# -*- coding: utf-8 -*- # """*********************************************************************************************""" # FileName [ expert.py ] # Synopsis [ the speech separation downstream wrapper ] # Source [ Reference some code from https://github.com/funcwj/uPIT-for-speech-separation and https://github.com/asteroid-team/asteroid ] # Author [ Zili Huang ] # Copyright [ Copyright(c), Johns Hopkins University ] """*********************************************************************************************""" ############### # IMPORTATION # ############### import os import math import random import h5py import numpy as np from pathlib import Path from collections import defaultdict import librosa # -------------# import torch import torch.nn as nn from torch.utils.data import DataLoader from torch.nn.utils.rnn import pack_sequence, pad_sequence import torch.nn.functional as F # -------------# from .model import SepRNN from .dataset import SeparationDataset from asteroid.metrics import get_metrics from .loss import SepLoss, SISDRLoss from itertools import permutations device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") COMPUTE_METRICS = ["si_sdr"] EPS = 1e-10 def match_length(feat_list, length_list): assert len(feat_list) == len(length_list) bs = len(length_list) new_feat_list = [] for i in range(bs): assert abs(feat_list[i].size(0) - length_list[i]) < 5 if feat_list[i].size(0) == length_list[i]: new_feat_list.append(feat_list[i]) elif feat_list[i].size(0) > length_list[i]: new_feat_list.append(feat_list[i][:length_list[i], :]) else: new_feat = torch.zeros(length_list[i], feat_list[i].size(1)).to(feat_list[i].device) new_feat[:feat_list[i].size(0), :] = feat_list[i] new_feat_list.append(new_feat) return new_feat_list class DownstreamExpert(nn.Module): """ Used to handle downstream-specific operations eg. downstream forward, metric computation, contents to log """ def __init__(self, upstream_dim, upstream_rate, downstream_expert, expdir, **kwargs): super(DownstreamExpert, self).__init__() self.upstream_dim = upstream_dim self.upstream_rate = upstream_rate self.datarc = downstream_expert["datarc"] self.loaderrc = downstream_expert["loaderrc"] self.modelrc = downstream_expert["modelrc"] self.expdir = expdir self.train_dataset = SeparationDataset( data_dir=self.loaderrc["train_dir"], rate=self.datarc['rate'], src=self.datarc['src'], tgt=self.datarc['tgt'], n_fft=self.datarc['n_fft'], hop_length=self.upstream_rate, win_length=self.datarc['win_length'], window=self.datarc['window'], center=self.datarc['center'], ) self.dev_dataset = SeparationDataset( data_dir=self.loaderrc["dev_dir"], rate=self.datarc['rate'], src=self.datarc['src'], tgt=self.datarc['tgt'], n_fft=self.datarc['n_fft'], hop_length=self.upstream_rate, win_length=self.datarc['win_length'], window=self.datarc['window'], center=self.datarc['center'], ) self.test_dataset = SeparationDataset( data_dir=self.loaderrc["test_dir"], rate=self.datarc['rate'], src=self.datarc['src'], tgt=self.datarc['tgt'], n_fft=self.datarc['n_fft'], hop_length=self.upstream_rate, win_length=self.datarc['win_length'], window=self.datarc['window'], center=self.datarc['center'], ) if self.modelrc["model"] == "SepRNN": self.model = SepRNN( input_dim=self.upstream_dim, num_bins=int(self.datarc['n_fft'] / 2 + 1), rnn=self.modelrc["rnn"], num_spks=self.datarc['num_speakers'], num_layers=self.modelrc["rnn_layers"], hidden_size=self.modelrc["hidden_size"], dropout=self.modelrc["dropout"], non_linear=self.modelrc["non_linear"], bidirectional=self.modelrc["bidirectional"] ) else: raise ValueError("Model type not defined.") self.loss_type = self.modelrc["loss_type"] self.log = self.modelrc["log"] self.objective = SepLoss(self.datarc['num_speakers'], self.loss_type, self.modelrc["mask_type"], self.log) self.register_buffer("best_score", torch.ones(1) * -10000) def _get_train_dataloader(self, dataset): return