upload p2

.gitattributes

@@ -52,3 +52,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+egs/datasets/audio/libritts/data/test.data filter=lfs diff=lfs merge=lfs -text
+egs/datasets/audio/libritts/data/valid.data filter=lfs diff=lfs merge=lfs -text

egs/datasets/audio/libritts/base_text2mel.yaml

@@ -0,0 +1,14 @@
+raw_data_dir: 'egs/datasets/audio/libritts/data'
+processed_data_dir: 'egs/datasets/audio/libritts/data'
+binary_data_dir: 'egs/datasets/audio/libritts/data'
+pre_align_cls: egs.datasets.audio.libritts.pre_align.LibrittsPreAlign
+binarization_args:
+  shuffle: true
+use_spk_id: true
+test_num: 200
+num_spk: 2320
+pitch_type: frame
+min_frames: 128
+num_test_samples: 30
+mel_loss: "ssim:0.5|l1:0.5"
+vocoder_ckpt: ''

egs/datasets/audio/libritts/data/emo_map.json

@@ -0,0 +1 @@
+{"\"Angry\"": 0, "\"Happy\"": 1, "\"Neutral\"": 2, "\"Sad\"": 3, "\"Surprise\"": 4}

egs/datasets/audio/libritts/data/mfa_model.zip

@@ -0,0 +1,3 @@
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+size 23850075

egs/datasets/audio/libritts/data/phone_set.json

@@ -0,0 +1 @@
+["!", ",", ".", ":", ";", "<BOS>", "<EOS>", "?", "AA0", "AA1", "AA2", "AE0", "AE1", "AE2", "AH0", "AH1", "AH2", "AO0", "AO1", "AO2", "AW0", "AW1", "AW2", "AY0", "AY1", "AY2", "B", "CH", "D", "DH", "EH0", "EH1", "EH2", "ER0", "ER1", "ER2", "EY0", "EY1", "EY2", "F", "G", "HH", "IH0", "IH1", "IH2", "IY0", "IY1", "IY2", "JH", "K", "L", "M", "N", "NG", "OW0", "OW1", "OW2", "OY0", "OY1", "OY2", "P", "R", "S", "SH", "T", "TH", "UH0", "UH1", "UH2", "UW0", "UW1", "UW2", "V", "W", "Y", "Z", "ZH", "|"]

egs/datasets/audio/libritts/data/spk_map.json

@@ -0,0 +1 @@
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egs/datasets/audio/libritts/data/test.data

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egs/datasets/audio/libritts/data/test_f0s_mean_std.npy

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egs/datasets/audio/libritts/data/test_lengths.npy

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egs/datasets/audio/libritts/data/test_ph_lengths.npy

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egs/datasets/audio/libritts/data/train_f0s_mean_std.npy

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egs/datasets/audio/libritts/data/train_lengths.npy

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egs/datasets/audio/libritts/data/train_ph_lengths.npy

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egs/datasets/audio/libritts/data/valid.data

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egs/datasets/audio/libritts/data/valid_f0s_mean_std.npy

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egs/datasets/audio/libritts/data/valid_lengths.npy

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egs/datasets/audio/libritts/data/valid_ph_lengths.npy

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egs/datasets/audio/libritts/data/word_set.json

@@ -0,0 +1 @@
+[".", "the", ",", "a", "and", "of", "her", "these", "with", "is", "its", "we", "to", "at", "things", "for", "rainbow", "into", "as", "end", "will", "she", "can", "please", "call", "stella", "take", "shape", "long", "round", "arch", "path", "high", "above", "two", "ends", "apparently", "beyond", "horizon", "six", "spoons", "fresh", "snow", "peas", "five", "thick", "slabs", "blue", "cheese", "maybe", "snack", "brother", "bob", "i", "there", "according", "legend", "boiling", "pot", "gold", "one", "when", "sunlight", "strikes", "raindrops", "in", "air", "they", "act", "prism", "form", "ask", "bring", "from", "store", "scoop", "three", "red", "bags", "go", "meet", "wednesday", "train", "station", "also", "need", "small", "plastic", "snake", "big", "toy", "frog", "kids", "division", "white", "light", "many", "beautiful", "colors", "you", "your", "say", "he", "have", "be", "just", "know", "because", "was", "man", "infinite", "resource", "sagacity", "shouldnt", "pricked", "him", "horn", "all", "this", "won", "by", "our", "labour", "neither", "yea", "nor", "nay", "but", "if", "hadnt", "done", "them", "emperor", "no", "admittance", "except", "on", "party", "business", "smiled", "calmly", "mother", "knows", "that", "best", "smile", "id", "soon", "swim", "way", "others", "do", "searched", "through", "box", "name", "more", "hilarious", "?", "words", "behind", "ears", "nonsense", "tom", "fell", "cloven", "head", "vowed", "hed", "change", "pigtails", "place", "shall", "good", "bye", "part", "fish", "mouth", "chew", "leaves", "quickly", "said", "rabbit", "pay", "half", "crown", "week", "extra", "daisy", "creams", "pink", "edges"]

egs/datasets/audio/libritts/fs2.yaml

@@ -0,0 +1,3 @@
+base_config:
+  - egs/egs_bases/tts/fs2.yaml
+  - ./base_text2mel.yaml

egs/datasets/audio/libritts/pre_align.py

@@ -0,0 +1,18 @@
+import os
+
+from data_gen.tts.base_pre_align import BasePreAlign
+import glob
+
+
+class LibrittsPreAlign(BasePreAlign):
+    def meta_data(self):
+        wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*/*.wav'))
+        for wav_fn in wav_fns:
+            item_name = os.path.basename(wav_fn)[:-4]
+            txt_fn = f'{wav_fn[:-4]}.normalized.txt'
+            spk = item_name.split("_")[0]
+            yield item_name, wav_fn, (self.load_txt, txt_fn), spk
+
+
+if __name__ == "__main__":
+    LibrittsPreAlign().process()

