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Singing Voice Conversion via diffusion model

License: GNU Affero General Public License v3.0

Python 16.75% Jupyter Notebook 83.25% Batchfile 0.01%

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diff-svc's Issues

IndexError: list index out of range

I got this error when I run this code on windows

 set PYTHONPATH=.
set CUDA_VISIBLE_DEVICES=0 
python preprocessing/binarize.py --config training/config.yaml

| Binarizer:  <class 'preprocessing.SVCpre.SVCBinarizer'>
Traceback (most recent call last):
  File "preprocessing/binarize.py", line 20, in <module>
    binarize()
  File "preprocessing/binarize.py", line 15, in binarize
    binarizer_cls().process()
  File "C:\Users\ASUS\Downloads\diff-svc\preprocessing\SVCpre.py", line 29, in __init__
    super().__init__(item_attributes)
  File "C:\Users\ASUS\Downloads\diff-svc\preprocessing\base_binarizer.py", line 52, in __init__
    assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
IndexError: list index out of range

can someone please make a svc diff with my voice please

AttributeError: 'HifiGAN' object has no attribute 'device'

on windows, anaconda

RuntimeError: Error(s) in loading state_dict for GaussianDiffusion:

In load model step, This error occurred.
How to fix it?

cannot import name 'consume_prefix_in_state_dict_if_present' from 'torch.nn.modules.utils'

nobody in the universe has had this happen to them, requirements.txt is incomplete and i still have to install 300 more modules that have errors just to use this with no luck.

Traceback (most recent call last):
  File "infer.py", line 9, in <module>
    from infer_tools import infer_tool
  File "x:\diffsvc\diff-svc\infer_tools\infer_tool.py", line 20, in <module>
    from preprocessing.hubertinfer import Hubertencoder
  File "x:\diffsvc\diff-svc\preprocessing\hubertinfer.py", line 8, in <module>
    from network.hubert.hubert_model import hubert_soft, get_units
  File "x:\diffsvc\diff-svc\network\hubert\hubert_model.py", line 11, in <module>
    from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
ImportError: cannot import name 'consume_prefix_in_state_dict_if_present' from 'torch.nn.modules.utils' (C:\Users\EzoGaming\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\utils.py)

ModuleNotFoundError: No module named 'torchaudio'

I have the right environment, I installed the right version of torchaudio, and I installed the requirements in the txt, but when I try to infer locally, it says "ModuleNotFoundError: No module named 'torchaudio'".

Model training question (batch cut samples)

So as the title said, I batch cut my samples so they'd be short enough to not consume all my colab ram. That left me with the question of would it affect the model's output generation if I train my model with data that has the cut off at the end and start mid vowel/consonant?

IndexError: list index out of range (when uploading my model in the Inference Notebook)

IndexError Traceback (most recent call last)
in
50 config_path="/content/drive/MyDrive/diff-svc/data/My_Model/config.yaml" #@param {type: "string"}
51 hubert_gpu=True
---> 52 svc_model = Svc(project_name,config_path,hubert_gpu, model_path)
53 print('model loaded')

/content/diff-svc/preprocessing/hubertinfer.py in init(self, pt_path)
20 self.hbt_model = load_model(pt_path)
21 else:
---> 22 pt_path = list(Path(pt_path).parent.rglob('*.pt'))[0]
23 if 'hubert_gpu' in hparams.keys():
24 self.use_gpu = hparams['hubert_gpu']

IndexError: list index out of range

No module named 'training.task'

I'm at the last step of training with

set CUDA_VISIBLE_DEVICES=0
python run.py --config training/config_nsf.yaml --exp_name NAME --reset

and it spits out the error for missing 'training.task'

Everything else works and I've followed all steps to train

Error: No module named 'utils.hparams'

Traceback (most recent call last):
  File "preprocessing/binarize.py", line 6, in <module>
    from utils.hparams import set_hparams, hparams
ModuleNotFoundError: No module named 'utils.hparams'

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.

Ever since I tried to get rid of a duplicate tensorbaord, I keep getting this error when trying to continue training. I have a 3090 24 GB GPU. Please help!

RuntimeError: Error(s) in loading state_dict for PitchExtractor:

(diffsvc) x:\diffsvc\diff-svc>python infer.py
| load 'model' from './checkpoints/model/model_ckpt_steps_600000.ckpt'.
Traceback (most recent call last):
  File "infer.py", line 93, in <module>
    model = Svc(project_name, config_path, hubert_gpu, model_path)
  File "x:\diffsvc\diff-svc\infer_tools\infer_tool.py", line 135, in __init__
    utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
  File "x:\diffsvc\diff-svc\utils\__init__.py", line 202, in load_ckpt
    cur_model.load_state_dict(state_dict, strict=strict)
  File "C:\Users\EzoGaming\anaconda3\envs\diffsvc\lib\site-packages\torch\nn\modules\module.py", line 1604, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for PitchExtractor:
        Missing key(s) in state_dict: "mel_prenet.layers.0.0.weight", "mel_prenet.layers.0.0.bias", "mel_prenet.layers.0.2.weight", "mel_prenet.layers.0.2.bias", "mel_prenet.layers.0.2.running_mean", "mel_prenet.layers.0.2.running_var", "mel_prenet.layers.1.0.weight", "mel_prenet.layers.1.0.bias", "mel_prenet.layers.1.2.weight", "mel_prenet.layers.1.2.bias", "mel_prenet.layers.1.2.running_mean", "mel_prenet.layers.1.2.running_var",
...

