Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleSpeech into add_license_cpp
TianYuan
3 years ago
commit
f176948fcd
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############################################
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# Network Architecture #
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############################################
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freeze_wav2vec2: False
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normalize_wav: True
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output_norm: True
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init_type: 'kaiming_uniform' # !Warning: need to convergence
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enc:
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input_shape: 1024
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dnn_blocks: 3
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dnn_neurons: 1024
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activation: True
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normalization: True
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dropout_rate: [0.15, 0.15, 0.0]
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ctc:
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enc_n_units: 1024
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blank_id: 0
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dropout_rate: 0.0
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audio_augment:
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speeds: [90, 100, 110]
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spec_augment:
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time_warp: True
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time_warp_window: 5
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time_warp_mode: bicubic
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freq_mask: True
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n_freq_mask: 2
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time_mask: True
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n_time_mask: 2
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replace_with_zero: False
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freq_mask_width: 30
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time_mask_width: 40
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wav2vec2_params_path: exp/wav2vec2/chinese-wav2vec2-large.pdparams
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############################################
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# Wav2Vec2.0 #
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############################################
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# vocab_size: 1000000
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hidden_size: 1024
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num_hidden_layers: 24
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num_attention_heads: 16
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intermediate_size: 4096
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hidden_act: gelu
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hidden_dropout: 0.1
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activation_dropout: 0.0
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attention_dropout: 0.1
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feat_proj_dropout: 0.1
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feat_quantizer_dropout: 0.0
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final_dropout: 0.0
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layerdrop: 0.1
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initializer_range: 0.02
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layer_norm_eps: 1e-5
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feat_extract_norm: layer
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feat_extract_activation: gelu
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conv_dim: [512, 512, 512, 512, 512, 512, 512]
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conv_stride: [5, 2, 2, 2, 2, 2, 2]
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conv_kernel: [10, 3, 3, 3, 3, 2, 2]
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conv_bias: True
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num_conv_pos_embeddings: 128
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num_conv_pos_embedding_groups: 16
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do_stable_layer_norm: True
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apply_spec_augment: False
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mask_channel_length: 10
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mask_channel_min_space: 1
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mask_channel_other: 0.0
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mask_channel_prob: 0.0
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mask_channel_selection: static
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mask_feature_length: 10
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mask_feature_min_masks: 0
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mask_feature_prob: 0.0
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mask_time_length: 10
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mask_time_min_masks: 2
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mask_time_min_space: 1
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mask_time_other: 0.0
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mask_time_prob: 0.075
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mask_time_selection: static
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num_codevectors_per_group: 320
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num_codevector_groups: 2
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contrastive_logits_temperature: 0.1
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num_negatives: 100
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codevector_dim: 256
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proj_codevector_dim: 256
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diversity_loss_weight: 0.1
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use_weighted_layer_sum: False
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# pad_token_id: 0
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# bos_token_id: 1
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# eos_token_id: 2
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add_adapter: False
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adapter_kernel_size: 3
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adapter_stride: 2
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num_adapter_layers: 3
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output_hidden_size: None
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###########################################
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# Data #
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###########################################
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train_manifest: data/manifest.train
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dev_manifest: data/manifest.dev
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test_manifest: data/manifest.test
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vocab_filepath: data/lang_char/vocab.txt
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###########################################
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# Dataloader #
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###########################################
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unit_type: 'char'
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tokenizer: bert-base-chinese
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mean_std_filepath:
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preprocess_config: conf/preprocess.yaml
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sortagrad: -1 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs
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batch_size: 5 # Different batch_size may cause large differences in results
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maxlen_in: 51200000000 # if input length > maxlen-in batchsize is automatically reduced
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maxlen_out: 1500000 # if output length > maxlen-out batchsize is automatically reduced
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minibatches: 0 # for debug
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batch_count: auto
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batch_bins: 0
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batch_frames_in: 0
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batch_frames_out: 0
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batch_frames_inout: 0
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num_workers: 6
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subsampling_factor: 1
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num_encs: 1
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dist_sampler: True
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shortest_first: True
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return_lens_rate: True
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###########################################
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# use speechbrain dataloader #
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###########################################
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use_sb_pipeline: True # whether use speechbrain pipeline. Default is True.
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sb_pipeline_conf: conf/train_with_wav2vec.yaml
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###########################################
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# Training #
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###########################################
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n_epoch: 80
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accum_grad: 1
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global_grad_clip: 5.0
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model_optim: adadelta
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model_optim_conf:
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lr: 1.0
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weight_decay: 0.0
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rho: 0.95
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epsilon: 1.0e-8
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wav2vec2_optim: adam
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wav2vec2_optim_conf:
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lr: 0.0001
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weight_decay: 0.0
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model_scheduler: newbobscheduler
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model_scheduler_conf:
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improvement_threshold: 0.0025
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annealing_factor: 0.8
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patient: 0
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wav2vec2_scheduler: newbobscheduler
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wav2vec2_scheduler_conf:
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improvement_threshold: 0.0025
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annealing_factor: 0.9
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patient: 0
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log_interval: 1
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checkpoint:
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kbest_n: 50
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latest_n: 5
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@ -0,0 +1,6 @@
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# Opencpop
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* svs1 - DiffSinger
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* voc1 - Parallel WaveGAN
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* voc5 - HiFiGAN
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@ -0,0 +1,139 @@
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# Parallel WaveGAN with Opencpop
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This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [Mandarin singing corpus](https://wenet.org.cn/opencpop/).
