diff --git a/conf/config_7Ch.yaml b/conf/config_7Ch.yaml new file mode 100644 index 00000000..411dd590 --- /dev/null +++ b/conf/config_7Ch.yaml @@ -0,0 +1,115 @@ +defaults: + - dset: test + - hydra/job_logging: colorlog + - hydra/hydra_logging: colorlog + +# Dataset related +sample_rate: 16000 +segment: 4 +stride: 1 # in seconds, how much to stride between training examples +pad: true # if training sample is too short, pad it +cv_maxlen: 8 +validfull: 1 # use entire samples at valid + +# Logging and printing, and does not impact training +num_prints: 5 +device: cuda +num_workers: 5 +verbose: 1 +show: 0 # just show the model and its size and exit + +# Checkpointing, by default automatically load last checkpoint +checkpoint: True +continue_from: '' # Only pass the name of the exp, like `exp_dset=wham` + # this arg is ignored for the naming of the exp! +continue_best: false +restart: False # Ignore existing checkpoints +checkpoint_file: checkpoint.th +history_file: history.json +samples_dir: samples + +# Other stuff +seed: 2036 +dummy: # use this if you want twice the same exp, with a name + +# Evaluation stuff +pesq: True # compute pesq? +eval_every: 10 +keep_last: 0 + +# Optimization related +optim: adam +lr: 5e-4 +beta2: 0.999 +stft_loss: False +stft_sc_factor: .5 +stft_mag_factor: .5 +epochs: 100 +batch_size: 2 +max_norm: 5 +# learning rate scheduling +lr_sched: step # can be either step or plateau +step: + step_size: 2 + gamma: 0.98 +plateau: + factor: 0.5 + patience: 5 + +# Models +model: swave # either demucs or dwave +swave: + N: 128 + L: 8 + H: 128 + R: 6 + C: 2 + input_normalize: False + +# Experiment launching, distributed +ddp: false +ddp_backend: nccl +rendezvous_file: ./rendezvous + +# Internal config, don't set manually +rank: +world_size: + +# Hydra config +hydra: + run: + dir: ./outputs/exp_${hydra.job.override_dirname} + job: + config: + # configuration for the ${hydra.job.override_dirname} runtime variable + override_dirname: + kv_sep: '=' + item_sep: ',' + # Remove all paths, as the / in them would mess up things + # Remove params that would not impact the training itself + # Remove all slurm and submit params. + # This is ugly I know... + exclude_keys: [ + 'hydra.job_logging.handles.file.filename', + 'dset.train', 'dset.valid', 'dset.test', 'dset.mix_json', 'dset.mix_dir', + 'num_prints', 'continue_from', + 'device', 'num_workers', 'print_freq', 'restart', 'verbose', + 'log', 'ddp', 'ddp_backend', 'rendezvous_file', 'rank', 'world_size'] + job_logging: + handlers: + file: + class: logging.FileHandler + mode: w + formatter: colorlog + filename: trainer.log + console: + class: logging.StreamHandler + formatter: colorlog + stream: ext://sys.stderr + + hydra_logging: + handlers: + console: + class: logging.StreamHandler + formatter: colorlog + stream: ext://sys.stderr diff --git a/conf/dset/test.yaml b/conf/dset/test.yaml new file mode 100644 index 00000000..2f75d5c8 --- /dev/null +++ b/conf/dset/test.yaml @@ -0,0 +1,7 @@ + +# @package dset +train: egs/test/tr +valid: egs/test/val +test: egs/test/te +mix_json: egs/test/tr/mix.json +mix_dir: diff --git a/egs/debug/tr/mix.json b/egs/debug/tr/mix.json index fd0424dd..b525328d 100644 --- a/egs/debug/tr/mix.json +++ b/egs/debug/tr/mix.json @@ -1,34 +1,34 @@ [ [ - "debug/mix/1919-142785-0045_6319-275224-0016.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/1919-142785-0045_6319-275224-0016.wav", 39840 ], [ - "debug/mix/2803-154328-0017_8297-275155-0016.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/2803-154328-0017_8297-275155-0016.wav", 59480 ], [ - "debug/mix/2803-161169-0001_7976-110523-0004.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/2803-161169-0001_7976-110523-0004.wav", 58080 ], [ - "debug/mix/2902-9006-0008_2412-153954-0015.