IndexError: list index out of range

| Hparams chains:  ['training/config.yaml']
| Hparams: 
;33;mK_step: 1000, ;33;maccumulate_grad_batches: 1, ;33;maudio_num_mel_bins: 80, ;33;maudio_sample_rate: 24000, ;33;mbinarization_args: {'shuffle': False, 'with_align': True, 'with_f0': True, 'with_hubert': True, 'with_spk_embed': False, 'with_wav': False}, 
;33;mbinarizer_cls: preprocessing.SVCpre.SVCBinarizer, ;33;mbinary_data_dir: data/binary/atri, ;33;mcheck_val_every_n_epoch: 10, ;33;mchoose_test_manually: False, ;33;mclip_grad_norm: 1, 
;33;mconfig_path: training/config.yaml, ;33;mcontent_cond_steps: [], ;33;mcwt_add_f0_loss: False, ;33;mcwt_hidden_size: 128, ;33;mcwt_layers: 2, 
;33;mcwt_loss: l1, ;33;mcwt_std_scale: 0.8, ;33;mdatasets: ['opencpop'], ;33;mdebug: False, ;33;mdec_ffn_kernel_size: 9, 
;33;mdec_layers: 4, ;33;mdecay_steps: 40000, ;33;mdecoder_type: fft, ;33;mdict_dir: , ;33;mdiff_decoder_type: wavenet, 
;33;mdiff_loss_type: l2, ;33;mdilation_cycle_length: 4, ;33;mdropout: 0.1, ;33;mds_workers: 4, ;33;mdur_enc_hidden_stride_kernel: ['0,2,3', '0,2,3', '0,1,3'], 
;33;mdur_loss: mse, ;33;mdur_predictor_kernel: 3, ;33;mdur_predictor_layers: 5, ;33;menc_ffn_kernel_size: 9, ;33;menc_layers: 4, 
;33;mencoder_K: 8, ;33;mencoder_type: fft, ;33;mendless_ds: False, ;33;mf0_bin: 256, ;33;mf0_max: 1100.0, 
;33;mf0_min: 50.0, ;33;mffn_act: gelu, ;33;mffn_padding: SAME, ;33;mfft_size: 512, ;33;mfmax: 12000, 
;33;mfmin: 30, ;33;mfs2_ckpt: , ;33;mgaussian_start: True, ;33;mgen_dir_name: , ;33;mgen_tgt_spk_id: -1, 
;33;mhidden_size: 256, ;33;mhop_size: 128, ;33;mhubert_gpu: True, ;33;mhubert_path: checkpoints/hubert/hubert_soft.pt, ;33;minfer: False, 
;33;mkeep_bins: 80, ;33;mlambda_commit: 0.25, ;33;mlambda_energy: 0.0, ;33;mlambda_f0: 1.0, ;33;mlambda_ph_dur: 0.3, 
;33;mlambda_sent_dur: 1.0, ;33;mlambda_uv: 1.0, ;33;mlambda_word_dur: 1.0, ;33;mload_ckpt: , ;33;mlog_interval: 100, 
;33;mloud_norm: False, ;33;mlr: 0.0004, ;33;mmax_beta: 0.02, ;33;mmax_epochs: 3000, ;33;mmax_eval_sentences: 1, 
;33;mmax_eval_tokens: 60000, ;33;mmax_frames: 42000, ;33;mmax_input_tokens: 6000, ;33;mmax_sentences: 88, ;33;mmax_tokens: 128000, 
;33;mmax_updates: 1000000, ;33;mmel_loss: ssim:0.5|l1:0.5, ;33;mmel_vmax: 1.5, ;33;mmel_vmin: -6.0, ;33;mmin_level_db: -120, 
;33;mnorm_type: gn, ;33;mnum_ckpt_keep: 10, ;33;mnum_heads: 2, ;33;mnum_sanity_val_steps: 1, ;33;mnum_spk: 1, 
;33;mnum_test_samples: 0, ;33;mnum_valid_plots: 10, ;33;moptimizer_adam_beta1: 0.9, ;33;moptimizer_adam_beta2: 0.98, ;33;mout_wav_norm: False, 
;33;mpe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt, ;33;mpe_enable: False, ;33;mperform_enhance: True, ;33;mpitch_ar: False, ;33;mpitch_enc_hidden_stride_kernel: ['0,2,5', '0,2,5', '0,2,5'], 
;33;mpitch_extractor: parselmouth, ;33;mpitch_loss: l2, ;33;mpitch_norm: log, ;33;mpitch_type: frame, ;33;mpndm_speedup: 10, 
;33;mpre_align_args: {'allow_no_txt': False, 'denoise': False, 'forced_align': 'mfa', 'txt_processor': 'zh_g2pM', 'use_sox': True, 'use_tone': False}, ;33;mpre_align_cls: data_gen.singing.pre_align.SingingPreAlign, ;33;mpredictor_dropout: 0.5, ;33;mpredictor_grad: 0.1, ;33;mpredictor_hidden: -1, 
;33;mpredictor_kernel: 5, ;33;mpredictor_layers: 5, ;33;mprenet_dropout: 0.5, ;33;mprenet_hidden_size: 256, ;33;mpretrain_fs_ckpt: , 
;33;mprocessed_data_dir: xxx, ;33;mprofile_infer: False, ;33;mraw_data_dir: data/raw/atri, ;33;mref_norm_layer: bn, ;33;mrel_pos: True, 
;33;mreset_phone_dict: True, ;33;mresidual_channels: 256, ;33;mresidual_layers: 20, ;33;msave_best: False, ;33;msave_ckpt: True, 
;33;msave_codes: ['configs', 'modules', 'src', 'utils'], ;33;msave_f0: True, ;33;msave_gt: False, ;33;mschedule_type: linear, ;33;mseed: 1234, 
;33;msort_by_len: True, ;33;mspeaker_id: atri, ;33;mspec_max: [-1.5451143980026245, -1.5177826881408691, -1.1807013750076294, -0.6732071042060852, -0.47006210684776306, -0.271837055683136, -0.27174147963523865, -0.3395537734031677, -0.2529868483543396, -0.22453370690345764, -0.24767500162124634, -0.22861438989639282, -0.28668588399887085, -0.335957795381546, -0.3118636906147003, -0.34530898928642273, -0.35274678468704224, -0.4730182886123657, -0.45395755767822266, -0.4338522255420685, -0.41395917534828186, -0.29468369483947754, -0.16852207481861115, -0.3900046646595001, -0.6241626739501953, -0.5899035930633545, -0.6534764170646667, -0.6667397022247314, -0.6992383003234863, -0.7867978811264038, -0.8457596302032471, -0.43350857496261597, -0.629216730594635, -0.9135912656784058, -0.9230040311813354, -0.6756577491760254, -0.8399246335029602, -0.8495144248008728, -0.781493067741394, -1.0347247123718262, -1.0051935911178589, -1.1246198415756226, -1.021154522895813, -0.851677417755127, -0.8443652987480164, -0.9016147255897522, -0.7618780732154846, -1.0490750074386597, -1.2046996355056763, -1.2022035121917725, -0.9753153324127197, -1.2503044605255127, -1.0664823055267334, -1.1236635446548462, -1.2223032712936401, -1.0116488933563232, -1.2263423204421997, -1.2552075386047363, -1.3846945762634277, -1.2681812047958374, -1.3416036367416382, -1.264938235282898, -1.2763726711273193, -1.4651004076004028, -1.4880361557006836, -1.5735552310943604, -1.4097294807434082, -1.468385100364685, -1.3768259286880493, -1.3312186002731323, -1.3547866344451904, -1.4387739896774292, -1.1861546039581299, -1.1709729433059692, -1.1812609434127808, -1.1489264965057373, -1.5605546236038208, -2.2702553272247314, -4.064557075500488, -5.809507846832275], ;33;mspec_min: [-5.740882873535156, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -5.959110260009766, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -5.999546527862549, -6.0, -5.995517730712891, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -5.960205078125, -5.93423318862915, -6.0, -5.933608531951904, -6.0, -6.0, -6.0, -5.953958511352539, -5.908934593200684, -5.911312580108643, -5.882552623748779, -5.932425498962402, -5.91495943069458, -5.826524257659912, -5.777952671051025, -5.775007724761963, -5.849961280822754, -5.7793660163879395, -5.781087875366211, -5.818603992462158, -5.765895366668701, -5.834509372711182, -5.817623615264893, -5.855445384979248, -5.844409465789795, -5.760529518127441, -5.713063716888428, -5.74588680267334, -5.855954647064209, -5.874588489532471, -5.81571626663208, -5.849369049072266, -5.963766574859619, -5.8541646003723145, -5.922942161560059, -6.0, -6.0, -6.0, -6.0], ;33;mspk_cond_steps: [], 
;33;mstop_token_weight: 5.0, ;33;mtask_cls: training.task.SVC_task.SVCTask, ;33;mtest_ids: [], ;33;mtest_input_dir: , ;33;mtest_num: 0, 
;33;mtest_prefixes: ['test'], ;33;mtest_set_name: test, ;33;mtimesteps: 1000, ;33;mtrain_set_name: train, ;33;muse_crepe: False, 
;33;muse_denoise: False, ;33;muse_energy_embed: False, ;33;muse_gt_dur: False, ;33;muse_gt_f0: False, ;33;muse_midi: False, 
;33;muse_nsf: True, ;33;muse_pitch_embed: True, ;33;muse_pos_embed: True, ;33;muse_spk_embed: False, ;33;muse_spk_id: False, 
;33;muse_split_spk_id: False, ;33;muse_uv: True, ;33;muse_var_enc: False, ;33;mval_check_interval: 2000, ;33;mvalid_num: 0, 
;33;mvalid_set_name: valid, ;33;mvalidate: False, ;33;mvocoder: network.vocoders.hifigan.HifiGAN, ;33;mvocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128, ;33;mwarmup_updates: 2000, 
;33;mwav2spec_eps: 1e-6, ;33;mweight_decay: 0, ;33;mwin_size: 512, ;33;mwork_dir: , 
| Binarizer:  <class 'preprocessing.SVCpre.SVCBinarizer'>
Traceback (most recent call last):
  File "preprocessing/binarize.py", line 20, in <module>
    binarize()
  File "preprocessing/binarize.py", line 15, in binarize
    binarizer_cls().process()
  File "/content/diff-svc/preprocessing/SVCpre.py", line 29, in __init__
    super().__init__(item_attributes)
  File "/content/diff-svc/preprocessing/base_binarizer.py", line 52, in __init__
    assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
IndexError: list index out of range

How to lower batch size ?

How to lower batch size ?
I have out of memory issue
RuntimeError: CUDA out of memory. Tried to allocate 150.00 MiB (GPU 0; 4.00 GiB total capacity; 3.33 GiB already allocated; 0 bytes free; 3.53 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

Paging File Error

OSError: [WinError 1455] The paging file is too small for this operation to complete. Error loading "C:\Users\phill\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\lib\cudnn_adv_infer64_8.dll" or one of its dependencies.

I've tried to change the batch size to something smaller, but then I get this error.

Pretraining not working.

