def filter_dataset(input_path, output_path, duration_in_hours, cer, seed):
dataset = transcripts.load(input_path)
random.seed(seed)
random.shuffle(dataset)
print('initial set hours: ',
sum(transcripts.compute_duration(t, hours=True) for t in dataset),
'hours')
if cer:
dataset = [e for e in dataset if e['cer'] <= cer]
print(
'after cer filtering hours: ',
sum(transcripts.compute_duration(t, hours=True) for t in dataset),
'hours')
if duration_in_hours is not None:
s = []
set_duration = 0
while set_duration <= duration_in_hours and len(dataset) > 0:
t = dataset.pop()
set_duration += transcripts.compute_duration(t, hours=True)
s.append(t)
dataset = s
print('after duration filtering hours: ',
sum(transcripts.compute_duration(t, hours=True) for t in dataset),
'hours')
print(output_path)
transcripts.save(output_path, dataset)
def eval(ref, hyp, html, debug_audio, sample_rate=100):
if os.path.isfile(ref) and os.path.isfile(hyp):
print(der(ref_rttm_path=ref, hyp_rttm_path=hyp))
elif os.path.isdir(ref) and os.path.isdir(hyp):
errs = []
diarization_transcript = []
for rttm in os.listdir(ref):
if not rttm.endswith('.rttm'):
continue
print(rttm)
audio_path = transcripts.load(
os.path.join(hyp, rttm).replace('.rttm',
'.json'))[0]['audio_path']
ref_rttm_path, hyp_rttm_path = os.path.join(ref,
rttm), os.path.join(
hyp, rttm)
ref_transcript, hyp_transcript = map(
transcripts.load, [ref_rttm_path, hyp_rttm_path])
ser_err, hyp_perm = speaker_error(
ref=ref_transcript,
hyp=hyp_transcript,
num_speakers=2,
sample_rate=sample_rate,
ignore_silence_and_overlapped_speech=True)
der_err, *_ = speaker_error(
ref=ref_transcript,
hyp=hyp_transcript,
num_speakers=2,
sample_rate=sample_rate,
ignore_silence_and_overlapped_speech=False)
der_err_ = der(ref_rttm_path=ref_rttm_path,
hyp_rttm_path=hyp_rttm_path)
transcripts.remap_speaker(hyp_transcript, hyp_perm)
err = dict(ser=ser_err, der=der_err, der_=der_err_)
diarization_transcript.append(
dict(audio_path=audio_path,
audio_name=transcripts.audio_name(audio_path),
ref=ref_transcript,
hyp=hyp_transcript,
**err))
print(rttm, '{ser:.2f}, {der:.2f} | {der_:.2f}'.format(**err))
print()
errs.append(err)
print('===')
print({
k: sum(e) / len(e)
for k in errs[0] for e in [[err[k] for err in errs]]
})
if html:
print(
vis.diarization(
sorted(diarization_transcript,
key=lambda t: t['ser'],
reverse=True), html, debug_audio))
def main(args, ext_json=['.json', '.json.gz']):
utils.enable_jit_fusion()
assert args.output_json or args.output_html or args.output_txt or args.output_csv, \
'at least one of the output formats must be provided'
os.makedirs(args.output_path, exist_ok=True)
audio_data_paths = set(
p for f in args.input_path
for p in ([os.path.join(f, g)
for g in os.listdir(f)] if os.path.isdir(f) else [f])
if os.path.isfile(p) and any(map(p.endswith, args.ext)))
json_data_paths = set(
p for p in args.input_path if any(map(p.endswith, ext_json))
and not utils.strip_suffixes(p, ext_json) in audio_data_paths)
data_paths = list(audio_data_paths | json_data_paths)
exclude = set([
os.path.splitext(basename)[0]
for basename in os.listdir(args.output_path)
if basename.endswith('.json')
]) if args.skip_processed else None
data_paths = [
path for path in data_paths
if exclude is None or os.path.basename(path) not in exclude
]
text_pipeline, frontend, model, generator = setup(args)
val_dataset = datasets.AudioTextDataset(
data_paths, [text_pipeline],
args.sample_rate,
frontend=frontend if not args.frontend_in_model else None,
mono=args.mono,
time_padding_multiple=args.batch_time_padding_multiple,
audio_backend=args.audio_backend,
exclude=exclude,
max_duration=args.transcribe_first_n_sec,
mode='batched_channels'
if args.join_transcript else 'batched_transcript',
string_array_encoding=args.dataset_string_array_encoding,
debug_short_long_records_features_from_whole_normalized_signal=args.
