I want to test opencpop preitrain model on unseen song. I don't know how to generate the wav file.

1. What data I should prepare for model?
2. How to do it? I saw test_step in FastSpeech2Task, but it seems for tts task. So I need override test_step in DiffSingerMIDITask? Is there other way to solve this? Without packing data into dataloader, just load model, and infer.

Hi. Since the DiffSinger(PopCS) needs ground-truth f0 information at inference, is it possible to synthesize an unseen song (with phoneme labels, phoneme duration and notes provided) using the DIffSinger(PopCS) model?

Hi, thanks for your contribution on DiffSinger! and also thanks for mentioning my implementation, I just realized it yesterday:)

With your detailed documentation in README and paper, I can reproduce the training & inference procedure and the results with this repo. But during that, I found some missing parts to get the full training with shallow version: I think the current code only supports forced K (which is 71) with the pre-trained FastSpeech2 (especially of the decoder). If I understood correctly, we need a process for the boundary prediction and pre-training of FastSpeech2 before training DiffSpeech in shallow. Maybe I missed somewhere in the repo, but if it is not yet pushed, I wonder whether you have planned to provide that part soon or not.

Thanks in advance!

Best, keon

According to README-SVS-opencpop-cascade.md, I made my own datasets and tried training.

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/midi/cascade/mydatasets/aux_rel.yaml --exp_name MY_DATASETS_FS_EXP --reset

The training went to Epoch 50 but I didn't any checkpoint files. Which is the checkpoint file, or could anyone tell me which config should be changed to make checkpoint files (in shorter interval)?

Having successfully run step 1, data preparation, I am now trying to run inference. I am using the given dataset preview. Running CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/popcs_fs2.yaml --exp_name popcs_fs2_pmf0_1230 --reset --infer according to the readme.md, I end up with this error:

| model Trainable Parameters: 24.253M
Traceback (most recent call last):
  File "tasks/run.py", line 15, in <module>
    run_task()
  File "tasks/run.py", line 10, in run_task
    task_cls.start()
  File "/.../DiffSinger/tasks/base_task.py", line 258, in start
    trainer.test(task)
  File "/.../DiffSinger/utils/pl_utils.py", line 586, in test
    self.fit(model)
  File "/.../DiffSinger/utils/pl_utils.py", line 489, in fit
    self.run_pretrain_routine(model)
  File "/.../DiffSinger/utils/pl_utils.py", line 541, in run_pretrain_routine
    self.restore_weights(model)
  File "/.../DiffSinger/utils/pl_utils.py", line 617, in restore_weights
    self.restore_state_if_checkpoint_exists(model)
  File "/.../DiffSinger/utils/pl_utils.py", line 655, in restore_state_if_checkpoint_exists
    self.restore(last_ckpt_path, self.on_gpu)
  File "/.../DiffSinger/utils/pl_utils.py", line 668, in restore
    model.load_state_dict(checkpoint['state_dict'], strict=False)
  File "/.../envs/DiffSinger/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for FastSpeech2Task:
	size mismatch for model.encoder_embed_tokens.weight: copying a param with shape torch.Size([62, 256]) from checkpoint, the shape in current model is torch.Size([57, 256]).
	size mismatch for model.encoder.embed_tokens.weight: copying a param with shape torch.Size([62, 256]) from checkpoint, the shape in current model is torch.Size([57, 256]).

Do you have any ideas on what could be wrong here and how to resolve it?

Thanks for your wonderful work! I was running the inference of 0128_opencpop_ds58_midi, but there's a problem that bothers me.

https://github.com/MoonInTheRiver/DiffSinger/blob/master/tasks/tts/fs2.py#L348

    ############
    # infer
    ############
    def test_step(self, sample, batch_idx):
        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        txt_tokens = sample['txt_tokens']
        mel2ph, uv, f0 = None, None, None
        ref_mels = None
        if hparams['profile_infer']:
            pass
        else:
            if hparams['use_gt_dur']:
                mel2ph = sample['mel2ph']
            if hparams['use_gt_f0']:
                f0 = sample['f0']
                uv = sample['uv']
                print('Here using gt f0!!')
            if hparams.get('use_midi') is not None and hparams['use_midi']:
                outputs = self.model(
                    txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True,
                    pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur'))
            else:
                outputs = self.model(
                    txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True)

The param use_gt_dur is True, that is, the model takes the phoneme duration as input when inference. Is it correct?

Hi @MoonInTheRiver ，

In the midi version, why also feed in f0 and uv?

f0 and uv is generated from raw wav, but during the infer, only txt_token and midi are given, how to get f0 and uv?

Hello! Can you please tell me if I can use your universal vocoder (trained on ~70 hours singing data) to get a DiffSinger (SVS) model by training on English data or do I need to train it from scratch on English data? If so, how can I do it? I want to get a model that synthesizes English singing without a Chinese accent. I want to make sure that there won't be any problems due to different phonemes.