egs/datasets/audio/libritts/pwg.yaml

@@ -0,0 +1,8 @@
+base_config: egs/egs_bases/tts/vocoder/pwg.yaml
+raw_data_dir: 'egs/datasets/audio/libritts/data'
+processed_data_dir: 'egs/datasets/audio/libritts/data'
+binary_data_dir: 'egs/datasets/audio/libritts/data'
+generator_params:
+  kernel_size: 5
+num_spk: 400
+max_samples: 20480

egs/datasets/audio/lj/base_mel2wav.yaml

@@ -0,0 +1,5 @@
+raw_data_dir: 'data/raw/LJSpeech-1.1'
+processed_data_dir: 'data/processed/ljspeech'
+binary_data_dir: 'data/binary/ljspeech_wav'
+binarization_args:
+  with_spk_embed: false

egs/datasets/audio/lj/pre_align.py

@@ -0,0 +1,13 @@
+from data_gen.tts.base_preprocess import BasePreprocessor
+
+
+class LJPreAlign(BasePreprocessor):
+    def meta_data(self):
+        for l in open(f'{self.raw_data_dir}/metadata.csv').readlines():
+            item_name, _, txt = l.strip().split("|")
+            wav_fn = f"{self.raw_data_dir}/wavs/{item_name}.wav"
+            yield item_name, wav_fn, txt, 'SPK1'
+
+
+if __name__ == "__main__":
+    LJPreAlign().process()

egs/datasets/audio/lj/pwg.yaml

@@ -0,0 +1,3 @@
+base_config:
+  - egs/egs_bases/tts/vocoder/pwg.yaml
+  - ./base_mel2wav.yaml

egs/datasets/audio/vctk/base_mel2wav.yaml

@@ -0,0 +1,3 @@
+raw_data_dir: 'data/raw/VCTK-Corpus'
+processed_data_dir: 'data/processed/vctk'
+binary_data_dir: 'data/binary/vctk_wav'

egs/datasets/audio/vctk/fs2.yaml

@@ -0,0 +1,12 @@
+base_config:
+  - egs/egs_bases/tts/fs2.yaml
+raw_data_dir: 'data/raw/VCTK-Corpus'
+processed_data_dir: 'data/processed/vctk'
+binary_data_dir: 'data/binary/vctk'
+pre_align_cls: egs.datasets.audio.vctk.pre_align.VCTKPreAlign
+use_spk_id: true
+test_num: 200
+num_spk: 400
+binarization_args:
+  shuffle: true
+  trim_eos_bos: true

egs/datasets/audio/vctk/pre_align.py

@@ -0,0 +1,22 @@
+import os
+
+from data_gen.tts.base_pre_align import BasePreAlign
+import glob
+
+
+class VCTKPreAlign(BasePreAlign):
+    def meta_data(self):
+        wav_fns = glob.glob(f'{self.raw_data_dir}/wav48/*/*.wav')
+        for wav_fn in wav_fns:
+            item_name = os.path.basename(wav_fn)[:-4]
+            spk = item_name.split("_")[0]
+            txt_fn = wav_fn.split("/")
+            txt_fn[-1] = f'{item_name}.txt'
+            txt_fn[-3] = f'txt'
+            txt_fn = "/".join(txt_fn)
+            if os.path.exists(txt_fn) and os.path.exists(wav_fn):
+                yield item_name, wav_fn, (self.load_txt, txt_fn), spk
+
+
+if __name__ == "__main__":
+    VCTKPreAlign().process()

egs/datasets/audio/vctk/pwg.yaml

@@ -0,0 +1,6 @@
+base_config:
+  - egs/egs_bases/tts/vocoder/pwg.yaml
+  - ./base_mel2wav.yaml
+
+num_spk: 400
+max_samples: 20480

egs/egs_bases/config_base.yaml

@@ -0,0 +1,46 @@
+# task
+binary_data_dir: ''
+work_dir: '' # experiment directory.
+infer: false # inference
+amp: false
+seed: 1234
+debug: false
+save_codes: []
+#  - configs
+#  - modules
+#  - tasks
+#  - utils
+#  - usr
+
+#############
+# dataset
+#############
+ds_workers: 1
+test_num: 100
+endless_ds: false
+sort_by_len: true
+
+#########
+# train and eval
+#########
+print_nan_grads: false
+load_ckpt: ''
+save_best: true
+num_ckpt_keep: 3
+clip_grad_norm: 0
+accumulate_grad_batches: 1
+tb_log_interval: 100
+num_sanity_val_steps: 5  # steps of validation at the beginning
+check_val_every_n_epoch: 10
+val_check_interval: 2000
+valid_monitor_key: 'val_loss'
+valid_monitor_mode: 'min'
+max_epochs: 1000
+max_updates: 1000000
+max_tokens: 31250
+max_sentences: 100000
+max_valid_tokens: -1
+max_valid_sentences: -1
+test_input_dir: ''
+resume_from_checkpoint: 0
+rename_tmux: true