DataLoader( dataset, batch_size=self.loaderrc["train_batchsize"], shuffle=True, num_workers=self.loaderrc["num_workers"], drop_last=False, pin_memory=True, collate_fn=dataset.collate_fn, ) def _get_eval_dataloader(self, dataset): return DataLoader( dataset, batch_size=self.loaderrc["eval_batchsize"], shuffle=False, num_workers=self.loaderrc["num_workers"], drop_last=False, pin_memory=True, collate_fn=dataset.collate_fn, ) def get_dataloader(self, mode): """ Args: mode: string 'train', 'dev' or 'test' Return: a torch.utils.data.DataLoader returning each batch in the format of: [wav1, wav2, ...], your_other_contents1, your_other_contents2, ... where wav1, wav2 ... are in variable length each wav is torch.FloatTensor in cpu with: 1. dim() == 1 2. sample_rate == 16000 3. directly loaded by torchaudio """ if mode == "train": return self._get_train_dataloader(self.train_dataset) elif mode == "dev": return self._get_eval_dataloader(self.dev_dataset) elif mode == "test": return self._get_eval_dataloader(self.test_dataset) def forward(self, mode, features, uttname_list, source_attr, source_wav, target_attr, target_wav_list, feat_length, wav_length, records, **kwargs): """ Args: mode: string 'train', 'dev' or 'test' for this forward step features: list of unpadded features [feat1, feat2, ...] each feat is in torch.FloatTensor and already put in the device assigned by command-line args uttname_list: list of utterance names source_attr: source_attr is a dict containing the STFT information for the mixture. source_attr['magnitude'] stores the STFT magnitude, source_attr['phase'] stores the STFT phase and source_attr['stft'] stores the raw STFT feature. The shape is [bs, max_length, feat_dim] source_wav: source_wav contains the raw waveform for the mixture, and it has the shape of [bs, max_wav_length] target_attr: similar to source_attr, it contains the STFT information for individual sources. It only has two keys ('magnitude' and 'phase') target_attr['magnitude'] is a list of length n_srcs, and target_attr['magnitude'][i] has the shape [bs, max_length, feat_dim] target_wav_list: target_wav_list contains the raw waveform for the individual sources, and it is a list of length n_srcs. target_wav_list[0] has the shape [bs, max_wav_length] feat_length: length of STFT features wav_length: length of raw waveform records: defaultdict(list), by appending contents into records, these contents can be averaged and logged on Tensorboard later by self.log_records every log_step Return: loss: the loss to be optimized, should not be detached """ # match the feature length to STFT feature length features = match_length(features, feat_length) features = pack_sequence(features) mask_list = self.model(features) # evaluate the separation quality of predict sources if mode == 'dev' or mode == 'test': if self.log == 'none': predict_stfts = [torch.squeeze(m.cpu() * source_attr['stft']) for m in mask_list] predict_stfts_np = [np.transpose(s.data.numpy()) for s in predict_stfts] elif self.log == 'log1p': phase = source_attr['stft'] / (source_attr['stft'].abs() + EPS) predict_stfts = [torch.squeeze(torch.expm1(m.cpu() * torch.log1p(source_attr['stft'].abs())) * phase) for m in mask_list] predict_stfts_np = [np.transpose(s.data.numpy()) for s in predict_stfts] else: raise ValueError("log type not defined.") assert len(wav_length) == 1 # reconstruct the signal using iSTFT predict_srcs_np = [librosa.util.fix_length(librosa.istft(stft_mat, hop_length=self.upstream_rate, win_length=self.datarc['win_length'], window=self.datarc['window'], center=self.datarc['center']), size=wav_length[0]) for stft_mat in predict_stfts_np] predict_srcs_np = np.stack(predict_srcs_np, 0) gt_srcs_np = torch.cat(target_wav_list, 0).data.cpu().numpy() mix_np = source_wav.data.cpu().numpy() perm_list = [list(perm) for perm in list(permutations(range(len(gt_srcs_np))))] utt_metrics_list = [get_metrics( mix_np, gt_srcs_np, predict_srcs_np[perm, :], sample_rate = self.datarc['rate'], metrics_list = COMPUTE_METRICS, compute_permutation=False, ) for