File size: 3,717 Bytes
f55a095 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import torch
from transformers import AutoFeatureExtractor
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
from preprocessing.ast_processor import ast
from util_stats.local_stats import local_extract_phn_frame_probs
from util_stats.global_stats import global_extract_phn_frame_probs
import numpy as np
import pickle
import torch.nn.functional as F
from load_data.extract_fbanks import Mel_Spectrogram
extractor = Mel_Spectrogram()
with open('new_lbl2ind.pkl', 'rb') as f:
lbl2ind = pickle.load(f)
with open('new_spk.pkl', 'rb') as f:
unique_speaker_ids = pickle.load(f)
# change the labels
number_Of_spks = len(unique_speaker_ids)
@dataclass
class DataCollatorWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
processor (:class:`~transformers.Wav2Vec2Processor`)
The processor used for proccessing the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
max_length_labels (:obj:`int`, `optional`):
Maximum length of the ``labels`` returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
"""
padding: Union[bool, str] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
pad_to_multiple_of_labels: Optional[int] = None
flag_global_local: Optional[str] = None
dic_train_phn_frequency: Optional [dict] = None
dic_train_frame_frequency: Optional [dict] = None
lbl2ind: Optional [dict] = None
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need
# different padding methods
batch={}
batch['input_values']= [features[idx]['audio_tensor'].squeeze(0) for idx in range(len(features))]
batch["prompt"] = [features[idx]["prompt"] for idx in range(len(features))]
batch["answer"] = [features[idx]["answer"] for idx in range(len(features))]
batch["filename"] = [features[idx]["filename"] for idx in range(len(features))]
# batch["no_hot_encode"] = torch.tensor([lbl2ind[features[idx]['sid']] for idx in range(len(features))])
batch["no_hot_encode"] = torch.tensor([0 for idx in range(len(features))])
# if batch["no_hot_encode"].numel():
batch["labels"]= F.one_hot(batch["no_hot_encode"], number_Of_spks)
batch['input_values'] = extractor(torch.stack(batch['input_values']))
return batch
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