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| # -------------------------------------------------------- | |
| # ArTST: Arabic Text and Speech Transformer (https://arxiv.org/abs/2310.16621) | |
| # Github source: https://github.com/mbzuai-nlp/ArTST | |
| # Based on speecht5, fairseq and espnet code bases | |
| # https://github.com/microsoft/SpeechT5/tree/main/SpeechT5; https://github.com/pytorch/fairseq; https://github.com/espnet/espnet | |
| # -------------------------------------------------------- | |
| import itertools | |
| import logging | |
| import os | |
| import mmap | |
| from typing import Any, List, Optional | |
| import numpy as np | |
| import torch | |
| torch.set_printoptions(profile="full") | |
| import torch.nn.functional as F | |
| from fairseq.data import data_utils, Dictionary | |
| from fairseq.data.fairseq_dataset import FairseqDataset | |
| logger = logging.getLogger(__name__) | |
| def load_audio(manifest_path, max_keep, min_keep): | |
| n_long, n_short = 0, 0 | |
| names, inds, sizes = [], [], [] | |
| with open(manifest_path) as f: | |
| root = f.readline().strip() | |
| for ind, line in enumerate(f): | |
| items = line.strip().split("\t") | |
| assert len(items) >= 2, line | |
| sz = int(items[1]) | |
| if min_keep is not None and sz < min_keep: | |
| n_short += 1 | |
| elif max_keep is not None and sz > max_keep: | |
| n_long += 1 | |
| else: | |
| names.append(items[0]) | |
| inds.append(ind) | |
| sizes.append(sz) | |
| tot = ind + 1 | |
| logger.info( | |
| ( | |
| f"max_keep={max_keep}, min_keep={min_keep}, " | |
| f"loaded {len(names)}, skipped {n_short} short and {n_long} long, " | |
| f"longest-loaded={max(sizes)}, shortest-loaded={min(sizes)}" | |
| ) | |
| ) | |
| return root, names, inds, tot, sizes | |
| def load_label(label_path, inds, tot): | |
| with open(label_path) as f: | |
| labels = [line.rstrip() for line in f] | |
| assert ( | |
| len(labels) == tot | |
| ), f"number of labels does not match ({len(labels)} != {tot})" | |
| labels = [labels[i] for i in inds] | |
| return labels | |
| def load_label_offset(label_path, inds, tot): | |
| with open(label_path) as f: | |
| # Hawau: | |
| # changed line length reading as it's incorrect | |
| code_lengths = [len(line.encode("utf-8")) for line in f] #original | |
| # code_lengths = [len(line) for line in f] #fix | |
| assert ( | |
| len(code_lengths) == tot | |
| ), f"number of labels does not match ({len(code_lengths)} != {tot})" | |
| offsets = list(itertools.accumulate([0] + code_lengths)) | |
| offsets = [(offsets[i], offsets[i + 1]) for i in inds] | |
| return offsets | |
| class SpeechToTextDataset(FairseqDataset): | |
| def __init__( | |
| self, | |
| manifest_path: str, | |
| sample_rate: float, | |
| label_paths: List[str], | |
| label_processors: Optional[List[Any]] = None, | |
| max_keep_sample_size: Optional[int] = None, | |
| min_keep_sample_size: Optional[int] = None, | |
| shuffle: bool = True, | |
| normalize: bool = False, | |
| store_labels: bool = True, | |
| tgt_dict: Optional[Dictionary] = None, | |
| tokenizer = None, | |
| ): | |
| self.audio_root, self.audio_names, inds, tot, self.wav_sizes = load_audio( | |
| manifest_path, max_keep_sample_size, min_keep_sample_size | |
| ) | |
| self.sample_rate = sample_rate | |
| self.shuffle = shuffle | |
| self.tgt_dict = tgt_dict | |
| self.tokenizer = tokenizer | |
| self.num_labels = len(label_paths) | |
| self.label_processors = label_processors | |
| self.store_labels = store_labels | |
| if store_labels: | |
| self.label_list = [load_label(p, inds, tot) for p in label_paths] | |
| logger.info(f"label_list: {self.label_list}") | |
| else: | |
| self.label_paths = label_paths | |
| self.label_offsets_list = [ | |
| load_label_offset(p, inds, tot) for p in label_paths | |
| ] | |
| # logger.info(f"label_offsets_list: {self.label_offsets_list}") | |
| assert label_processors is None or len(label_processors) == self.num_labels | |
| self.normalize = normalize | |
| logger.info( | |
| f"normalize={normalize}" | |
| ) | |
| def get_audio(self, index): | |
| import soundfile as sf | |
| # Hawau: | |
| # logger.info(f"loaded_audio: {self.audio_names[index]}") | |
| wav_path = os.path.join(self.audio_root, self.audio_names[index]) | |
| wav, cur_sample_rate = sf.read(wav_path) | |
| wav = torch.from_numpy(wav).float() | |
| wav = self.postprocess(wav, cur_sample_rate) | |
| return wav | |
| def get_label(self, index, label_idx): | |
| if self.store_labels: | |
| label = self.label_list[label_idx][index] | |
| else: | |
| # list slicing method | |
| # with open(self.label_paths[label_idx]) as f: | |
| # offset_s, offset_e = self.label_offsets_list[label_idx][index] | |
| # # Hawau: | |
| # # f.seek(offset_s) | |
| # # label = f.read(offset_e - offset_s) | |
| # label = f.read()[offset_s : offset_e] | |