perm in perm_list] utt_metrics = {} for metric in COMPUTE_METRICS: input_metric = "input_" + metric utt_metrics[input_metric] = utt_metrics_list[0][input_metric] utt_metrics[metric] = np.max([k[metric] for k in utt_metrics_list]) for metric in COMPUTE_METRICS: input_metric = "input_" + metric assert metric in utt_metrics and input_metric in utt_metrics imp = utt_metrics[metric] - utt_metrics[input_metric] if metric not in records: records[metric] = [] if metric == "si_sdr": records[metric].append(imp) elif metric == "stoi" or metric == "pesq": records[metric].append(utt_metrics[metric]) else: raise ValueError("Metric type not defined.") assert 'batch_id' in kwargs if kwargs['batch_id'] % 1000 == 0: # Save the prediction every 1000 examples records['mix'].append(mix_np) records['hypo'].append(predict_srcs_np) records['ref'].append(gt_srcs_np) records['uttname'].append(uttname_list[0]) if self.loss_type == "MSE" or self.loss_type == "L1": loss = self.objective.compute_loss(mask_list, feat_length, source_attr, target_attr) else: raise ValueError("Loss type not defined.") records["loss"].append(loss.item()) return loss # interface def log_records( self, mode, records, logger, global_step, batch_ids, total_batch_num, **kwargs ): """ Args: mode: string 'train': records and batchids contain contents for `log_step` batches `log_step` is defined in your downstream config eg. downstream/example/config.yaml 'dev' or 'test' : records and batchids contain contents for the entire evaluation dataset records: defaultdict(list), contents already appended logger: Tensorboard SummaryWriter please use f'{prefix}your_content_name' as key name to log your customized contents global_step: The global_step when training, which is helpful for Tensorboard logging batch_ids: The batches contained in records when enumerating over the dataloader total_batch_num: The total amount of batches in the dataloader Return: a list of string Each string is a filename we wish to use to save the current model according to the evaluation result, like the best.ckpt on the dev set You can return nothing or an empty list when no need to save the checkpoint """ if mode == 'train': avg_loss = np.mean(records["loss"]) logger.add_scalar( f"separation_stft2/{mode}-loss", avg_loss, global_step=global_step ) return [] else: avg_loss = np.mean(records["loss"]) logger.add_scalar( f"separation_stft2/{mode}-loss", avg_loss, global_step=global_step ) with (Path(self.expdir) / f"{mode}_metrics.txt").open("w") as output: for metric in COMPUTE_METRICS: avg_metric = np.mean(records[metric]) if mode == "test" or mode == "dev": print("Average {} of {} utts: {:.4f}".format(metric, len(records[metric]), avg_metric)) print(metric, avg_metric, file=output) logger.add_scalar( f'separation_stft2/{mode}-'+metric, avg_metric, global_step=global_step ) save_ckpt = [] assert 'si_sdr' in records if mode == "dev" and np.mean(records['si_sdr']) > self.best_score: self.best_score = torch.ones(1) * np.mean(records['si_sdr']) save_ckpt.append(f"best-states-{mode}.ckpt") for s in ['mix', 'ref', 'hypo', 'uttname']: assert s in records for i in range(len(records['uttname'])): utt = records['uttname'][i] mix_wav = records['mix'][i][0, :] mix_wav = librosa.util.normalize(mix_wav, norm=np.inf, axis=None) logger.add_audio('step{:06d}_{}_mix.wav'.format(global_step, utt), mix_wav, global_step=global_step, sample_rate=self.datarc['rate']) for j in range(records['ref'][i].shape[0]): ref_wav = records['ref'][i][j, :] hypo_wav = records['hypo'][i][j, :] ref_wav = librosa.util.normalize(ref_wav, norm=np.inf, axis=None) hypo_wav = librosa.util.normalize(hypo_wav, norm=np.inf, axis=None) logger.add_audio('step{:06d}_{}_ref_s{}.wav'.format(global_step, utt, j+1), ref_wav, global_step=global_step, sample_rate=self.datarc['rate']) logger.add_audio('step{:06d}_{}_hypo_s{}.wav'.format(global_step, utt, j+1), hypo_wav, global_step=global_step, sample_rate=self.datarc['rate']) return save_ckpt