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/2902-9006-0008_2412-153954-0015.wav", 88640 ], [ - "debug/mix/3752-4943-0021_84-121550-0018.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/3752-4943-0021_84-121550-0018.wav", 36120 ], [ - "debug/mix/5694-64038-0005_251-137823-0002.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/5694-64038-0005_251-137823-0002.wav", 25040 ], [ - "debug/mix/6313-76958-0012_5536-43363-0016.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/6313-76958-0012_5536-43363-0016.wav", 52720 ], [ - "debug/mix/8842-302201-0004_3081-166546-0049.wav", + "D:/work2/kaist/svoice/dataset/debug/mix/8842-302201-0004_3081-166546-0049.wav", 27960 ] ] diff --git a/egs/debug/tr/s1.json b/egs/debug/tr/s1.json index 2f228cfe..b8c015fd 100644 --- a/egs/debug/tr/s1.json +++ b/egs/debug/tr/s1.json @@ -1,34 +1,34 @@ [ [ - "debug/s1/1919-142785-0045_6319-275224-0016.wav", + "D:/work2/kaist/svoice/dataset/debug/s1/1919-142785-0045_6319-275224-0016.wav", 39840 ], [ - "debug/s1/2803-154328-0017_8297-275155-0016.wav", + "D:/work2/kaist/svoice/dataset/debug/s1/2803-154328-0017_8297-275155-0016.wav", 59480 ], [ - 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7CHs/origin/cmu_arctic_female_axb_1.wav", + 144529 + ] +] \ No newline at end of file diff --git a/egs/test/val/s2.json b/egs/test/val/s2.json new file mode 100644 index 00000000..4951e070 --- /dev/null +++ b/egs/test/val/s2.json @@ -0,0 +1,22 @@ +[ + [ + "D:/work2/kaist/wav_fpas 7CHs/origin/cmu_arctic_female_eey_3.wav", + 129455 + ], + [ + "D:/work2/kaist/wav_fpas 7CHs/origin/cmu_arctic_male_ahw_2.wav", + 130205 + ], + [ + "D:/work2/kaist/wav_fpas 7CHs/origin/cmu_arctic_male_aew_3.wav", + 120040 + ], + [ + "D:/work2/kaist/wav_fpas 7CHs/origin/cmu_arctic_female_lnh_5.wav", + 138496 + ], + [ + "D:/work2/kaist/wav_fpas 7CHs/origin/cmu_arctic_female_ljm_4.wav", + 144529 + ] +] \ No newline at end of file diff --git a/separate.py b/separate.py new file mode 100644 index 00000000..75c9d064 --- /dev/null +++ b/separate.py @@ -0,0 +1,135 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Authors: Yossi Adi (adiyoss) + +import argparse +import logging +import os +import sys + +import librosa +import torch +import tqdm +import soundfile as sf + +from svoice.data.data import EvalDataLoader, EvalDataset +from svoice import distrib +from svoice.utils import remove_pad + +from svoice.utils import bold, deserialize_model, LogProgress +logger = logging.getLogger(__name__) + + +parser = argparse.ArgumentParser("Speech separation using MulCat blocks") +parser.add_argument("--model_path", type=str, default="checkpoint.th", help="Model name") +parser.add_argument("--out_dir", type=str, default="exp/result", + help="Directory putting enhanced wav files") +parser.add_argument("--mix_dir", type=str, default=None, + help="Directory including mix wav files") +parser.add_argument("--mix_json", type=str, default="test/mix.json", + help="Json file including mix wav files") +parser.add_argument('--device', default="cuda") +parser.add_argument("--sample_rate", default=16000, + type=int, help="Sample rate") +parser.add_argument("--batch_size", default=1, type=int, help="Batch size") +parser.add_argument('-v', '--verbose', action='store_const', const=logging.DEBUG, + default=logging.INFO, help="More loggging") + + +def save_wavs(estimate_source, mix_sig, lengths, filenames, out_dir, sr=16000): + # Remove padding and flat + flat_estimate = remove_pad(estimate_source, lengths) + mix_sig = remove_pad(mix_sig, lengths) + # Write result + for i, filename in enumerate(filenames): + filename = os.path.join( + out_dir, os.path.basename(filename).strip(".wav")) + #write(mix_sig[i], filename + ".wav", sr=sr) + C = flat_estimate[i].shape[0] + # future support for wave playing + for c in range(C): + write(flat_estimate[i][c], filename + f"_s{c + 1}.wav", sr=sr) + + +def