Hi, thank you very much for your valuable SVS corpus and code. I strictly follow your instruction until step "2. Training Example" for SVS, in https://github.com/MoonInTheRiver/DiffSinger . Then I am somewhat stuck here. The error message is: Validation sanity check: 0%| | 0/1 [00:00<?, ?batch/s] Traceback (most recent call last): File "tasks/run.py", line 19, in run_task() File "tasks/run.py", line 14, in run_task task_cls.start() File "/data/juicefs_speech_tts_v2/public_data/tts_public_data/11090357/singing/diffsinger/DiffSinger/tasks/base_task.py", line 256, in start trainer.fit(task) File "/data/juicefs_speech_tts_v2/public_data/tts_public_data/11090357/singing/diffsinger/DiffSinger/utils/pl_utils.py", line 489, in fit self.run_pretrain_routine(model) File "/data/juicefs_speech_tts_v2/public_data/tts_public_data/11090357/singing/diffsinger/DiffSinger/utils/pl_utils.py", line 565, in run_pretrain_routine self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing) File "/data/juicefs_speech_tts_v2/public_data/tts_public_data/11090357/singing/diffsinger/DiffSinger/utils/pl_utils.py", line 1173, in evaluate for batch_idx, batch in enumerate(dataloader): File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 363, in next data = self._next_data() File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 989, in _next_data return self._process_data(data) File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1014, in _process_data data.reraise() File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/torch/_utils.py", line 395, in reraise raise self.exc_type(msg) FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0. Original Traceback (most recent call last): File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop data = fetcher.fetch(index) File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/data/juicefs_speech_tts_v2/public_data/tts_public_data/11090357/singing/diffsinger/DiffSinger/usr/diffsinger_task.py", line 93, in getitem fs2_mel = torch.Tensor(np.load(f'{fs2_ckpt}/P_mels_npy/{item_name}.npy')) # ~M generated by FFT-singer. File "/root/miniconda3/envs/diffsinger/lib/python3.8/site-packages/numpy/lib/npyio.py", line 416, in load fid = stack.enter_context(open(os_fspath(file), "rb")) FileNotFoundError: [Errno 2] No such file or directory: 'checkpoints/popcs_fs2_pmf0_1230/P_mels_npy/popcs-说散就散-0000.npy' It seems that the required file is not properly process in "1. Data Preparation" step, though the first step was passed successfully with the following prompt: test_input_dir: , test_num: 0, test_prefixes: ['popcs-说散就散', 'popcs-隐形的翅膀'], test_set_name: test, timesteps: 100, train_set_name: train, use_denoise: False, use_energy_embed: False, use_gt_dur: True, use_gt_f0: True, 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: True, use_var_enc: False, val_check_interval: 2000, valid_num: 0, valid_set_name: valid, validate: False, vocoder: vocoders.hifigan.HifiGAN, vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128, warmup_updates: 2000, weight_decay: 0, win_size: 512, work_dir: , | Binarizer: <class 'data_gen.singing.binarize.SingingBinarizer'> | spk_map: {'SPK1': 0} | Build phone set: ['', '', '', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h', 'i', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'iou', 'j', 'k', 'l', 'm', 'n', 'o', 'ong', 'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'uei', 'uen', 'uo', 'v', 'van', 've', 'vn', 'x', 'z', 'zh', '|'] 100%|████████████████████████████████████████████| 27/27 [00:13<00:00, 2.01it/s] | valid total duration: 330.677s 100%|████████████████████████████████████████████| 27/27 [00:13<00:00, 2.04it/s] | test total duration: 330.677s 100%|████████████████████████████████████████████| 1624/1624 [11:55<00:00, 2.27it/s] | train total duration: 20878.560s I guess the output of Step 1 and input of Step 2 are possibly not chained perfectly. Any help or hints will be welcome. Thank you in advance.

Yes, you are right. There is a problem. You need run "CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/popcs_fs2.yaml --exp_name popcs_fs2_pmf0_1230 --reset --infer" in advance to produce the files "P_mels_npy". I have fixed the readme file. Thanks for your report!

I have a question about it, if we are training from scratch and we don't have any saved models for inference, how does the P_mels_npy(predicted mels I guess) generated?

Originally posted by @Cescfangs in https://github.com/MoonInTheRiver/DiffSinger/issues/11#issuecomment-1038568795

Thank you for sharing this project! I am trying to run inference on a pre-trained model (DiffSinger), following the directions in the README. I have downloaded your dataset preview and trained models. I have tried both using a symlink to the dataset as instructed and also placing everything in data/processed/popcs/ directly.

