def __init__(self,
data_dir,
manifest_fpaths,
tokenizer,
sample_rate=16000,
min_duration=0.1,
max_duration=float("inf"),
max_utts=0,
normalize_transcripts=True,
trim_silence=False,
speed_perturbation=None,
ignore_offline_speed_perturbation=False):
"""Loads audio, transcript and durations listed in a .json file.
Args:
data_dir: absolute path to dataset folder
manifest_filepath: relative path from dataset folder
to manifest json as described above. Can be coma-separated paths.
tokenizer: class converting transcript to tokens
min_duration (int): skip audio shorter than threshold
max_duration (int): skip audio longer than threshold
max_utts (int): limit number of utterances
normalize_transcripts (bool): normalize transcript text
trim_silence (bool): trim leading and trailing silence from audio
ignore_offline_speed_perturbation (bool): use precomputed speed perturbation
Returns:
tuple of Tensors
"""
self.data_dir = data_dir
self.tokenizer = tokenizer
self.punctuation_map = punctuation_map(self.tokenizer.charset)
self.max_utts = max_utts
self.normalize_transcripts = normalize_transcripts
self.ignore_offline_speed_perturbation = ignore_offline_speed_perturbation
self.min_duration = min_duration
self.max_duration = max_duration
self.trim_silence = trim_silence
self.sample_rate = sample_rate
perturbations = []
if speed_perturbation is not None:
perturbations.append(SpeedPerturbation(**speed_perturbation))
self.perturbations = perturbations
self.max_duration = max_duration
self.samples = []
self.duration = 0.0
self.duration_filtered = 0.0
for fpath in manifest_fpaths:
self._load_json_manifest(fpath)
def tokenize(self, transcripts):
transcripts = [transcripts[i] for i in range(len(transcripts))]
if self.normalize_transcripts:
transcripts = [
normalize_string(
t,
self.tokenizer.charset,
punctuation_map(self.tokenizer.charset)
) for t in transcripts
]
transcripts = [self.tokenizer.tokenize(t) for t in transcripts]
transcripts = [torch.tensor(t) for t in transcripts]
self.tr = np.array(transcripts, dtype=object)
self.t_sizes = torch.tensor([len(t) for t in transcripts], dtype=torch.int32)
def _gen_transcripts(self, labels, normalize_transcripts: bool = True):
"""
Generate transcripts in format expected by NN
"""
lists = [
self._str2list(
normalize_string(self.transcripts[lab.item()], self.symbols,
punctuation_map(self.symbols)))
for lab in labels
] if normalize_transcripts else [
self._str2list(self.transcripts[lab.item()]) for lab in labels
]
sizes = self._pad_lists(lists)
return torch.tensor(lists).cuda(), torch.tensor(
sizes, dtype=torch.int32).cuda()
def tokenize(self, transcripts):
transcripts = [transcripts[i] for i in range(len(transcripts))]
if self.normalize_transcripts:
transcripts = [
normalize_string(t, self.tokenizer.charset,
punctuation_map(self.tokenizer.charset))
for t in transcripts
]
transcripts = [self.tokenizer.tokenize(t) for t in transcripts]
transcripts = [torch.tensor(t) for t in transcripts]
self.tr = np.array(transcripts, dtype=object)
self.t_sizes = torch.tensor([len(t) for t in transcripts],
dtype=torch.int32)
self._min_token_sequence_len = int(torch.min(self.t_sizes))
self._max_token_sequence_len = int(torch.max(self.t_sizes))
self.padded_tr = [
np.pad(t, (0, self._max_token_sequence_len - ts))
for t, ts in zip(self.tr, self.t_sizes)
]
self.padded_tr = np.array(self.padded_tr)