update model

Conv-Tasnet/results/convtasnet_4-mix/1234/env.log

@@ -1,93 +0,0 @@
-SpeechBrain system description
-==============================
-Python version:
-3.11.13 (main, Jun  5 2025, 13:12:00) [GCC 11.2.0]
-==============================
-Installed Python packages:
-black==24.3.0
-certifi==2025.6.15
-cffi==1.17.1
-cfgv==3.4.0
-charset-normalizer==3.4.2
-click==8.1.7
-distlib==0.3.9
-docstring_parser_fork==0.0.12
-filelock==3.18.0
-flake8==7.0.0
-fsspec==2025.5.1
-future==1.0.0
-hf-xet==1.1.5
-huggingface-hub==0.33.0
-HyperPyYAML==1.2.2
-identify==2.6.12
-idna==3.10
-iniconfig==2.1.0
-isort==5.13.2
-Jinja2==3.1.6
-joblib==1.5.1
-MarkupSafe==3.0.2
-mccabe==0.7.0
-mir_eval==0.6
-mpmath==1.3.0
-mypy_extensions==1.1.0
-networkx==3.5
-nodeenv==1.9.1
-numpy==2.3.1
-nvidia-cublas-cu12==12.6.4.1
-nvidia-cuda-cupti-cu12==12.6.80
-nvidia-cuda-nvrtc-cu12==12.6.77
-nvidia-cuda-runtime-cu12==12.6.77
-nvidia-cudnn-cu12==9.5.1.17
-nvidia-cufft-cu12==11.3.0.4
-nvidia-cufile-cu12==1.11.1.6
-nvidia-curand-cu12==10.3.7.77
-nvidia-cusolver-cu12==11.7.1.2
-nvidia-cusparse-cu12==12.5.4.2
-nvidia-cusparselt-cu12==0.6.3
-nvidia-nccl-cu12==2.26.2
-nvidia-nvjitlink-cu12==12.6.85
-nvidia-nvtx-cu12==12.6.77
-packaging==25.0
-pandas==2.3.0
-pathspec==0.12.1
-platformdirs==4.3.8
-pluggy==1.6.0
-pre_commit==4.2.0
-pycodestyle==2.11.0
-pycparser==2.22
-pydoclint==0.4.1
-pyflakes==3.2.0
-pyloudnorm==0.1.1
-pytest==7.4.0
-python-dateutil==2.9.0.post0
-pytz==2025.2
-PyYAML==6.0.2
-regex==2024.11.6
-requests==2.32.4
-ruamel.yaml==0.18.14
-ruamel.yaml.clib==0.2.12
-safetensors==0.5.3
-scipy==1.16.0
-sentencepiece==0.2.0
-six==1.17.0
-soundfile==0.13.1
-sox==1.5.0
--e git+ssh://git@github.com/speechbrain/speechbrain.git@c75ab5489431fd0a2a7d21160bc37677801cb506#egg=speechbrain
-sympy==1.14.0
-tokenizers==0.21.2
-torch==2.7.1
-torchaudio==2.7.1
-tqdm==4.67.1
-transformers==4.52.4
-triton==3.3.1
-typing_extensions==4.14.0
-tzdata==2025.2
-urllib3==2.5.0
-virtualenv==20.31.2
-yamllint==1.35.1
-==============================
-Git revision:
-c75ab5489
-==============================
-CUDA version:
-12.6

Conv-Tasnet/results/convtasnet_4-mix/1234/hyperparams.yaml

@@ -1,179 +0,0 @@
-# Generated 2025-06-26 from:
-# /work106/youzhenghai/project/speechbrain/myegs/FORHUAWEI_TASNET/separation/hparams/convtasnet_4mix.yaml
-# yamllint disable
-# ################################
-# Model: SepFormer for source separation
-# https://arxiv.org/abs/2010.13154
-# Dataset : WSJ0-2mix and WSJ0-3mix
-# ################################
-# Basic parameters
-# Seed needs to be set at top of yaml, before objects with parameters are made
-#
-seed: 1234
-__set_seed: !apply:speechbrain.utils.seed_everything [1234]
-
-# Data params
-
-# e.g. '/yourpath/wsj0-mix/2speakers'
-# end with 2speakers for wsj0-2mix or 3speakers for wsj0-3mix
-data_folder: /work105/youzhenghai/data/wsj0_2mix
-
-# the path for wsj0/si_tr_s/ folder -- only needed if dynamic mixing is used
-# e.g. /yourpath/wsj0-processed/si_tr_s/
-base_folder_dm: /yourpath/wsj0-processed/si_tr_s/
-
-experiment_name: convtasnet_4-mix
-output_folder: results/convtasnet_4-mix/1234
-train_log: results/convtasnet_4-mix/1234/train_log.txt
-save_folder: results/convtasnet_4-mix/1234/save
-train_data: results/convtasnet_4-mix/1234/save/record_tr.csv
-valid_data: results/convtasnet_4-mix/1234/save/record_val.csv
-test_data: results/convtasnet_4-mix/1234/save/test_data.csv
-skip_prep: false
-
-
-# Experiment params
-precision: fp32 # bf16, fp16 or fp32
-num_spks: 4 # set to 3 for wsj0-3mix
-noprogressbar: false
-save_audio: false # Save estimated sources on disk
-sample_rate: 16000
-
-####################### Training Parameters ####################################
-N_epochs: 200
-batch_size: 2
-lr: 0.00015
-clip_grad_norm: 5
-loss_upper_lim: 999999  # this is the upper limit for an acceptable loss
-# if True, the training sequences are cut to a specified length
-limit_training_signal_len: true
-# this is the length of sequences if we choose to limit
-# the signal length of training sequences
-training_signal_len: 64000000
-
-# Set it to True to dynamically create mixtures at training time
-dynamic_mixing: false
-
-# Parameters for data augmentation
-use_wavedrop: false
-use_speedperturb: true
-use_rand_shift: false
-min_shift: -8000
-max_shift: 8000
-
-# Speed perturbation
-speed_changes: &id001 [95, 100, 105]
-
-# Frequency drop: randomly drops a number of frequency bands to zero.
-speed_perturb: !new:speechbrain.augment.time_domain.SpeedPerturb
-  orig_freq: 16000
-  speeds: *id001
-drop_freq_low: 0  # Min frequency band dropout probability
-drop_freq_high: 1  # Max frequency band dropout probability
-drop_freq_count_low: 1  # Min number of frequency bands to drop
-drop_freq_count_high: 3  # Max number of frequency bands to drop
-drop_freq_width: 0.05  # Width of frequency bands to drop
-
-drop_freq: !new:speechbrain.augment.time_domain.DropFreq
-  drop_freq_low: 0
-  drop_freq_high: 1
-  drop_freq_count_low: 1
-  drop_freq_count_high: 3
-  drop_freq_width: 0.05
-
-# Time drop: randomly drops a number of temporal chunks.
-drop_chunk_count_low: 1  # Min number of audio chunks to drop
-drop_chunk_count_high: 5  # Max number of audio chunks to drop
-drop_chunk_length_low: 1000  # Min length of audio chunks to drop
-drop_chunk_length_high: 2000  # Max length of audio chunks to drop
-
-drop_chunk: !new:speechbrain.augment.time_domain.DropChunk
-  drop_length_low: 1000
-  drop_length_high: 2000
-  drop_count_low: 1
-  drop_count_high: 5
-
-# loss thresholding -- this thresholds the training loss
-threshold_byloss: true
-threshold: -30
-
-# Encoder parameters
-N_encoder_out: 256
-# out_channels: 256
-kernel_size: 32
-kernel_stride: 16
-
-# Dataloader options
-dataloader_opts:
-  batch_size: 2
-  num_workers: 3
-
-
-# Specifying the network
-Encoder: &id002 !new:speechbrain.lobes.models.dual_path.Encoder
-  kernel_size: 32
-  out_channels: 256
-
-# intra: !new:speechbrain.lobes.models.dual_path.SBRNNBlock
-#    num_layers: 1
-#    input_size: !ref <out_channels>
-#    hidden_channels: !ref <out_channels>
-#    dropout: 0
-#    bidirectional: True
-
-# inter: !new:speechbrain.lobes.models.dual_path.SBRNNBlock
-#    num_layers: 1
-#    input_size: !ref <out_channels>
-#    hidden_channels: !ref <out_channels>
-#    dropout: 0
-#    bidirectional: True
-
-MaskNet: &id004 !new:speechbrain.lobes.models.conv_tasnet.MaskNet
-
-  N: 256
-  B: 256
-  H: 512
-  P: 3
-  X: 6
-  R: 4
-  C: 4
-  norm_type: gLN
-  causal: true
-  mask_nonlinear: relu
-
-Decoder: &id003 !new:speechbrain.lobes.models.dual_path.Decoder
-  in_channels: 256
-  out_channels: 1
-  kernel_size: 32
-  stride: 16
-  bias: false
-
-optimizer: !name:torch.optim.Adam
-  lr: 0.00015
-  weight_decay: 0
-
-loss: !name:speechbrain.nnet.losses.get_si_snr_with_pitwrapper
-
-lr_scheduler: &id006 !new:speechbrain.nnet.schedulers.ReduceLROnPlateau
-
-  factor: 0.5
-  patience: 2
-  dont_halve_until_epoch: 85
-
-epoch_counter: &id005 !new:speechbrain.utils.epoch_loop.EpochCounter
-  limit: 200
-
-modules:
-  encoder: *id002
-  decoder: *id003
-  masknet: *id004
-checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
-  checkpoints_dir: results/convtasnet_4-mix/1234/save
-  recoverables:
-    encoder: *id002
-    decoder: *id003
-    masknet: *id004
-    counter: *id005
-    lr_scheduler: *id006
-train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
-  save_file: results/convtasnet_4-mix/1234/train_log.txt

Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/CKPT.yaml

@@ -1,4 +0,0 @@
-# yamllint disable
-end-of-epoch: true
-si-snr: 22.240427712364045
-unixtime: 1750961143.069555

Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/brain.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/counter.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/dataloader-TRAIN.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/decoder.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/encoder.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/lr_scheduler.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/masknet.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+02-05-43+00/optimizer.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+11-17-00+00/CKPT.yaml

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-# yamllint disable
-end-of-epoch: true
-si-snr: 22.256136728080673
-unixtime: 1750994220.6695538

Conv-Tasnet/results/convtasnet_4-mix/1234/save/CKPT+2025-06-27+11-17-00+00/brain.ckpt