I tried to pretrain a model, and it didn't work. I put the pretrained directory in config.yaml, but it still doesn't work. I checked that the configs for pretraining, training, and the checkpoint for my non pretrained model are the same.

interpolate and extrapolate voices

Is there a way to interpolate voices and also to extrapolate voices, I think that if I extrapolate the voice it would sound even more similar to the voice of the trained model.

input voice ------------------------model voice -----------o----
^
extrapolation

Torch==0.12.1+cu113

I have a problem installing torch==1.12.1+cu113, apparently installing it in anaconda is complicated

RuntimeError: CUDA error: device-side assert triggered

| Hparams chains:  ['training/config.yaml']
| Hparams: 
;33;mK_step: 1000, ;33;maccumulate_grad_batches: 1, ;33;maudio_num_mel_bins: 80, ;33;maudio_sample_rate: 24000, ;33;mbinarization_args: {'shuffle': False, 'with_align': True, 'with_f0': True, 'with_hubert': True, 'with_spk_embed': False, 'with_wav': False}, 
;33;mbinarizer_cls: preprocessing.SVCpre.SVCBinarizer, ;33;mbinary_data_dir: raw1, ;33;mcheck_val_every_n_epoch: 10, ;33;mchoose_test_manually: False, ;33;mclip_grad_norm: 1, 
;33;mconfig_path: training/config.yaml, ;33;mcontent_cond_steps: [], ;33;mcwt_add_f0_loss: False, ;33;mcwt_hidden_size: 128, ;33;mcwt_layers: 2, 
;33;mcwt_loss: l1, ;33;mcwt_std_scale: 0.8, ;33;mdatasets: ['opencpop'], ;33;mdebug: False, ;33;mdec_ffn_kernel_size: 9, 
;33;mdec_layers: 4, ;33;mdecay_steps: 40000, ;33;mdecoder_type: fft, ;33;mdict_dir: , ;33;mdiff_decoder_type: wavenet, 
;33;mdiff_loss_type: l2, ;33;mdilation_cycle_length: 4, ;33;mdropout: 0.1, ;33;mds_workers: 4, ;33;mdur_enc_hidden_stride_kernel: ['0,2,3', '0,2,3', '0,1,3'], 
;33;mdur_loss: mse, ;33;mdur_predictor_kernel: 3, ;33;mdur_predictor_layers: 5, ;33;menc_ffn_kernel_size: 9, ;33;menc_layers: 4, 
;33;mencoder_K: 8, ;33;mencoder_type: fft, ;33;mendless_ds: False, ;33;mf0_bin: 256, ;33;mf0_max: 1100.0, 
;33;mf0_min: 50.0, ;33;mffn_act: gelu, ;33;mffn_padding: SAME, ;33;mfft_size: 512, ;33;mfmax: 12000, 
;33;mfmin: 30, ;33;mfs2_ckpt: , ;33;mgaussian_start: True, ;33;mgen_dir_name: , ;33;mgen_tgt_spk_id: -1, 
;33;mhidden_size: 256, ;33;mhop_size: 128, ;33;mhubert_gpu: True, ;33;mhubert_path: checkpoints/hubert/hubert_soft.pt, ;33;minfer: False, 
;33;mkeep_bins: 80, ;33;mlambda_commit: 0.25, ;33;mlambda_energy: 0.0, ;33;mlambda_f0: 1.0, ;33;mlambda_ph_dur: 0.3, 
;33;mlambda_sent_dur: 1.0, ;33;mlambda_uv: 1.0, ;33;mlambda_word_dur: 1.0, ;33;mload_ckpt: , ;33;mlog_interval: 100, 
;33;mloud_norm: False, ;33;mlr: 0.0004, ;33;mmax_beta: 0.02, ;33;mmax_epochs: 3000, ;33;mmax_eval_sentences: 1, 
;33;mmax_eval_tokens: 60000, ;33;mmax_frames: 42000, ;33;mmax_input_tokens: 6000, ;33;mmax_sentences: 88, ;33;mmax_tokens: 128000, 
;33;mmax_updates: 1000000, ;33;mmel_loss: ssim:0.5|l1:0.5, ;33;mmel_vmax: 1.5, ;33;mmel_vmin: -6.0, ;33;mmin_level_db: -120, 
;33;mnorm_type: gn, ;33;mnum_ckpt_keep: 10, ;33;mnum_heads: 2, ;33;mnum_sanity_val_steps: 1, ;33;mnum_spk: 1, 
;33;mnum_test_samples: 0, ;33;mnum_valid_plots: 10, ;33;moptimizer_adam_beta1: 0.9, ;33;moptimizer_adam_beta2: 0.98, ;33;mout_wav_norm: False, 
;33;mpe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt, ;33;mpe_enable: False, ;33;mperform_enhance: True, ;33;mpitch_ar: False, ;33;mpitch_enc_hidden_stride_kernel: ['0,2,5', '0,2,5', '0,2,5'], 
;33;mpitch_extractor: parselmouth, ;33;mpitch_loss: l2, ;33;mpitch_norm: log, ;33;mpitch_type: frame, ;33;mpndm_speedup: 10, 
;33;mpre_align_args: {'allow_no_txt': False, 'denoise': False, 'forced_align': 'mfa', 'txt_processor': 'zh_g2pM', 'use_sox': True, 'use_tone': False}, ;33;mpre_align_cls: data_gen.singing.pre_align.SingingPreAlign, ;33;mpredictor_dropout: 0.5, ;33;mpredictor_grad: 0.1, ;33;mpredictor_hidden: -1, 
;33;mpredictor_kernel: 5, ;33;mpredictor_layers: 5, ;33;mprenet_dropout: 0.5, ;33;mprenet_hidden_size: 256, ;33;mpretrain_fs_ckpt: , 
;33;mprocessed_data_dir: xxx, ;33;mprofile_infer: False, ;33;mraw_data_dir: raw, ;33;mref_norm_layer: bn, ;33;mrel_pos: True, 
;33;mreset_phone_dict: True, ;33;mresidual_channels: 256, ;33;mresidual_layers: 20, ;33;msave_best: False, ;33;msave_ckpt: True, 
;33;msave_codes: ['configs', 'modules', 'src', 'utils'], ;33;msave_f0: True, ;33;msave_gt: False, ;33;mschedule_type: linear, ;33;mseed: 1234, 
;33;msort_by_len: True, ;33;mspeaker_id: atri, ;33;mspec_max: [-1.0607465505599976, -0.7033716440200806, -0.4173407256603241, -0.16794806718826294, -0.028824420645833015, -0.1262795627117157, -0.24279162287712097, -0.1512477993965149, -0.15455038845539093, -0.15768705308437347, -0.01602056622505188, -0.06075224652886391, -0.22821667790412903, -0.04766678437590599, 0.08978565037250519, 0.01753641478717327, 0.08169093728065491, -0.04328322410583496, -0.06052879989147186, -0.0872504860162735, -0.1409643292427063, -0.4341391623020172, -0.330179363489151, -0.3497491180896759, -0.34290072321891785, -0.4050096869468689, -0.5106686949729919, -0.5009798407554626, -0.43373051285743713, -0.5322016477584839, -0.5622034072875977, -0.48023879528045654, -0.45307499170303345, -0.44846850633621216, -0.6325342059135437, -0.6576830148696899, -0.643975019454956, -0.6216201186180115, -0.640028178691864, -0.6955752968788147, -0.8267902135848999, -0.9362852573394775, -0.9073840379714966, -0.9252982139587402, -0.6987875699996948, -0.7051966190338135, -0.7560497522354126, -1.0179532766342163, -1.30494225025177, -1.1002845764160156, -0.7018898129463196, -0.5612887144088745, -0.7958599328994751, -0.7234693169593811, -0.8277182579040527, -0.7889002561569214, -0.9493206739425659, -0.717327892780304, -0.9719500541687012, -0.7878120541572571, -0.9345341920852661, -0.6350462436676025, -0.7313671112060547, -0.7564242482185364, -0.8549256920814514, -0.7617059350013733, -0.7894893884658813, -0.9214569926261902, -0.967815637588501, -1.0261434316635132, -1.1173886060714722, -1.3690667152404785, -1.4494295120239258, -1.554869532585144, -1.5558702945709229, -1.5336979627609253, -1.6113662719726562, -1.8248108625411987, -2.303267478942871, -3.0580945014953613], ;33;mspec_min: [-4.822337627410889, -5.5127854347229, -5.5074357986450195, -5.679365634918213, -6.0, -5.86112117767334, -6.0, -5.932244300842285, -5.8914947509765625, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -5.915018558502197, -5.90329647064209, -5.91562557220459, -6.0, -6.0, -6.0, -6.0], ;33;mspk_cond_steps: [], 
;33;mstop_token_weight: 5.0, ;33;mtask_cls: training.task.SVC_task.SVCTask, ;33;mtest_ids: [], ;33;mtest_input_dir: , ;33;mtest_num: 0, 
;33;mtest_prefixes: ['test'], ;33;mtest_set_name: test, ;33;mtimesteps: 1000, ;33;mtrain_set_name: train, ;33;muse_crepe: False, 
;33;muse_denoise: False, ;33;muse_energy_embed: False, ;33;muse_gt_dur: False, ;33;muse_gt_f0: False, ;33;muse_midi: False, 
;33;muse_nsf: True, ;33;muse_pitch_embed: True, ;33;muse_pos_embed: True, ;33;muse_spk_embed: False, ;33;muse_spk_id: False, 
;33;muse_split_spk_id: False, ;33;muse_uv: True, ;33;muse_var_enc: False, ;33;muse_vec: False, ;33;mval_check_interval: 2000, 
;33;mvalid_num: 0, ;33;mvalid_set_name: valid, ;33;mvalidate: False, ;33;mvocoder: network.vocoders.hifigan.HifiGAN, ;33;mvocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128, 
;33;mwarmup_updates: 2000, ;33;mwav2spec_eps: 1e-6, ;33;mweight_decay: 0, ;33;mwin_size: 512, ;33;mwork_dir: checkpoints/porter, 