debug_short_long_records_features_from_whole_normalized_signal)
print('Examples count: ', len(val_dataset))
val_meta = val_dataset.pop_meta()
val_data_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=None,
collate_fn=val_dataset.collate_fn,
num_workers=args.num_workers)
csv_sep = dict(tab='\t', comma=',')[args.csv_sep]
csv_lines = [] # only used if args.output_csv is True
oom_handler = utils.OomHandler(max_retries=args.oom_retries)
for i, (meta, s, x, xlen, y, ylen) in enumerate(val_data_loader):
print(f'Processing: {i}/{len(val_dataset)}')
meta = [val_meta[t['example_id']] for t in meta]
audio_path = meta[0]['audio_path']
audio_name = transcripts.audio_name(audio_path)
begin_end = [dict(begin=t['begin'], end=t['end']) for t in meta]
begin = torch.tensor([t['begin'] for t in begin_end],
dtype=torch.float)
end = torch.tensor([t['end'] for t in begin_end], dtype=torch.float)
#TODO WARNING assumes frontend not in dataset
if not args.frontend_in_model:
print('\n' * 10 + 'WARNING\n' * 5)
print(
'transcribe.py assumes frontend in model, in other case time alignment was incorrect'
)
print('WARNING\n' * 5 + '\n')
duration = x.shape[-1] / args.sample_rate
channel = [t['channel'] for t in meta]
speaker = [t['speaker'] for t in meta]
speaker_name = [t['speaker_name'] for t in meta]
if x.numel() == 0:
print(f'Skipping empty [{audio_path}].')
continue
try:
tic = time.time()
y, ylen = y.to(args.device), ylen.to(args.device)
log_probs, logits, olen = model(
x.squeeze(1).to(args.device), xlen.to(args.device))
print('Input:', audio_name)
print('Input time steps:', log_probs.shape[-1],
'| target time steps:', y.shape[-1])
print(
'Time: audio {audio:.02f} sec | processing {processing:.02f} sec'
.format(audio=sum(map(transcripts.compute_duration, meta)),
processing=time.time() - tic))
ts: shaping.Bt = duration * torch.linspace(
0, 1, steps=log_probs.shape[-1],
device=log_probs.device).unsqueeze(0).expand(x.shape[0], -1)
ref_segments = [[
dict(channel=channel[i],
begin=begin_end[i]['begin'],
end=begin_end[i]['end'],
ref=text_pipeline.postprocess(
text_pipeline.preprocess(meta[i]['ref'])))
] for i in range(len(meta))]
hyp_segments = [
alternatives[0] for alternatives in generator.generate(
tokenizer=text_pipeline.tokenizer,
log_probs=log_probs,
begin=begin,
end=end,
output_lengths=olen,
time_stamps=ts,
segment_text_key='hyp',
segment_extra_info=[
dict(speaker=s, speaker_name=sn, channel=c)
for s, sn, c in zip(speaker, speaker_name, channel)
])
]
hyp_segments = [
transcripts.map_text(text_pipeline.postprocess, hyp=hyp)
for hyp in hyp_segments
]
hyp, ref = '\n'.join(
transcripts.join(hyp=h)
for h in hyp_segments).strip(), '\n'.join(
transcripts.join(ref=r) for r in ref_segments).strip()
if args.verbose:
print('HYP:', hyp)
print('CER: {cer:.02%}'.format(cer=metrics.cer(hyp=hyp, ref=ref)))
tic_alignment = time.time()
if args.align and y.numel() > 0:
alignment: shaping.BY = ctc.alignment(
log_probs.permute(2, 0, 1),
y[:, 0, :], # assumed that 0 channel is char labels
olen,
ylen[:, 0],
blank=text_pipeline.tokenizer.eps_id,
pack_backpointers=args.pack_backpointers)
aligned_ts: shaping.Bt = ts.gather(1, alignment)
ref_segments = [
alternatives[0] for alternatives in generator.generate(
tokenizer=text_pipeline.tokenizer,
log_probs=torch.nn.functional.one_hot(
y[:,
0, :], num_classes=log_probs.shape[1]).permute(
0, 2, 1),
begin=begin,
end=end,
output_lengths=ylen,
time_stamps=aligned_ts,
segment_text_key='ref',
segment_extra_info=[
dict(speaker=s, speaker_name=sn, channel=c)
for s, sn, c in zip(speaker, speaker_name, channel)
])
]
ref_segments = [