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Conv-Tasnet/results/convtasnet_4-mix/1234/test.py

@@ -1,628 +0,0 @@
-#!/usr/bin/env/python3
-"""Recipe for training a neural speech separation system on the wsjmix
-dataset. The system employs an encoder, a decoder, and a masking network.
-
-To run this recipe, do the following:
-> python train.py hparams/sepformer.yaml
-> python train.py hparams/dualpath_rnn.yaml
-> python train.py hparams/convtasnet.yaml
-
-The experiment file is flexible enough to support different neural
-networks. By properly changing the parameter files, you can try
-different architectures. The script supports both wsj2mix and
-wsj3mix.
-
-# 4-mix 主要根据 num_spks 修改 train.py 和 config
-Authors
- * Cem Subakan 2020
- * Mirco Ravanelli 2020
- * Samuele Cornell 2020
- * Mirko Bronzi 2020
- * Jianyuan Zhong 2020
-"""
-
-import csv
-import os
-import sys
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torchaudio
-from hyperpyyaml import load_hyperpyyaml
-from tqdm import tqdm
-
-import speechbrain as sb
-import speechbrain.nnet.schedulers as schedulers
-from speechbrain.utils.distributed import run_on_main
-from speechbrain.utils.logger import get_logger
-
-
-# Define training procedure
-class Separation(sb.Brain):
-    def compute_forward(self, mix, targets, stage, noise=None):
-        """Forward computations from the mixture to the separated signals."""
-
-        # Unpack lists and put tensors in the right device
-        mix, mix_lens = mix
-        mix, mix_lens = mix.to(self.device), mix_lens.to(self.device)
-
-        # Convert targets to tensor
-        targets = torch.cat(
-            [targets[i][0].unsqueeze(-1) for i in range(self.hparams.num_spks)],
-            dim=-1,
-        ).to(self.device)
-
-        # Add speech distortions
-        if stage == sb.Stage.TRAIN:
-            with torch.no_grad():
-                if self.hparams.use_speedperturb:
-                    mix, targets = self.add_speed_perturb(targets, mix_lens)
-
-                    mix = targets.sum(-1)
-
-                if self.hparams.use_wavedrop:
-                    mix = self.hparams.drop_chunk(mix, mix_lens)
-                    mix = self.hparams.drop_freq(mix)
-
-                if self.hparams.limit_training_signal_len:
-                    mix, targets = self.cut_signals(mix, targets)
-
-        # Separation
-        mix_w = self.hparams.Encoder(mix)
-        est_mask = self.hparams.MaskNet(mix_w)
-        mix_w = torch.stack([mix_w] * self.hparams.num_spks)
-        sep_h = mix_w * est_mask
-
-        # Decoding
-        est_source = torch.cat(
-            [
-                self.hparams.Decoder(sep_h[i]).unsqueeze(-1)
-                for i in range(self.hparams.num_spks)
-            ],
-            dim=-1,
-        )
-
-        # T changed after conv1d in encoder, fix it here
-        T_origin = mix.size(1)
-        T_est = est_source.size(1)
-        if T_origin > T_est:
-            est_source = F.pad(est_source, (0, 0, 0, T_origin - T_est))
-        else:
-            est_source = est_source[:, :T_origin, :]
-
-        return est_source, targets
-
-    def compute_objectives(self, predictions, targets):
-        """Computes the sinr loss"""
-        return self.hparams.loss(targets, predictions)
-
-    def fit_batch(self, batch):
-        """Trains one batch"""
-
-        # Unpacking batch list
-        mixture = batch.mix_sig
-        targets = [batch.s1_sig, batch.s2_sig]
-
-        if self.hparams.num_spks == 3:
-            targets.append(batch.s3_sig)
-
-        if self.hparams.num_spks == 4:
-            targets.append(batch.s3_sig)
-            targets.append(batch.s4_sig)
-
-        with self.training_ctx:
-            predictions, targets = self.compute_forward(
-                mixture, targets, sb.Stage.TRAIN
-            )
-            loss = self.compute_objectives(predictions, targets)
-
-            # hard threshold the easy dataitems
-            if self.hparams.threshold_byloss:
-                th = self.hparams.threshold
-                loss = loss[loss > th]
-                if loss.nelement() > 0:
-                    loss = loss.mean()
-            else:
-                loss = loss.mean()
-
-        if loss.nelement() > 0 and loss < self.hparams.loss_upper_lim:
-            self.scaler.scale(loss).backward()
-            if self.hparams.clip_grad_norm >= 0:
-                self.scaler.unscale_(self.optimizer)
-                torch.nn.utils.clip_grad_norm_(
-                    self.modules.parameters(),
-                    self.hparams.clip_grad_norm,
-                )
-            self.scaler.step(self.optimizer)
-            self.scaler.update()
-        else:
-            self.nonfinite_count += 1
-            logger.info(
-                "infinite loss or empty loss! it happened {} times so far - skipping this batch".format(
-                    self.nonfinite_count
-                )
-            )
-            loss.data = torch.tensor(0.0).to(self.device)
-        self.optimizer.zero_grad()
-
-        return loss.detach().cpu()
-
-    def evaluate_batch(self, batch, stage):
-        """Computations needed for validation/test batches"""
-        snt_id = batch.id
-        mixture = batch.mix_sig
-        targets = [batch.s1_sig, batch.s2_sig]
-        if self.hparams.num_spks == 3:
-            targets.append(batch.s3_sig)
-
-        if self.hparams.num_spks == 4:
-            targets.append(batch.s3_sig)
-            targets.append(batch.s4_sig)
-
-        with torch.no_grad():
-            predictions, targets = self.compute_forward(mixture, targets, stage)
-            loss = self.compute_objectives(predictions, targets)
-
-        # Manage audio file saving
-        if stage == sb.Stage.TEST and self.hparams.save_audio:
-            if hasattr(self.hparams, "n_audio_to_save"):
-                if self.hparams.n_audio_to_save > 0:
-                    self.save_audio(snt_id[0], mixture, targets, predictions)
-                    self.hparams.n_audio_to_save += -1
-            else:
-                self.save_audio(snt_id[0], mixture, targets, predictions)
-
-        return loss.mean().detach()
-
-    def on_stage_end(self, stage, stage_loss, epoch):
-        """Gets called at the end of a epoch."""
-        # Compute/store important stats
-        stage_stats = {"si-snr": stage_loss}
-        if stage == sb.Stage.TRAIN:
-            self.train_stats = stage_stats
-
-        # Perform end-of-iteration things, like annealing, logging, etc.
-        if stage == sb.Stage.VALID:
-            # Learning rate annealing
-            if isinstance(
-                self.hparams.lr_scheduler, schedulers.ReduceLROnPlateau
-            ):
-                current_lr, next_lr = self.hparams.lr_scheduler(
-                    [self.optimizer], epoch, stage_loss
-                )
-                schedulers.update_learning_rate(self.optimizer, next_lr)
-            else:
-                # if we do not use the reducelronplateau, we do not change the lr
-                current_lr = self.hparams.optimizer.optim.param_groups[0]["lr"]
-
-            self.hparams.train_logger.log_stats(
-                stats_meta={"epoch": epoch, "lr": current_lr},
-                train_stats=self.train_stats,
-                valid_stats=stage_stats,
-            )
-            self.checkpointer.save_and_keep_only(
-                meta={"si-snr": stage_stats["si-snr"]}, min_keys=["si-snr"]
-            )
-        elif stage == sb.Stage.TEST:
-            self.hparams.train_logger.log_stats(
-                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
-                test_stats=stage_stats,
-            )
-
-    def add_speed_perturb(self, targets, targ_lens):
-        """Adds speed perturbation and random_shift to the input signals"""
-
-        min_len = -1
-        recombine = False
-
-        if self.hparams.use_speedperturb or self.hparams.use_rand_shift:
-            # Performing speed change (independently on each source)
-            new_targets = []
-            recombine = True
-
-            for i in range(targets.shape[-1]):
-                new_target = self.hparams.speed_perturb(targets[:, :, i])
-                new_targets.append(new_target)
-                if i == 0:
-                    min_len = new_target.shape[-1]
-                else:
-                    if new_target.shape[-1] < min_len:
-                        min_len = new_target.shape[-1]
-
-            if self.hparams.use_rand_shift:
-                # Performing random_shift (independently on each source)
-                recombine = True
-                for i in range(targets.shape[-1]):
-                    rand_shift = torch.randint(
-                        self.hparams.min_shift, self.hparams.max_shift, (1,)
-                    )
-                    new_targets[i] = new_targets[i].to(self.device)
-                    new_targets[i] = torch.roll(
-                        new_targets[i], shifts=(rand_shift[0],), dims=1
-                    )
-
-            # Re-combination
-            if recombine:
-                if self.hparams.use_speedperturb:
-                    targets = torch.zeros(
-                        targets.shape[0],
-                        min_len,
-                        targets.shape[-1],
-                        device=targets.device,
-                        dtype=torch.float,
-                    )
-                for i, new_target in enumerate(new_targets):
-                    targets[:, :, i] = new_targets[i][:, 0:min_len]
-
-        mix = targets.sum(-1)
-        return mix, targets
-
-    def cut_signals(self, mixture, targets):
-        """This function selects a random segment of a given length within the mixture.
-        The corresponding targets are selected accordingly"""
-        randstart = torch.randint(
-            0,
-            1 + max(0, mixture.shape[1] - self.hparams.training_signal_len),
-            (1,),
-        ).item()
-        targets = targets[
-            :, randstart : randstart + self.hparams.training_signal_len, :
-        ]
-        mixture = mixture[
-            :, randstart : randstart + self.hparams.training_signal_len
-        ]
-        return mixture, targets
-
-    def reset_layer_recursively(self, layer):
-        """Reinitializes the parameters of the neural networks"""
-        if hasattr(layer, "reset_parameters"):
-            layer.reset_parameters()
-        for child_layer in layer.modules():
-            if layer != child_layer:
-                self.reset_layer_recursively(child_layer)
-
-    def save_results(self, test_data):
-        """This script computes the SDR and SI-SNR metrics and saves
-        them into a csv file"""
-
-        # This package is required for SDR computation
-        from mir_eval.separation import bss_eval_sources
-
-        # Create folders where to store audio
-        save_file = os.path.join(self.hparams.output_folder, "test_results.csv")
-
-        # Variable init
-        all_sdrs = []
-        all_sdrs_i = []
-        all_sisnrs = []
-        all_sisnrs_i = []
-        csv_columns = ["snt_id", "sdr", "sdr_i", "si-snr", "si-snr_i"]
-
-        test_loader = sb.dataio.dataloader.make_dataloader(
-            test_data, **self.hparams.dataloader_opts
-        )
-
-        with open(save_file, "w", newline="", encoding="utf-8") as results_csv:
-            writer = csv.DictWriter(results_csv, fieldnames=csv_columns)
-            writer.writeheader()
-
-            # Loop over all test sentence
-            with tqdm(test_loader, dynamic_ncols=True) as t:
-                for i, batch in enumerate(t):
-                    # Apply Separation
-                    mixture, mix_len = batch.mix_sig
-                    snt_id = batch.id
-                    targets = [batch.s1_sig, batch.s2_sig]