At step 1, packing the dataset, I seem to run into a problem:

(DiffSinger) user@ubuntu:~/.../DiffSinger$ CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config usr/configs/popcs_ds_beta6.yaml

| Hparams chains:  ['configs/config_base.yaml', 'configs/tts/base.yaml', 'configs/tts/fs2.yaml', 'configs/tts/base_zh.yaml', 'configs/singing/base.yaml', 'usr/configs/base.yaml', 'usr/configs/popcs_ds_beta6.yaml']
| Hparams: 
K_step: 51, accumulate_grad_batches: 1, audio_num_mel_bins: 80, audio_sample_rate: 24000, base_config: ['configs/tts/fs2.yaml', 'configs/singing/base.yaml', './base.yaml'], 
binarization_args: {'shuffle': False, 'with_txt': True, 'with_wav': True, 'with_align': True, 'with_spk_embed': False, 'with_f0': True, 'with_f0cwt': True}, binarizer_cls: data_gen.singing.binarize.SingingBinarizer, binary_data_dir: data/binary/popcs-pmf0, check_val_every_n_epoch: 10, clip_grad_norm: 1, 
content_cond_steps: [], cwt_add_f0_loss: False, cwt_hidden_size: 128, cwt_layers: 2, cwt_loss: l1, 
cwt_std_scale: 0.8, datasets: ['popcs'], debug: False, dec_ffn_kernel_size: 9, dec_layers: 4, 
decay_steps: 50000, decoder_type: fft, dict_dir: , diff_decoder_type: wavenet, diff_loss_type: l1, 
dilation_cycle_length: 1, 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: 2, enc_ffn_kernel_size: 9, enc_layers: 4, encoder_K: 8, 
encoder_type: fft, endless_ds: True, ffn_act: gelu, ffn_padding: SAME, fft_size: 512, 
fmax: 12000, fmin: 30, fs2_ckpt: , gen_dir_name: , gen_tgt_spk_id: -1, 
hidden_size: 256, hop_size: 128, infer: False, keep_bins: 80, lambda_commit: 0.25, 
lambda_energy: 0.0, lambda_f0: 0.0, lambda_ph_dur: 0.0, lambda_sent_dur: 0.0, lambda_uv: 0.0, 
lambda_word_dur: 0.0, load_ckpt: , log_interval: 100, loud_norm: False, lr: 0.001, 
max_beta: 0.06, max_epochs: 1000, max_eval_sentences: 1, max_eval_tokens: 60000, max_frames: 5000, 
max_input_tokens: 1550, max_sentences: 48, max_tokens: 20000, max_updates: 160000, mel_loss: ssim:0.5|l1:0.5, 
mel_vmax: 1.5, mel_vmin: -6, min_level_db: -120, norm_type: gn, num_ckpt_keep: 3, 
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, pitch_ar: False, pitch_enc_hidden_stride_kernel: ['0,2,5', '0,2,5', '0,2,5'], 
pitch_extractor: parselmouth, pitch_loss: l1, pitch_norm: log, pitch_type: frame, pre_align_args: {'use_tone': False, 'forced_align': 'mfa', 'use_sox': True, 'txt_processor': 'zh_g2pM', 'allow_no_txt': False, 'denoise': False}, 
pre_align_cls: data_gen.singing.pre_align.SingingPreAlign, predictor_dropout: 0.5, predictor_grad: 0.0, predictor_hidden: -1, predictor_kernel: 5, 
predictor_layers: 2, prenet_dropout: 0.5, prenet_hidden_size: 256, pretrain_fs_ckpt: , processed_data_dir: data/processed/popcs, 
profile_infer: False, raw_data_dir: data/raw/popcs, ref_norm_layer: bn, reset_phone_dict: True, residual_channels: 256, 
residual_layers: 20, save_best: False, save_ckpt: True, save_codes: ['configs', 'modules', 'tasks', 'utils', 'usr'], save_f0: True, 
save_gt: False, schedule_type: linear, seed: 1234, sort_by_len: True, spec_max: [0.2645, 0.0583, -0.2344, -0.0184, 0.1227, 0.1533, 0.1103, 0.1212, 0.2421, 0.1809, 0.2134, 0.3161, 0.3301, 0.3289, 0.2667, 0.2421, 0.2581, 0.26, 0.1394, 0.1907, 0.1082, 0.1474, 0.168, 0.255, 0.1057, 0.0826, 0.0423, 0.1203, -0.0701, -0.0056, 0.0477, -0.0639, -0.0272, -0.0728, -0.1648, -0.0855, -0.2652, -0.1998, -0.1547, -0.2167, -0.4181, -0.5463, -0.4161, -0.4733, -0.6518, -0.5387, -0.429, -0.4191, -0.4151, -0.3042, -0.381, -0.416, -0.4496, -0.2847, -0.4676, -0.4658, -0.4931, -0.4885, -0.5547, -0.5481, -0.6948, -0.7968, -0.8455, -0.8392, -0.877, -0.952, -0.8749, -0.7297, -0.8374, -0.8667, -0.7157, -0.9035, -0.9219, -0.8801, -0.9298, -0.9009, -0.9604, -1.0537, -1.0781, -1.3766], 
spec_min: [-6.8276, -7.027, -6.8142, -7.1429, -7.6669, -7.6, -7.1148, -6.964, -6.8414, -6.6596, -6.688, -6.7439, -6.7986, -7.494, -7.7845, -7.6586, -6.9288, -6.7639, -6.9118, -6.8246, -6.7183, -7.1769, -6.9794, -7.4513, -7.3422, -7.5623, -6.961, -6.8158, -6.9595, -6.8403, -6.5688, -6.6356, -7.0209, -6.5002, -6.7819, -6.5232, -6.6927, -6.5701, -6.5531, -6.7069, -6.6462, -6.4523, -6.5954, -6.4264, -6.4487, -6.707, -6.4025, -6.3042, -6.4008, -6.3857, -6.3903, -6.3094, -6.2491, -6.3518, -6.3566, -6.4168, -6.2481, -6.3624, -6.2858, -6.2575, -6.3638, -6.452, -6.1835, -6.2754, -6.1253, -6.1645, -6.0638, -6.1262, -6.071, -6.1039, -6.4428, -6.1363, -6.1054, -6.1252, -6.1797, -6.0235, -6.0758, -5.9453, -6.0213, -6.0446], spk_cond_steps: [], stop_token_weight: 5.0, task_cls: usr.diffsinger_task.DiffSingerTask, test_ids: [], 
test_input_dir: , test_num: 0, test_prefixes: ['popcs-说散就散', 'popcs-隐形的翅膀'], test_set_name: test, timesteps: 100, 
train_set_name: train, use_denoise: False, use_energy_embed: False, use_gt_dur: True, use_gt_f0: True, 
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: True, use_var_enc: False, val_check_interval: 2000, valid_num: 0, 
valid_set_name: valid, validate: False, vocoder: vocoders.hifigan.HifiGAN, vocoder_ckpt: checkpoints/0109_hifigan_bigpopcs_hop128, warmup_updates: 2000, 
weight_decay: 0, win_size: 512, work_dir: , 
| Binarizer:  <class 'data_gen.singing.binarize.SingingBinarizer'>
| spk_map:  {}
| Build phone set:  []
0it [00:00, ?it/s]
| valid total duration: 0.000s
0it [00:00, ?it/s]
| test total duration: 0.000s
0it [00:00, ?it/s]
| train total duration: 0.000s