write(inputs, filename, sr=8000): + #librosa.output.write_wav(filename, inputs, sr, norm=True) + inputs = librosa.util.normalize(inputs) + sf.write(filename, inputs, sr) + +def get_mix_paths(args): + mix_dir = None + mix_json = None + # fix mix dir + try: + if args.dset.mix_dir: + mix_dir = args.dset.mix_dir + except: + mix_dir = args.mix_dir + + # fix mix json + try: + if args.dset.mix_json: + mix_json = args.dset.mix_json + except: + mix_json = args.mix_json + return mix_dir, mix_json + + +def separate(args, model=None, local_out_dir=None): + mix_dir, mix_json = get_mix_paths(args) + if not mix_json and not mix_dir: + logger.error("Must provide mix_dir or mix_json! " + "When providing mix_dir, mix_json is ignored.") + # Load model + if not model: + # model + pkg = torch.load(args.model_path) + if 'model' in pkg: + model = pkg['model'] + else: + model = pkg + model = deserialize_model(model) + logger.debug(model) + model.eval() + model.to(args.device) + if local_out_dir: + out_dir = local_out_dir + else: + out_dir = args.out_dir + + # Load data + eval_dataset = EvalDataset( + mix_dir, + mix_json, + batch_size=args.batch_size, + sample_rate=args.sample_rate, + ) + eval_loader = distrib.loader( + eval_dataset, batch_size=1, klass=EvalDataLoader) + + if distrib.rank == 0: + os.makedirs(out_dir, exist_ok=True) + distrib.barrier() + + with torch.no_grad(): + for i, data in enumerate(tqdm.tqdm(eval_loader, ncols=120)): + # Get batch data + mixture, lengths, filenames = data + mixture = mixture.to(args.device) + lengths = lengths.to(args.device) + # Forward + estimate_sources = model(mixture)[-1] + # save wav files + save_wavs(estimate_sources, mixture, lengths, + filenames, out_dir, sr=args.sample_rate) + + +if __name__ == "__main__": + args = parser.parse_args() + logging.basicConfig(stream=sys.stderr, level=args.verbose) + logger.debug(args) + separate(args, local_out_dir=args.out_dir) diff --git a/svoice/data/audio.py b/svoice/data/audio.py index af749d31..d130196f 100644 --- a/svoice/data/audio.py +++ b/svoice/data/audio.py @@ -39,15 +39,17 @@ def find_audio_files(path, exts=[".wav"], progress=True): class Audioset: - def __init__(self, files, length=None, stride=None, pad=True, augment=None): + def __init__(self, files, is_mix = False, length=None, stride=None, pad=True, augment=None): """ files should be a list [(file, length)] """ self.files = files self.num_examples = [] + self.file_length = [] self.length = length self.stride = stride or length self.augment = augment + self.mix_set = is_mix for file, file_length in self.files: if length is None: examples = 1 @@ -59,6 +61,7 @@ def __init__(self, files, length=None, stride=None, pad=True, augment=None): else: examples = (file_length - self.length) // self.stride + 1 self.num_examples.append(examples) + self.file_length.append(file_length) def __len__(self): return sum(self.num_examples) @@ -74,13 +77,35 @@ def __getitem__(self, index): offset = self.stride * index num_frames = self.length # out = th.Tensor(sf.read(str(file), start=offset, frames=num_frames)[0]).unsqueeze(0) - out = torchaudio.load(str(file), offset=offset, - num_frames=num_frames)[0] - if self.augment: - out = self.augment(out.squeeze(0).numpy()).unsqueeze(0) - if num_frames: - out = F.pad(out, (0, num_frames - out.shape[-1])) - return out[0] + else : + num_frames = self.file_length[index] + + if self.mix_set : + mix_files = file + mix_array = [] + for file_path in mix_files: + mix = torchaudio.load(str(file_path), frame_offset=offset, num_frames=num_frames)[0] + + if self.augment: + mix = self.augment(mix.squeeze(0).numpy()).unsqueeze(0) + if num_frames: + mix = F.pad(mix, (0, num_frames - mix.shape[-1])) + + mix_array.append(mix[0]) + result = mix_array + else : + out = torchaudio.load(str(file), frame_offset=offset, num_frames=num_frames)[0] + + if