-                    if self.hparams.num_spks == 3:
-                        targets.append(batch.s3_sig)
-
-                    if self.hparams.num_spks == 4:
-                        targets.append(batch.s3_sig)
-                        targets.append(batch.s4_sig)
-
-                    with torch.no_grad():
-                        predictions, targets = self.compute_forward(
-                            batch.mix_sig, targets, sb.Stage.TEST
-                        )
-
-                    # Compute SI-SNR
-                    sisnr = self.compute_objectives(predictions, targets)
-
-                    # Compute SI-SNR improvement
-                    mixture_signal = torch.stack(
-                        [mixture] * self.hparams.num_spks, dim=-1
-                    )
-                    mixture_signal = mixture_signal.to(targets.device)
-                    sisnr_baseline = self.compute_objectives(
-                        mixture_signal, targets
-                    )
-                    sisnr_i = sisnr - sisnr_baseline
-
-                    # Compute SDR
-                    sdr, _, _, _ = bss_eval_sources(
-                        targets[0].t().cpu().numpy(),
-                        predictions[0].t().detach().cpu().numpy(),
-                    )
-
-                    sdr_baseline, _, _, _ = bss_eval_sources(
-                        targets[0].t().cpu().numpy(),
-                        mixture_signal[0].t().detach().cpu().numpy(),
-                    )
-
-                    sdr_i = sdr.mean() - sdr_baseline.mean()
-
-                    # Saving on a csv file
-                    row = {
-                        "snt_id": snt_id[0],
-                        "sdr": sdr.mean(),
-                        "sdr_i": sdr_i,
-                        "si-snr": -sisnr.item(),
-                        "si-snr_i": -sisnr_i.item(),
-                    }
-                    writer.writerow(row)
-
-                    # Metric Accumulation
-                    all_sdrs.append(sdr.mean())
-                    all_sdrs_i.append(sdr_i.mean())
-                    all_sisnrs.append(-sisnr.item())
-                    all_sisnrs_i.append(-sisnr_i.item())
-
-                row = {
-                    "snt_id": "avg",
-                    "sdr": np.array(all_sdrs).mean(),
-                    "sdr_i": np.array(all_sdrs_i).mean(),
-                    "si-snr": np.array(all_sisnrs).mean(),
-                    "si-snr_i": np.array(all_sisnrs_i).mean(),
-                }
-                writer.writerow(row)
-
-        logger.info("Mean SISNR is {}".format(np.array(all_sisnrs).mean()))
-        logger.info("Mean SISNRi is {}".format(np.array(all_sisnrs_i).mean()))
-        logger.info("Mean SDR is {}".format(np.array(all_sdrs).mean()))
-        logger.info("Mean SDRi is {}".format(np.array(all_sdrs_i).mean()))
-
-    def save_audio(self, snt_id, mixture, targets, predictions):
-        "saves the test audio (mixture, targets, and estimated sources) on disk"
-
-        # Create output folder
-        save_path = os.path.join(self.hparams.save_folder, "audio_results")
-        if not os.path.exists(save_path):
-            os.mkdir(save_path)
-
-        for ns in range(self.hparams.num_spks):
-            # Estimated source
-            signal = predictions[0, :, ns]
-            signal = signal / signal.abs().max()
-            save_file = os.path.join(
-                save_path, "item{}_source{}hat.wav".format(snt_id, ns + 1)
-            )
-            torchaudio.save(
-                save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
-            )
-
-            # Original source
-            signal = targets[0, :, ns]
-            signal = signal / signal.abs().max()
-            save_file = os.path.join(
-                save_path, "item{}_source{}.wav".format(snt_id, ns + 1)
-            )
-            torchaudio.save(
-                save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
-            )
-
-        # Mixture
-        signal = mixture[0][0, :]
-        signal = signal / signal.abs().max()
-        save_file = os.path.join(save_path, "item{}_mix.wav".format(snt_id))
-        torchaudio.save(
-            save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
-        )
-
-
-def dataio_prep(hparams):
-    """Creates data processing pipeline"""
-
-    # 1. Define datasets
-    train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["train_data"],
-        replacements={"data_root": hparams["data_folder"]},
-    )
-
-    valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["valid_data"],
-        replacements={"data_root": hparams["data_folder"]},
-    )
-
-    test_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["test_data"],
-        replacements={"data_root": hparams["data_folder"]},
-    )
-
-    datasets = [train_data, valid_data, test_data]
-
-    # 2. Provide audio pipelines
-
-    @sb.utils.data_pipeline.takes("mix_wav")
-    @sb.utils.data_pipeline.provides("mix_sig")
-    def audio_pipeline_mix(mix_wav):
-        mix_sig = sb.dataio.dataio.read_audio(mix_wav)
-        return mix_sig
-
-    @sb.utils.data_pipeline.takes("s1_wav")
-    @sb.utils.data_pipeline.provides("s1_sig")
-    def audio_pipeline_s1(s1_wav):
-        s1_sig = sb.dataio.dataio.read_audio(s1_wav)
-        return s1_sig
-
-    @sb.utils.data_pipeline.takes("s2_wav")
-    @sb.utils.data_pipeline.provides("s2_sig")
-    def audio_pipeline_s2(s2_wav):
-        s2_sig = sb.dataio.dataio.read_audio(s2_wav)
-        return s2_sig
-
-    # --- 如果说话人 >= 3，定义第 3 路 ---
-    if hparams["num_spks"] >= 3:
-        @sb.utils.data_pipeline.takes("s3_wav")
-        @sb.utils.data_pipeline.provides("s3_sig")
-        def audio_pipeline_s3(s3_wav):
-            return sb.dataio.dataio.read_audio(s3_wav)
-
-    # --- 如果说话人 == 4，定义第 4 路 ---
-    if hparams["num_spks"] == 4:
-        @sb.utils.data_pipeline.takes("s4_wav")
-        @sb.utils.data_pipeline.provides("s4_sig")
-        def audio_pipeline_s4(s4_wav):
-            return sb.dataio.dataio.read_audio(s4_wav)
-
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_mix)
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s1)
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s2)
-    if hparams["num_spks"] == 3:
-        sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s3)
-        sb.dataio.dataset.set_output_keys(
-            datasets, ["id", "mix_sig", "s1_sig", "s2_sig", "s3_sig"]
-        )
-    elif hparams["num_spks"] == 4 :
-        sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s3)
-        sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s4)
-        sb.dataio.dataset.set_output_keys(
-            datasets,
-            ["id", "mix_sig", "s1_sig", "s2_sig", "s3_sig", "s4_sig"],
-        )    
-    else:
-        sb.dataio.dataset.set_output_keys(
-            datasets, ["id", "mix_sig", "s1_sig", "s2_sig"]
-        )
-
-    return train_data, valid_data, test_data
-
-
-if __name__ == "__main__":
-    # Load hyperparameters file with command-line overrides
-    hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
-    with open(hparams_file, encoding="utf-8") as fin:
-        hparams = load_hyperpyyaml(fin, overrides)
-
-    # Initialize ddp (useful only for multi-GPU DDP training)
-    sb.utils.distributed.ddp_init_group(run_opts)
-
-    # Logger info
-    logger = get_logger(__name__)
-
-    # Create experiment directory
-    sb.create_experiment_directory(
-        experiment_directory=hparams["output_folder"],
-        hyperparams_to_save=hparams_file,
-        overrides=overrides,
-    )
-
-    # Update precision to bf16 if the device is CPU and precision is fp16
-    if run_opts.get("device") == "cpu" and hparams.get("precision") == "fp16":
-        hparams["precision"] = "bf16"
-
-    # Check if wsj0_tr is set with dynamic mixing
-    if hparams["dynamic_mixing"] and not os.path.exists(
-        hparams["base_folder_dm"]
-    ):
-        raise ValueError(
-            "Please, specify a valid base_folder_dm folder when using dynamic mixing"
-        )
-
-    # Data preparation
-    from prepare_data import prepare_wsjmix  # noqa
-
-    # run_on_main(
-    #     prepare_wsjmix,
-    #     kwargs={
-    #         "datapath": hparams["data_folder"],
-    #         "savepath": hparams["save_folder"],
-    #         "n_spks": hparams["num_spks"],
-    #         "skip_prep": hparams["skip_prep"],
-    #         "fs": hparams["sample_rate"],
-    #     },
-    # )
-
-    # Create dataset objects
-    if hparams["dynamic_mixing"]:
-        from dynamic_mixing import dynamic_mix_data_prep
-
-        # if the base_folder for dm is not processed, preprocess them
-        if "processed" not in hparams["base_folder_dm"]:
-            # if the processed folder already exists we just use it otherwise we do the preprocessing
-            if not os.path.exists(
-                os.path.normpath(hparams["base_folder_dm"]) + "_processed"
-            ):
-                from preprocess_dynamic_mixing import resample_folder
-
-                print("Resampling the base folder")
-                run_on_main(
-                    resample_folder,
-                    kwargs={
-                        "input_folder": hparams["base_folder_dm"],
-                        "output_folder": os.path.normpath(
-                            hparams["base_folder_dm"]
-                        )
-                        + "_processed",
-                        "fs": hparams["sample_rate"],
-                        "regex": "**/*.wav",
-                    },
-                )
-                # adjust the base_folder_dm path
-                hparams["base_folder_dm"] = (
-                    os.path.normpath(hparams["base_folder_dm"]) + "_processed"
-                )
-            else:
-                print(
-                    "Using the existing processed folder on the same directory as base_folder_dm"
-                )
-                hparams["base_folder_dm"] = (
-                    os.path.normpath(hparams["base_folder_dm"]) + "_processed"
-                )
-
-        # Collecting the hparams for dynamic batching
-        dm_hparams = {
-            "train_data": hparams["train_data"],
-            "data_folder": hparams["data_folder"],
-            "base_folder_dm": hparams["base_folder_dm"],
-            "sample_rate": hparams["sample_rate"],
-            "num_spks": hparams["num_spks"],
-            "training_signal_len": hparams["training_signal_len"],
-            "dataloader_opts": hparams["dataloader_opts"],
-        }
-        train_data = dynamic_mix_data_prep(dm_hparams)
-        _, valid_data, test_data = dataio_prep(hparams)
-    else:
-        train_data, valid_data, test_data = dataio_prep(hparams)
-
-    # Load pretrained model if pretrained_separator is present in the yaml
-    if "pretrained_separator" in hparams:
-        run_on_main(hparams["pretrained_separator"].collect_files)
-        hparams["pretrained_separator"].load_collected()
-
-    # Brain class initialization
-    separator = Separation(
-        modules=hparams["modules"],
-        opt_class=hparams["optimizer"],
-        hparams=hparams,
-        run_opts=run_opts,
-        checkpointer=hparams["checkpointer"],
-    )
-
-    # re-initialize the parameters if we don't use a pretrained model
-    if "pretrained_separator" not in hparams:
-        for module in separator.modules.values():
-            separator.reset_layer_recursively(module)
-
-    # # Training
-    # separator.fit(
-    #     separator.hparams.epoch_counter,
-    #     train_data,
-    #     valid_data,
-    #     train_loader_kwargs=hparams["dataloader_opts"],
-    #     valid_loader_kwargs=hparams["dataloader_opts"],
-    # )
-
-    # Eval
-    separator.evaluate(test_data, min_key="si-snr")
-    separator.save_results(test_data)

