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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 logging | |
| import os | |
| from typing import Any, List, Optional | |
| import librosa | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from fairseq.data.fairseq_dataset import FairseqDataset | |
| logger = logging.getLogger(__name__) | |
| def _collate_frames( | |
| frames: List[torch.Tensor], is_audio_input: bool = False | |
| ): | |
| """ | |
| Convert a list of 2D frames into a padded 3D tensor | |
| Args: | |
| frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is | |
| length of i-th frame and f_dim is static dimension of features | |
| Returns: | |
| 3D tensor of size len(frames)*len_max*f_dim where len_max is max of L[i] | |
| """ | |
| max_len = max(frame.size(0) for frame in frames) | |
| if is_audio_input: | |
| out = frames[0].new_zeros((len(frames), max_len)) | |
| else: | |
| out = frames[0].new_zeros((len(frames), max_len, frames[0].size(1))) | |
| for i, v in enumerate(frames): | |
| out[i, : v.size(0)] = v | |
| return out | |
| def load_audio(manifest_path, max_keep, min_keep): | |
| """manifest tsv: src_wav, src_nframe, tgt_wav, tgt_nframe, tgt_spkemb""" | |
| n_long, n_short = 0, 0 | |
| src_names, tgt_names, inds, sizes, tgt_sizes, spk_embeds = [], [], [], [], [], [] | |
| 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: | |
| src_names.append(items[0]) | |
| tgt_names.append(items[2]) | |
| tgt_sizes.append(items[3]) | |
| spk_embeds.append(items[4]) | |
| inds.append(ind) | |
| sizes.append(sz) | |
| tot = ind + 1 | |
| logger.info( | |
| ( | |
| f"max_keep={max_keep}, min_keep={min_keep}, " | |
| f"loaded {len(src_names)}, skipped {n_short} short and {n_long} long, " | |
| f"longest-loaded={max(sizes)}, shortest-loaded={min(sizes)}" | |
| ) | |
| ) | |
| return root, src_names, inds, tot, sizes, tgt_names, tgt_sizes, spk_embeds | |
| def logmelfilterbank( | |
| audio, | |
| sampling_rate, | |
| fft_size=1024, | |
| hop_size=256, | |
| win_length=None, | |
| window="hann", | |
| num_mels=80, | |
| fmin=80, | |
| fmax=7600, | |
| eps=1e-10, | |
| ): | |
| """Compute log-Mel filterbank feature. | |
| (https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/parallel_wavegan/bin/preprocess.py) | |
| Args: | |
| audio (ndarray): Audio signal (T,). | |
| sampling_rate (int): Sampling rate. | |
| fft_size (int): FFT size. | |
| hop_size (int): Hop size. | |
| win_length (int): Window length. If set to None, it will be the same as fft_size. | |
| window (str): Window function type. | |
| num_mels (int): Number of mel basis. | |
| fmin (int): Minimum frequency in mel basis calculation. | |
| fmax (int): Maximum frequency in mel basis calculation. | |
| eps (float): Epsilon value to avoid inf in log calculation. | |
| Returns: | |
| ndarray: Log Mel filterbank feature (#frames, num_mels). | |
| """ | |
| # get amplitude spectrogram | |
| x_stft = librosa.stft(audio, n_fft=fft_size, hop_length=hop_size, | |
| win_length=win_length, window=window, pad_mode="reflect") | |
| spc = np.abs(x_stft).T # (#frames, #bins) | |
| # get mel basis | |
| fmin = 0 if fmin is None else fmin | |
| fmax = sampling_rate / 2 if fmax is None else fmax | |
| mel_basis = librosa.filters.mel(sr=sampling_rate, n_fft=fft_size, n_mels=num_mels, fmin=fmin, fmax=fmax) | |
| return np.log10(np.maximum(eps, np.dot(spc, mel_basis.T))) | |
| class SpeechToSpeechDataset(FairseqDataset): | |
| def __init__( | |
| self, | |
| manifest_path: str, | |
| sample_rate: float, | |
| max_keep_sample_size: Optional[int] = None, | |
| min_keep_sample_size: Optional[int] = None, | |
| shuffle: bool = True, | |
| normalize: bool = False, | |
| reduction_factor: int = 1, | |
| ): | |
| self.audio_root, self.audio_names, inds, tot, self.wav_sizes, self.tgt_audios, self.tgt_sizes, self.tgt_spkembs = load_audio( | |