egs/egs_bases/tts/base.yaml

@@ -0,0 +1,112 @@
+# task
+base_config: ../config_base.yaml
+task_cls: ''
+#############
+# dataset
+#############
+raw_data_dir: ''
+processed_data_dir: ''
+binary_data_dir: ''
+dict_dir: ''
+pre_align_cls: ''
+binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer
+pre_align_args:
+  txt_processor: en
+  use_tone: true # for ZH
+  sox_resample: false
+  sox_to_wav: false
+  allow_no_txt: false
+  trim_sil: false
+  denoise: false
+binarization_args:
+  shuffle: false
+  with_txt: true
+  with_wav: false
+  with_align: true
+  with_spk_embed: false
+  with_spk_id: true
+  with_f0: true
+  with_f0cwt: false
+  with_linear: false
+  with_word: true
+  trim_sil: false
+  trim_eos_bos: false
+  reset_phone_dict: true
+  reset_word_dict: true
+word_size: 30000
+pitch_extractor: parselmouth
+
+loud_norm: false
+endless_ds: true
+
+test_num: 100
+min_frames: 0
+max_frames: 1548
+frames_multiple: 1
+max_input_tokens: 1550
+audio_num_mel_bins: 80
+audio_sample_rate: 22050
+hop_size: 256  # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate)
+win_size: 1024  # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate)
+fmin: 80  # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
+fmax: 7600  # To be increased/reduced depending on data.
+fft_size: 1024  # Extra window size is filled with 0 paddings to match this parameter
+min_level_db: -100
+ref_level_db: 20
+griffin_lim_iters: 60
+num_spk: 1
+mel_vmin: -6
+mel_vmax: 1.5
+ds_workers: 1
+
+#########
+# model
+#########
+dropout: 0.1
+enc_layers: 4
+dec_layers: 4
+hidden_size: 256
+num_heads: 2
+enc_ffn_kernel_size: 9
+dec_ffn_kernel_size: 9
+ffn_act: gelu
+ffn_padding: 'SAME'
+use_spk_id: true
+use_split_spk_id: false
+use_spk_embed: false
+
+
+###########
+# optimization
+###########
+lr: 2.0
+scheduler: rsqrt # rsqrt|none
+warmup_updates: 8000
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+weight_decay: 0
+clip_grad_norm: 1
+clip_grad_value: 0
+
+
+###########
+# train and eval
+###########
+max_tokens: 30000
+max_sentences: 100000
+max_valid_sentences: 1
+max_valid_tokens: 60000
+valid_infer_interval: 10000
+train_set_name: 'train'
+train_sets: ''
+valid_set_name: 'valid'
+test_set_name: 'test'
+num_test_samples: 0
+num_valid_plots: 10
+test_ids: [ ]
+vocoder_denoise_c: 0.0
+profile_infer: false
+out_wav_norm: false
+save_gt: true
+save_f0: false
+gen_dir_name: ''

egs/egs_bases/tts/fs2.yaml

@@ -0,0 +1,102 @@
+base_config: ./base.yaml
+task_cls: tasks.tts.fs2.FastSpeech2Task
+
+# model
+hidden_size: 256
+dropout: 0.1
+encoder_type: fft # rel_fft|fft|tacotron|tacotron2|conformer
+decoder_type: fft # fft|rnn|conv|conformer|wn
+
+# rnn enc/dec
+encoder_K: 8
+decoder_rnn_dim: 0 # for rnn decoder, 0 -> hidden_size * 2
+
+# fft enc/dec
+use_pos_embed: true
+dec_num_heads: 2
+dec_layers: 4
+ffn_hidden_size: 1024
+enc_ffn_kernel_size: 9
+dec_ffn_kernel_size: 9
+
+# conv enc/dec
+enc_dec_norm: ln
+conv_use_pos: false
+layers_in_block: 2
+enc_dilations: [ 1, 1, 1, 1 ]
+enc_kernel_size: 5
+dec_dilations: [ 1, 1, 1, 1 ] # for conv decoder
+dec_kernel_size: 5
+dur_loss: mse # huber|mol
+
+# duration
+predictor_hidden: -1
+predictor_kernel: 5
+predictor_layers: 2
+dur_predictor_kernel: 3
+dur_predictor_layers: 2
+predictor_dropout: 0.5
+
+# pitch and energy
+pitch_norm: standard # standard|log
+use_pitch_embed: true
+pitch_type: frame # frame|ph|cwt
+use_uv: true
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_add_f0_loss: false
+cwt_std_scale: 0.8
+
+pitch_ar: false
+pitch_embed_type: 0
+pitch_loss: 'l1' # l1|l2|ssim
+pitch_ssim_win: 11
+use_energy_embed: false
+
+# reference encoder and speaker embedding
+use_ref_enc: false
+use_var_enc: false
+lambda_commit: 0.25
+var_enc_vq_codes: 64
+ref_norm_layer: bn
+dec_inp_add_noise: false
+sil_add_noise: false
+ref_hidden_stride_kernel:
+  - 0,3,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size
+  - 0,3,5
+  - 0,2,5
+  - 0,2,5
+  - 0,2,5
+pitch_enc_hidden_stride_kernel:
+  - 0,2,5 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size
+  - 0,2,5
+  - 0,2,5
+dur_enc_hidden_stride_kernel:
+  - 0,2,3 # conv_hidden_size, conv_stride, conv_kernel_size. conv_hidden_size=0: use hidden_size
+  - 0,2,3
+  - 0,1,3
+
+# mel
+mel_loss: l1:0.5|ssim:0.5 # l1|l2|gdl|ssim or l1:0.5|ssim:0.5
+
+# loss lambda
+lambda_f0: 1.0
+lambda_uv: 1.0
+lambda_energy: 0.1
+lambda_ph_dur: 0.1
+lambda_sent_dur: 1.0
+lambda_word_dur: 1.0
+predictor_grad: 0.1
+
+# train and eval
+pretrain_fs_ckpt: ''
+warmup_updates: 2000
+max_tokens: 32000
+max_sentences: 100000
+max_valid_sentences: 1
+max_updates: 120000
+use_gt_dur: false
+use_gt_f0: false
+ds_workers: 2
+lr: 1.0

