| import logging |
| import torch |
| import torch.nn.functional as F |
| from fairseq.data.audio.raw_audio_dataset import RawAudioDataset |
| from typing import Tuple |
| try: |
| import kaldiio |
| except: |
| kaldiio = None |
| import warnings |
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| class ArkDataset(RawAudioDataset): |
| def __init__( |
| self, |
| wav_scp, |
| dur_scp, |
| sr = 24000, |
| max_dur = 20, |
| num_buckets=0, |
| normalize=False, |
| ): |
| super().__init__( |
| sample_rate=sr, |
| max_sample_size=max_dur*sr, |
| min_sample_size=1200, |
| shuffle=True, |
| pad=True, |
| normalize=normalize, |
| compute_mask=False, |
| ) |
| self.sr = sr |
| self.max_dur = max_dur |
| self.normalize = normalize |
|
|
| logger.info("Loading Kaldi scp files from {}".format(wav_scp)) |
|
|
| self.wav_data = kaldiio.load_scp(wav_scp) |
| self.keys = list(self.wav_data.keys()) |
| dur_data = {} |
| keys_set = set(self.keys) |
| |
| with open(dur_scp, 'r') as f: |
| for line in f: |
| line = line.strip().split() |
| if line[0] in keys_set: |
| dur_data[line[0]] = float(line[-1]) |
| self.sizes = [int(dur_data[k]*self.sr/100) for k in self.keys] |
|
|
| logger.info("Loading Kaldi scp files done") |
|
|
| self.dataset_len = len(self.keys) |
| self.set_bucket_info(num_buckets) |
|
|
| def __len__(self): |
| return self.dataset_len |
|
|
| def __getitem__(self, idx): |
| pass |
|
|
| def size(self, idx): |
| pass |
| |
| def postprocess(self, wav): |
| pass |
| |
| def collater(self, samples): |
| pass |
|
|
|
|