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## Dataset
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### Download and Extract
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Download Opencpop from it's [Official Website](https://wenet.org.cn/opencpop/download/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/Opencpop`.
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## Get Started
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Assume the path to the dataset is `~/datasets/Opencpop`.
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Run the command below to
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1. **source path**.
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2. preprocess the dataset.
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3. train the model.
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4. synthesize wavs.
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- synthesize waveform from `metadata.jsonl`.
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```bash
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./run.sh
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```
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You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
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```bash
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./run.sh --stage 0 --stop-stage 0
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```
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### Data Preprocessing
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```bash
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./local/preprocess.sh ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── feats_stats.npy
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```
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The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
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Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
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### Model Training
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
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```
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`./local/train.sh` calls `${BIN_DIR}/train.py`.
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Here's the complete help message.
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```text
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usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
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[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
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[--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
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[--run-benchmark RUN_BENCHMARK]
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[--profiler_options PROFILER_OPTIONS]
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Train a ParallelWaveGAN model.
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optional arguments:
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-h, --help show this help message and exit
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--config CONFIG ParallelWaveGAN config file.
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--train-metadata TRAIN_METADATA
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training data.
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--dev-metadata DEV_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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benchmark:
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arguments related to benchmark.
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--batch-size BATCH_SIZE
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batch size.
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--max-iter MAX_ITER train max steps.
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--run-benchmark RUN_BENCHMARK
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runing benchmark or not, if True, use the --batch-size
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and --max-iter.
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--profiler_options PROFILER_OPTIONS
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The option of profiler, which should be in format
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"key1=value1;key2=value2;key3=value3".
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```
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1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
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2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
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3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
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4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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### Synthesizing
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`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
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```
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```text
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usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
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[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
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[--output-dir OUTPUT_DIR] [--ngpu NGPU]
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Synthesize with GANVocoder.
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optional arguments:
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-h, --help show this help message and exit
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--generator-type GENERATOR_TYPE
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type of GANVocoder, should in {pwgan, mb_melgan,
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style_melgan, } now
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--config CONFIG GANVocoder config file.
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--checkpoint CHECKPOINT
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snapshot to load.
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--test-metadata TEST_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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```
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1. `--config` parallel wavegan config file. You should use the same config with which the model is trained.
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2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
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3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
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4. `--output-dir` is the directory to save the synthesized audio files.
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5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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## Pretrained Models
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The pretrained model can be downloaded here:
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- [pwgan_opencpop_ckpt_1.4.0](https://paddlespeech.bj.bcebos.com/t2s/svs/opencpop/pwgan_opencpop_ckpt_1.4.0.zip)
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Parallel WaveGAN checkpoint contains files listed below.
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```text
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pwgan_opencpop_ckpt_1.4.0
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├── default.yaml # default config used to train parallel wavegan
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├── snapshot_iter_100000.pdz # generator parameters of parallel wavegan
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└── feats_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
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```
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## Acknowledgement
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We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
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@ -0,0 +1,119 @@
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# This is the hyperparameter configuration file for Parallel WaveGAN.
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# Please make sure this is adjusted for the CSMSC dataset. If you want to
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# apply to the other dataset, you might need to carefully change some parameters.
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# This configuration requires 12 GB GPU memory and takes ~3 days on RTX TITAN.
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # Sampling rate.
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n_fft: 512 # FFT size (samples).
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n_shift: 128 # Hop size (samples). 12.5ms
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win_length: 512 # Window length (samples). 50ms
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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n_mels: 80 # Number of mel basis.
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fmin: 30 # Minimum freq in mel basis calculation. (Hz)
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fmax: 12000 # Maximum frequency in mel basis calculation. (Hz)
|
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|
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|
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###########################################################
|
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# GENERATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
|
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generator_params:
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in_channels: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_size: 3 # Kernel size of dilated convolution.
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layers: 30 # Number of residual block layers.
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stacks: 3 # Number of stacks i.e., dilation cycles.
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residual_channels: 64 # Number of channels in residual conv.
|
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gate_channels: 128 # Number of channels in gated conv.
|
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skip_channels: 64 # Number of channels in skip conv.