egs/egs_bases/tts/vocoder/base.yaml

@@ -0,0 +1,34 @@
+base_config: ../base.yaml
+binarization_args:
+  with_wav: true
+  with_spk_embed: false
+  with_align: false
+  with_word: false
+  with_txt: false
+
+###########
+# train and eval
+###########
+max_samples: 25600
+max_sentences: 5
+max_valid_sentences: 1
+max_updates: 1000000
+val_check_interval: 2000
+
+###########################################################
+#                FEATURE EXTRACTION SETTING               #
+###########################################################
+fft_size: 1024           # FFT size.
+hop_size: 256            # Hop size.
+win_length: null         # Window length.
+# If set to null, it will be the same as fft_size.
+window: "hann"           # Window function.
+num_mels: 80             # Number of mel basis.
+fmin: 80                 # Minimum freq in mel basis calculation.
+fmax: 7600               # Maximum frequency in mel basis calculation.
+aux_context_window: 0 # Context window size for auxiliary feature.
+use_pitch_embed: false
+
+generator_grad_norm: 10    # Generator's gradient norm.
+discriminator_grad_norm: 1 # Discriminator's gradient norm.
+disc_start_steps: 40000 # Number of steps to start to train discriminator.

egs/egs_bases/tts/vocoder/pwg.yaml

@@ -0,0 +1,82 @@
+base_config: ./base.yaml
+task_cls: tasks.vocoder.pwg.PwgTask
+
+aux_context_window: 2 # Context window size for auxiliary feature.
+use_pitch_embed: false
+###########################################################
+#         GENERATOR NETWORK ARCHITECTURE SETTING          #
+###########################################################
+generator_params:
+  in_channels: 1        # Number of input channels.
+  out_channels: 1       # Number of output channels.
+  kernel_size: 3        # Kernel size of dilated convolution.
+  layers: 30            # Number of residual block layers.
+  stacks: 3             # Number of stacks i.e., dilation cycles.
+  residual_channels: 64 # Number of channels in residual conv.
+  gate_channels: 128    # Number of channels in gated conv.
+  skip_channels: 64     # Number of channels in skip conv.
+  aux_channels: 80      # Number of channels for auxiliary feature conv.
+  # Must be the same as num_mels.
+  # If set to 2, previous 2 and future 2 frames will be considered.
+  dropout: 0.0          # Dropout rate. 0.0 means no dropout applied.
+  use_weight_norm: true # Whether to use weight norm.
+  # If set to true, it will be applied to all of the conv layers.
+  upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture.
+  upsample_params:                      # Upsampling network parameters.
+    upsample_scales: [4, 4, 4, 4]     # Upsampling scales. Prodcut of these must be the same as hop size.
+  use_pitch_embed: false
+  use_nsf: false
+###########################################################
+#       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.
+rerun_gen: true
+
+###########################################################
+#                   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"         # Window function for STFT-based loss
+use_mel_loss: false
+
+###########################################################
+#               ADVERSARIAL LOSS SETTING                  #
+###########################################################
+lambda_adv: 4.0  # Loss balancing coefficient.
+
+###########################################################
+#             OPTIMIZER & SCHEDULER SETTING               #
+###########################################################
+generator_optimizer_params:
+  lr: 0.0001             # Generator's learning rate.
+  eps: 1.0e-6            # Generator's epsilon.
+  weight_decay: 0.0      # Generator's weight decay coefficient.
+generator_scheduler_params:
+  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:
+  lr: 0.00005            # Discriminator's learning rate.
+  eps: 1.0e-6            # Discriminator's epsilon.
+  weight_decay: 0.0      # Discriminator's weight decay coefficient.
+discriminator_scheduler_params:
+  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.
+disc_start_steps: 40000 # Number of steps to start to train discriminator.

inference/ProDiff.py

@@ -0,0 +1,49 @@
+import torch
+from inference.base_tts_infer import BaseTTSInfer
+from utils.ckpt_utils import load_ckpt, get_last_checkpoint
+from utils.hparams import hparams
+from modules.ProDiff.model.ProDiff import GaussianDiffusion
+from usr.diff.net import DiffNet
+import os
+import numpy as np
+from functools import partial
+
+class ProDiffInfer(BaseTTSInfer):
+    def build_model(self):
+        f0_stats_fn = f'{hparams["binary_data_dir"]}/train_f0s_mean_std.npy'
+        if os.path.exists(f0_stats_fn):
+            hparams['f0_mean'], hparams['f0_std'] = np.load(f0_stats_fn)
+            hparams['f0_mean'] = float(hparams['f0_mean'])
+            hparams['f0_std'] = float(hparams['f0_std'])
+        model = GaussianDiffusion(
+            phone_encoder=self.ph_encoder,
+            out_dims=80, denoise_fn=DiffNet(hparams['audio_num_mel_bins']),
+            timesteps=hparams['timesteps'],
+            loss_type=hparams['diff_loss_type'],
+            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
+        )
+        checkpoint = torch.load(hparams['teacher_ckpt'], map_location='cpu')["state_dict"]['model']
+        teacher_timesteps = int(checkpoint['timesteps'].item())
+        teacher_timescales = int(checkpoint['timescale'].item())
+        student_timesteps = teacher_timesteps // 2
+        student_timescales = teacher_timescales * 2
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+        model.register_buffer('timesteps', to_torch(student_timesteps))      # beta
+        model.register_buffer('timescale', to_torch(student_timescales))      # beta
+        model.eval()
+        load_ckpt(model, hparams['work_dir'], 'model')
+        return model
+
+    def forward_model(self, inp):
+        sample = self.input_to_batch(inp)
+        txt_tokens = sample['txt_tokens']  # [B, T_t]
+        with torch.no_grad():
+            output = self.model(txt_tokens, infer=True)
+            mel_out = output['mel_out']
+            wav_out = self.run_vocoder(mel_out)
+        wav_out = wav_out.squeeze().cpu().numpy()
+        return wav_out
+
+
+if __name__ == '__main__':
+    ProDiffInfer.example_run()