transcripts.map_text(text_pipeline.postprocess, hyp=ref)
for ref in ref_segments
]
oom_handler.reset()
except:
if oom_handler.try_recover(model.parameters()):
print(f'Skipping {i} / {len(val_dataset)}')
continue
else:
raise
print('Alignment time: {:.02f} sec'.format(time.time() -
tic_alignment))
ref_transcript, hyp_transcript = [
sorted(transcripts.flatten(segments), key=transcripts.sort_key)
for segments in [ref_segments, hyp_segments]
]
if args.max_segment_duration:
if ref:
ref_segments = list(
transcripts.segment_by_time(ref_transcript,
args.max_segment_duration))
hyp_segments = list(
transcripts.segment_by_ref(hyp_transcript, ref_segments))
else:
hyp_segments = list(
transcripts.segment_by_time(hyp_transcript,
args.max_segment_duration))
ref_segments = [[] for _ in hyp_segments]
#### HACK for diarization
elif args.ref_transcript_path and args.join_transcript:
audio_name_hack = audio_name.split('.')[0]
#TODO: normalize ref field
ref_segments = [[t] for t in sorted(transcripts.load(
os.path.join(args.ref_transcript_path, audio_name_hack +
'.json')),
key=transcripts.sort_key)]
hyp_segments = list(
transcripts.segment_by_ref(hyp_transcript,
ref_segments,
set_speaker=True,
soft=False))
#### END OF HACK
has_ref = bool(transcripts.join(ref=transcripts.flatten(ref_segments)))
transcript = []
for hyp_transcript, ref_transcript in zip(hyp_segments, ref_segments):
hyp, ref = transcripts.join(hyp=hyp_transcript), transcripts.join(
ref=ref_transcript)
transcript.append(
dict(audio_path=audio_path,
ref=ref,
hyp=hyp,
speaker_name=transcripts.speaker_name(ref=ref_transcript,
hyp=hyp_transcript),
words=metrics.align_words(
*metrics.align_strings(hyp=hyp, ref=ref))
if args.align_words else [],
words_ref=ref_transcript,
words_hyp=hyp_transcript,
**transcripts.summary(hyp_transcript),
**(dict(cer=metrics.cer(hyp=hyp, ref=ref))
if has_ref else {})))
transcripts.collect_speaker_names(transcript,
set_speaker_data=True,
num_speakers=2)
filtered_transcript = list(
transcripts.prune(transcript,
align_boundary_words=args.align_boundary_words,
cer=args.prune_cer,
duration=args.prune_duration,
gap=args.prune_gap,
allowed_unk_count=args.prune_unk,
num_speakers=args.prune_num_speakers))
print('Filtered segments:', len(filtered_transcript), 'out of',
len(transcript))
if args.output_json:
transcript_path = os.path.join(args.output_path,
audio_name + '.json')
print(transcripts.save(transcript_path, filtered_transcript))
if args.output_html:
transcript_path = os.path.join(args.output_path,
audio_name + '.html')
print(
vis.transcript(transcript_path, args.sample_rate, args.mono,
transcript, filtered_transcript))
if args.output_txt:
transcript_path = os.path.join(args.output_path,
audio_name + '.txt')
with open(transcript_path, 'w') as f:
f.write(' '.join(t['hyp'].strip()
for t in filtered_transcript))
print(transcript_path)
if args.output_csv:
assert len({t['audio_path'] for t in filtered_transcript}) == 1
audio_path = filtered_transcript[0]['audio_path']
hyp = ' '.join(t['hyp'].strip() for t in filtered_transcript)
begin = min(t['begin'] for t in filtered_transcript)
end = max(t['end'] for t in filtered_transcript)
csv_lines.append(
csv_sep.join([audio_path, hyp,
str(begin),
str(end)]))
if args.logits:
logits_file_path = os.path.join(args.output_path,
audio_name + '.pt')
if args.logits_crop:
begin_end = [
dict(
zip(['begin', 'end'], [
t['begin'] + c / float(o) * (t['end'] - t['begin'])
for c in args.logits_crop
])) for o, t in zip(olen, begin_end)
]
logits_crop = [slice(*args.logits_crop) for o in olen]
else:
logits_crop = [slice(int(o)) for o in olen]