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aux_channels: 80 # Number of channels for auxiliary feature conv.
|
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# Must be the same as num_mels.
|
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aux_context_window: 2 # Context window size for auxiliary feature.
|
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# If set to 2, previous 2 and future 2 frames will be considered.
|
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dropout: 0.0 # Dropout rate. 0.0 means no dropout applied.
|
||||
bias: True # use bias in residual blocks
|
||||
use_weight_norm: True # Whether to use weight norm.
|
||||
# If set to true, it will be applied to all of the conv layers.
|
||||
use_causal_conv: False # use causal conv in residual blocks and upsample layers
|
||||
upsample_scales: [8, 4, 2, 2] # Upsampling scales. Prodcut of these must be the same as hop size.
|
||||
interpolate_mode: "nearest" # upsample net interpolate mode
|
||||
freq_axis_kernel_size: 1 # upsamling net: convolution kernel size in frequencey axis
|
||||
nonlinear_activation: null
|
||||
nonlinear_activation_params: {}
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_size: 3 # Number of output channels.
|
||||
layers: 10 # Number of conv layers.
|
||||
conv_channels: 64 # Number of chnn layers.
|
||||
bias: True # Whether to use bias parameter in conv.
|
||||
use_weight_norm: True # Whether to use weight norm.
|
||||
# If set to true, it will be applied to all of the conv layers.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear function after each conv.
|
||||
nonlinear_activation_params: # Nonlinear function parameters
|
||||
negative_slope: 0.2 # Alpha in leakyrelu.
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
stft_loss_params:
|
||||
fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss.
|
||||
hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss
|
||||
win_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
|
||||
window: "hann" # Window function for STFT-based loss
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_adv: 4.0 # Loss balancing coefficient.
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
batch_size: 8 # Batch size.
|
||||
batch_max_steps: 25500 # Length of each audio in batch. Make sure dividable by n_shift.
|
||||
num_workers: 1 # Number of workers in DataLoader.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
epsilon: 1.0e-6 # Generator's epsilon.
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 0.0001 # Generator's learning rate.
|
||||
step_size: 200000 # Generator's scheduler step size.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
# At each step size, lr will be multiplied by this parameter.
|
||||
generator_grad_norm: 10 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
epsilon: 1.0e-6 # Discriminator's epsilon.
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 0.00005 # Discriminator's learning rate.
|
||||
step_size: 200000 # Discriminator's scheduler step size.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
# At each step size, lr will be multiplied by this parameter.
|
||||
discriminator_grad_norm: 1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 400000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_save_intermediate_results: 4 # Number of results to be saved as intermediate results.
|
||||
num_snapshots: 10 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1 @@
|
||||
../../../csmsc/voc1/local/PTQ_static.sh
|
||||
@ -0,0 +1,15 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/../../dygraph_to_static.py \
|
||||
--type=voc \
|
||||
--voc=pwgan_opencpop \
|
||||
--voc_config=${config_path} \
|
||||
--voc_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--voc_stat=dump/train/feats_stats.npy \
|
||||
--inference_dir=exp/default/inference/
|
||||
@ -0,0 +1,47 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
config_path=$1
|
||||
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# extract features
|
||||
echo "Extract features ..."
|
||||
python3 ${BIN_DIR}/../preprocess.py \
|
||||
--rootdir=~/datasets/Opencpop/segments/ \
|
||||
--dataset=opencpop \
|
||||
--dumpdir=dump \
|
||||
--dur-file=~/datasets/Opencpop/segments/transcriptions.txt \
|
||||
--config=${config_path} \
|
||||
--cut-sil=False \
|
||||
--num-cpu=20
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="feats"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# normalize, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump/train/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump/dev/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
fi
|
||||
@ -0,0 +1 @@
|
||||
../../../csmsc/voc1/local/synthesize.sh
|
||||
@ -0,0 +1 @@
|
||||
../../../csmsc/voc1/local/train.sh
|
||||
@ -0,0 +1 @@
|
||||
../../csmsc/voc1/path.sh
|
||||
@ -0,0 +1,42 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_100000.pdz
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
|
||||
# dygraph to static
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/dygraph_to_static.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
|
||||
# PTQ_static
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/PTQ_static.sh ${train_output_path} pwgan_opencpop || exit -1
|
||||
fi
|
||||
@ -0,0 +1,167 @@
|
||||
# This is the configuration file for CSMSC dataset.
|
||||
# This configuration is based on HiFiGAN V1, which is an official configuration.
|
||||
# But I found that the optimizer setting does not work well with my implementation.
|
||||
# So I changed optimizer settings as follows:
|
||||
# - AdamW -> Adam
|
||||
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
|
||||
# - Scheduler: ExponentialLR -> MultiStepLR
|
||||
# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
|
||||
|
||||
###########################################################
|
||||
# FEATURE EXTRACTION SETTING #
|
||||
###########################################################
|
||||
fs: 24000 # Sampling rate.
|
||||
n_fft: 512 # FFT size (samples).