inference/ProDiff_Teacher.py

@@ -0,0 +1,41 @@
+import torch
+from inference.base_tts_infer import BaseTTSInfer
+from utils.ckpt_utils import load_ckpt, get_last_checkpoint
+from utils.hparams import hparams
+from modules.ProDiff.model.ProDiff_teacher import GaussianDiffusion
+from usr.diff.net import DiffNet
+import os
+import numpy as np
+
+class ProDiffTeacherInfer(BaseTTSInfer):
+    def build_model(self):
+        f0_stats_fn = f'{hparams["binary_data_dir"]}/train_f0s_mean_std.npy'
+        if os.path.exists(f0_stats_fn):
+            hparams['f0_mean'], hparams['f0_std'] = np.load(f0_stats_fn)
+            hparams['f0_mean'] = float(hparams['f0_mean'])
+            hparams['f0_std'] = float(hparams['f0_std'])
+        model = GaussianDiffusion(
+            phone_encoder=self.ph_encoder,
+            out_dims=80, denoise_fn=DiffNet(hparams['audio_num_mel_bins']),
+            timesteps=hparams['timesteps'],
+            loss_type=hparams['diff_loss_type'],
+            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
+        )
+
+        model.eval()
+        load_ckpt(model, hparams['work_dir'], 'model')
+        return model
+
+    def forward_model(self, inp):
+        sample = self.input_to_batch(inp)
+        txt_tokens = sample['txt_tokens']  # [B, T_t]
+        with torch.no_grad():
+            output = self.model(txt_tokens, infer=True)
+            mel_out = output['mel_out']
+            wav_out = self.run_vocoder(mel_out)
+        wav_out = wav_out.squeeze().cpu().numpy()
+        return wav_out
+
+
+if __name__ == '__main__':
+    ProDiffTeacherInfer.example_run()