Conv-Tasnet/results/convtasnet_4-mix/1234/test_results.csv

@@ -1 +0,0 @@
-snt_id,sdr,sdr_i,si-snr,si-snr_i

Conv-Tasnet/results/convtasnet_4-mix/1234/train.py

@@ -1,628 +0,0 @@
-#!/usr/bin/env/python3
-"""Recipe for training a neural speech separation system on the wsjmix
-dataset. The system employs an encoder, a decoder, and a masking network.
-
-To run this recipe, do the following:
-> python train.py hparams/sepformer.yaml
-> python train.py hparams/dualpath_rnn.yaml
-> python train.py hparams/convtasnet.yaml
-
-The experiment file is flexible enough to support different neural
-networks. By properly changing the parameter files, you can try
-different architectures. The script supports both wsj2mix and
-wsj3mix.
-
-# 4-mix 主要根据 num_spks 修改 train.py 和 config
-Authors
- * Cem Subakan 2020
- * Mirco Ravanelli 2020
- * Samuele Cornell 2020
- * Mirko Bronzi 2020
- * Jianyuan Zhong 2020
-"""
-
-import csv
-import os
-import sys
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torchaudio
-from hyperpyyaml import load_hyperpyyaml
-from tqdm import tqdm
-
-import speechbrain as sb
-import speechbrain.nnet.schedulers as schedulers
-from speechbrain.utils.distributed import run_on_main
-from speechbrain.utils.logger import get_logger
-
-
-# Define training procedure
-class Separation(sb.Brain):
-    def compute_forward(self, mix, targets, stage, noise=None):
-        """Forward computations from the mixture to the separated signals."""
-
-        # Unpack lists and put tensors in the right device
-        mix, mix_lens = mix
-        mix, mix_lens = mix.to(self.device), mix_lens.to(self.device)
-
-        # Convert targets to tensor
-        targets = torch.cat(
-            [targets[i][0].unsqueeze(-1) for i in range(self.hparams.num_spks)],
-            dim=-1,
-        ).to(self.device)
-
-        # Add speech distortions
-        if stage == sb.Stage.TRAIN:
-            with torch.no_grad():
-                if self.hparams.use_speedperturb:
-                    mix, targets = self.add_speed_perturb(targets, mix_lens)
-
-                    mix = targets.sum(-1)
-
-                if self.hparams.use_wavedrop:
-                    mix = self.hparams.drop_chunk(mix, mix_lens)
-                    mix = self.hparams.drop_freq(mix)
-
-                if self.hparams.limit_training_signal_len:
-                    mix, targets = self.cut_signals(mix, targets)
-
-        # Separation
-        mix_w = self.hparams.Encoder(mix)
-        est_mask = self.hparams.MaskNet(mix_w)
-        mix_w = torch.stack([mix_w] * self.hparams.num_spks)
-        sep_h = mix_w * est_mask
-
-        # Decoding
-        est_source = torch.cat(
-            [
-                self.hparams.Decoder(sep_h[i]).unsqueeze(-1)
-                for i in range(self.hparams.num_spks)
-            ],
-            dim=-1,
-        )
-
-        # T changed after conv1d in encoder, fix it here
-        T_origin = mix.size(1)
-        T_est = est_source.size(1)
-        if T_origin > T_est:
-            est_source = F.pad(est_source, (0, 0, 0, T_origin - T_est))
-        else:
-            est_source = est_source[:, :T_origin, :]
-
-        return est_source, targets
-
-    def compute_objectives(self, predictions, targets):
-        """Computes the sinr loss"""
-        return self.hparams.loss(targets, predictions)
-
-    def fit_batch(self, batch):
-        """Trains one batch"""
-
-        # Unpacking batch list
-        mixture = batch.mix_sig
-        targets = [batch.s1_sig, batch.s2_sig]
-
-        if self.hparams.num_spks == 3:
-            targets.append(batch.s3_sig)
-
-        if self.hparams.num_spks == 4:
-            targets.append(batch.s3_sig)
-            targets.append(batch.s4_sig)
-
-        with self.training_ctx:
-            predictions, targets = self.compute_forward(
-                mixture, targets, sb.Stage.TRAIN
-            )
-            loss = self.compute_objectives(predictions, targets)
-
-            # hard threshold the easy dataitems
-            if self.hparams.threshold_byloss:
-                th = self.hparams.threshold
-                loss = loss[loss > th]
-                if loss.nelement() > 0:
-                    loss = loss.mean()
-            else:
-                loss = loss.mean()
-
-        if loss.nelement() > 0 and loss < self.hparams.loss_upper_lim:
-            self.scaler.scale(loss).backward()
-            if self.hparams.clip_grad_norm >= 0:
-                self.scaler.unscale_(self.optimizer)
-                torch.nn.utils.clip_grad_norm_(
-                    self.modules.parameters(),
-                    self.hparams.clip_grad_norm,
-                )
-            self.scaler.step(self.optimizer)
-            self.scaler.update()
-        else:
-            self.nonfinite_count += 1
-            logger.info(
-                "infinite loss or empty loss! it happened {} times so far - skipping this batch".format(
-                    self.nonfinite_count
-                )
-            )
-            loss.data = torch.tensor(0.0).to(self.device)
-        self.optimizer.zero_grad()
-
-        return loss.detach().cpu()
-
-    def evaluate_batch(self, batch, stage):
-        """Computations needed for validation/test batches"""
-        snt_id = batch.id
-        mixture = batch.mix_sig
-        targets = [batch.s1_sig, batch.s2_sig]
-        if self.hparams.num_spks == 3:
-            targets.append(batch.s3_sig)
-
-        if self.hparams.num_spks == 4:
-            targets.append(batch.s3_sig)
-            targets.append(batch.s4_sig)
-
-        with torch.no_grad():
-            predictions, targets = self.compute_forward(mixture, targets, stage)
-            loss = self.compute_objectives(predictions, targets)
-
-        # Manage audio file saving
-        if stage == sb.Stage.TEST and self.hparams.save_audio:
-            if hasattr(self.hparams, "n_audio_to_save"):
-                if self.hparams.n_audio_to_save > 0:
-                    self.save_audio(snt_id[0], mixture, targets, predictions)
-                    self.hparams.n_audio_to_save += -1
-            else:
-                self.save_audio(snt_id[0], mixture, targets, predictions)
-
-        return loss.mean().detach()
-
-    def on_stage_end(self, stage, stage_loss, epoch):
-        """Gets called at the end of a epoch."""
-        # Compute/store important stats
-        stage_stats = {"si-snr": stage_loss}
-        if stage == sb.Stage.TRAIN:
-            self.train_stats = stage_stats
-
-        # Perform end-of-iteration things, like annealing, logging, etc.
-        if stage == sb.Stage.VALID:
-            # Learning rate annealing
-            if isinstance(
-                self.hparams.lr_scheduler, schedulers.ReduceLROnPlateau
-            ):
-                current_lr, next_lr = self.hparams.lr_scheduler(
-                    [self.optimizer], epoch, stage_loss
-                )
-                schedulers.update_learning_rate(self.optimizer, next_lr)
-            else:
-                # if we do not use the reducelronplateau, we do not change the lr
-                current_lr = self.hparams.optimizer.optim.param_groups[0]["lr"]
-
-            self.hparams.train_logger.log_stats(
-                stats_meta={"epoch": epoch, "lr": current_lr},
-                train_stats=self.train_stats,
-                valid_stats=stage_stats,
-            )
-            self.checkpointer.save_and_keep_only(
-                meta={"si-snr": stage_stats["si-snr"]}, min_keys=["si-snr"]
-            )
-        elif stage == sb.Stage.TEST:
-            self.hparams.train_logger.log_stats(
-                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
-                test_stats=stage_stats,
-            )
-
-    def add_speed_perturb(self, targets, targ_lens):
-        """Adds speed perturbation and random_shift to the input signals"""
-
-        min_len = -1
-        recombine = False
-
-        if self.hparams.use_speedperturb or self.hparams.use_rand_shift:
-            # Performing speed change (independently on each source)
-            new_targets = []
-            recombine = True
-
-            for i in range(targets.shape[-1]):
-                new_target = self.hparams.speed_perturb(targets[:, :, i])
-                new_targets.append(new_target)
-                if i == 0:
-                    min_len = new_target.shape[-1]
-                else:
-                    if new_target.shape[-1] < min_len:
-                        min_len = new_target.shape[-1]
-
-            if self.hparams.use_rand_shift:
-                # Performing random_shift (independently on each source)
-                recombine = True
-                for i in range(targets.shape[-1]):
-                    rand_shift = torch.randint(
-                        self.hparams.min_shift, self.hparams.max_shift, (1,)
-                    )
-                    new_targets[i] = new_targets[i].to(self.device)
-                    new_targets[i] = torch.roll(
-                        new_targets[i], shifts=(rand_shift[0],), dims=1
-                    )
-
-            # Re-combination
-            if recombine:
-                if self.hparams.use_speedperturb:
-                    targets = torch.zeros(
-                        targets.shape[0],
-                        min_len,
-                        targets.shape[-1],
-                        device=targets.device,
-                        dtype=torch.float,
-                    )
-                for i, new_target in enumerate(new_targets):
-                    targets[:, :, i] = new_targets[i][:, 0:min_len]
-
-        mix = targets.sum(-1)
-        return mix, targets
-
-    def cut_signals(self, mixture, targets):
-        """This function selects a random segment of a given length within the mixture.
-        The corresponding targets are selected accordingly"""
-        randstart = torch.randint(
-            0,
-            1 + max(0, mixture.shape[1] - self.hparams.training_signal_len),
-            (1,),
-        ).item()
-        targets = targets[
-            :, randstart : randstart + self.hparams.training_signal_len, :
-        ]
-        mixture = mixture[
-            :, randstart : randstart + self.hparams.training_signal_len
-        ]
-        return mixture, targets
-
-    def reset_layer_recursively(self, layer):
-        """Reinitializes the parameters of the neural networks"""
-        if hasattr(layer, "reset_parameters"):
-            layer.reset_parameters()
-        for child_layer in layer.modules():
-            if layer != child_layer:
-                self.reset_layer_recursively(child_layer)
-
-    def save_results(self, test_data):
-        """This script computes the SDR and SI-SNR metrics and saves
-        them into a csv file"""
-
-        # This package is required for SDR computation
-        from mir_eval.separation import bss_eval_sources
-
-        # Create folders where to store audio
-        save_file = os.path.join(self.hparams.output_folder, "test_results.csv")
-
-        # Variable init
-        all_sdrs = []
-        all_sdrs_i = []
-        all_sisnrs = []
-        all_sisnrs_i = []
-        csv_columns = ["snt_id", "sdr", "sdr_i", "si-snr", "si-snr_i"]
-
-        test_loader = sb.dataio.dataloader.make_dataloader(
-            test_data, **self.hparams.dataloader_opts
-        )
-
-        with open(save_file, "w", newline="", encoding="utf-8") as results_csv:
-            writer = csv.DictWriter(results_csv, fieldnames=csv_columns)
-            writer.writeheader()
-
-            # Loop over all test sentence
-            with tqdm(test_loader, dynamic_ncols=True) as t:
-                for i, batch in enumerate(t):
-                    # Apply Separation
-                    mixture, mix_len = batch.mix_sig
-                    snt_id = batch.id
-                    targets = [batch.s1_sig, batch.s2_sig]