|
||||
n_shift: 128 # Hop size (samples). 12.5ms
|
||||
win_length: 512 # Window length (samples). 50ms
|
||||
# If set to null, it will be the same as fft_size.
|
||||
window: "hann" # Window function.
|
||||
n_mels: 80 # Number of mel basis.
|
||||
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
|
||||
fmax: 12000 # Maximum frequency in mel basis calculation. (Hz)
|
||||
|
||||
###########################################################
|
||||
# GENERATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
generator_params:
|
||||
in_channels: 80 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
channels: 512 # Number of initial channels.
|
||||
kernel_size: 7 # Kernel size of initial and final conv layers.
|
||||
upsample_scales: [8, 4, 2, 2] # Upsampling scales.
|
||||
upsample_kernel_sizes: [16, 8, 4, 4] # Kernel size for upsampling layers.
|
||||
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
|
||||
resblock_dilations: # Dilations for residual blocks.
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
|
||||
bias: True # Whether to use bias parameter in conv.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
scales: 3 # Number of multi-scale discriminator.
|
||||
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
|
||||
scale_downsample_pooling_params:
|
||||
kernel_size: 4 # Pooling kernel size.
|
||||
stride: 2 # Pooling stride.
|
||||
padding: 2 # Padding size.
|
||||
scale_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
|
||||
channels: 128 # Initial number of channels.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
max_groups: 16 # Maximum number of groups in downsampling conv layers.
|
||||
bias: True
|
||||
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params:
|
||||
negative_slope: 0.1
|
||||
follow_official_norm: True # Whether to follow the official norm setting.
|
||||
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
|
||||
period_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [5, 3] # List of kernel sizes.
|
||||
channels: 32 # Initial number of channels.
|
||||
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
bias: True # Whether to use bias parameter in conv layer."
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
use_spectral_norm: False # Whether to apply spectral normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
use_stft_loss: False # Whether to use multi-resolution STFT loss.
|
||||
use_mel_loss: True # Whether to use Mel-spectrogram loss.
|
||||
mel_loss_params:
|
||||
fs: 24000
|
||||
fft_size: 512
|
||||
hop_size: 128
|
||||
win_length: 512
|
||||
window: "hann"
|
||||
num_mels: 80
|
||||
fmin: 30
|
||||
fmax: 12000
|
||||
log_base: null
|
||||
generator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
discriminator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
use_feat_match_loss: True
|
||||
feat_match_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
average_by_layers: False # Whether to average loss by #layers in each discriminator.
|
||||
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
|
||||
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
|
||||
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
batch_size: 16 # Batch size.
|
||||
batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
|
||||
num_workers: 1 # Number of workers in DataLoader.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Generator's learning rate.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
generator_grad_norm: -1 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Discriminator's learning rate.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
discriminator_grad_norm: -1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
|
||||
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 2500000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_snapshots: 4 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1,168 @@
|
||||
# This is the configuration file for CSMSC dataset.
|
||||
# This configuration is based on HiFiGAN V1, which is an official configuration.
|
||||
# But I found that the optimizer setting does not work well with my implementation.
|
||||
# So I changed optimizer settings as follows:
|
||||
# - AdamW -> Adam
|
||||
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
|
||||
# - Scheduler: ExponentialLR -> MultiStepLR
|
||||
# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
|
||||
|
||||
###########################################################
|
||||
# FEATURE EXTRACTION SETTING #
|
||||
###########################################################
|
||||
fs: 24000 # Sampling rate.
|
||||
n_fft: 512 # FFT size (samples).
|
||||
n_shift: 128 # Hop size (samples). 12.5ms
|
||||
win_length: 512 # Window length (samples). 50ms
|
||||
# If set to null, it will be the same as fft_size.
|
||||
window: "hann" # Window function.
|
||||
n_mels: 80 # Number of mel basis.
|
||||
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
|
||||
fmax: 12000 # Maximum frequency in mel basis calculation. (Hz)
|
||||
|
||||
###########################################################
|
||||
# GENERATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
generator_params:
|
||||
in_channels: 80 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
channels: 512 # Number of initial channels.
|
||||
kernel_size: 7 # Kernel size of initial and final conv layers.
|
||||
upsample_scales: [8, 4, 2, 2] # Upsampling scales.
|
||||
upsample_kernel_sizes: [16, 8, 4, 4] # Kernel size for upsampling layers.
|
||||
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
|
||||
resblock_dilations: # Dilations for residual blocks.
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
|
||||
bias: True # Whether to use bias parameter in conv.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
scales: 3 # Number of multi-scale discriminator.
|
||||
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
|
||||
scale_downsample_pooling_params:
|
||||
kernel_size: 4 # Pooling kernel size.
|
||||
stride: 2 # Pooling stride.
|
||||
padding: 2 # Padding size.
|
||||
scale_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
|
||||
channels: 128 # Initial number of channels.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
max_groups: 16 # Maximum number of groups in downsampling conv layers.
|
||||
bias: True
|
||||
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params:
|
||||
negative_slope: 0.1
|
||||
follow_official_norm: True # Whether to follow the official norm setting.
|
||||
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
|
||||
period_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [5, 3] # List of kernel sizes.
|
||||
channels: 32 # Initial number of channels.
|
||||
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
bias: True # Whether to use bias parameter in conv layer."