inference/base_tts_infer.py

@@ -0,0 +1,173 @@
+import os
+
+import torch
+
+from tasks.tts.dataset_utils import FastSpeechWordDataset
+from tasks.tts.tts_utils import load_data_preprocessor
+import numpy as np
+from modules.FastDiff.module.util import compute_hyperparams_given_schedule, sampling_given_noise_schedule
+
+import os
+
+import torch
+
+from modules.FastDiff.module.FastDiff_model import FastDiff
+from utils.ckpt_utils import load_ckpt
+from utils.hparams import set_hparams
+
+
+class BaseTTSInfer:
+    def __init__(self, hparams, device=None):
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.hparams = hparams
+        self.device = device
+        self.data_dir = hparams['binary_data_dir']
+        self.preprocessor, self.preprocess_args = load_data_preprocessor()
+        self.ph_encoder = self.preprocessor.load_dict(self.data_dir)
+        self.spk_map = self.preprocessor.load_spk_map(self.data_dir)
+        self.ds_cls = FastSpeechWordDataset
+        self.model = self.build_model()
+        self.model.eval()
+        self.model.to(self.device)
+        self.vocoder, self.diffusion_hyperparams, self.noise_schedule = self.build_vocoder()
+        self.vocoder.eval()
+        self.vocoder.to(self.device)
+
+    def build_model(self):
+        raise NotImplementedError
+
+    def forward_model(self, inp):
+        raise NotImplementedError
+
+    def build_vocoder(self):
+        base_dir = self.hparams['vocoder_ckpt']
+        config_path = f'{base_dir}/config.yaml'
+        config = set_hparams(config_path, global_hparams=False)
+        vocoder = FastDiff(audio_channels=config['audio_channels'],
+                 inner_channels=config['inner_channels'],
+                 cond_channels=config['cond_channels'],
+                 upsample_ratios=config['upsample_ratios'],
+                 lvc_layers_each_block=config['lvc_layers_each_block'],
+                 lvc_kernel_size=config['lvc_kernel_size'],
+                 kpnet_hidden_channels=config['kpnet_hidden_channels'],
+                 kpnet_conv_size=config['kpnet_conv_size'],
+                 dropout=config['dropout'],
+                 diffusion_step_embed_dim_in=config['diffusion_step_embed_dim_in'],
+                 diffusion_step_embed_dim_mid=config['diffusion_step_embed_dim_mid'],
+                 diffusion_step_embed_dim_out=config['diffusion_step_embed_dim_out'],
+                 use_weight_norm=config['use_weight_norm'])
+        load_ckpt(vocoder, base_dir, 'model')
+
+        # Init hyperparameters by linear schedule
+        noise_schedule = torch.linspace(float(config["beta_0"]), float(config["beta_T"]), int(config["T"])).cuda()
+        diffusion_hyperparams = compute_hyperparams_given_schedule(noise_schedule)
+
+        # map diffusion hyperparameters to gpu
+        for key in diffusion_hyperparams:
+            if key in ["beta", "alpha", "sigma"]:
+                diffusion_hyperparams[key] = diffusion_hyperparams[key].cuda()
+        diffusion_hyperparams = diffusion_hyperparams
+
+        if config['noise_schedule'] != '':
+            noise_schedule = config['noise_schedule']
+            if isinstance(noise_schedule, list):
+                noise_schedule = torch.FloatTensor(noise_schedule).cuda()
+        else:
+            # Select Schedule
+            try:
+                reverse_step = int(self.hparams.get('N'))
+            except:
+                print(
+                    'Please specify $N (the number of revere iterations) in config file. Now denoise with 4 iterations.')
+                reverse_step = 4
+            if reverse_step == 1000:
+                noise_schedule = torch.linspace(0.000001, 0.01, 1000).cuda()
+            elif reverse_step == 200:
+                noise_schedule = torch.linspace(0.0001, 0.02, 200).cuda()
+
+            # Below are schedules derived by Noise Predictor.
+            # We will release codes of noise predictor training process & noise scheduling process soon. Please Stay Tuned!
+            elif reverse_step == 8:
+                noise_schedule = [6.689325005027058e-07, 1.0033881153503899e-05, 0.00015496854030061513,
+                                  0.002387222135439515, 0.035597629845142365, 0.3681158423423767, 0.4735414385795593,
+                                  0.5]
+            elif reverse_step == 6:
+                noise_schedule = [1.7838445955931093e-06, 2.7984189728158526e-05, 0.00043231004383414984,
+                                  0.006634317338466644, 0.09357017278671265, 0.6000000238418579]
+            elif reverse_step == 4:
+                noise_schedule = [3.2176e-04, 2.5743e-03, 2.5376e-02, 7.0414e-01]
+            elif reverse_step == 3:
+                noise_schedule = [9.0000e-05, 9.0000e-03, 6.0000e-01]
+            else:
+                raise NotImplementedError
+
+        if isinstance(noise_schedule, list):
+            noise_schedule = torch.FloatTensor(noise_schedule).cuda()
+
+        return vocoder, diffusion_hyperparams, noise_schedule
+
+    def run_vocoder(self, c):
+        c = c.transpose(2, 1)
+        audio_length = c.shape[-1] * self.hparams["hop_size"]
+        y = sampling_given_noise_schedule(
+            self.vocoder, (1, 1, audio_length), self.diffusion_hyperparams, self.noise_schedule, condition=c, ddim=False, return_sequence=False)
+        return y
+
+    def preprocess_input(self, inp):
+        """
+        :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)}
+        :return:
+        """
+        preprocessor, preprocess_args = self.preprocessor, self.preprocess_args
+        text_raw = inp['text']
+        item_name = inp.get('item_name', '<ITEM_NAME>')
+        spk_name = inp.get('spk_name', 'SPK1')
+        ph, txt = preprocessor.txt_to_ph(
+            preprocessor.txt_processor, text_raw, preprocess_args)
+        ph_token = self.ph_encoder.encode(ph)
+        spk_id = self.spk_map[spk_name]
+        item = {'item_name': item_name, 'text': txt, 'ph': ph, 'spk_id': spk_id, 'ph_token': ph_token}
+        item['ph_len'] = len(item['ph_token'])
+        return item
+
+    def input_to_batch(self, item):
+        item_names = [item['item_name']]
+        text = [item['text']]
+        ph = [item['ph']]
+        txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device)
+        txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device)
+        spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device)
+        batch = {
+            'item_name': item_names,
+            'text': text,
+            'ph': ph,
+            'txt_tokens': txt_tokens,
+            'txt_lengths': txt_lengths,
+            'spk_ids': spk_ids,
+        }
+        return batch
+
+    def postprocess_output(self, output):
+        return output
+
+    def infer_once(self, inp):
+        inp = self.preprocess_input(inp)
+        output = self.forward_model(inp)
+        output = self.postprocess_output(output)
+        return output
+
+    @classmethod
+    def example_run(cls):
+        from utils.hparams import set_hparams
+        from utils.hparams import hparams as hp
+        from utils.audio import save_wav
+
+        set_hparams()
+        inp = {
+            'text': hp['text']
+        }
+        infer_ins = cls(hp)
+        out = infer_ins.infer_once(inp)
+        os.makedirs('infer_out', exist_ok=True)
+        save_wav(out, f'infer_out/{hp["text"]}.wav', hp['audio_sample_rate'])

inference/gradio/gradio_settings.yaml

@@ -0,0 +1,13 @@
+title: 'Rongjiehuang/ProDiff'
+description: |
+  Gradio demo for Rongjiehuang/ProDiff. To use it, simply add your audio, or click one of the examples to load them.
+article: |
+  Link to <a href='https://github.com/Rongjiehuang/ProDiff' style='color:blue;' target='_blank\'>Github REPO</a>
+example_inputs:
+  - |-
+    the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing.
+  - |-
+    produced the block books, which were the immediate predecessors of the true printed book,
+inference_cls: inference.ProDiff_Teacher.ProDiffTeacherInfer
+exp_name: ProDiff_Teacher
+config: modules/ProDiff/config/prodiff_teacher.yaml