| Mel losses: {'ssim': 0.5, 'l1': 0.5}
| load HifiGAN:  checkpoints/0109_hifigan_bigpopcs_hop128/model_ckpt_steps_1512000.ckpt
Removing weight norm...
| Loaded model parameters from checkpoints/0109_hifigan_bigpopcs_hop128/model_ckpt_steps_1512000.ckpt.
| HifiGAN device: cuda.
11/12 12:21:58 AM gpu available: True, used: True
| model Trainable Parameters: 39.915M
Validation sanity check:   0% 0/1 [00:00<?, ?batch/s]/pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:115: operator(): block: [22556,0,0], thread: [32,0,0] Assertion `idx_dim >= 0 && idx_dim < index_size && "index out of bounds"` failed.
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/pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:115: operator(): block: [22618Traceback (most recent call last):
,0,0], thread: [7,0,0] Assertion `idx_dim >= 0 && idx_dim < index_size && "index out of bounds"  File "run.py", line 15, in <module>
` failed.
/pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:115: operator(): block: [22618,0,0], thread: [8,0,0] Assertion `idx_dim >= 0 && idx_dim < index_size && "index out of bounds"` failed.
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    run_task()
  File "run.py", line 11, in run_task
    task_cls.start()
  File "/content/diff-svc/training/task/base_task.py", line 234, in start
    trainer.fit(task)
  File "/content/diff-svc/utils/pl_utils.py", line 495, in fit
    self.run_pretrain_routine(model)
  File "/content/diff-svc/utils/pl_utils.py", line 571, in run_pretrain_routine
    self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing)
  File "/content/diff-svc/utils/pl_utils.py", line 1196, in evaluate
    test)
  File "/content/diff-svc/utils/pl_utils.py", line 1316, in evaluation_forward
    output = model.validation_step(*args)
  File "/content/diff-svc/training/task/SVC_task.py", line 139, in validation_step
    outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
  File "/content/diff-svc/training/task/SVC_task.py", line 94, in run_model
    ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/diff-svc/network/diff/diffusion.py", line 233, in forward
    skip_decoder=True, infer=infer, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/diff-svc/modules/fastspeech/fs2.py", line 132, in forward
    decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_)  # [B, T, H]
RuntimeError: CUDA error: device-side assert triggered

Thank you for helping so far. Any idea what this next error means?

What version of python should I be using?

Just a question

Is there a way to optimize the settings in order for training to run the fastest it can for a 3090 24 GB card?

Files ckpt and yaml

can you tell me how to create a ckpt, and yaml file? because I think it's different from compressing the audio files into a zip file and then renaming it as a ckpt file.

librosa api change

File "D:\clone\ml\vits\diff-svc\network\hubert\hubert_model.py", line 240, in get_units
wav16 = librosa.resample(wav, sr, 16000)
TypeError: resample() takes 1 positional argument but 3 were given

call to resample should be wav16 = librosa.resample(wav, orig_sr=sr, target_sr=16000) in newer version of librosa

also whats weird is that, multiple call to librosa.resample have key specifiy and others do not
like the call in vec_model.py line 28 and nvSTFT.py line 41
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))

Skip item (Empty gt f0) f0 all zero!

what do these mean?

New error

| Binarizer:  <class 'preprocessing.SVCpre.SVCBinarizer'>
spkers:  {'atri'}
| spk_map:  {'atri': 0}
100% 2/2 [00:04<00:00,  2.15s/it]
| valid total duration: 45.280s
100% 2/2 [00:02<00:00,  1.14s/it]
| test total duration: 45.280s
0it [00:00, ?it/s]
Traceback (most recent call last):
  File "preprocessing/binarize.py", line 20, in <module>
    binarize()
  File "preprocessing/binarize.py", line 15, in binarize
    binarizer_cls().process()
  File "/content/diff-svc/preprocessing/base_binarizer.py", line 137, in process
    self.process_data_split('train')
  File "/content/diff-svc/preprocessing/base_binarizer.py", line 175, in process_data_split
    spec_max=np.max(spec_max,0)
  File "<__array_function__ internals>", line 6, in amax
  File "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py", line 2755, in amax
    keepdims=keepdims, initial=initial, where=where)
  File "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py", line 86, in _wrapreduction
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
ValueError: zero-size array to reduction operation maximum which has no identity

What does this mean?

manu

qq频道不能用

qq频道不能用，显示处于内测，只部分用户可用。请问训练需要的模型在哪里能下载？

Error with input and output sizes

Yesterday I was inferencing fine, but now it just throws this error:

load chunks from temp
#=====segment start, 7.569s======
jump empty segment
#=====segment start, 27.321s======
load temp crepe f0
executing 'get_pitch' costed 0.045s
hubert (on cuda) time used 0.7498652935028076
sample time step: 100%|██████████| 50/50 [00:03<00:00, 13.22it/s]
executing 'diff_infer' costed 3.807s
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
[<ipython-input-10-ae1ee0a3d8df>](https://localhost:8080/#) in <module>
      6 wav_gen='michael2.wav'
      7 f0_tst, f0_pred, audio = run_clip(svc_model,file_path=wav_fn, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.05,
----> 8                                         use_gt_mel=False, add_noise_step=500,project_name=project_name,out_path=wav_gen)

9 frames
[/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py](https://localhost:8080/#) in interpolate(input, size, scale_factor, mode, align_corners, recompute_scale_factor, antialias)
   3906 
   3907     if input.dim() == 3 and mode == "nearest":
-> 3908         return torch._C._nn.upsample_nearest1d(input, output_size, scale_factors)
   3909     if input.dim() == 4 and mode == "nearest":
   3910         return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)

RuntimeError: Input and output sizes should be greater than 0, but got input (W: 0) and output (W: 0)

endless_ds lr problem

endless_ds: true messing with the learning rate, like 0.0008 becomes 1.85e36

qq 频道码不能用

您好，
感谢您的工作和分享，但是README中频道码现在不能用了，请问怎么能够下载预训练模型吗？谢谢，期待您的回复。

Number counting wrong

Epoch 1 finished, but show Epoch 0 ended

Python Notebook fails on training

Hi! Jus trying to run a simple demo. Currently following all the demos/examples/other people's tutorials and I have everything set up the same as them, but i keep getting errors. I'm wondering if you would please point me in the right direction?