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
use_spectral_norm: False # Whether to apply spectral normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
use_stft_loss: False # Whether to use multi-resolution STFT loss.
|
||||
use_mel_loss: True # Whether to use Mel-spectrogram loss.
|
||||
mel_loss_params:
|
||||
fs: 24000
|
||||
fft_size: 512
|
||||
hop_size: 128
|
||||
win_length: 512
|
||||
window: "hann"
|
||||
num_mels: 80
|
||||
fmin: 30
|
||||
fmax: 12000
|
||||
log_base: null
|
||||
generator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
discriminator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
use_feat_match_loss: True
|
||||
feat_match_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
average_by_layers: False # Whether to average loss by #layers in each discriminator.
|
||||
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
|
||||
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
|
||||
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
#batch_size: 16 # Batch size.
|
||||
batch_size: 1 # Batch size.
|
||||
batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
|
||||
num_workers: 1 # Number of workers in DataLoader.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Generator's learning rate.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
generator_grad_norm: -1 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Discriminator's learning rate.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
discriminator_grad_norm: -1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
|
||||
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 2600000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_snapshots: 4 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1,74 @@
|
||||
#!/bin/bash
|
||||
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
python3 ${MAIN_ROOT}/paddlespeech/t2s/exps/diffsinger/gen_gta_mel.py \
|
||||
--diffsinger-config=diffsinger_opencpop_ckpt_1.4.0/default.yaml \
|
||||
--diffsinger-checkpoint=diffsinger_opencpop_ckpt_1.4.0/snapshot_iter_160000.pdz \
|
||||
--diffsinger-stat=diffsinger_opencpop_ckpt_1.4.0/speech_stats.npy \
|
||||
--diffsinger-stretch=diffsinger_opencpop_ckpt_1.4.0/speech_stretchs.npy \
|
||||
--dur-file=~/datasets/Opencpop/segments/transcriptions.txt \
|
||||
--output-dir=dump_finetune \
|
||||
--phones-dict=diffsinger_opencpop_ckpt_1.4.0/phone_id_map.txt \
|
||||
--dataset=opencpop \
|
||||
--rootdir=~/datasets/Opencpop/segments/
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
python3 ${MAIN_ROOT}/utils/link_wav.py \
|
||||
--old-dump-dir=dump \
|
||||
--dump-dir=dump_finetune
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
cp dump/train/feats_stats.npy dump_finetune/train/
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# normalize, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump_finetune/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump_finetune/train/norm \
|
||||
--stats=dump_finetune/train/feats_stats.npy
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump_finetune/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump_finetune/dev/norm \
|
||||
--stats=dump_finetune/train/feats_stats.npy
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump_finetune/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump_finetune/test/norm \
|
||||
--stats=dump_finetune/train/feats_stats.npy
|
||||
fi
|
||||
|
||||
# create finetune env
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
echo "create finetune env"
|
||||
python3 local/prepare_env.py \
|
||||
--pretrained_model_dir=exp/default/checkpoints/ \
|
||||
--output_dir=exp/finetune/
|
||||
fi
|
||||
|
||||
# finetune
|
||||
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
|
||||
CUDA_VISIBLE_DEVICES=${gpus} \
|
||||
FLAGS_cudnn_exhaustive_search=true \
|
||||
FLAGS_conv_workspace_size_limit=4000 \
|
||||
python ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump_finetune/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump_finetune/dev/norm/metadata.jsonl \
|
||||
--config=conf/finetune.yaml \
|
||||
--output-dir=exp/finetune \
|
||||
--ngpu=1
|
||||
fi
|
||||
@ -0,0 +1 @@
|
||||
../../../csmsc/voc1/local/PTQ_static.sh
|
||||
@ -0,0 +1,15 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/../../dygraph_to_static.py \
|
||||
--type=voc \
|
||||
--voc=hifigan_opencpop \
|
||||
--voc_config=${config_path} \
|
||||
