parser = parser
--> 135     self.collection = collections.ASRAudioText(
    136         manifests_files=manifest_filepath,
    137         parser=parser,
    138         min_duration=min_duration,
    139         max_duration=max_duration,
    140         max_number=max_utts,
    141         index_by_file_id=index_by_file_id,
    142     )
    144     self.eos_id = eos_id
    145     self.bos_id = bos_id

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/collections.py:206, in ASRAudioText.__init__(self, manifests_files, *args, **kwargs)
    196 """Parse lists of audio files, durations and transcripts texts.
    197 
    198 Args:
   (...)
    202     **kwargs: Kwargs to pass to `AudioText` constructor.
    203 """
    205 ids, audio_files, durations, texts, offsets, speakers, orig_srs, langs = [], [], [], [], [], [], [], []
--> 206 for item in manifest.item_iter(manifests_files):
    207     ids.append(item['id'])
    208     audio_files.append(item['audio_file'])

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py:72, in item_iter(manifests_files, parse_func)
     70 for line in f:
     71     k += 1
---> 72     item = parse_func(line, manifest_file)
     73     item['id'] = k
     75     yield item

File ~/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/collections/common/parts/preprocessing/manifest.py:79, in __parse_item(line, manifest_file)
     78 def __parse_item(line: str, manifest_file: str) -> Dict[str, Any]:
---> 79     item = json.loads(line)
     81     # Audio file
     82     if 'audio_filename' in item:

File ~/anaconda3/envs/nemo/lib/python3.8/json/__init__.py:357, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
    352     del kw['encoding']
    354 if (cls is None and object_hook is None and
    355         parse_int is None and parse_float is None and
    356         parse_constant is None and object_pairs_hook is None and not kw):
--> 357     return _default_decoder.decode(s)
    358 if cls is None:
    359     cls = JSONDecoder

File ~/anaconda3/envs/nemo/lib/python3.8/json/decoder.py:340, in JSONDecoder.decode(self, s, _w)
    338 end = _w(s, end).end()
    339 if end != len(s):
--> 340     raise JSONDecodeError("Extra data", s, end)
    341 return obj

JSONDecodeError: Extra data: line 1 column 126 (char 125)
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