| manifest_path, max_keep_sample_size, min_keep_sample_size | |
| ) | |
| self.sample_rate = sample_rate | |
| self.shuffle = shuffle | |
| self.normalize = normalize | |
| self.reduction_factor = reduction_factor | |
| logger.info( | |
| f"reduction_factor={reduction_factor}, normalize={normalize}" | |
| ) | |
| def get_audio(self, index): | |
| import soundfile as sf | |
| wav_fbank = [] | |
| for name in [self.audio_names[index], self.tgt_audios[index]]: | |
| wav_path = os.path.join(self.audio_root, name) | |
| wav, cur_sample_rate = sf.read(wav_path) | |
| wav = torch.from_numpy(wav).float() | |
| fbank = logmelfilterbank( | |
| wav.view(-1).cpu().numpy(), 16000 | |
| ) | |
| fbank = torch.from_numpy(fbank).float() | |
| wav = self.postprocess(wav, cur_sample_rate) | |
| wav_fbank.append(wav) | |
| wav_fbank.append(fbank) | |
| src_wav, src_fbank, tgt_wav, tgt_fbank = wav_fbank | |
| return src_wav, src_fbank, tgt_wav, tgt_fbank | |
| def __getitem__(self, index): | |
| src_wav, src_fbank, tgt_wav, tgt_fbank = self.get_audio(index) | |
| spkembs = np.load(os.path.join(self.audio_root, self.tgt_spkembs[index])) | |
| spkembs = torch.from_numpy(spkembs).float() | |
| name = self.audio_names[index].replace("/", ".").replace(".wav", "") + "-" + self.tgt_audios[index].replace("/", ".").replace(".wav", "") + ".wav" | |
| return {"id": index, "source": src_wav, "target": tgt_fbank, "spkembs": spkembs, "audio_name": name, "tgt_name": self.tgt_audios[index]} | |
| 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 | |
| ) | |
| fbanks = [s["target"] for s in samples] | |
| fbank_sizes = [len(s) for s in fbanks] | |
| collated_fbanks = _collate_frames(fbanks) | |
| collated_fbanks_size = torch.tensor(fbank_sizes, dtype=torch.long) | |
| # thin out frames for reduction factor (B, Lmax, odim) -> (B, Lmax//r, odim) | |
| if self.reduction_factor > 1: | |
| collated_fbanks_in = collated_fbanks[:, self.reduction_factor - 1 :: self.reduction_factor] | |
| collated_fbanks_size_in = collated_fbanks_size.new([torch.div(olen, self.reduction_factor, rounding_mode='floor') for olen in collated_fbanks_size]) | |
| else: | |
| collated_fbanks_in, collated_fbanks_size_in = collated_fbanks, collated_fbanks_size | |
| prev_output_tokens = torch.cat( | |
| [collated_fbanks_in.new_zeros((collated_fbanks_in.shape[0], 1, collated_fbanks_in.shape[2])), collated_fbanks_in[:, :-1]], dim=1 | |
| ) | |
| # make labels for stop prediction | |
| labels = collated_fbanks.new_zeros(collated_fbanks.size(0), collated_fbanks.size(1)) | |
| for i, l in enumerate(fbank_sizes): | |
| labels[i, l - 1 :] = 1.0 | |
| spkembs = _collate_frames([s["spkembs"] for s in samples], is_audio_input=True) | |
| net_input = { | |
| "source": collated_audios, | |
| "padding_mask": padding_mask, | |
| "prev_output_tokens": prev_output_tokens, | |
| "tgt_lengths": collated_fbanks_size_in, | |
| "spkembs": spkembs, | |
| "task_name": "s2s", | |
| } | |
| batch = { | |
| "id": torch.LongTensor([s["id"] for s in samples]), | |
| "name": [s["audio_name"] for s in samples], | |
| "tgt_name": [s["tgt_name"] for s in samples], | |
| "net_input": net_input, | |
| "labels": labels, | |
| "dec_target": collated_fbanks, | |
| "dec_target_lengths": collated_fbanks_size, | |
| "src_lengths": torch.LongTensor(audio_sizes), | |
| "task_name": "s2s", | |
| "ntokens": sum(audio_sizes), | |
| "target": collated_fbanks, | |
| } | |
| 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 num_tokens(self, index): | |
| return self.wav_sizes[index] | |
| def size(self, index): | |
| return self.wav_sizes[index], self.tgt_sizes[index] | |
| def sizes(self): | |
| return np.array(self.wav_sizes) | |
| def can_reuse_epoch_itr_across_epochs(self): | |
| """No cache dataset if dataset is large-scale. Cache dataset for small dataset.""" | |
| return True | |
| 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 | |