-                    if self.hparams.num_spks == 3:
-                        targets.append(batch.s3_sig)
-
-                    if self.hparams.num_spks == 4:
-                        targets.append(batch.s3_sig)
-                        targets.append(batch.s4_sig)
-
-                    with torch.no_grad():
-                        predictions, targets = self.compute_forward(
-                            batch.mix_sig, targets, sb.Stage.TEST
-                        )
-
-                    # Compute SI-SNR
-                    sisnr = self.compute_objectives(predictions, targets)
-
-                    # Compute SI-SNR improvement
-                    mixture_signal = torch.stack(
-                        [mixture] * self.hparams.num_spks, dim=-1
-                    )
-                    mixture_signal = mixture_signal.to(targets.device)
-                    sisnr_baseline = self.compute_objectives(
-                        mixture_signal, targets
-                    )
-                    sisnr_i = sisnr - sisnr_baseline
-
-                    # Compute SDR
-                    sdr, _, _, _ = bss_eval_sources(
-                        targets[0].t().cpu().numpy(),
-                        predictions[0].t().detach().cpu().numpy(),
-                    )
-
-                    sdr_baseline, _, _, _ = bss_eval_sources(
-                        targets[0].t().cpu().numpy(),
-                        mixture_signal[0].t().detach().cpu().numpy(),
-                    )
-
-                    sdr_i = sdr.mean() - sdr_baseline.mean()
-
-                    # Saving on a csv file
-                    row = {
-                        "snt_id": snt_id[0],
-                        "sdr": sdr.mean(),
-                        "sdr_i": sdr_i,
-                        "si-snr": -sisnr.item(),
-                        "si-snr_i": -sisnr_i.item(),
-                    }
-                    writer.writerow(row)
-
-                    # Metric Accumulation
-                    all_sdrs.append(sdr.mean())
-                    all_sdrs_i.append(sdr_i.mean())
-                    all_sisnrs.append(-sisnr.item())
-                    all_sisnrs_i.append(-sisnr_i.item())
-
-                row = {
-                    "snt_id": "avg",
-                    "sdr": np.array(all_sdrs).mean(),
-                    "sdr_i": np.array(all_sdrs_i).mean(),
-                    "si-snr": np.array(all_sisnrs).mean(),
-                    "si-snr_i": np.array(all_sisnrs_i).mean(),
-                }
-                writer.writerow(row)
-
-        logger.info("Mean SISNR is {}".format(np.array(all_sisnrs).mean()))
-        logger.info("Mean SISNRi is {}".format(np.array(all_sisnrs_i).mean()))
-        logger.info("Mean SDR is {}".format(np.array(all_sdrs).mean()))
-        logger.info("Mean SDRi is {}".format(np.array(all_sdrs_i).mean()))
-
-    def save_audio(self, snt_id, mixture, targets, predictions):
-        "saves the test audio (mixture, targets, and estimated sources) on disk"
-
-        # Create output folder
-        save_path = os.path.join(self.hparams.save_folder, "audio_results")
-        if not os.path.exists(save_path):
-            os.mkdir(save_path)
-
-        for ns in range(self.hparams.num_spks):
-            # Estimated source
-            signal = predictions[0, :, ns]
-            signal = signal / signal.abs().max()
-            save_file = os.path.join(
-                save_path, "item{}_source{}hat.wav".format(snt_id, ns + 1)
-            )
-            torchaudio.save(
-                save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
-            )
-
-            # Original source
-            signal = targets[0, :, ns]
-            signal = signal / signal.abs().max()
-            save_file = os.path.join(
-                save_path, "item{}_source{}.wav".format(snt_id, ns + 1)
-            )
-            torchaudio.save(
-                save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
-            )
-
-        # Mixture
-        signal = mixture[0][0, :]
-        signal = signal / signal.abs().max()
-        save_file = os.path.join(save_path, "item{}_mix.wav".format(snt_id))
-        torchaudio.save(
-            save_file, signal.unsqueeze(0).cpu(), self.hparams.sample_rate
-        )
-
-
-def dataio_prep(hparams):
-    """Creates data processing pipeline"""
-
-    # 1. Define datasets
-    train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["train_data"],
-        replacements={"data_root": hparams["data_folder"]},
-    )
-
-    valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["valid_data"],
-        replacements={"data_root": hparams["data_folder"]},
-    )
-
-    test_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["test_data"],
-        replacements={"data_root": hparams["data_folder"]},
-    )
-
-    datasets = [train_data, valid_data, test_data]
-
-    # 2. Provide audio pipelines
-
-    @sb.utils.data_pipeline.takes("mix_wav")
-    @sb.utils.data_pipeline.provides("mix_sig")
-    def audio_pipeline_mix(mix_wav):
-        mix_sig = sb.dataio.dataio.read_audio(mix_wav)
-        return mix_sig
-
-    @sb.utils.data_pipeline.takes("s1_wav")
-    @sb.utils.data_pipeline.provides("s1_sig")
-    def audio_pipeline_s1(s1_wav):
-        s1_sig = sb.dataio.dataio.read_audio(s1_wav)
-        return s1_sig
-
-    @sb.utils.data_pipeline.takes("s2_wav")
-    @sb.utils.data_pipeline.provides("s2_sig")
-    def audio_pipeline_s2(s2_wav):
-        s2_sig = sb.dataio.dataio.read_audio(s2_wav)
-        return s2_sig
-
-    # --- 如果说话人 >= 3，定义第 3 路 ---
-    if hparams["num_spks"] >= 3:
-        @sb.utils.data_pipeline.takes("s3_wav")
-        @sb.utils.data_pipeline.provides("s3_sig")
-        def audio_pipeline_s3(s3_wav):
-            return sb.dataio.dataio.read_audio(s3_wav)
-
-    # --- 如果说话人 == 4，定义第 4 路 ---
-    if hparams["num_spks"] == 4:
-        @sb.utils.data_pipeline.takes("s4_wav")
-        @sb.utils.data_pipeline.provides("s4_sig")
-        def audio_pipeline_s4(s4_wav):
-            return sb.dataio.dataio.read_audio(s4_wav)
-
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_mix)
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s1)
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s2)
-    if hparams["num_spks"] == 3:
-        sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s3)
-        sb.dataio.dataset.set_output_keys(
-            datasets, ["id", "mix_sig", "s1_sig", "s2_sig", "s3_sig"]
-        )
-    elif hparams["num_spks"] == 4 :
-        sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s3)
-        sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s4)
-        sb.dataio.dataset.set_output_keys(
-            datasets,
-            ["id", "mix_sig", "s1_sig", "s2_sig", "s3_sig", "s4_sig"],
-        )    
-    else:
-        sb.dataio.dataset.set_output_keys(
-            datasets, ["id", "mix_sig", "s1_sig", "s2_sig"]
-        )
-
-    return train_data, valid_data, test_data
-
-
-if __name__ == "__main__":
-    # Load hyperparameters file with command-line overrides
-    hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:])
-    with open(hparams_file, encoding="utf-8") as fin:
-        hparams = load_hyperpyyaml(fin, overrides)
-
-    # Initialize ddp (useful only for multi-GPU DDP training)
-    sb.utils.distributed.ddp_init_group(run_opts)
-
-    # Logger info
-    logger = get_logger(__name__)
-
-    # Create experiment directory
-    sb.create_experiment_directory(
-        experiment_directory=hparams["output_folder"],
-        hyperparams_to_save=hparams_file,
-        overrides=overrides,
-    )
-
-    # Update precision to bf16 if the device is CPU and precision is fp16
-    if run_opts.get("device") == "cpu" and hparams.get("precision") == "fp16":
-        hparams["precision"] = "bf16"
-
-    # Check if wsj0_tr is set with dynamic mixing
-    if hparams["dynamic_mixing"] and not os.path.exists(
-        hparams["base_folder_dm"]
-    ):
-        raise ValueError(
-            "Please, specify a valid base_folder_dm folder when using dynamic mixing"
-        )
-
-    # Data preparation
-    from prepare_data import prepare_wsjmix  # noqa
-
-    # run_on_main(
-    #     prepare_wsjmix,
-    #     kwargs={
-    #         "datapath": hparams["data_folder"],
-    #         "savepath": hparams["save_folder"],
-    #         "n_spks": hparams["num_spks"],
-    #         "skip_prep": hparams["skip_prep"],
-    #         "fs": hparams["sample_rate"],
-    #     },
-    # )
-
-    # Create dataset objects
-    if hparams["dynamic_mixing"]:
-        from dynamic_mixing import dynamic_mix_data_prep
-
-        # if the base_folder for dm is not processed, preprocess them
-        if "processed" not in hparams["base_folder_dm"]:
-            # if the processed folder already exists we just use it otherwise we do the preprocessing
-            if not os.path.exists(
-                os.path.normpath(hparams["base_folder_dm"]) + "_processed"
-            ):
-                from preprocess_dynamic_mixing import resample_folder
-
-                print("Resampling the base folder")
-                run_on_main(
-                    resample_folder,
-                    kwargs={
-                        "input_folder": hparams["base_folder_dm"],
-                        "output_folder": os.path.normpath(
-                            hparams["base_folder_dm"]
-                        )
-                        + "_processed",
-                        "fs": hparams["sample_rate"],
-                        "regex": "**/*.wav",
-                    },
-                )
-                # adjust the base_folder_dm path
-                hparams["base_folder_dm"] = (
-                    os.path.normpath(hparams["base_folder_dm"]) + "_processed"
-                )
-            else:
-                print(
-                    "Using the existing processed folder on the same directory as base_folder_dm"
-                )
-                hparams["base_folder_dm"] = (
-                    os.path.normpath(hparams["base_folder_dm"]) + "_processed"
-                )
-
-        # Collecting the hparams for dynamic batching
-        dm_hparams = {
-            "train_data": hparams["train_data"],
-            "data_folder": hparams["data_folder"],
-            "base_folder_dm": hparams["base_folder_dm"],
-            "sample_rate": hparams["sample_rate"],
-            "num_spks": hparams["num_spks"],
-            "training_signal_len": hparams["training_signal_len"],
-            "dataloader_opts": hparams["dataloader_opts"],
-        }
-        train_data = dynamic_mix_data_prep(dm_hparams)
-        _, valid_data, test_data = dataio_prep(hparams)
-    else:
-        train_data, valid_data, test_data = dataio_prep(hparams)
-
-    # Load pretrained model if pretrained_separator is present in the yaml
-    if "pretrained_separator" in hparams:
-        run_on_main(hparams["pretrained_separator"].collect_files)
-        hparams["pretrained_separator"].load_collected()
-
-    # Brain class initialization
-    separator = Separation(
-        modules=hparams["modules"],
-        opt_class=hparams["optimizer"],
-        hparams=hparams,
-        run_opts=run_opts,
-        checkpointer=hparams["checkpointer"],
-    )
-
-    # re-initialize the parameters if we don't use a pretrained model
-    if "pretrained_separator" not in hparams:
-        for module in separator.modules.values():
-            separator.reset_layer_recursively(module)
-
-    # Training
-    separator.fit(
-        separator.hparams.epoch_counter,
-        train_data,
-        valid_data,
-        train_loader_kwargs=hparams["dataloader_opts"],
-        valid_loader_kwargs=hparams["dataloader_opts"],
-    )
-
-    # Eval
-    separator.evaluate(test_data, min_key="si-snr")
-    separator.save_results(test_data)