/content/diff-svc
| Hparams chains:  ['/content/diff-svc/training/config_nsf.yaml']
| Hparams: 
;33;mK_step: 1000, ;33;maccumulate_grad_batches: 1, ;33;maudio_num_mel_bins: 128, ;33;maudio_sample_rate: 44100, ;33;mbinarization_args: {'shuffle': False, 'with_align': True, 'with_f0': True, 'with_hubert': True, 'with_spk_embed': False, 'with_wav': False}, 
;33;mbinarizer_cls: preprocessing.SVCpre.SVCBinarizer, ;33;mbinary_data_dir: data/binary/neer, ;33;mcheck_val_every_n_epoch: 10, ;33;mchoose_test_manually: False, ;33;mclip_grad_norm: 1, 
;33;mconfig_path: training/config_nsf.yaml, ;33;mcontent_cond_steps: [], ;33;mcwt_add_f0_loss: False, ;33;mcwt_hidden_size: 128, ;33;mcwt_layers: 2, 
;33;mcwt_loss: l1, ;33;mcwt_std_scale: 0.8, ;33;mdatasets: ['opencpop'], ;33;mdebug: False, ;33;mdec_ffn_kernel_size: 9, 
;33;mdec_layers: 4, ;33;mdecay_steps: 20000, ;33;mdecoder_type: fft, ;33;mdict_dir: , ;33;mdiff_decoder_type: wavenet, 
;33;mdiff_loss_type: l2, ;33;mdilation_cycle_length: 4, ;33;mdropout: 0.1, ;33;mds_workers: 4, ;33;mdur_enc_hidden_stride_kernel: ['0,2,3', '0,2,3', '0,1,3'], 
;33;mdur_loss: mse, ;33;mdur_predictor_kernel: 3, ;33;mdur_predictor_layers: 5, ;33;menc_ffn_kernel_size: 9, ;33;menc_layers: 4, 
;33;mencoder_K: 8, ;33;mencoder_type: fft, ;33;mendless_ds: True, ;33;mf0_bin: 256, ;33;mf0_max: 1100.0, 
;33;mf0_min: 40.0, ;33;mffn_act: gelu, ;33;mffn_padding: SAME, ;33;mfft_size: 2048, ;33;mfmax: 16000, 
;33;mfmin: 40, ;33;mfs2_ckpt: , ;33;mgaussian_start: True, ;33;mgen_dir_name: , ;33;mgen_tgt_spk_id: -1, 
;33;mhidden_size: 256, ;33;mhop_size: 512, ;33;mhubert_gpu: True, ;33;mhubert_path: checkpoints/hubert/hubert_soft.pt, ;33;minfer: False, 
;33;mkeep_bins: 128, ;33;mlambda_commit: 0.25, ;33;mlambda_energy: 0.0, ;33;mlambda_f0: 1.0, ;33;mlambda_ph_dur: 0.3, 
;33;mlambda_sent_dur: 1.0, ;33;mlambda_uv: 1.0, ;33;mlambda_word_dur: 1.0, ;33;mload_ckpt: /content/diff-svc/pretrain/nehito.ckpt, ;33;mlog_interval: 100, 
;33;mloud_norm: False, ;33;mlr: 0.0008, ;33;mmax_beta: 0.02, ;33;mmax_epochs: 3000, ;33;mmax_eval_sentences: 1, 
;33;mmax_eval_tokens: 60000, ;33;mmax_frames: 42000, ;33;mmax_input_tokens: 60000, ;33;mmax_sentences: 12, ;33;mmax_tokens: 128000, 
;33;mmax_updates: 1000000, ;33;mmel_loss: ssim:0.5|l1:0.5, ;33;mmel_vmax: 1.5, ;33;mmel_vmin: -6.0, ;33;mmin_level_db: -120, 
;33;mno_fs2: True, ;33;mnorm_type: gn, ;33;mnum_ckpt_keep: 10, ;33;mnum_heads: 2, ;33;mnum_sanity_val_steps: 1, 
;33;mnum_spk: 1, ;33;mnum_test_samples: 0, ;33;mnum_valid_plots: 10, ;33;moptimizer_adam_beta1: 0.9, ;33;moptimizer_adam_beta2: 0.98, 
;33;mout_wav_norm: False, ;33;mpe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt, ;33;mpe_enable: False, ;33;mperform_enhance: True, ;33;mpitch_ar: False, 
;33;mpitch_enc_hidden_stride_kernel: ['0,2,5', '0,2,5', '0,2,5'], ;33;mpitch_extractor: parselmouth, ;33;mpitch_loss: l2, ;33;mpitch_norm: log, ;33;mpitch_type: frame, 
;33;mpndm_speedup: 10, ;33;mpre_align_args: {'allow_no_txt': False, 'denoise': False, 'forced_align': 'mfa', 'txt_processor': 'zh_g2pM', 'use_sox': True, 'use_tone': False}, ;33;mpre_align_cls: data_gen.singing.pre_align.SingingPreAlign, ;33;mpredictor_dropout: 0.5, ;33;mpredictor_grad: 0.1, 
;33;mpredictor_hidden: -1, ;33;mpredictor_kernel: 5, ;33;mpredictor_layers: 5, ;33;mprenet_dropout: 0.5, ;33;mprenet_hidden_size: 256, 
;33;mpretrain_fs_ckpt: , ;33;mprocessed_data_dir: xxx, ;33;mprofile_infer: False, ;33;mraw_data_dir: data/raw/neer, ;33;mref_norm_layer: bn, 
;33;mrel_pos: True, ;33;mreset_phone_dict: True, ;33;mresidual_channels: 384, ;33;mresidual_layers: 20, ;33;msave_best: False, 
;33;msave_ckpt: True, ;33;msave_codes: ['configs', 'modules', 'src', 'utils'], ;33;msave_f0: True, ;33;msave_gt: False, ;33;mschedule_type: linear, 
;33;mseed: 1234, ;33;msort_by_len: True, ;33;mspeaker_id: neer, ;33;mspec_max: [-0.07976219058036804, 0.3064012825489044, 0.45079874992370605, 0.48896849155426025, 0.38102585077285767, 0.5545408129692078, 0.6556591391563416, 0.5011460781097412, 0.7585625052452087, 0.7933887243270874, 0.7276718020439148, 0.6568117141723633, 0.8160334825515747, 0.7098748087882996, 0.7070586681365967, 0.9631615281105042, 0.8693066835403442, 0.8992214202880859, 0.8334618210792542, 0.9382892847061157, 0.761588454246521, 1.0139938592910767, 0.8147022128105164, 0.8377708196640015, 0.8404781818389893, 0.5279245376586914, 0.7715780735015869, 0.5754967331886292, 0.19373822212219238, 0.11457031220197678, -0.048836078494787216, 0.2835775315761566, 0.1506994366645813, -0.016768964007496834, 0.07266628742218018, -0.05616551637649536, -0.010572524741292, 0.1133032739162445, 0.16342110931873322, 0.035064052790403366, 0.3116454482078552, 0.16785651445388794, 0.1354154646396637, 0.36229264736175537, 0.372775673866272, -0.10152062773704529, 0.22035335004329681, 0.183604434132576, 0.04665748029947281, 0.23221279680728912, 0.21843412518501282, 0.049887944012880325, -0.05100967362523079, -0.0010432127164676785, -0.06516791135072708, 0.07901491224765778, -0.18570756912231445, -0.14707334339618683, -0.11538795381784439, -0.1341129094362259, -0.15978987514972687, -0.18778416514396667, -0.2038293480873108, -0.25516536831855774, -0.24493663012981415, -0.15004149079322815, -0.016140246763825417, -0.07177135348320007, -0.3963303565979004, -0.3779948353767395, -0.25783461332321167, -0.16094177961349487, -0.23505426943302155, -0.3541640043258667, -0.34247317910194397, -0.3881177306175232, -0.4593522846698761, -0.5756832957267761, -0.35765165090560913, -0.542741060256958, -0.4082295298576355, -0.4770561158657074, -0.17004281282424927, -0.27877169847488403, -0.15326324105262756, -0.4180527925491333, -0.27339401841163635, -0.23254677653312683, -0.29365968704223633, -0.33521631360054016, -0.3491170406341553, -0.18533602356910706, -0.29260891675949097, -0.44137561321258545, -0.6128101944923401, -0.731763482093811, -0.6580878496170044, -0.11427026987075806, -0.3944733738899231, -0.6505616903305054, -0.6488122344017029, -0.7484522461891174, -0.7040322422981262, -0.6145080924034119, -0.531133770942688, -0.5737754702568054, -0.6910640597343445, -0.6721180081367493, -0.8550227284431458, -0.7104114294052124, -0.6984644532203674, -0.8648133277893066, -1.0164130926132202, -1.0275567770004272, -1.1420173645019531, -1.068782925605774, -1.244425654411316, -1.302030086517334, -1.5661638975143433, -1.639020562171936, -1.697121500968933, -1.9838589429855347, -2.2957139015197754, -2.2596089839935303, -2.119849443435669, -2.2869279384613037, -2.358459711074829, -2.3582921028137207], ;33;mspec_min: [-4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102, -4.999994277954102], 
;33;mspk_cond_steps: [], ;33;mstop_token_weight: 5.0, ;33;mtask_cls: training.task.SVC_task.SVCTask, ;33;mtest_ids: [], ;33;mtest_input_dir: , 
;33;mtest_num: 0, ;33;mtest_prefixes: ['test'], ;33;mtest_set_name: test, ;33;mtimesteps: 1000, ;33;mtrain_set_name: train, 
;33;muse_crepe: True, ;33;muse_denoise: False, ;33;muse_energy_embed: False, ;33;muse_gt_dur: False, ;33;muse_gt_f0: False, 