It creates the folder data/binary/popcs-pmf0 with 11 files, but they seem to be essentially empty. Can you please tell what I am missing, why it does not find or use the dataset?

The MFA outputs don't really provide the durations/frames between the words, and I checked that this project uses the duration of the SEG token (word separator). It is many times 0 and other times not, so I wanted to ask how did you get that on preprocessing step?

I try to run training on my dataset. Valid data is processed correctly and this error does not occur at this stage. But when training data is used, a RuntimeError always occurs. I tried to analyze the tensors, look at their sizes, but there are no ideas, because they are identical to the valid ones. The only thing I noticed is that I have a lot of zero tensors at the end. But I'm not sure that this is an important point. Valid data was taken randomly of course. In fact, this part of code works correctly for valid data, but does not work for training data:

torch.gather(F.pad(encoder_out, [0, 0, 1, 0]), 1, mel2ph)

Please help, I would be glad to any ideas to solve this problem!

https://github.com/MoonInTheRiver/DiffSinger/blob/5f2f6eb3c42635f9446363a302602a2ef1d41d70/modules/diffsinger_midi/fs2.py#L100

    Hello, I have issue as I try to use another english dataset. And I'm wondering why Inference from packed test set can work (`CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME --reset --infer`) but inference model from raw input (`python inference/svs/ds_e2e.py --config usr/configs/midi/e2e/opencpop/ds100_adj_rel.yaml --exp_name $MY_DS_EXP_NAME`) needs same phoneme set size?

Originally posted by @Wayne-wonderai in https://github.com/MoonInTheRiver/DiffSinger/issues/29#issuecomment-1260673475

Hi, with data preview we have create 72 phonemes, is there a way to train the model such that it doesn't use the existing phone_set file with 62 phonemes and can use up to 72 phonemes?

Thanks

In the e2e trainning mode of opencpop, skip_decoder is true and the decoder part is not trainned at all, right? But in the inference, you still use run_decoder to get mel_out and use it as a start for q_sample, right? Why run_decoder can also used here?

Is that why you use k=60 in cascade mode but k=1000 in e2e mode?