Conv-Tasnet/results/convtasnet_4-mix/1234/train_log.txt

@@ -1,242 +0,0 @@
-epoch: 1, lr: 1.50e-04 - train si-snr: 2.76 - valid si-snr: 12.09
-epoch: 2, lr: 1.50e-04 - train si-snr: 2.15 - valid si-snr: 11.84
-epoch: 3, lr: 1.50e-04 - train si-snr: 1.96 - valid si-snr: 11.70
-epoch: 4, lr: 1.50e-04 - train si-snr: 1.70 - valid si-snr: 11.63
-epoch: 5, lr: 1.50e-04 - train si-snr: 1.58 - valid si-snr: 11.57
-epoch: 6, lr: 1.50e-04 - train si-snr: 1.45 - valid si-snr: 11.55
-epoch: 7, lr: 1.50e-04 - train si-snr: 1.33 - valid si-snr: 11.45
-epoch: 8, lr: 1.50e-04 - train si-snr: 1.20 - valid si-snr: 11.33
-epoch: 9, lr: 1.50e-04 - train si-snr: 1.10 - valid si-snr: 11.35
-epoch: 10, lr: 1.50e-04 - train si-snr: 1.01 - valid si-snr: 11.30
-epoch: 11, lr: 1.50e-04 - train si-snr: 9.25e-01 - valid si-snr: 11.33
-epoch: 12, lr: 1.50e-04 - train si-snr: 7.83e-01 - valid si-snr: 11.16
-epoch: 13, lr: 1.50e-04 - train si-snr: 7.61e-01 - valid si-snr: 11.19
-epoch: 14, lr: 1.50e-04 - train si-snr: 6.87e-01 - valid si-snr: 11.13
-epoch: 15, lr: 1.50e-04 - train si-snr: 6.31e-01 - valid si-snr: 11.13
-epoch: 16, lr: 1.50e-04 - train si-snr: 5.54e-01 - valid si-snr: 11.10
-epoch: 17, lr: 1.50e-04 - train si-snr: 4.47e-01 - valid si-snr: 11.02
-epoch: 18, lr: 1.50e-04 - train si-snr: 4.65e-01 - valid si-snr: 11.04
-epoch: 19, lr: 1.50e-04 - train si-snr: 3.32e-01 - valid si-snr: 11.01
-epoch: 20, lr: 1.50e-04 - train si-snr: 3.27e-01 - valid si-snr: 10.95
-epoch: 21, lr: 1.50e-04 - train si-snr: 2.78e-01 - valid si-snr: 10.97
-epoch: 22, lr: 1.50e-04 - train si-snr: 2.18e-01 - valid si-snr: 10.88
-epoch: 23, lr: 1.50e-04 - train si-snr: 1.74e-01 - valid si-snr: 10.87
-epoch: 24, lr: 1.50e-04 - train si-snr: 1.03e-01 - valid si-snr: 10.95
-epoch: 25, lr: 1.50e-04 - train si-snr: 6.04e-02 - valid si-snr: 10.84
-epoch: 26, lr: 1.50e-04 - train si-snr: -2.94e-02 - valid si-snr: 10.79
-epoch: 27, lr: 1.50e-04 - train si-snr: -5.32e-02 - valid si-snr: 10.77
-epoch: 28, lr: 1.50e-04 - train si-snr: -5.68e-02 - valid si-snr: 10.74
-epoch: 29, lr: 1.50e-04 - train si-snr: -1.04e-01 - valid si-snr: 10.79
-epoch: 30, lr: 1.50e-04 - train si-snr: -1.57e-01 - valid si-snr: 10.73
-epoch: 31, lr: 1.50e-04 - train si-snr: -1.64e-01 - valid si-snr: 10.67
-epoch: 32, lr: 1.50e-04 - train si-snr: -2.11e-01 - valid si-snr: 10.71
-epoch: 33, lr: 1.50e-04 - train si-snr: -2.48e-01 - valid si-snr: 10.73
-epoch: 34, lr: 1.50e-04 - train si-snr: -2.79e-01 - valid si-snr: 10.69
-epoch: 35, lr: 1.50e-04 - train si-snr: -3.55e-01 - valid si-snr: 10.69
-epoch: 36, lr: 1.50e-04 - train si-snr: -3.32e-01 - valid si-snr: 10.64
-epoch: 37, lr: 1.50e-04 - train si-snr: -3.97e-01 - valid si-snr: 10.63
-epoch: 38, lr: 1.50e-04 - train si-snr: -4.11e-01 - valid si-snr: 10.71
-epoch: 39, lr: 1.50e-04 - train si-snr: -4.18e-01 - valid si-snr: 10.56
-epoch: 40, lr: 1.50e-04 - train si-snr: -4.74e-01 - valid si-snr: 10.55
-epoch: 41, lr: 1.50e-04 - train si-snr: -4.71e-01 - valid si-snr: 10.52
-epoch: 1, lr: 1.50e-04 - train si-snr: 6.31 - valid si-snr: 23.11
-epoch: 2, lr: 1.50e-04 - train si-snr: 4.85 - valid si-snr: 23.05
-epoch: 3, lr: 1.50e-04 - train si-snr: 4.79 - valid si-snr: 22.98
-epoch: 4, lr: 1.50e-04 - train si-snr: 4.56 - valid si-snr: 22.79
-epoch: 5, lr: 1.50e-04 - train si-snr: 4.28 - valid si-snr: 23.05
-epoch: 6, lr: 1.50e-04 - train si-snr: 4.27 - valid si-snr: 22.88
-epoch: 7, lr: 1.50e-04 - train si-snr: 4.11 - valid si-snr: 22.86
-epoch: 8, lr: 1.50e-04 - train si-snr: 4.11 - valid si-snr: 22.80
-epoch: 9, lr: 1.50e-04 - train si-snr: 3.96 - valid si-snr: 22.80
-epoch: 10, lr: 1.50e-04 - train si-snr: 3.91 - valid si-snr: 22.75
-epoch: 11, lr: 1.50e-04 - train si-snr: 3.76 - valid si-snr: 22.72
-epoch: 12, lr: 1.50e-04 - train si-snr: 3.82 - valid si-snr: 22.69
-epoch: 13, lr: 1.50e-04 - train si-snr: 3.71 - valid si-snr: 22.86
-epoch: 14, lr: 1.50e-04 - train si-snr: 3.64 - valid si-snr: 22.71
-epoch: 15, lr: 1.50e-04 - train si-snr: 3.59 - valid si-snr: 22.89
-epoch: 16, lr: 1.50e-04 - train si-snr: 3.39 - valid si-snr: 22.79
-epoch: 17, lr: 1.50e-04 - train si-snr: 3.30 - valid si-snr: 22.69
-epoch: 18, lr: 1.50e-04 - train si-snr: 3.29 - valid si-snr: 22.82
-epoch: 19, lr: 1.50e-04 - train si-snr: 3.32 - valid si-snr: 22.75
-epoch: 20, lr: 1.50e-04 - train si-snr: 3.14 - valid si-snr: 22.49
-epoch: 21, lr: 1.50e-04 - train si-snr: 3.11 - valid si-snr: 22.83
-epoch: 22, lr: 1.50e-04 - train si-snr: 3.12 - valid si-snr: 22.69
-epoch: 23, lr: 1.50e-04 - train si-snr: 2.93 - valid si-snr: 22.66
-epoch: 24, lr: 1.50e-04 - train si-snr: 2.96 - valid si-snr: 22.72
-epoch: 25, lr: 1.50e-04 - train si-snr: 2.96 - valid si-snr: 22.83
-epoch: 26, lr: 1.50e-04 - train si-snr: 2.88 - valid si-snr: 22.61
-epoch: 27, lr: 1.50e-04 - train si-snr: 2.86 - valid si-snr: 22.83
-epoch: 28, lr: 1.50e-04 - train si-snr: 2.80 - valid si-snr: 22.67
-epoch: 29, lr: 1.50e-04 - train si-snr: 2.73 - valid si-snr: 22.67
-epoch: 30, lr: 1.50e-04 - train si-snr: 2.65 - valid si-snr: 22.62
-epoch: 31, lr: 1.50e-04 - train si-snr: 2.62 - valid si-snr: 22.63
-epoch: 32, lr: 1.50e-04 - train si-snr: 2.61 - valid si-snr: 22.61
-epoch: 33, lr: 1.50e-04 - train si-snr: 2.44 - valid si-snr: 22.55
-epoch: 34, lr: 1.50e-04 - train si-snr: 2.50 - valid si-snr: 22.55
-epoch: 35, lr: 1.50e-04 - train si-snr: 2.47 - valid si-snr: 22.60
-epoch: 36, lr: 1.50e-04 - train si-snr: 2.44 - valid si-snr: 22.66
-epoch: 37, lr: 1.50e-04 - train si-snr: 2.24 - valid si-snr: 22.64
-epoch: 38, lr: 1.50e-04 - train si-snr: 2.28 - valid si-snr: 22.66
-epoch: 39, lr: 1.50e-04 - train si-snr: 2.15 - valid si-snr: 22.62
-epoch: 40, lr: 1.50e-04 - train si-snr: 2.19 - valid si-snr: 22.48
-epoch: 41, lr: 1.50e-04 - train si-snr: 2.26 - valid si-snr: 22.66