;33;muse_midi: False, ;33;muse_nsf: True, ;33;muse_pitch_embed: True, ;33;muse_pos_embed: True, ;33;muse_spk_embed: False, 
;33;muse_spk_id: False, ;33;muse_split_spk_id: False, ;33;muse_uv: False, ;33;muse_var_enc: False, ;33;muse_vec: False, 
;33;mval_check_interval: 1000, ;33;mvalid_num: 0, ;33;mvalid_set_name: valid, ;33;mvalidate: False, ;33;mvocoder: network.vocoders.nsf_hifigan.NsfHifiGAN, 
;33;mvocoder_ckpt: checkpoints/nsf_hifigan/model, ;33;mwarmup_updates: 2000, ;33;mwav2spec_eps: 1e-6, ;33;mweight_decay: 0, ;33;mwin_size: 2048, 
;33;mwork_dir: checkpoints/neer, 
| Mel losses: {'ssim': 0.5, 'l1': 0.5}
| Load HifiGAN:  checkpoints/nsf_hifigan/model
Removing weight norm...
12/14 10:52:47 PM gpu available: True, used: True
Traceback (most recent call last):
  File "run.py", line 15, in <module>
    run_task()
  File "run.py", line 11, in run_task
    task_cls.start()
  File "/content/diff-svc/training/task/base_task.py", line 234, in start
    trainer.fit(task)
  File "/content/diff-svc/utils/pl_utils.py", line 487, in fit
    model.model = model.build_model()
  File "/content/diff-svc/training/task/fs2.py", line 75, in build_model
    self.load_ckpt(hparams['load_ckpt'], strict=True)
  File "/content/diff-svc/training/task/base_task.py", line 84, in load_ckpt
    utils.load_ckpt(self.__getattr__(current_model_name), ckpt_base_dir, current_model_name, force, strict)
  File "/content/diff-svc/utils/__init__.py", line 202, in load_ckpt
    cur_model.load_state_dict(state_dict, strict=strict)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1604, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for GaussianDiffusion:
	Unexpected key(s) in state_dict: "fs2.encoder.layers.0.op.layer_norm1.weight", "fs2.encoder.layers.0.op.layer_norm1.bias", "fs2.encoder.layers.0.op.self_attn.in_proj_weight", "fs2.encoder.layers.0.op.self_attn.out_proj.weight", "fs2.encoder.layers.0.op.layer_norm2.weight", "fs2.encoder.layers.0.op.layer_norm2.bias", "fs2.encoder.layers.0.op.ffn.ffn_1.weight", "fs2.encoder.layers.0.op.ffn.ffn_1.bias", "fs2.encoder.layers.0.op.ffn.ffn_2.weight", "fs2.encoder.layers.0.op.ffn.ffn_2.bias", "fs2.encoder.layers.1.op.layer_norm1.weight", "fs2.encoder.layers.1.op.layer_norm1.bias", "fs2.encoder.layers.1.op.self_attn.in_proj_weight", "fs2.encoder.layers.1.op.self_attn.out_proj.weight", "fs2.encoder.layers.1.op.layer_norm2.weight", "fs2.encoder.layers.1.op.layer_norm2.bias", "fs2.encoder.layers.1.op.ffn.ffn_1.weight", "fs2.encoder.layers.1.op.ffn.ffn_1.bias", "fs2.encoder.layers.1.op.ffn.ffn_2.weight", "fs2.encoder.layers.1.op.ffn.ffn_2.bias", "fs2.encoder.layers.2.op.layer_norm1.weight", "fs2.encoder.layers.2.op.layer_norm1.bias", "fs2.encoder.layers.2.op.self_attn.in_proj_weight", "fs2.encoder.layers.2.op.self_attn.out_proj.weight", "fs2.encoder.layers.2.op.layer_norm2.weight", "fs2.encoder.layers.2.op.layer_norm2.bias", "fs2.encoder.layers.2.op.ffn.ffn_1.weight", "fs2.encoder.layers.2.op.ffn.ffn_1.bias", "fs2.encoder.layers.2.op.ffn.ffn_2.weight", "fs2.encoder.layers.2.op.ffn.ffn_2.bias", "fs2.encoder.layers.3.op.layer_norm1.weight", "fs2.encoder.layers.3.op.layer_norm1.bias", "fs2.encoder.layers.3.op.self_attn.in_proj_weight", "fs2.encoder.layers.3.op.self_attn.out_proj.weight", "fs2.encoder.layers.3.op.layer_norm2.weight", "fs2.encoder.layers.3.op.layer_norm2.bias", "fs2.encoder.layers.3.op.ffn.ffn_1.weight", "fs2.encoder.layers.3.op.ffn.ffn_1.bias", "fs2.encoder.layers.3.op.ffn.ffn_2.weight", "fs2.encoder.layers.3.op.ffn.ffn_2.bias", "fs2.encoder.layer_norm.weight", "fs2.encoder.layer_norm.bias", "fs2.decoder.pos_embed_alpha", "fs2.decoder.embed_positions._float_tensor", "fs2.decoder.layers.0.op.layer_norm1.weight", "fs2.decoder.layers.0.op.layer_norm1.bias", "fs2.decoder.layers.0.op.self_attn.in_proj_weight", "fs2.decoder.layers.0.op.self_attn.out_proj.weight", "fs2.decoder.layers.0.op.layer_norm2.weight", "fs2.decoder.layers.0.op.layer_norm2.bias", "fs2.decoder.layers.0.op.ffn.ffn_1.weight", "fs2.decoder.layers.0.op.ffn.ffn_1.bias", "fs2.decoder.layers.0.op.ffn.ffn_2.weight", "fs2.decoder.layers.0.op.ffn.ffn_2.bias", "fs2.decoder.layers.1.op.layer_norm1.weight", "fs2.decoder.layers.1.op.layer_norm1.bias", "fs2.decoder.layers.1.op.self_attn.in_proj_weight", "fs2.decoder.layers.1.op.self_attn.out_proj.weight", "fs2.decoder.layers.1.op.layer_norm2.weight", "fs2.decoder.layers.1.op.layer_norm2.bias", "fs2.decoder.layers.1.op.ffn.ffn_1.weight", "fs2.decoder.layers.1.op.ffn.ffn_1.bias", "fs2.decoder.layers.1.op.ffn.ffn_2.weight", "fs2.decoder.layers.1.op.ffn.ffn_2.bias", "fs2.decoder.layers.2.op.layer_norm1.weight", "fs2.decoder.layers.2.op.layer_norm1.bias", "fs2.decoder.layers.2.op.self_attn.in_proj_weight", "fs2.decoder.layers.2.op.self_attn.out_proj.weight", "fs2.decoder.layers.2.op.layer_norm2.weight", "fs2.decoder.layers.2.op.layer_norm2.bias", "fs2.decoder.layers.2.op.ffn.ffn_1.weight", "fs2.decoder.layers.2.op.ffn.ffn_1.bias", "fs2.decoder.layers.2.op.ffn.ffn_2.weight", "fs2.decoder.layers.2.op.ffn.ffn_2.bias", "fs2.decoder.layers.3.op.layer_norm1.weight", "fs2.decoder.layers.3.op.layer_norm1.bias", "fs2.decoder.layers.3.op.self_attn.in_proj_weight", "fs2.decoder.layers.3.op.self_attn.out_proj.weight", "fs2.decoder.layers.3.op.layer_norm2.weight", "fs2.decoder.layers.3.op.layer_norm2.bias", "fs2.decoder.layers.3.op.ffn.ffn_1.weight", "fs2.decoder.layers.3.op.ffn.ffn_1.bias", "fs2.decoder.layers.3.op.ffn.ffn_2.weight", "fs2.decoder.layers.3.op.ffn.ffn_2.bias", "fs2.decoder.layer_norm.weight", "fs2.decoder.layer_norm.bias". 
	size mismatch for spec_min: copying a param with shape torch.Size([1, 1, 80]) from checkpoint, the shape in current model is torch.Size([1, 1, 128]).
	size mismatch for spec_max: copying a param with shape torch.Size([1, 1, 80]) from checkpoint, the shape in current model is torch.Size([1, 1, 128]).
	size mismatch for denoise_fn.input_projection.weight: copying a param with shape torch.Size([256, 80, 1]) from checkpoint, the shape in current model is torch.Size([384, 128, 1]).
	size mismatch for denoise_fn.input_projection.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
	size mismatch for denoise_fn.mlp.0.weight: copying a param with shape torch.Size([1024, 256]) from checkpoint, the shape in current model is torch.Size([1536, 384]).
	size mismatch for denoise_fn.mlp.0.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([1536]).
	size mismatch for denoise_fn.mlp.2.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([384, 1536]).
	size mismatch for denoise_fn.mlp.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([384]).
	size mismatch for denoise_fn.residual_layers.0.dilated_conv.weight: copying a param with shape torch.Size([512, 256, 3]) from checkpoint, the shape in current model is torch.Size([768, 384, 3]).
	size mismatch for denoise_fn.residual_layers.0.dilated_conv.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([768]).