inference/gradio/infer.py

@@ -0,0 +1,69 @@
+import importlib
+import re
+
+import gradio as gr
+import yaml
+from gradio.inputs import Textbox
+
+from inference.base_tts_infer import BaseTTSInfer
+from utils.hparams import set_hparams
+from utils.hparams import hparams as hp
+import numpy as np
+
+from data_gen.tts.data_gen_utils import is_sil_phoneme, PUNCS
+
+class GradioInfer:
+    def __init__(self, exp_name, config, inference_cls, title, description, article, example_inputs):
+        self.exp_name = exp_name
+        self.config = config
+        self.title = title
+        self.description = description
+        self.article = article
+        self.example_inputs = example_inputs
+        pkg = ".".join(inference_cls.split(".")[:-1])
+        cls_name = inference_cls.split(".")[-1]
+        self.inference_cls = getattr(importlib.import_module(pkg), cls_name)
+
+    def greet(self, text):
+        sents = re.split(rf'([{PUNCS}])', text.replace('\n', ','))
+        if sents[-1] not in list(PUNCS):
+            sents = sents + ['.']
+        audio_outs = []
+        s = ""
+        for i in range(0, len(sents), 2):
+            if len(sents[i]) > 0:
+                s += sents[i] + sents[i + 1]
+            if len(s) >= 400 or (i >= len(sents) - 2 and len(s) > 0):
+                audio_out = self.infer_ins.infer_once({
+                    'text': s
+                })
+                audio_out = audio_out * 32767
+                audio_out = audio_out.astype(np.int16)
+                audio_outs.append(audio_out)
+                audio_outs.append(np.zeros(int(hp['audio_sample_rate'] * 0.3)).astype(np.int16))
+                s = ""
+        audio_outs = np.concatenate(audio_outs)
+        return hp['audio_sample_rate'], audio_outs
+
+    def run(self):
+        set_hparams(exp_name=self.exp_name, config=self.config)
+        infer_cls = self.inference_cls
+        self.infer_ins: BaseTTSInfer = infer_cls(hp)
+        example_inputs = self.example_inputs
+        iface = gr.Interface(fn=self.greet,
+                             inputs=Textbox(
+                                 lines=10, placeholder=None, default=example_inputs[0], label="input text"),
+                             outputs="audio",
+                             allow_flagging="never",
+                             title=self.title,
+                             description=self.description,
+                             article=self.article,
+                             examples=example_inputs,
+                             enable_queue=True)
+        iface.launch(share=True,cache_examples=True)
+
+
+if __name__ == '__main__':
+    gradio_config = yaml.safe_load(open('inference/gradio/gradio_settings.yaml'))
+    g = GradioInfer(**gradio_config)
+    g.run()

modules/FastDiff/config/FastDiff.yaml

@@ -0,0 +1,7 @@
+base_config:
+  - ./base.yaml
+
+audio_sample_rate: 22050
+raw_data_dir: 'data/raw/LJSpeech-1.1'
+processed_data_dir: 'egs/datasets/audio/libritts/data'
+binary_data_dir: 'egs/datasets/audio/libritts/data'

modules/FastDiff/config/FastDiff_libritts.yaml

@@ -0,0 +1,7 @@
+base_config:
+  - ./base.yaml
+
+audio_sample_rate: 22050
+raw_data_dir: 'data/raw/LibriTTS'
+processed_data_dir: 'data/processed/LibriTTS'
+binary_data_dir: 'data/binary/LibriTTS'

modules/FastDiff/config/FastDiff_sc09.yaml

@@ -0,0 +1,25 @@
+base_config:
+  - egs/egs_bases/tts/vocoder/base.yaml
+  - egs/datasets/audio/lj/base_mel2wav.yaml
+  - ./base.yaml
+
+#raw_data_dir: '/home1/huangrongjie/dataset/sc09/data/'
+#processed_data_dir: 'data/processed/SC09'
+#binary_data_dir: 'data/binary/SC09'
+
+raw_data_dir: '/home1/huangrongjie/Project/AdaGrad/data/raw/SC09/'
+processed_data_dir: 'data/processed/SC09_ten_processed'
+binary_data_dir: 'data/binary/SC09_ten_processed'
+
+pre_align_cls: egs.datasets.audio.sc09.pre_align.Sc09PreAlign
+audio_sample_rate: 16000
+max_samples: 12800
+
+pre_align_args:
+  sox_resample: false
+  sox_to_wav: false
+  allow_no_txt: true
+  trim_sil: true
+  denoise: true
+
+loud_norm: true

modules/FastDiff/config/FastDiff_tacotron.yaml

@@ -0,0 +1,58 @@
+base_config:
+  - egs/egs_bases/tts/vocoder/pwg.yaml
+  - egs/egs_bases/tts/base_mel2wav.yaml
+  - egs/datasets/audio/lj/pwg.yaml
+
+raw_data_dir: 'data/raw/LJSpeech-1.1'
+processed_data_dir: 'data/processed/LJSpeech_FastDiff'
+#binary_data_dir: 'data/binary/LJSpeech_Taco'
+binary_data_dir: /apdcephfs/private_nlphuang/preprocess/AdaGrad/data/binary/LJSpeech_Taco
+
+binarizer_cls: data_gen.tts.vocoder_binarizer.VocoderBinarizer
+pre_align_cls: egs.datasets.audio.lj.pre_align.LJPreAlign
+task_cls: modules.FastDiff.task.FastDiff.FastDiffTask
+binarization_args:
+  with_wav: true
+  with_spk_embed: false
+  with_align: false
+  with_word: false
+  with_txt: false
+  with_f0: false
+
+# data
+num_spk: 400
+max_samples: 25600
+aux_context_window: 0
+max_sentences: 20
+test_input_dir: '' # 'wavs' # wav->wav inference
+test_mel_dir: '' # 'mels' # mel->wav inference
+use_wav: True # mel->wav inference
+
+# training
+num_sanity_val_steps: -1
+max_updates: 1000000
+lr: 2e-4
+weight_decay: 0
+
+# FastDiff
+audio_channels: 1
+inner_channels: 32
+cond_channels: 80
+upsample_ratios: [8, 8, 4]
+lvc_layers_each_block: 4
+lvc_kernel_size: 3
+kpnet_hidden_channels: 64
+kpnet_conv_size: 3
+dropout: 0.0
+diffusion_step_embed_dim_in: 128
+diffusion_step_embed_dim_mid: 512
+diffusion_step_embed_dim_out: 512
+use_weight_norm: True
+
+# Diffusion
+T: 1000
+beta_0: 0.000001
+beta_T: 0.01
+noise_schedule: ''
+N: ''
+

