--- tags: - espnet - audio - singing-voice-synthesis language: zh datasets: - m4singer license: cc-by-4.0 --- ## ESPnet2 SVS model ### `espnet/m4singer_svs_xiaoice` This model was trained by ftshijt using m4singer recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash 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_xiaoice ``` ## SVS config
expand ``` config: conf/tuning/train_xiaoice.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_xiaoice_raw_phn_None_zh ngpu: 1 seed: 0 num_workers: 10 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: 5 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: 500 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: - - - i - - - 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 - 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: xiaoice svs_conf: midi_dim: 129 duration_dim: 1000 adim: 384 aheads: 4 elayers: 6 eunits: 1536 dlayers: 6 dunits: 1536 postnet_layers: 5 postnet_chans: 512 postnet_filts: 5 postnet_dropout_rate: 0.5 use_batch_norm: true reduction_factor: 1 init_type: pytorch use_masking: true loss_function: XiaoiceSing2 loss_type: L1 lambda_mel: 1 lambda_dur: 0.1 lambda_pitch: 0.01 lambda_vuv: 0.01 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 ```BibTex @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: ```bibtex @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} } ```