... blah blah blah more of the same

looking for a video tutorial

Hi is there any video tutorial about how to train voice Could you please make a tutorial Thank you

is there any way to resume training?

Hi! i need to know if there is a way to resume the training of a model. i have the ckpt and the config.yaml. do i have to point a specific path inside the config.yaml that is in the training directory? thanks!

60000or6000

Who to contact regarding commercial uses?

Hello, I'd like to speak with the creator to share some plans for a product involving this as well as approve those usecases. (As noted in the readme)

What's the best way to contact them?

No module named 'utils' when running hubert_model.py

Before running the normal command for training, I tried running python python ./network/hubert/hubert_model.py from the diff-svc root directory. However, after running it, I just get the following issue:

Traceback (most recent call last):
  File "D:\Utau\diff-svc-main\network\hubert\hubert_model.py", line 13, in <module>
    from utils import hparams
ModuleNotFoundError: No module named 'utils'

Attempting to do a quick fix by copying hparam.py to the same folder as hubert_model.py and directly importing it from there gives me another issue:

Traceback (most recent call last):
  File "D:\Utau\diff-svc-main\network\hubert\hubert_model.py", line 266, in <module>
    hbt_model = hubert_soft(str(list(Path(hparams['hubert_path']).home().rglob('*.pt'))[0]))
TypeError: 'module' object is not subscriptable

Any idea what might be causing this and how I can fix it? If it helps, I'm running this on Windows 10.

Quick Question

Around how many epochs does it take for the voice to start being recognizable?

IndexError: list index out of range

AttributeError: 'NsfHifiGAN' object has no attribute 'h'

  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\run.py", line 15, in <module>
    run_task()
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\run.py", line 11, in run_task
    task_cls.start()
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\training\task\base_task.py", line 234, in start
    trainer.fit(task)
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\utils\pl_utils.py", line 495, in fit
    self.run_pretrain_routine(model)
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\utils\pl_utils.py", line 571, in run_pretrain_routine
    self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing)
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\utils\pl_utils.py", line 1192, in evaluate
    output = self.evaluation_forward(model,
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\utils\pl_utils.py", line 1316, in evaluation_forward
    output = model.validation_step(*args)
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\training\task\SVC_task.py", line 155, in validation_step
    self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\training\task\SVC_task.py", line 218, in plot_wav
    gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0)
  File "E:\다운로드\diff-svc-main (1)\diff-svc-main\network\vocoders\nsf_hifigan.py", line 48, in spec2wav
    if self.h.sampling_rate != hparams['audio_sample_rate']:
AttributeError: 'NsfHifiGAN' object has no attribute 'h'

error occured on training
used anaconda

Something I noticed

Setting use_gt_mel to True seems to yield much better results than setting it to False, especially at lower steps. Why is this?

TypeError: argument of type 'NoneType' is not iterable

This is an error during inferencing, I have not changed anything other than the project name, pitching, and location of model

TypeError                                 Traceback (most recent call last)
Cell In[1], line 20
     18 config_path=f'./checkpoints/{project_name}/config.yaml
     19 hubert_gpu=True
---> 20 svc_model = Svc(project_name,config_path,hubert_gpu, model_path)
     21 print('model loaded')

File c:\Users\AzoSBear\Desktop\Folders\Artificial Inteligence\Diff-SVC\Diff-SVC\infer_tools\infer_tool.py:107, in Svc.__init__(self, project_name, config_name, hubert_gpu, model_path)
    104 self.model_path = model_path
    105 self.dev = torch.device("cuda")
--> 107 self._ = set_hparams(config=config_name, exp_name=self.project_name, infer=True,
    108                      reset=True,
    109                      hparams_str='',
    110                      print_hparams=False)
    112 self.mel_bins = hparams['audio_num_mel_bins']
    113 self.model = GaussianDiffusion(
    114     phone_encoder=Hubertencoder(hparams['hubert_path']),
    115     out_dims=self.mel_bins, denoise_fn=self.DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
   (...)
    119     spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
    120 )

File c:\Users\AzoSBear\Desktop\Folders\Artificial Inteligence\Diff-SVC\Diff-SVC\utils\hparams.py:90, in set_hparams(config, exp_name, hparams_str, print_hparams, global_hparams, reset, infer)
     86         args.config = ckpt_config_path
...
---> 58 if 'base_config' in hparams_:
     59     ret_hparams = {}
     60     if not isinstance(hparams_['base_config'], list):

TypeError: argument of type 'NoneType' is not iterable

TypeError: argument of type 'NoneType' is not iterable

attempting to start training for my dataset but I keep getting this error, changed settings and restarted runtime but still no luck?