-epoch: 42, lr: 1.50e-04 - train si-snr: 2.09 - valid si-snr: 22.57
-epoch: 43, lr: 1.50e-04 - train si-snr: 2.15 - valid si-snr: 22.47
-epoch: 44, lr: 1.50e-04 - train si-snr: 2.00 - valid si-snr: 22.63
-epoch: 45, lr: 1.50e-04 - train si-snr: 2.13 - valid si-snr: 22.52
-epoch: 46, lr: 1.50e-04 - train si-snr: 2.00 - valid si-snr: 22.57
-epoch: 47, lr: 1.50e-04 - train si-snr: 1.90 - valid si-snr: 22.50
-epoch: 48, lr: 1.50e-04 - train si-snr: 1.89 - valid si-snr: 22.49
-epoch: 49, lr: 1.50e-04 - train si-snr: 1.94 - valid si-snr: 22.54
-epoch: 50, lr: 1.50e-04 - train si-snr: 1.89 - valid si-snr: 22.50
-epoch: 51, lr: 1.50e-04 - train si-snr: 1.85 - valid si-snr: 22.55
-epoch: 52, lr: 1.50e-04 - train si-snr: 1.66 - valid si-snr: 22.51
-epoch: 53, lr: 1.50e-04 - train si-snr: 1.65 - valid si-snr: 22.52
-epoch: 54, lr: 1.50e-04 - train si-snr: 1.77 - valid si-snr: 22.45
-epoch: 55, lr: 1.50e-04 - train si-snr: 1.62 - valid si-snr: 22.45
-epoch: 56, lr: 1.50e-04 - train si-snr: 1.52 - valid si-snr: 22.42
-epoch: 57, lr: 1.50e-04 - train si-snr: 1.53 - valid si-snr: 22.39
-epoch: 58, lr: 1.50e-04 - train si-snr: 1.52 - valid si-snr: 22.40
-epoch: 59, lr: 1.50e-04 - train si-snr: 1.55 - valid si-snr: 22.43
-epoch: 60, lr: 1.50e-04 - train si-snr: 1.64 - valid si-snr: 22.43
-epoch: 61, lr: 1.50e-04 - train si-snr: 1.42 - valid si-snr: 22.38
-epoch: 62, lr: 1.50e-04 - train si-snr: 1.50 - valid si-snr: 22.29
-epoch: 63, lr: 1.50e-04 - train si-snr: 1.34 - valid si-snr: 22.51
-epoch: 64, lr: 1.50e-04 - train si-snr: 1.25 - valid si-snr: 22.55
-epoch: 65, lr: 1.50e-04 - train si-snr: 1.43 - valid si-snr: 22.35
-epoch: 66, lr: 1.50e-04 - train si-snr: 1.33 - valid si-snr: 22.54
-epoch: 67, lr: 1.50e-04 - train si-snr: 1.35 - valid si-snr: 22.44
-epoch: 68, lr: 1.50e-04 - train si-snr: 1.35 - valid si-snr: 22.33
-epoch: 69, lr: 1.50e-04 - train si-snr: 1.13 - valid si-snr: 22.38
-epoch: 70, lr: 1.50e-04 - train si-snr: 1.18 - valid si-snr: 22.37
-epoch: 71, lr: 1.50e-04 - train si-snr: 1.04 - valid si-snr: 22.35
-epoch: 72, lr: 1.50e-04 - train si-snr: 1.24 - valid si-snr: 22.49
-epoch: 73, lr: 1.50e-04 - train si-snr: 1.25 - valid si-snr: 22.35
-epoch: 74, lr: 1.50e-04 - train si-snr: 1.07 - valid si-snr: 22.37
-epoch: 75, lr: 1.50e-04 - train si-snr: 1.04 - valid si-snr: 22.37
-epoch: 76, lr: 1.50e-04 - train si-snr: 1.11 - valid si-snr: 22.48
-epoch: 77, lr: 1.50e-04 - train si-snr: 1.03 - valid si-snr: 22.46
-epoch: 78, lr: 1.50e-04 - train si-snr: 9.65e-01 - valid si-snr: 22.31
-epoch: 79, lr: 1.50e-04 - train si-snr: 1.06 - valid si-snr: 22.34
-epoch: 80, lr: 1.50e-04 - train si-snr: 1.03 - valid si-snr: 22.32
-epoch: 81, lr: 1.50e-04 - train si-snr: 8.12e-01 - valid si-snr: 22.32
-epoch: 82, lr: 1.50e-04 - train si-snr: 8.76e-01 - valid si-snr: 22.33
-epoch: 83, lr: 1.50e-04 - train si-snr: 8.91e-01 - valid si-snr: 22.32
-epoch: 84, lr: 1.50e-04 - train si-snr: 9.11e-01 - valid si-snr: 22.34
-epoch: 85, lr: 1.50e-04 - train si-snr: 7.24e-01 - valid si-snr: 22.39
-epoch: 86, lr: 1.50e-04 - train si-snr: 7.65e-01 - valid si-snr: 22.34
-epoch: 87, lr: 1.50e-04 - train si-snr: 7.10e-01 - valid si-snr: 22.29
-epoch: 88, lr: 1.50e-04 - train si-snr: 7.65e-01 - valid si-snr: 22.42
-epoch: 89, lr: 1.50e-04 - train si-snr: 7.09e-01 - valid si-snr: 22.35
-epoch: 90, lr: 1.50e-04 - train si-snr: 8.13e-01 - valid si-snr: 22.38
-epoch: 91, lr: 7.50e-05 - train si-snr: 5.81e-01 - valid si-snr: 22.24
-epoch: 92, lr: 7.50e-05 - train si-snr: 3.71e-01 - valid si-snr: 22.33
-epoch: 93, lr: 7.50e-05 - train si-snr: 3.21e-01 - valid si-snr: 22.33
-epoch: 94, lr: 7.50e-05 - train si-snr: 3.48e-01 - valid si-snr: 22.29
-epoch: 95, lr: 3.75e-05 - train si-snr: 4.08e-01 - valid si-snr: 22.34
-epoch: 96, lr: 3.75e-05 - train si-snr: 2.29e-01 - valid si-snr: 22.33
-epoch: 97, lr: 3.75e-05 - train si-snr: 2.27e-01 - valid si-snr: 22.29
-epoch: 98, lr: 1.87e-05 - train si-snr: 1.28e-01 - valid si-snr: 22.27
-epoch: 99, lr: 1.87e-05 - train si-snr: 3.17e-02 - valid si-snr: 22.27
-epoch: 100, lr: 1.87e-05 - train si-snr: 6.84e-02 - valid si-snr: 22.24
-epoch: 101, lr: 1.87e-05 - train si-snr: 6.90e-02 - valid si-snr: 22.25
-epoch: 102, lr: 1.87e-05 - train si-snr: 1.53e-01 - valid si-snr: 22.28
-epoch: 103, lr: 1.87e-05 - train si-snr: 4.23e-02 - valid si-snr: 22.28
-epoch: 104, lr: 9.37e-06 - train si-snr: 7.48e-02 - valid si-snr: 22.24
-epoch: 105, lr: 9.37e-06 - train si-snr: 8.28e-02 - valid si-snr: 22.27
-epoch: 106, lr: 9.37e-06 - train si-snr: -1.19e-01 - valid si-snr: 22.26
-epoch: 107, lr: 9.37e-06 - train si-snr: 2.27e-02 - valid si-snr: 22.26
-epoch: 108, lr: 4.69e-06 - train si-snr: -9.19e-02 - valid si-snr: 22.24
-epoch: 109, lr: 4.69e-06 - train si-snr: -1.86e-02 - valid si-snr: 22.26
-epoch: 110, lr: 4.69e-06 - train si-snr: -1.29e-01 - valid si-snr: 22.26
-epoch: 111, lr: 2.34e-06 - train si-snr: -1.28e-01 - valid si-snr: 22.26
-epoch: 112, lr: 2.34e-06 - train si-snr: 1.96e-02 - valid si-snr: 22.26
-epoch: 113, lr: 2.34e-06 - train si-snr: -8.82e-02 - valid si-snr: 22.26
-epoch: 114, lr: 1.17e-06 - train si-snr: -2.95e-02 - valid si-snr: 22.25
-epoch: 115, lr: 1.17e-06 - train si-snr: 1.44e-02 - valid si-snr: 22.26
-epoch: 116, lr: 1.17e-06 - train si-snr: -2.01e-02 - valid si-snr: 22.25
-epoch: 117, lr: 5.86e-07 - train si-snr: -6.14e-02 - valid si-snr: 22.25
-epoch: 118, lr: 5.86e-07 - train si-snr: 1.49e-02 - valid si-snr: 22.25
-epoch: 119, lr: 5.86e-07 - train si-snr: -2.11e-02 - valid si-snr: 22.25
-epoch: 120, lr: 2.93e-07 - train si-snr: -8.56e-02 - valid si-snr: 22.25
-epoch: 121, lr: 2.93e-07 - train si-snr: 3.46e-02 - valid si-snr: 22.25
-epoch: 122, lr: 2.93e-07 - train si-snr: -4.48e-02 - valid si-snr: 22.26