# TODO: filter ref / hyp by channel?
torch.save([
dict(audio_path=audio_path,
logits=l[..., logits_crop[i]],
**begin_end[i],
ref=ref,
hyp=hyp) for i, l in enumerate(logits.cpu())
], logits_file_path)
print(logits_file_path)
print('Done: {:.02f} sec\n'.format(time.time() - tic))
if args.output_csv:
transcript_path = os.path.join(args.output_path, 'transcripts.csv')
with open(transcript_path, 'w') as f:
f.write('\n'.join(csv_lines))
print(transcript_path)
def errors(
input_paths: typing.List[str],
output_path: typing.Optional[str] = None,
include_metrics: typing.List[str] = ('cer', 'wer',),
debug_audio: bool = False,
filter_fn: typing.Optional[typing.Callable[[typing.Tuple[dict]], bool]] = lambda x: True,
sort_fn: typing.Optional[typing.Callable[[typing.List[typing.Tuple[dict]]], typing.List[typing.Tuple[dict]]]] = lambda x: x
) -> str:
'''
Parameters:
input_paths: paths to json files with list of analyzed examples
output_path: path to output html (default: input_path[0]+.html)
debug_audio: include audio data into html if true
filter_fn: function to filter tuples of examples grouped by `audio_path`,
function input: tuple of examples in order same as in `input_paths`
function output: true to include examples into html, false otherwise
sort_fn: function to sort tuples of examples grouped by `audio_path`,
function input: list of tuples of examples, each tuple has same order as in `input_paths`
function output: same list but in sorted order
'''
grouped_examples = collections.defaultdict(list)
examples_count = {}
for path in input_paths:
examples = transcripts.load(path)
examples_count[path] = len(examples)
for example in examples:
grouped_examples[example['audio_path']].append(example)
grouped_examples = list(filter(lambda x: len(x) == len(input_paths), grouped_examples.values()))
not_found_examples_count = {path: count - len(grouped_examples) for path, count in examples_count.items()}
grouped_examples = list(filter(filter_fn, grouped_examples))
filtered_examples_count = {path: count - len(grouped_examples) - not_found_examples_count[path] for path, count in examples_count.items()}
grouped_examples = sort_fn(grouped_examples)
style = '''
.filters_table b.warning {color: red;}
table.metrics_table {border-collapse:collapse;}
.metrics_table th {padding: 5px; padding-left: 10px; text-align: left}
.metrics_table tr {padding: 5px;}
.metrics_table tr.new_section {border-top: 1px solid black; padding: 5px;}
.metrics_table td {border-left: 1px dashed black; border-right: 1px dashed black; padding: 5px; padding-left: 10px;}
'''
template = '''
<html>
<head>
<meta charset="utf-8">
<style>
{style}
</style>
<script>
{scripts}
</script>
</head>
<body>
<b style="padding: 10px">Filters</b><br><br>
Dropped (example not found in other files):<br>
<table class="filters_table">
{filter_not_found_table}
</table><br>
Dropped (filter_fn):
<table class="filters_table">
{filter_fn_table}
</table><br>
<table class="metrics_table">
{metrics_table}
</table>
</body>
</html>
'''
# Make filter "not found" table
def fmt_filter_table(filtered_count: dict) -> str:
filtered_table = []
for file_path, count in filtered_count.items():
css_class = 'warning' if count > 0 else ''
file_name = os.path.basename(file_path)
filtered_table.append(f'<tr><td>{file_name}</td><td><b class="{css_class}">{count}</b></td></tr>')
return '\n'.join(filtered_table)
filter_not_found_table = fmt_filter_table(not_found_examples_count)
# Make filter "filter_fn" table
filter_fn_table = fmt_filter_table(filtered_examples_count)
# Make averages table
def fmt_averages_table(include_metrics: typing.List[str], averages: dict) -> str:
header = '<tr><th>Averages</th>' + '<th></th>' * (len(include_metrics) + 2) + '</tr>\n'
header += '<tr><th></th>' + ''.join(f'<th>{metric_name}</th>' for metric_name in include_metrics) + '<th></th>' * 2 + '</tr>\n'
content = []
for i, (file_name, metric_values) in enumerate(averages.items()):
content_line = f'<td><b>{file_name}</b></td>' + ''.join(
f'<td>{metric_value:.2%}</td>' for metric_value in metric_values) + '<td></td>' * 2
if i == 0:
content_line = '<tr class="new_section">' + content_line + '</tr>'
else:
content_line = '<tr>' + content_line + '</tr>'
content.append(content_line)
content = '\n'.join(content)
footer = '<tr class="new_section" style="height: 30px">' + '<th></th>' * (len(include_metrics) + 3) + '</tr>\n'
return header + content + footer
averages = {}
for i, input_file in enumerate(input_paths):
file_name = os.path.basename(input_file)
file_examples = [examples[i] for examples in grouped_examples]
averages[file_name] = [metrics.nanmean(file_examples, metric_name) for metric_name in include_metrics]