RuntimeError: stft requires the return_complex parameter

(python3.8) D:\application\diff-svc>python preprocessing/binarize.py --config training/config_nsf.yaml
| Hparams chains:  ['training/config_nsf.yaml']
| Hparams:
K_step: 1000, accumulate_grad_batches: 1, audio_num_mel_bins: 128, audio_sample_rate: 44100, binarization_args: {'shuffle': False, 'with_align': True, 'with_f0': True, 'with_hubert': True, 'with_spk_embed': False, 'with_wav': False},
binarizer_cls: preprocessing.SVCpre.SVCBinarizer, binary_data_dir: data/binary/Ili_Union, check_val_every_n_epoch: 10, choose_test_manually: False, clip_grad_norm: 1,
config_path: training/config.yaml, content_cond_steps: [], cwt_add_f0_loss: False, cwt_hidden_size: 128, cwt_layers: 2,
cwt_loss: l1, cwt_std_scale: 0.8, datasets: ['opencpop'], debug: False, dec_ffn_kernel_size: 9,
dec_layers: 4, decay_steps: 30000, decoder_type: fft, dict_dir: , diff_decoder_type: wavenet,
diff_loss_type: l2, dilation_cycle_length: 4, dropout: 0.1, ds_workers: 4, dur_enc_hidden_stride_kernel: ['0,2,3', '0,2,3', '0,1,3'],
dur_loss: mse, dur_predictor_kernel: 3, dur_predictor_layers: 5, enc_ffn_kernel_size: 9, enc_layers: 4,
encoder_K: 8, encoder_type: fft, endless_ds: False, f0_bin: 256, f0_max: 1100.0,
f0_min: 40.0, ffn_act: gelu, ffn_padding: SAME, fft_size: 2048, fmax: 16000,
fmin: 40, fs2_ckpt: , gaussian_start: True, gen_dir_name: , gen_tgt_spk_id: -1,
hidden_size: 256, hop_size: 512, hubert_gpu: True, hubert_path: checkpoints/hubert/hubert_soft.pt, infer: False,
keep_bins: 128, lambda_commit: 0.25, lambda_energy: 0.0, lambda_f0: 1.0, lambda_ph_dur: 0.3,
lambda_sent_dur: 1.0, lambda_uv: 1.0, lambda_word_dur: 1.0, load_ckpt: , log_interval: 100,
loud_norm: False, lr: 0.0008, max_beta: 0.02, max_epochs: 3000, max_eval_sentences: 1,
max_eval_tokens: 60000, max_frames: 42000, max_input_tokens: 60000, max_sentences: 88, max_tokens: 128000,
max_updates: 1000000, mel_loss: ssim:0.5|l1:0.5, mel_vmax: 1.5, mel_vmin: -6.0, min_level_db: -120,
norm_type: gn, num_ckpt_keep: 10, num_heads: 2, num_sanity_val_steps: 1, num_spk: 1,
num_test_samples: 0, num_valid_plots: 10, optimizer_adam_beta1: 0.9, optimizer_adam_beta2: 0.98, out_wav_norm: False,
pe_ckpt: checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt, pe_enable: False, perform_enhance: True, pitch_ar: False, pitch_enc_hidden_stride_kernel: ['0,2,5', '0,2,5', '0,2,5'],
pitch_extractor: parselmouth, pitch_loss: l2, pitch_norm: log, pitch_type: frame, pndm_speedup: 10,
pre_align_args: {'allow_no_txt': False, 'denoise': False, 'forced_align': 'mfa', 'txt_processor': 'zh_g2pM', 'use_sox': True, 'use_tone': False}, pre_align_cls: data_gen.singing.pre_align.SingingPreAlign, predictor_dropout: 0.5, predictor_grad: 0.1, predictor_hidden: -1,
predictor_kernel: 5, predictor_layers: 5, prenet_dropout: 0.5, prenet_hidden_size: 256, pretrain_fs_ckpt: ,
processed_data_dir: xxx, profile_infer: False, raw_data_dir: data/raw/Ili_Union, ref_norm_layer: bn, rel_pos: True,
reset_phone_dict: True, residual_channels: 384, residual_layers: 20, save_best: False, save_ckpt: True,
save_codes: ['configs', 'modules', 'src', 'utils'], save_f0: True, save_gt: False, schedule_type: linear, seed: 1234,
sort_by_len: True, speaker_id: Ili_Union, spec_max: [0.0], spec_min: [-5.0], spk_cond_steps: [],
stop_token_weight: 5.0, task_cls: training.task.SVC_task.SVCTask, test_ids: [], test_input_dir: , test_num: 0,
test_prefixes: ['test'], test_set_name: test, timesteps: 1000, train_set_name: train, use_crepe: True,
use_denoise: False, use_energy_embed: False, use_gt_dur: False, use_gt_f0: False, use_midi: False,
use_nsf: True, use_pitch_embed: True, use_pos_embed: True, use_spk_embed: False, use_spk_id: False,
use_split_spk_id: False, use_uv: False, use_var_enc: False, use_vec: False, val_check_interval: 2000,
valid_num: 0, valid_set_name: valid, validate: False, vocoder: network.vocoders.nsf_hifigan.NsfHifiGAN, vocoder_ckpt: checkpoints/nsf_hifigan/g_00105000,
warmup_updates: 2000, wav2spec_eps: 1e-6, weight_decay: 0, win_size: 2048, work_dir: ,

| Binarizer:  <class 'preprocessing.SVCpre.SVCBinarizer'>
spkers:  {'Ili_Union'}
| spk_map:  {'Ili_Union': 0}
  0%|                                                                                            | 0/5 [00:01<?, ?it/s]
Traceback (most recent call last):
  File "preprocessing/binarize.py", line 20, in <module>
    binarize()
  File "preprocessing/binarize.py", line 15, in binarize
    binarizer_cls().process()
  File "D:\application\diff-svc\preprocessing\base_binarizer.py", line 135, in process
    self.process_data_split('valid')
  File "D:\application\diff-svc\preprocessing\base_binarizer.py", line 156, in process_data_split
    item = self.process_item(*a)
  File "D:\application\diff-svc\preprocessing\base_binarizer.py", line 194, in process_item
    return File2Batch.temporary_dict2processed_input(item_name, meta_data, self.phone_encoder, binarization_args)
  File "D:\application\diff-svc\preprocessing\process_pipeline.py", line 112, in temporary_dict2processed_input
    wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(temp_dict['wav_fn'])
  File "D:\application\diff-svc\network\vocoders\nsf_hifigan.py", line 89, in wav2spec
    mel_torch = stft.get_mel(wav_torch.unsqueeze(0).to(device)).squeeze(0).T
  File "D:\application\diff-svc\modules\nsf_hifigan\nvSTFT.py", line 95, in get_mel
    spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
  File "D:\application\diff-svc\python3.8\lib\site-packages\torch\functional.py", line 641, in stft
    return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
RuntimeError: stft requires the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release.

No CUDA GPUs are available

I notice when I try to preprocess with my 3090 GPU, it keeps saying "No CUDA GPUs are available". It's not a traceback error. And I have no idea what to do.

its a error on step #4

The specified procedure could not be found.

Traceback (most recent call last): File "preprocessing/binarize.py", line 20, in <module> binarize() File "preprocessing/binarize.py", line 13, in binarize binarizer_cls = getattr(importlib.import_module(pkg), cls_name) File "D:\Ana\envs\diff-svc\lib\importlib\__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "D:\DiffSVC\diff-svc-main\preprocessing\SVCpre.py", line 22, in <module> from preprocessing.process_pipeline import File2Batch File "D:\DiffSVC\diff-svc-main\preprocessing\process_pipeline.py", line 6, in <module> from network.vocoders.base_vocoder import VOCODERS File "D:\DiffSVC\diff-svc-main\network\vocoders\__init__.py", line 1, in <module> from network.vocoders import hifigan File "D:\DiffSVC\diff-svc-main\network\vocoders\hifigan.py", line 13, in <module> from network.vocoders.pwg import PWG File "D:\DiffSVC\diff-svc-main\network\vocoders\pwg.py", line 9, in <module> from modules.parallel_wavegan.utils import read_hdf5 File "D:\DiffSVC\diff-svc-main\modules\parallel_wavegan\utils\__init__.py", line 1, in <module> from .utils import * # NOQA File "D:\DiffSVC\diff-svc-main\modules\parallel_wavegan\utils\utils.py", line 13, in <module> import h5py File "D:\Ana\envs\diff-svc\lib\site-packages\h5py\__init__.py", line 34, in <module> from . import version File "D:\Ana\envs\diff-svc\lib\site-packages\h5py\version.py", line 17, in <module> from . import h5 as _h5 File "h5py\h5.pyx", line 1, in init h5py.h5 ImportError: DLL load failed while importing defs: The specified procedure could not be found.

This is my issue...

There is an error during the preprocessing process, but I don't know the cause.

(diff-svc) C:\diff-svc-main>python sep_wav.py
C:\Users\bytec\anaconda3\envs\diff-svc\lib\site-packages\pydub\utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work
warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning)
mp4 to wav 변환 작업 중...: 0%| | 0/6 [00:00<?, ?it/s]C:\Users\bytec\anaconda3\envs\diff-svc\lib\site-packages\pydub\utils.py:198: RuntimeWarning: Couldn't find ffprobe or avprobe - defaulting to ffprobe, but may not work
warn("Couldn't find ffprobe or avprobe - defaulting to ffprobe, but may not work", RuntimeWarning)
mp4 to wav 변환 작업 중...: 0%| | 0/6 [00:00<?, ?it/s]
Traceback (most recent call last):
File "C:\diff-svc-main\sep_wav.py", line 278, in
main(
File "C:\diff-svc-main\sep_wav.py", line 157, in main
mp4_to_wav(input_dir,filename)
File "C:\diff-svc-main\sep_wav.py", line 89, in mp4_to_wav
track = AudioSegment.from_file(os.path.join(input_dir,input_file), format= 'mp4')
File "C:\Users\bytec\anaconda3\envs\diff-svc\lib\site-packages\pydub\audio_segment.py", line 728, in from_file
info = mediainfo_json(orig_file, read_ahead_limit=read_ahead_limit)
File "C:\Users\bytec\anaconda3\envs\diff-svc\lib\site-packages\pydub\utils.py", line 274, in mediainfo_json
res = Popen(command, stdin=stdin_parameter, stdout=PIPE, stderr=PIPE)
File "C:\Users\bytec\anaconda3\envs\diff-svc\lib\subprocess.py", line 951, in init
self._execute_child(args, executable, preexec_fn, close_fds,
File "C:\Users\bytec\anaconda3\envs\diff-svc\lib\subprocess.py", line 1420, in _execute_child
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
FileNotFoundError: [WinError 2] 지정된 파일을 찾을 수 없습니다

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