--voc_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--voc_stat=dump/train/feats_stats.npy \
|
||||
--inference_dir=exp/default/inference/
|
||||
@ -0,0 +1 @@
|
||||
../../../other/tts_finetune/tts3/local/prepare_env.py
|
||||
@ -0,0 +1 @@
|
||||
../../voc1/local/preprocess.sh
|
||||
@ -0,0 +1 @@
|
||||
../../../csmsc/voc5/local/synthesize.sh
|
||||
@ -0,0 +1 @@
|
||||
../../../csmsc/voc1/local/train.sh
|
||||
@ -0,0 +1 @@
|
||||
../../csmsc/voc5/path.sh
|
||||
@ -0,0 +1,42 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_2500000.pdz
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
|
||||
# dygraph to static
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/dygraph_to_static.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
|
||||
# PTQ_static
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/PTQ_static.sh ${train_output_path} hifigan_opencpop || exit -1
|
||||
fi
|
||||
@ -0,0 +1,240 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# generate mels using durations.txt
|
||||
# for mb melgan finetune
|
||||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
import yaml
|
||||
from tqdm import tqdm
|
||||
from yacs.config import CfgNode
|
||||
|
||||
from paddlespeech.t2s.datasets.preprocess_utils import get_sentences_svs
|
||||
from paddlespeech.t2s.models.diffsinger import DiffSinger
|
||||
from paddlespeech.t2s.models.diffsinger import DiffSingerInference
|
||||
from paddlespeech.t2s.modules.normalizer import ZScore
|
||||
from paddlespeech.t2s.utils import str2bool
|
||||
|
||||
|
||||
def evaluate(args, diffsinger_config):
|
||||
rootdir = Path(args.rootdir).expanduser()
|
||||
assert rootdir.is_dir()
|
||||
|
||||
# construct dataset for evaluation
|
||||
with open(args.phones_dict, "r") as f:
|
||||
phn_id = [line.strip().split() for line in f.readlines()]
|
||||
vocab_size = len(phn_id)
|
||||
print("vocab_size:", vocab_size)
|
||||
|
||||
phone_dict = {}
|
||||
for phn, id in phn_id:
|
||||
phone_dict[phn] = int(id)
|
||||
|
||||
if args.speaker_dict:
|
||||
with open(args.speaker_dict, 'rt') as f:
|
||||
spk_id_list = [line.strip().split() for line in f.readlines()]
|
||||
spk_num = len(spk_id_list)
|
||||
else:
|
||||
spk_num = None
|
||||
|
||||
with open(args.diffsinger_stretch, "r") as f:
|
||||
spec_min = np.load(args.diffsinger_stretch)[0]
|
||||
spec_max = np.load(args.diffsinger_stretch)[1]
|
||||
spec_min = paddle.to_tensor(spec_min)
|
||||
spec_max = paddle.to_tensor(spec_max)
|
||||
print("min and max spec done!")
|
||||
|
||||
odim = diffsinger_config.n_mels
|
||||
diffsinger_config["model"]["fastspeech2_params"]["spk_num"] = spk_num
|
||||
model = DiffSinger(
|
||||
spec_min=spec_min,
|
||||
spec_max=spec_max,
|
||||
idim=vocab_size,
|
||||
odim=odim,
|
||||
**diffsinger_config["model"], )
|
||||
|
||||
model.set_state_dict(paddle.load(args.diffsinger_checkpoint)["main_params"])
|
||||
model.eval()
|
||||
|
||||
stat = np.load(args.diffsinger_stat)
|
||||
mu, std = stat
|
||||
mu = paddle.to_tensor(mu)
|
||||
std = paddle.to_tensor(std)
|
||||
diffsinger_normalizer = ZScore(mu, std)
|
||||
|
||||
diffsinger_inference = DiffSingerInference(diffsinger_normalizer, model)
|
||||
diffsinger_inference.eval()
|
||||
|
||||
output_dir = Path(args.output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
sentences, speaker_set = get_sentences_svs(
|
||||
args.dur_file,
|
||||
dataset=args.dataset,
|
||||
sample_rate=diffsinger_config.fs,
|
||||
n_shift=diffsinger_config.n_shift, )
|
||||
|
||||
if args.dataset == "opencpop":
|
||||
wavdir = rootdir / "wavs"
|
||||
# split data into 3 sections
|
||||
train_file = rootdir / "train.txt"
|
||||
train_wav_files = []
|
||||
with open(train_file, "r") as f_train:
|
||||
for line in f_train.readlines():
|
||||
utt = line.split("|")[0]
|
||||
wav_name = utt + ".wav"
|
||||
wav_path = wavdir / wav_name
|
||||
train_wav_files.append(wav_path)
|
||||
|
||||
test_file = rootdir / "test.txt"
|
||||
dev_wav_files = []
|
||||
test_wav_files = []
|
||||
num_dev = 106
|
||||
count = 0
|
||||
with open(test_file, "r") as f_test:
|
||||
for line in f_test.readlines():
|
||||
count += 1
|
||||
utt = line.split("|")[0]
|
||||
wav_name = utt + ".wav"
|
||||
wav_path = wavdir / wav_name
|
||||
if count > num_dev:
|
||||
test_wav_files.append(wav_path)
|
||||
else:
|
||||
dev_wav_files.append(wav_path)
|
||||
else:
|
||||
print("dataset should in {opencpop} now!")
