ESPnet2 SVS model
espnet/m4singer_svs_naive_rnn_dp
This model was trained by ftshijt using m4singer recipe in espnet.
Demo: How to use in ESPnet2
Follow the ESPnet installation instructions if you haven't done that already.
cd espnet
git checkout 5c4d7cf7feba8461de2e1080bf82182f0efaef38
pip install -e .
cd egs2/m4singer/svs1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/m4singer_svs_naive_rnn_dp
SVS config
expand
config: conf/tuning/train_naive_rnn_dp.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/svs_train_naive_rnn_dp_raw_phn_None_zh
ngpu: 1
seed: 0
num_workers: 8
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 500
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 2
nbest_averaging_interval: 0
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/svs_stats_raw_phn_None_zh/train/text_shape.phn
- exp/svs_stats_raw_phn_None_zh/train/singing_shape
valid_shape_file:
- exp/svs_stats_raw_phn_None_zh/valid/text_shape.phn
- exp/svs_stats_raw_phn_None_zh/valid/singing_shape
batch_type: sorted
valid_batch_type: null
fold_length:
- 150
- 240000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
train_data_path_and_name_and_type:
- - dump/raw/tr_no_dev/text
- text
- text
- - dump/raw/tr_no_dev/wav.scp
- singing
- sound
- - dump/raw/tr_no_dev/label
- label
- duration
- - dump/raw/tr_no_dev/score.scp
- score
- score
- - exp/svs_stats_raw_phn_None_zh/train/collect_feats/pitch.scp
- pitch
- npy
- - exp/svs_stats_raw_phn_None_zh/train/collect_feats/feats.scp
- feats
- npy
- - dump/raw/tr_no_dev/utt2sid
- sids
- text_int
valid_data_path_and_name_and_type:
- - dump/raw/dev/text
- text
- text
- - dump/raw/dev/wav.scp
- singing
- sound
- - dump/raw/dev/label
- label
- duration
- - dump/raw/dev/score.scp
- score
- score
- - exp/svs_stats_raw_phn_None_zh/valid/collect_feats/pitch.scp
- pitch
- npy
- - exp/svs_stats_raw_phn_None_zh/valid/collect_feats/feats.scp
- feats
- npy
- - dump/raw/dev/utt2sid
- sids
- text_int
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-06
weight_decay: 0.0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- i
- <AP>
- <SP>
- e
- d
- uo
- ai
- sh
- u
- ian
- n
- l
- h
- x
- j
- b
- zh
- m
- en
- uei
- an
- a
- eng
- iou
- z
- g
- ang
- ing
- ou
- q
- ei
- ao
- iang
- t
- ie
- ong
- r
- iao
- ch
- k
- f
- v
- in
- uang
- uan
- c
- s
- ve
- van
- p
- uen
- o
- ia
- ua
- iong
- uai
- vn
- er
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
fs: 24000
score_feats_extract: syllable_score_feats
score_feats_extract_conf:
fs: 24000
n_fft: 2048
win_length: 1200
hop_length: 300
feats_extract: fbank
feats_extract_conf:
n_fft: 2048
hop_length: 300
win_length: 1200
fs: 24000
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/svs_stats_raw_phn_None_zh/train/feats_stats.npz
svs: naive_rnn_dp
svs_conf:
midi_dim: 129
embed_dim: 512
duration_dim: 1000
eprenet_conv_layers: 0
eprenet_conv_chans: 256
eprenet_conv_filts: 3
elayers: 3
eunits: 256
ebidirectional: true
midi_embed_integration_type: add
dlayers: 2
dunits: 256
dbidirectional: true
postnet_layers: 5
postnet_chans: 512
postnet_filts: 5
use_batch_norm: true
reduction_factor: 1
eprenet_dropout_rate: 0.2
edropout_rate: 0.1
ddropout_rate: 0.1
postnet_dropout_rate: 0.5
init_type: pytorch
use_masking: true
spks: 21
pitch_extract: dio
pitch_extract_conf:
use_token_averaged_f0: false
fs: 24000
n_fft: 2048
hop_length: 300
f0max: 800
f0min: 80
reduction_factor: 1
pitch_normalize: global_mvn
pitch_normalize_conf:
stats_file: exp/svs_stats_raw_phn_None_zh/train/pitch_stats.npz
ying_extract: null
ying_extract_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202310'
distributed: false
Citing ESPnet
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{shi22d_interspeech,
author={Jiatong Shi and Shuai Guo and Tao Qian and Tomoki Hayashi and Yuning Wu and Fangzheng Xu and Xuankai Chang and Huazhe Li and Peter Wu and Shinji Watanabe and Qin Jin},
title={{Muskits: an End-to-end Music Processing Toolkit for Singing Voice Synthesis}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={4277--4281},
doi={10.21437/Interspeech.2022-10039}
}
or arXiv:
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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