self.augment: + out = self.augment(out.squeeze(0).numpy()).unsqueeze(0) + if num_frames: + out = F.pad(out, (0, num_frames - out.shape[-1])) + + result = out[0] + if result == None: + print(f"load file {file}") + + return result if __name__ == "__main__": diff --git a/svoice/data/data.py b/svoice/data/data.py index fe05b0b8..3df8ed64 100644 --- a/svoice/data/data.py +++ b/svoice/data/data.py @@ -47,20 +47,22 @@ def __init__(self, json_dir, sample_rate=16000, segment=4.0, stride=1.0, pad=Tru stride = int(sample_rate * stride) kw = {'length': length, 'stride': stride, 'pad': pad} - self.mix_set = Audioset(sort(mix_infos), **kw) + self.mix_set = Audioset(sort(mix_infos), True, **kw) self.sets = list() for s_info in s_infos: - self.sets.append(Audioset(sort(s_info), **kw)) + self.sets.append(Audioset(sort(s_info), False, **kw)) # verify all sets has the same size for s in self.sets: + print (f'{len(s)} and {len(self.mix_set)}') assert len(s) == len(self.mix_set) def __getitem__(self, index): mix_sig = self.mix_set[index] tgt_sig = [self.sets[i][index] for i in range(len(self.sets))] - return self.mix_set[index], torch.LongTensor([mix_sig.shape[0]]), torch.stack(tgt_sig) + + return torch.stack(mix_sig), torch.LongTensor([mix_sig[0].shape[0]]), torch.stack(tgt_sig) def __len__(self): return len(self.mix_set) @@ -84,17 +86,18 @@ def __init__(self, json_dir): for s_json in s_jsons: with open(s_json, 'r') as f: s_infos.append(json.load(f)) - self.mix_set = Audioset(sort(mix_infos)) + self.mix_set = Audioset(sort(mix_infos), True) self.sets = list() for s_info in s_infos: - self.sets.append(Audioset(sort(s_info))) + self.sets.append(Audioset(sort(s_info),False)) for s in self.sets: + print (f'{len(s)} and {len(self.mix_set)}') assert len(s) == len(self.mix_set) def __getitem__(self, index): mix_sig = self.mix_set[index] tgt_sig = [self.sets[i][index] for i in range(len(self.sets))] - return self.mix_set[index], torch.LongTensor([mix_sig.shape[0]]), torch.stack(tgt_sig) + return torch.stack(mix_sig), torch.LongTensor([mix_sig[0].shape[0]]), torch.stack(tgt_sig) def __len__(self): return len(self.mix_set) @@ -168,13 +171,18 @@ def _collate_fn_eval(batch): mixtures, filenames = load_mixtures(batch[0]) # get batch of lengths of input sequences - ilens = np.array([mix.shape[0] for mix in mixtures]) + ilens = np.array([mix[0].shape[0] for mix in mixtures]) # perform padding and convert to tensor pad_value = 0 - mixtures_pad = pad_list([torch.from_numpy(mix).float() - for mix in mixtures], pad_value) + # mixtures_pad = pad_list([torch.from_numpy(mix).float() + # for mix in mixtures], pad_value) ilens = torch.from_numpy(ilens) + + #mixtures_pad = mixtures_pad.permute((0, 2, 1)).contiguous() + mixtures_pad = torch.stack(mixtures[0]) + mixtures_pad = mixtures_pad.reshape(1,7,ilens[0]) + return mixtures_pad, ilens, filenames @@ -190,10 +198,20 @@ def load_mixtures(batch): # for each utterance for mix_info in mix_infos: mix_path = mix_info[0] + filename = mix_path[0][:-8] + ".wav" # read wav file - mix, _ = librosa.load(mix_path, sr=sample_rate) - mixtures.append(mix) - filenames.append(mix_path) + mix_files = mix_path + mix_array = [] + for file_path in mix_files: + mix, _ = librosa.load(file_path, sr=sample_rate) + mix_array.append(mix) + + min_size= np.min([len(mix) for mix in mix_array]) + mix_array = [torch.Tensor(mix[:min_size]) for mix in mix_array] + + mixtures.append(mix_array) + filenames.append(filename) + return mixtures, filenames diff --git a/svoice/evaluate.py b/svoice/evaluate.py index 22c888f6..a5e1a962 100755 --- a/svoice/evaluate.py +++ b/svoice/evaluate.py @@ -85,7 +85,7 @@ def evaluate(args, model=None, data_loader=None, sr=None): pendings.append( pool.submit(_run_metrics, sources, reorder_estimate, mixture, None, - sr=sr)) + sr=sr, pesq= args.pesq)) total_cnt += sources.shape[0] for pending in LogProgress(logger, pendings, updates, name="Eval metrics"): @@ -111,7 +111,8 @@ def _run_metrics(clean, estimate, mix, model, sr, pesq=False): estimate = estimate.numpy() clean = clean.numpy() mix = mix.numpy() - sisnr = cal_SISNRi(clean, estimate, mix) + #sisnr = cal_SISNRi(clean, estimate, mix) + sisnr = cal_SISNRt(clean, estimate) if pesq: pesq_i = cal_PESQ(clean, estimate, sr=sr) stoi_i = cal_STOI(clean, estimate, sr=sr) @@ -197,6 +198,23 @@ def cal_SISNRi(src_ref, src_est, mix): avg_SISNRi /= C return avg_SISNRi +def cal_SISNRt(src_ref, src_est): + """Calculate Scale-Invariant Source-to-Noise Ratio improvement (SI-SNRi) + Args: + src_ref: numpy.ndarray, [B, C, T] + src_est: numpy.ndarray, [B, C, T], reordered by best PIT permutation + mix: numpy.ndarray, [T] + Returns: + average_SISNRi + """ + avg_SISNRt = 0.0 + B, C, T = src_ref.shape + for c in range(C): + sisnr = cal_SISNR(src_ref[:, c], src_est[:, c]) + avg_SISNRt += (sisnr) + avg_SISNRt /= C + return avg_SISNRt + def main(): args = parser.parse_args() diff --git a/svoice/models/sisnr_loss.py b/svoice/models/sisnr_loss.py index 9005cb47..9f4865d3 100644 --- a/svoice/models/sisnr_loss.py +++ b/svoice/models/sisnr_loss.py @@ -40,7 +40,7 @@ def cal_si_snr_with_pit(source, estimate_source, source_lengths): B, C, T = source.size() # mask padding position along T mask = get_mask(source, source_lengths) - estimate_source *= mask + estimate_source = estimate_source*mask # Step 1. Zero-mean norm num_samples = source_lengths.view(-1, 1, 1).float() # [B, 1, 1] diff --git a/svoice/models/swave.py b/svoice/models/swave.py index 772f7378..bd6b34d7 100755 --- a/svoice/models/swave.py +++ b/svoice/models/swave.py @@ -268,15 +268,15 @@ def forward(self, mixture): class Encoder(nn.Module): - def __init__(self, L, N): + def __init__(self,L, N): super(Encoder, self).__init__() self.L, self.N = L, N # setting 50% overlap self.conv = nn.Conv1d( - 1, N, kernel_size=L, stride=L // 2, bias=False) + 7, N, kernel_size=L, stride=L // 2, bias=False) def forward(self, mixture): - mixture = torch.unsqueeze(mixture, 1) + #mixture = torch.unsqueeze(mixture, 1) mixture_w = F.relu(self.conv(mixture)) return mixture_w diff --git a/svoice/separate.py b/svoice/separate.py index 2aa985b1..7f863db7 100755 --- a/svoice/separate.py +++ b/svoice/separate.py @@ -14,6 +14,7 @@ import librosa import torch import tqdm +import soundfile as sf from .data.data import EvalDataLoader, EvalDataset from . import distrib @@ -47,7 +48,7 @@ def save_wavs(estimate_source, mix_sig, lengths, filenames, out_dir, sr=16000): for i, filename in enumerate(filenames): filename = os.path.join( out_dir, os.path.basename(filename).strip(".wav")) - write(mix_sig[i], filename + ".wav", sr=sr) + #write(mix_sig[i], filename + ".wav", sr=sr) C = flat_estimate[i].shape[0] # future support for wave playing for c in range(C): @@ -55,8 +56,9 @@ def save_wavs(estimate_source, mix_sig, lengths, filenames, out_dir, sr=16000): def write(inputs, filename, sr=8000): - librosa.output.write_wav(filename, inputs, sr, norm=True) - + #librosa.output.write_wav(filename, inputs, sr, norm=True) + inputs = librosa.util.normalize(inputs) + sf.write(filename, inputs, sr) def get_mix_paths(args): mix_dir = None @@ -103,7 +105,7 @@ def separate(args, model=None, local_out_dir=None): eval_dataset = EvalDataset( mix_dir, mix_json, - batch_size=args.batch_size, + batch_size=1, sample_rate=args.sample_rate, ) eval_loader = distrib.loader( diff --git a/train.py b/train.py index 4953b9d5..b8ed6cfc 100755 --- a/train.py +++ b/train.py @@ -112,7 +112,7 @@ def _main(args): run(args) -@hydra.main(config_path="conf", config_name='config.yaml') +@hydra.main(version_base="1.1", config_path="conf", config_name='config_7CH.yaml') def main(args): try: _main(args)