|
||||
|
||||
train_wav_files = [
|
||||
os.path.basename(str(str_path)) for str_path in train_wav_files
|
||||
]
|
||||
dev_wav_files = [
|
||||
os.path.basename(str(str_path)) for str_path in dev_wav_files
|
||||
]
|
||||
test_wav_files = [
|
||||
os.path.basename(str(str_path)) for str_path in test_wav_files
|
||||
]
|
||||
|
||||
for i, utt_id in enumerate(tqdm(sentences)):
|
||||
phones = sentences[utt_id][0]
|
||||
durations = sentences[utt_id][1]
|
||||
note = sentences[utt_id][2]
|
||||
note_dur = sentences[utt_id][3]
|
||||
is_slur = sentences[utt_id][4]
|
||||
speaker = sentences[utt_id][-1]
|
||||
|
||||
phone_ids = [phone_dict[phn] for phn in phones]
|
||||
phone_ids = paddle.to_tensor(np.array(phone_ids))
|
||||
|
||||
if args.speaker_dict:
|
||||
speaker_id = int(
|
||||
[item[1] for item in spk_id_list if speaker == item[0]][0])
|
||||
speaker_id = paddle.to_tensor(speaker_id)
|
||||
else:
|
||||
speaker_id = None
|
||||
|
||||
durations = paddle.to_tensor(np.array(durations))
|
||||
note = paddle.to_tensor(np.array(note))
|
||||
note_dur = paddle.to_tensor(np.array(note_dur))
|
||||
is_slur = paddle.to_tensor(np.array(is_slur))
|
||||
# 生成的和真实的可能有 1, 2 帧的差距，但是 batch_fn 会修复
|
||||
# split data into 3 sections
|
||||
|
||||
wav_path = utt_id + ".wav"
|
||||
|
||||
if wav_path in train_wav_files:
|
||||
sub_output_dir = output_dir / ("train/raw")
|
||||
elif wav_path in dev_wav_files:
|
||||
sub_output_dir = output_dir / ("dev/raw")
|
||||
elif wav_path in test_wav_files:
|
||||
sub_output_dir = output_dir / ("test/raw")
|
||||
|
||||
sub_output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
with paddle.no_grad():
|
||||
mel = diffsinger_inference(
|
||||
text=phone_ids,
|
||||
note=note,
|
||||
note_dur=note_dur,
|
||||
is_slur=is_slur,
|
||||
get_mel_fs2=False)
|
||||
np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
|
||||
|
||||
|
||||
def main():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Generate mel with diffsinger.")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
default="opencpop",
|
||||
type=str,
|
||||
help="name of dataset, should in {opencpop} now")
|
||||
parser.add_argument(
|
||||
"--rootdir", default=None, type=str, help="directory to dataset.")
|
||||
parser.add_argument(
|
||||
"--diffsinger-config", type=str, help="diffsinger config file.")
|
||||
parser.add_argument(
|
||||
"--diffsinger-checkpoint",
|
||||
type=str,
|
||||
help="diffsinger checkpoint to load.")
|
||||
parser.add_argument(
|
||||
"--diffsinger-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training diffsinger."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--diffsinger-stretch",
|
||||
type=str,
|
||||
help="min and max mel used to stretch before training diffusion.")
|
||||
|
||||
parser.add_argument(
|
||||
"--phones-dict",
|
||||
type=str,
|
||||
default="phone_id_map.txt",
|
||||
help="phone vocabulary file.")
|
||||
|
||||
parser.add_argument(
|
||||
"--speaker-dict", type=str, default=None, help="speaker id map file.")
|
||||
|
||||
parser.add_argument(
|
||||
"--dur-file", default=None, type=str, help="path to durations.txt.")
|
||||
parser.add_argument("--output-dir", type=str, help="output dir.")
|
||||
parser.add_argument(
|
||||
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.ngpu == 0:
|
||||
paddle.set_device("cpu")
|
||||
elif args.ngpu > 0:
|
||||
paddle.set_device("gpu")
|
||||
else:
|
||||
print("ngpu should >= 0 !")
|
||||
|
||||
with open(args.diffsinger_config) as f:
|
||||
diffsinger_config = CfgNode(yaml.safe_load(f))
|
||||
|
||||
print("========Args========")
|
||||
print(yaml.safe_dump(vars(args)))
|
||||
print("========Config========")
|
||||
print(diffsinger_config)
|
||||
|
||||
evaluate(args, diffsinger_config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,170 @@
|
||||
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import argparse
|
||||
|
||||
import yaml
|
||||
from yacs.config import CfgNode
|
||||
|
||||
from paddlespeech.t2s.exps.syn_utils import am_to_static
|
||||
from paddlespeech.t2s.exps.syn_utils import get_am_inference
|
||||
from paddlespeech.t2s.exps.syn_utils import get_voc_inference
|
||||
from paddlespeech.t2s.exps.syn_utils import voc_to_static
|
||||
|
||||
|
||||
def am_dygraph_to_static(args):
|
||||
with open(args.am_config) as f:
|
||||
am_config = CfgNode(yaml.safe_load(f))
|
||||
am_inference = get_am_inference(
|
||||
am=args.am,
|
||||
am_config=am_config,
|
||||
am_ckpt=args.am_ckpt,
|
||||
am_stat=args.am_stat,
|
||||
phones_dict=args.phones_dict,
|
||||
tones_dict=args.tones_dict,
|
||||
speaker_dict=args.speaker_dict)
|
||||
print("acoustic model done!")