-epoch: 123, lr: 1.46e-07 - train si-snr: -4.78e-02 - valid si-snr: 22.25
-epoch: 124, lr: 1.46e-07 - train si-snr: 4.87e-02 - valid si-snr: 22.26
-epoch: 125, lr: 1.46e-07 - train si-snr: -8.55e-02 - valid si-snr: 22.25
-epoch: 126, lr: 7.32e-08 - train si-snr: 4.56e-02 - valid si-snr: 22.25
-epoch: 127, lr: 7.32e-08 - train si-snr: -7.29e-02 - valid si-snr: 22.25
-epoch: 128, lr: 7.32e-08 - train si-snr: -4.80e-02 - valid si-snr: 22.26
-epoch: 129, lr: 3.66e-08 - train si-snr: -6.66e-02 - valid si-snr: 22.26
-epoch: 130, lr: 3.66e-08 - train si-snr: 6.62e-03 - valid si-snr: 22.26
-epoch: 131, lr: 3.66e-08 - train si-snr: -1.94e-02 - valid si-snr: 22.26
-epoch: 132, lr: 1.83e-08 - train si-snr: 1.16e-02 - valid si-snr: 22.26
-epoch: 133, lr: 1.83e-08 - train si-snr: -1.09e-01 - valid si-snr: 22.26
-epoch: 134, lr: 1.83e-08 - train si-snr: -1.16e-01 - valid si-snr: 22.26
-epoch: 135, lr: 1.00e-08 - train si-snr: 2.68e-02 - valid si-snr: 22.26
-epoch: 136, lr: 1.00e-08 - train si-snr: 3.10e-03 - valid si-snr: 22.26
-epoch: 137, lr: 1.00e-08 - train si-snr: -4.31e-02 - valid si-snr: 22.26
-epoch: 138, lr: 1.00e-08 - train si-snr: 7.30e-02 - valid si-snr: 22.26
-epoch: 139, lr: 1.00e-08 - train si-snr: -9.77e-02 - valid si-snr: 22.26
-epoch: 140, lr: 1.00e-08 - train si-snr: -1.41e-01 - valid si-snr: 22.26
-epoch: 141, lr: 1.00e-08 - train si-snr: -1.82e-02 - valid si-snr: 22.26
-epoch: 142, lr: 1.00e-08 - train si-snr: -5.03e-02 - valid si-snr: 22.26
-epoch: 143, lr: 1.00e-08 - train si-snr: -9.63e-02 - valid si-snr: 22.26
-epoch: 144, lr: 1.00e-08 - train si-snr: -1.29e-02 - valid si-snr: 22.26
-epoch: 145, lr: 1.00e-08 - train si-snr: -3.77e-02 - valid si-snr: 22.26
-epoch: 146, lr: 1.00e-08 - train si-snr: -1.36e-01 - valid si-snr: 22.26
-epoch: 147, lr: 1.00e-08 - train si-snr: -1.02e-01 - valid si-snr: 22.26
-epoch: 148, lr: 1.00e-08 - train si-snr: 1.05e-01 - valid si-snr: 22.26
-epoch: 149, lr: 1.00e-08 - train si-snr: -1.08e-01 - valid si-snr: 22.26
-epoch: 150, lr: 1.00e-08 - train si-snr: 1.28e-02 - valid si-snr: 22.26
-epoch: 151, lr: 1.00e-08 - train si-snr: -8.94e-02 - valid si-snr: 22.26
-epoch: 152, lr: 1.00e-08 - train si-snr: -9.64e-02 - valid si-snr: 22.26
-epoch: 153, lr: 1.00e-08 - train si-snr: -1.32e-01 - valid si-snr: 22.26
-epoch: 154, lr: 1.00e-08 - train si-snr: 2.86e-02 - valid si-snr: 22.26
-epoch: 155, lr: 1.00e-08 - train si-snr: -2.50e-02 - valid si-snr: 22.26
-epoch: 156, lr: 1.00e-08 - train si-snr: -1.44e-02 - valid si-snr: 22.26
-epoch: 157, lr: 1.00e-08 - train si-snr: 9.09e-02 - valid si-snr: 22.26
-epoch: 158, lr: 1.00e-08 - train si-snr: 6.12e-03 - valid si-snr: 22.26
-epoch: 159, lr: 1.00e-08 - train si-snr: -3.80e-02 - valid si-snr: 22.26
-epoch: 160, lr: 1.00e-08 - train si-snr: 4.51e-02 - valid si-snr: 22.26
-epoch: 161, lr: 1.00e-08 - train si-snr: -2.98e-02 - valid si-snr: 22.26
-epoch: 162, lr: 1.00e-08 - train si-snr: -2.20e-03 - valid si-snr: 22.26
-epoch: 163, lr: 1.00e-08 - train si-snr: -1.64e-01 - valid si-snr: 22.26
-epoch: 164, lr: 1.00e-08 - train si-snr: -3.20e-02 - valid si-snr: 22.26
-epoch: 165, lr: 1.00e-08 - train si-snr: 3.47e-03 - valid si-snr: 22.26
-epoch: 166, lr: 1.00e-08 - train si-snr: -8.60e-02 - valid si-snr: 22.26
-epoch: 167, lr: 1.00e-08 - train si-snr: 6.45e-03 - valid si-snr: 22.26
-epoch: 168, lr: 1.00e-08 - train si-snr: 1.17e-02 - valid si-snr: 22.26
-epoch: 169, lr: 1.00e-08 - train si-snr: -4.05e-02 - valid si-snr: 22.26
-epoch: 170, lr: 1.00e-08 - train si-snr: -1.26e-01 - valid si-snr: 22.26
-epoch: 171, lr: 1.00e-08 - train si-snr: -1.06e-01 - valid si-snr: 22.26
-epoch: 172, lr: 1.00e-08 - train si-snr: -1.26e-01 - valid si-snr: 22.26
-epoch: 173, lr: 1.00e-08 - train si-snr: -7.41e-02 - valid si-snr: 22.26
-epoch: 174, lr: 1.00e-08 - train si-snr: 1.57e-02 - valid si-snr: 22.26
-epoch: 175, lr: 1.00e-08 - train si-snr: -1.48e-02 - valid si-snr: 22.26
-epoch: 176, lr: 1.00e-08 - train si-snr: 6.87e-02 - valid si-snr: 22.26
-epoch: 177, lr: 1.00e-08 - train si-snr: -6.77e-02 - valid si-snr: 22.26
-epoch: 178, lr: 1.00e-08 - train si-snr: -1.75e-01 - valid si-snr: 22.26
-epoch: 179, lr: 1.00e-08 - train si-snr: -8.73e-02 - valid si-snr: 22.26
-epoch: 180, lr: 1.00e-08 - train si-snr: -7.13e-02 - valid si-snr: 22.26
-epoch: 181, lr: 1.00e-08 - train si-snr: -1.28e-01 - valid si-snr: 22.26
-epoch: 182, lr: 1.00e-08 - train si-snr: 2.53e-02 - valid si-snr: 22.26
-epoch: 183, lr: 1.00e-08 - train si-snr: 5.30e-02 - valid si-snr: 22.26
-epoch: 184, lr: 1.00e-08 - train si-snr: -6.50e-02 - valid si-snr: 22.26
-epoch: 185, lr: 1.00e-08 - train si-snr: -7.48e-02 - valid si-snr: 22.26
-epoch: 186, lr: 1.00e-08 - train si-snr: -6.33e-02 - valid si-snr: 22.26
-epoch: 187, lr: 1.00e-08 - train si-snr: -5.01e-02 - valid si-snr: 22.26
-epoch: 188, lr: 1.00e-08 - train si-snr: -2.82e-03 - valid si-snr: 22.26
-epoch: 189, lr: 1.00e-08 - train si-snr: -1.37e-01 - valid si-snr: 22.26
-epoch: 190, lr: 1.00e-08 - train si-snr: -3.86e-02 - valid si-snr: 22.26
-epoch: 191, lr: 1.00e-08 - train si-snr: -4.23e-02 - valid si-snr: 22.26
-epoch: 192, lr: 1.00e-08 - train si-snr: -7.80e-02 - valid si-snr: 22.26
-epoch: 193, lr: 1.00e-08 - train si-snr: -2.90e-02 - valid si-snr: 22.26
-epoch: 194, lr: 1.00e-08 - train si-snr: -1.21e-01 - valid si-snr: 22.26
-epoch: 195, lr: 1.00e-08 - train si-snr: 8.91e-03 - valid si-snr: 22.26
-epoch: 196, lr: 1.00e-08 - train si-snr: -5.28e-02 - valid si-snr: 22.26
-epoch: 197, lr: 1.00e-08 - train si-snr: 9.40e-02 - valid si-snr: 22.26
-epoch: 198, lr: 1.00e-08 - train si-snr: -4.55e-02 - valid si-snr: 22.26
-epoch: 199, lr: 1.00e-08 - train si-snr: -6.24e-02 - valid si-snr: 22.26
-epoch: 200, lr: 1.00e-08 - train si-snr: 5.69e-03 - valid si-snr: 22.26
-Epoch loaded: 104 - test si-snr: 20.22