average_table = fmt_averages_table(include_metrics, averages)
# Make examples table
def fmt_examples_table(include_metrics: typing.List[str], table_data: typing.List[dict], debug_audio: bool) -> str:
header = '<tr><th>Examples</th>' + '<th></th>' * (len(include_metrics) + 2) + '</tr>\n'
content = []
for i, examples_data in enumerate(table_data):
ref = '<pre>' + examples_data['ref'] + '</pre>'
audio_path = examples_data['audio_path']
embedded_audio = fmt_audio(audio_path, i) if debug_audio else ''
examples_header = f'<tr class="new_section"><td colspan="{len(include_metrics)+1}"><b>{i}.</b>{audio_path}</td><td>{embedded_audio}</td><td>ref: <pre>{ref}</pre></td></tr>'
examples_content = []
for i, example_data in enumerate(examples_data['examples']):
metric_values = [f'{value:.2%}' if value is not None else '-' for value in example_data['metric_values']]
file_name = example_data['file_name']
alignment = example_data['alignment']
hyp = '<pre>' + example_data['hyp'] + '</pre>'
content_line = (f'<td>{file_name}</td>' + ''.join(map('<td>{}</td>'.format, metric_values)) + f'<td>{alignment}</td><td>{hyp}</td>')
if i == 0:
examples_content.append('<tr class="new_section">' + content_line + '</tr>')
else:
examples_content.append('<tr>' + content_line + '</tr>')
content.append(examples_header)
content.extend(examples_content)
return header + '\n'.join(content)
table_data = []
for examples in grouped_examples:
examples_data = dict(
audio_path = examples[0]['audio_path'],
ref = examples[0]['ref_orig'],
examples = [])
for i, input_file in enumerate(input_paths):
examples_data['examples'].append(dict(
file_name = os.path.basename(input_file),
metric_values = [metrics.extract_metric_value(examples[i], metric_name) for metric_name in include_metrics],
alignment = fmt_alignment(examples[i]['alignment']),
hyp = examples[i]["hyp"]))
table_data.append(examples_data)
examples_data = fmt_examples_table(include_metrics, table_data, debug_audio)
# make output html
metrics_table = average_table + examples_data
report = template.format(style = style,
scripts = play_script if debug_audio else '',
filter_not_found_table = filter_not_found_table,
filter_fn_table = filter_fn_table,
metrics_table = metrics_table)
html_path = output_path or (input_paths[0] + '.html')
open(html_path, 'w').write(report)
return html_path
def __init__(
self,
data_paths: typing.List[str],
text_pipelines: typing.List[text_processing.ProcessingPipeline],
sample_rate: int,
mode: str = DEFAULT_MODE,
frontend: typing.Optional[torch.nn.Module] = None,
speaker_names: typing.Optional[typing.List[str]] = None,
max_audio_file_size: typing.Optional[float] = None, #bytes
min_duration: typing.Optional[float] = None,
max_duration: typing.Optional[float] = None,
max_num_channels: int = 2,
mono: bool = True,
audio_dtype: str = 'float32',
time_padding_multiple: int = 1,
audio_backend: typing.Optional[str] = None,
exclude: typing.Optional[typing.Set] = None,
bucket_fn: typing.Callable[[typing.List[typing.Dict]],
int] = lambda transcript: 0,
pop_meta: bool = False,
string_array_encoding: str = 'utf_16_le',
_print: typing.Callable = print,
debug_short_long_records_features_from_whole_normalized_signal:
bool = False,
duration_from_transcripts: bool = False,
):
self.debug_short_long_records_features_from_whole_normalized_signal = debug_short_long_records_features_from_whole_normalized_signal
self.mode = mode
self.min_duration = min_duration
self.max_duration = max_duration
self.max_audio_file_size = max_audio_file_size
self.text_pipelines = text_pipelines
self.frontend = frontend
self.sample_rate = sample_rate
self.time_padding_multiple = time_padding_multiple
self.mono = mono
self.audio_backend = audio_backend
self.audio_dtype = audio_dtype
data_paths = data_paths if isinstance(data_paths,
list) else [data_paths]
data_paths_ = []
for data_path in data_paths:
if os.path.isdir(data_path):
data_paths_.extend(
os.path.join(data_path, filename) for filename in filter(
audio.is_audio, os.listdir(data_path)))
else:
data_paths_.append(data_path)
data_paths = data_paths_
tic = time.time()
segments = []
for path in data_paths:
if audio.is_audio(path):
assert self.mono or self.mode != AudioTextDataset.DEFAULT_MODE, 'Only mono audio files allowed as dataset input in default mode'
if self.mono:
transcript = [
dict(audio_path=path,
channel=transcripts.channel_missing)
]
else:
transcript = [
dict(audio_path=path, channel=c)
for c in range(max_num_channels)
]
else:
transcript = transcripts.load(path)
segments.extend(transcript)
_print('Dataset reading time: ', time.time() - tic)
tic = time.time()