| # Hawau: | |
| # mmap method | |
| with open(self.label_paths[label_idx], encoding='utf-8') as f: | |
| with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm: | |
| offset_s, offset_e = self.label_offsets_list[label_idx][index] | |
| label = mm[offset_s:offset_e].decode("utf-8") | |
| # Hawau: | |
| # logger.info(f"loaded_label: {label}") | |
| if self.tokenizer is not None: | |
| label = self.tokenizer.encode(label) | |
| if self.label_processors is not None: | |
| label = self.label_processors[label_idx](label) | |
| # logger.info(f"processed_label: {label}") | |
| return label | |
| def get_labels(self, index): | |
| return [self.get_label(index, i) for i in range(self.num_labels)] | |
| def __getitem__(self, index): | |
| wav = self.get_audio(index) | |
| labels = self.get_labels(index) | |
| return {"id": index, "source": wav, "label_list": labels} | |
| def __len__(self): | |
| return len(self.wav_sizes) | |
| def collater(self, samples): | |
| samples = [s for s in samples if s["source"] is not None] | |
| if len(samples) == 0: | |
| return {} | |
| audios = [s["source"] for s in samples] | |
| audio_sizes = [len(s) for s in audios] | |
| audio_size = max(audio_sizes) | |
| collated_audios, padding_mask = self.collater_audio( | |
| audios, audio_size | |
| ) | |
| targets_by_label = [ | |
| [s["label_list"][i] for s in samples] for i in range(self.num_labels) | |
| ] | |
| targets_list, lengths_list, ntokens_list = self.collater_label(targets_by_label) | |
| # Hawau: | |
| # logger.info(f'targets_list: {targets_list}') | |
| decoder_label = [ | |
| torch.cat((targets_list[0][i, :lengths_list[0][i]], torch.tensor([self.tgt_dict.eos()])), 0).long() | |
| for i in range(targets_list[0].size(0)) | |
| ] | |
| decoder_target = data_utils.collate_tokens( | |
| decoder_label, | |
| self.tgt_dict.pad(), | |
| self.tgt_dict.eos(), | |
| left_pad=False, | |
| move_eos_to_beginning=False, | |
| ) | |
| decoder_target_lengths = torch.tensor( | |
| [x.size(0) for x in decoder_label], dtype=torch.long | |
| ) | |
| prev_output_tokens = data_utils.collate_tokens( | |
| decoder_label, | |
| self.tgt_dict.pad(), | |
| self.tgt_dict.eos(), | |
| left_pad=False, | |
| move_eos_to_beginning=True, | |
| ) | |
| net_input = { | |
| "source": collated_audios, | |
| "padding_mask": padding_mask, | |
| "prev_output_tokens": prev_output_tokens, | |
| "task_name": "s2t", | |
| } | |
| batch = { | |
| "id": torch.LongTensor([s["id"] for s in samples]), | |
| "net_input": net_input, | |
| "target": decoder_target, | |
| "target_lengths": decoder_target_lengths, | |
| "task_name": "s2t", | |
| "ntokens": ntokens_list[0] | |
| } | |
| return batch | |
| def collater_audio(self, audios, audio_size): | |
| collated_audios = audios[0].new_zeros(len(audios), audio_size) | |
| padding_mask = ( | |
| torch.BoolTensor(collated_audios.shape).fill_(False) | |
| ) | |
| for i, audio in enumerate(audios): | |
| diff = len(audio) - audio_size | |
| if diff == 0: | |
| collated_audios[i] = audio | |
| elif diff < 0: | |
| collated_audios[i] = torch.cat([audio, audio.new_full((-diff,), 0.0)]) | |
| padding_mask[i, diff:] = True | |
| else: | |
| raise Exception("Diff should not be larger than 0") | |
| return collated_audios, padding_mask | |
| def collater_seq_label(self, targets, pad): | |
| lengths = torch.LongTensor([len(t) for t in targets]) | |
| ntokens = lengths.sum().item() | |
| targets = data_utils.collate_tokens(targets, pad_idx=pad, left_pad=False) | |
| return targets, lengths, ntokens | |
| def collater_label(self, targets_by_label): | |
| targets_list, lengths_list, ntokens_list = [], [], [] | |
| itr = zip(targets_by_label, [self.tgt_dict.pad()]) | |
| for targets, pad in itr: | |
| # Hawau: | |
| # logger.info(f'targets: {targets}') | |
| targets, lengths, ntokens = self.collater_seq_label(targets, pad) | |
| targets_list.append(targets) | |
| lengths_list.append(lengths) | |
| ntokens_list.append(ntokens) | |
| return targets_list, lengths_list, ntokens_list | |
| def num_tokens(self, index): | |
| return self.size(index) | |
| def size(self, index): | |
| return self.wav_sizes[index] | |
| def sizes(self): | |
| return np.array(self.wav_sizes) | |
| def ordered_indices(self): | |
| if self.shuffle: | |
| order = [np.random.permutation(len(self))] | |
| else: | |
| order = [np.arange(len(self))] | |
| order.append(self.wav_sizes) | |
| return np.lexsort(order)[::-1] | |
| def postprocess(self, wav, cur_sample_rate): | |
| if wav.dim() == 2: | |
| wav = wav.mean(-1) | |
| assert wav.dim() == 1, wav.dim() | |
| if cur_sample_rate != self.sample_rate: | |
| raise Exception(f"sr {cur_sample_rate} != {self.sample_rate}") | |
| if self.normalize: | |
| with torch.no_grad(): | |
| wav = F.layer_norm(wav, wav.shape) | |
| return wav | |