I want to use dynamic mixing of four people's voices, but I encounter the following problem.

(new0913-env) wenyu@wenyu:~/桌面/speechbrain-develop0912/speechbrain-develop/recipes/LibriMix/separation$ CUDA_VISIBLE_DEVICES=1 python train4.py hparams/sepformer-libri4mix8max104000DMall.yaml
speechbrain.core - Beginning experiment!
speechbrain.core - Experiment folder: results/sepformer-libri4mix-DM-all/111
Using the existing processed folder on the same directory as base_folder_dm
speechbrain.core - Exception:
Traceback (most recent call last):
File "train4.py", line 671, in
train_data = dynamic_mix_data_prep(dm_hparams)
File "/home/wenyu/桌面/speechbrain-develop0912/speechbrain-develop/recipes/LibriMix/separation/dynamic_mixing.py", line 89, in dynamic_mix_data_prep_librimix
train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
File "/home/wenyu/桌面/speechbrain-develop0912/new0913-env/lib/python3.8/site-packages/speechbrain/dataio/dataset.py", line 408, in from_csv
data = load_data_csv(csv_path, replacements)
File "/home/wenyu/桌面/speechbrain-develop0912/new0913-env/lib/python3.8/site-packages/speechbrain/dataio/dataio.py", line 129, in load_data_csv
with open(csv_path, newline="") as csvfile:
FileNotFoundError: [Errno 2] No such file or directory: 'results/sepformer-libri4mix-DM-all/111/save/libri4mix_train-all.csv'

Hello, this is my yaml file.
Even with dynamic mixing, there must be a Libri4Mix dataset to produce the correct csv file, right?
Because I didn't have enough computer space, I only generated the development and test datasets, but not the training dataset. Is this why the train csv file cannot be generated?
Thanks for the answer

Model: SepFormer for source separation

https://arxiv.org/abs/2010.13154

Dataset : Libri4Mix

Basic parameters

Seed needs to be set at top of yaml, before objects with parameters are made

seed: 111
__set_seed: !apply:torch.manual_seed [!ref ]

Data params

e.g. '/yourpath/Libri4Mix/train-clean-360/'

the data folder is needed even if dynamic mixing is applied

data_folder: /media/wenyu/Seagate Backup Plus Drive/LibriMix-master/storage_dir/Libri4Mix

This is needed only if dynamic mixing is applied

base_folder_dm: /home/wenyu/桌面/LibriMix-master0912/LibriMix-master/storage_dir/LibriSpeech/train-all/

experiment_name: sepformer-libri4mix-DM-all
output_folder: !ref results/<experiment_name>/
train_log: !ref <output_folder>/train_log.txt
save_folder: !ref <output_folder>/save
train_data: !ref <save_folder>/libri4mix_train-all.csv
valid_data: !ref <save_folder>/libri4mix_dev.csv
test_data: !ref <save_folder>/libri4mix_test.csv
skip_prep: False

ckpt_interval_minutes: 60

Experiment params

precision: fp16 # bf16, fp16 or fp32 # Set it to True for mixed precision
num_spks: 4
noprogressbar: False
save_audio: True # Save estimated sources on disk
sample_rate: 8000

####################### Training Parameters ####################################
N_epochs: 250
batch_size: 1
train_size: 40000
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: 104000

Set it to True to dynamically create mixtures at training time

dynamic_mixing: True
use_wham_noise: 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: [95, 100, 105] # List of speed changes for time-stretching

speed_perturb: !new:speechbrain.augment.time_domain.SpeedPerturb
orig_freq: !ref <sample_rate>
speeds: !ref <speed_changes>

Frequency drop: randomly drops a number of frequency bands to zero.

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: !ref <drop_freq_low>
drop_freq_high: !ref <drop_freq_high>
drop_freq_count_low: !ref <drop_freq_count_low>
drop_freq_count_high: !ref <drop_freq_count_high>
drop_freq_width: !ref <drop_freq_width>

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: !ref <drop_chunk_length_low>
drop_length_high: !ref <drop_chunk_length_high>
drop_count_low: !ref <drop_chunk_count_low>
drop_count_high: !ref <drop_chunk_count_high>

loss thresholding -- this thresholds the training loss

threshold_byloss: True
threshold: -30

Encoder parameters

N_encoder_out: 256
out_channels: 256
kernel_size: 16
kernel_stride: 8
d_ffn: 1024

Dataloader options

dataloader_opts:
batch_size: !ref <batch_size>
num_workers: 3

Specifying the network

Encoder: !new:speechbrain.lobes.models.dual_path.Encoder
kernel_size: !ref <kernel_size>
out_channels: !ref <N_encoder_out>

SBtfintra: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
num_layers: 8
d_model: !ref <out_channels>
nhead: 8
d_ffn: !ref <d_ffn>
dropout: 0
use_positional_encoding: True
norm_before: True

SBtfinter: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock
num_layers: 8
d_model: !ref <out_channels>
nhead: 8
d_ffn: !ref <d_ffn>
dropout: 0
use_positional_encoding: True
norm_before: True

MaskNet: !new:speechbrain.lobes.models.dual_path.Dual_Path_Model
num_spks: !ref <num_spks>
in_channels: !ref <N_encoder_out>
out_channels: !ref <out_channels>
num_layers: 2
K: 250
intra_model: !ref
inter_model: !ref
norm: ln
linear_layer_after_inter_intra: False
skip_around_intra: True

Decoder: !new:speechbrain.lobes.models.dual_path.Decoder
in_channels: !ref <N_encoder_out>
out_channels: 1
kernel_size: !ref <kernel_size>
stride: !ref <kernel_stride>
bias: False

optimizer: !name:torch.optim.Adam
lr: !ref
weight_decay: 0

loss: !name:speechbrain.nnet.losses.get_si_snr_with_pitwrapper

lr_scheduler: !new:speechbrain.nnet.schedulers.ReduceLROnPlateau
factor: 0.5
patience: 2
dont_halve_until_epoch: 5

epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
limit: !ref <N_epochs>

modules:
encoder: !ref
decoder: !ref
masknet: !ref

checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
checkpoints_dir: !ref <save_folder>
recoverables:
encoder: !ref
decoder: !ref
masknet: !ref
counter: !ref <epoch_counter>
# lr_scheduler: !ref <lr_scheduler>

train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
save_file: !ref <train_log>