|
||||
|
||||
# dygraph to static
|
||||
am_inference = am_to_static(
|
||||
am_inference=am_inference,
|
||||
am=args.am,
|
||||
inference_dir=args.inference_dir,
|
||||
speaker_dict=args.speaker_dict)
|
||||
print("finish to convert dygraph acoustic model to static!")
|
||||
|
||||
|
||||
def voc_dygraph_to_static(args):
|
||||
with open(args.voc_config) as f:
|
||||
voc_config = CfgNode(yaml.safe_load(f))
|
||||
voc_inference = get_voc_inference(
|
||||
voc=args.voc,
|
||||
voc_config=voc_config,
|
||||
voc_ckpt=args.voc_ckpt,
|
||||
voc_stat=args.voc_stat)
|
||||
print("voc done!")
|
||||
|
||||
# dygraph to static
|
||||
voc_inference = voc_to_static(
|
||||
voc_inference=voc_inference,
|
||||
voc=args.voc,
|
||||
inference_dir=args.inference_dir)
|
||||
print("finish to convert dygraph vocoder model to static!")
|
||||
|
||||
|
||||
def parse_args():
|
||||
# parse args and config
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Synthesize with acoustic model & vocoder")
|
||||
parser.add_argument(
|
||||
'--type',
|
||||
type=str,
|
||||
required=True,
|
||||
choices=["am", "voc"],
|
||||
help='Choose the model type of dynamic to static, am or voc')
|
||||
# acoustic model
|
||||
parser.add_argument(
|
||||
'--am',
|
||||
type=str,
|
||||
default='fastspeech2_csmsc',
|
||||
choices=[
|
||||
'speedyspeech_csmsc',
|
||||
'speedyspeech_aishell3',
|
||||
'fastspeech2_csmsc',
|
||||
'fastspeech2_ljspeech',
|
||||
'fastspeech2_aishell3',
|
||||
'fastspeech2_vctk',
|
||||
'tacotron2_csmsc',
|
||||
'tacotron2_ljspeech',
|
||||
'fastspeech2_mix',
|
||||
'fastspeech2_canton',
|
||||
'fastspeech2_male-zh',
|
||||
'fastspeech2_male-en',
|
||||
'fastspeech2_male-mix',
|
||||
],
|
||||
help='Choose acoustic model type of tts task.')
|
||||
parser.add_argument(
|
||||
'--am_config', type=str, default=None, help='Config of acoustic model.')
|
||||
parser.add_argument(
|
||||
'--am_ckpt',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Checkpoint file of acoustic model.')
|
||||
parser.add_argument(
|
||||
"--am_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--tones_dict", type=str, default=None, help="tone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--speaker_dict", type=str, default=None, help="speaker id map file.")
|
||||
# vocoder
|
||||
parser.add_argument(
|
||||
'--voc',
|
||||
type=str,
|
||||
default='pwgan_csmsc',
|
||||
choices=[
|
||||
'pwgan_csmsc',
|
||||
'pwgan_ljspeech',
|
||||
'pwgan_aishell3',
|
||||
'pwgan_vctk',
|
||||
'mb_melgan_csmsc',
|
||||
'style_melgan_csmsc',
|
||||
'hifigan_csmsc',
|
||||
'hifigan_ljspeech',
|
||||
'hifigan_aishell3',
|
||||
'hifigan_vctk',
|
||||
'wavernn_csmsc',
|
||||
'pwgan_male',
|
||||
'hifigan_male',
|
||||
'pwgan_opencpop',
|
||||
'hifigan_opencpop',
|
||||
],
|
||||
help='Choose vocoder type of tts task.')
|
||||
parser.add_argument(
|
||||
'--voc_config', type=str, default=None, help='Config of voc.')
|
||||
parser.add_argument(
|
||||
'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
|
||||
parser.add_argument(
|
||||
"--voc_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training voc."
|
||||
)
|
||||
# other
|
||||
parser.add_argument(
|
||||
"--inference_dir",
|
||||
type=str,
|
||||
default=None,
|
||||
help="dir to save inference models")
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
if args.type == "am":
|
||||
am_dygraph_to_static(args)
|
||||
elif args.type == "voc":
|
||||
voc_dygraph_to_static(args)
|
||||
else:
|
||||
print("type should be in ['am', 'voc'] !")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in new issue