# get_or_else required because dictionary could contain None values which we want to replace.
# dict.get doesnt work in this case
get_or_else = lambda dictionary, key, default: dictionary[
key] if dictionary.get(key) is not None else default
for t in segments:
t['ref'] = get_or_else(t, 'ref', transcripts.ref_missing)
t['begin'] = get_or_else(t, 'begin', transcripts.time_missing)
t['end'] = get_or_else(t, 'end', transcripts.time_missing)
t['channel'] = get_or_else(
t, 'channel', transcripts.channel_missing
) if not self.mono else transcripts.channel_missing
transcripts.collect_speaker_names(segments,
speaker_names=speaker_names or [],
num_speakers=max_num_channels,
set_speaker_data=True)
buckets = []
grouped_segments = []
transcripts_len = []
speakers_len = []
if self.mode == AudioTextDataset.DEFAULT_MODE:
groupped_transcripts = ((i, [t]) for i, t in enumerate(segments))
else:
groupped_transcripts = itertools.groupby(
sorted(segments, key=transcripts.group_key),
transcripts.group_key)
for group_key, transcript in groupped_transcripts:
transcript = sorted(transcript, key=transcripts.sort_key)
if self.mode == AudioTextDataset.BATCHED_CHANNELS_MODE:
transcript = transcripts.join_transcript(
transcript,
self.mono,
duration_from_transcripts=duration_from_transcripts)
if exclude is not None:
allowed_audio_names = set(
transcripts.audio_name(t) for t in transcript
if transcripts.audio_name(t) not in exclude)
else:
allowed_audio_names = None
transcript = transcripts.prune(
transcript,
allowed_audio_names=allowed_audio_names,
duration=(
min_duration if min_duration is not None else 0.0,
max_duration if max_duration is not None else 24.0 * 3600,
), #24h
max_audio_file_size=max_audio_file_size)
transcript = list(transcript)
for t in transcript:
t['example_id'] = AudioTextDataset.get_example_id(t)
if len(transcript) == 0:
continue
bucket = bucket_fn(transcript)
for t in transcript:
t['bucket'] = bucket
speakers_len.append(
len(t['speaker']) if (
isinstance(t['speaker'], list)) else 1)
buckets.append(bucket)
grouped_segments.extend(transcript)
transcripts_len.append(len(transcript))
_print('Dataset construction time: ', time.time() - tic)
tic = time.time()
self.bucket = torch.tensor(buckets, dtype=torch.short)
self.audio_path = utils.TensorBackedStringArray(
[t['audio_path'] for t in grouped_segments],
encoding=string_array_encoding)
self.ref = utils.TensorBackedStringArray(
[t['ref'] for t in grouped_segments],
encoding=string_array_encoding)
self.begin = torch.tensor([t['begin'] for t in grouped_segments],
dtype=torch.float64)
self.end = torch.tensor([t['end'] for t in grouped_segments],
dtype=torch.float64)
self.channel = torch.tensor([t['channel'] for t in grouped_segments],
dtype=torch.int8)
self.example_id = utils.TensorBackedStringArray(
[t['example_id'] for t in grouped_segments],
encoding=string_array_encoding)
if self.mode == AudioTextDataset.BATCHED_CHANNELS_MODE:
self.speaker = torch.tensor([
speaker for t in grouped_segments for speaker in t['speaker']
],
dtype=torch.int64)
else:
self.speaker = torch.tensor(
[t['speaker'] for t in grouped_segments], dtype=torch.int64)
self.speaker_len = torch.tensor(speakers_len, dtype=torch.int16)
self.transcript_cumlen = torch.tensor(transcripts_len,
dtype=torch.int16).cumsum(
dim=0, dtype=torch.int64)
if pop_meta:
self.meta = {}
else:
self.meta = {t['example_id']: t for t in grouped_segments}
_print('Dataset tensors creation time: ', time.time() - tic)