modules/FastDiff/config/FastDiff_vctk.yaml

@@ -0,0 +1,7 @@
+base_config:
+  - ./base.yaml
+
+audio_sample_rate: 22050
+raw_data_dir: 'data/raw/VCTK'
+processed_data_dir: 'data/processed/VCTK'
+binary_data_dir: 'data/binary/VCTK'

modules/FastDiff/config/base.yaml

@@ -0,0 +1,157 @@
+#############
+# Custom dataset preprocess
+#############
+audio_num_mel_bins: 80
+audio_sample_rate: 22050
+hop_size: 256  # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate)
+win_size: 1024  # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate)
+fmin: 80  # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
+fmax: 7600  # To be increased/reduced depending on data.
+fft_size: 1024  # Extra window size is filled with 0 paddings to match this parameter
+min_level_db: -100
+ref_level_db: 20
+griffin_lim_iters: 60
+num_spk: 1 # number of speakers
+mel_vmin: -6
+mel_vmax: 1.5
+
+#############
+# FastDiff Model
+#############
+audio_channels: 1
+inner_channels: 32
+cond_channels: 80
+upsample_ratios: [8, 8, 4]
+lvc_layers_each_block: 4
+lvc_kernel_size: 3
+kpnet_hidden_channels: 64
+kpnet_conv_size: 3
+dropout: 0.0
+diffusion_step_embed_dim_in: 128
+diffusion_step_embed_dim_mid: 512
+diffusion_step_embed_dim_out: 512
+use_weight_norm: True
+
+###########
+# Diffusion
+###########
+T: 1000
+beta_0: 0.000001
+beta_T: 0.01
+noise_schedule: ''
+N: ''
+
+
+###########
+# train and eval
+###########
+task_cls: modules.FastDiff.task.FastDiff.FastDiffTask
+max_updates: 1000000 # max training steps
+max_samples: 25600 # audio length in training
+max_sentences: 20 # max batch size in training
+num_sanity_val_steps: -1
+max_valid_sentences: 1
+valid_infer_interval: 10000
+val_check_interval: 2000
+num_test_samples: 0
+num_valid_plots: 10
+
+
+#############
+# Stage 1 of data processing
+#############
+pre_align_cls: egs.datasets.audio.pre_align.PreAlign
+pre_align_args:
+  nsample_per_mfa_group: 1000
+  txt_processor: en
+  use_tone: true # for ZH
+  sox_resample: false
+  sox_to_wav: false
+  allow_no_txt: true
+  trim_sil: false
+  denoise: false
+
+
+#############
+# Stage 2 of data processing
+#############
+binarizer_cls: data_gen.tts.vocoder_binarizer.VocoderBinarizer
+binarization_args:
+  with_wav: true
+  with_spk_embed: false
+  with_align: false
+  with_word: false
+  with_txt: false
+  with_f0: false
+  shuffle: false
+  with_spk_id: true
+  with_f0cwt: false
+  with_linear: false
+  trim_eos_bos: false
+  reset_phone_dict: true
+  reset_word_dict: true
+
+
+###########
+# optimization
+###########
+lr: 2e-4    # learning rate
+weight_decay: 0
+scheduler: rsqrt # rsqrt|none
+optimizer_adam_beta1: 0.9
+optimizer_adam_beta2: 0.98
+clip_grad_norm: 1
+clip_grad_value: 0
+
+#############
+# Setting for this Pytorch framework
+#############
+max_input_tokens: 1550
+frames_multiple: 1
+use_word_input: false
+vocoder: FastDiff
+vocoder_ckpt: checkpoints/FastDiff
+vocoder_denoise_c: 0.0
+max_tokens: 30000
+max_valid_tokens: 60000
+test_ids: [ ]
+profile_infer: false
+out_wav_norm: false
+save_gt: true
+save_f0: false
+aux_context_window: 0
+test_input_dir: '' # 'wavs' # wav->wav inference
+test_mel_dir: '' # 'mels' # mel->wav inference
+use_wav: True # mel->wav inference
+pitch_extractor: parselmouth
+loud_norm: false
+endless_ds: true
+test_num: 100
+min_frames: 0
+max_frames: 1548
+ds_workers: 1
+gen_dir_name: ''
+accumulate_grad_batches: 1
+tb_log_interval: 100
+print_nan_grads: false
+work_dir: '' # experiment directory.
+infer: false # inference
+amp: false
+debug: false
+save_codes: []
+save_best: true
+num_ckpt_keep: 3
+sort_by_len: true
+load_ckpt: ''
+check_val_every_n_epoch: 10
+max_epochs: 1000
+eval_max_batches: -1
+resume_from_checkpoint: 0
+rename_tmux: true
+valid_monitor_key: 'val_loss'
+valid_monitor_mode: 'min'
+train_set_name: 'train'
+train_sets: ''
+valid_set_name: 'valid'
+test_set_name: 'test'
+seed: 1234

modules/FastDiff/module/FastDiff_model.py

@@ -0,0 +1,123 @@
+import torch.nn as nn
+import torch
+import logging
+from modules.FastDiff.module.modules import DiffusionDBlock, TimeAware_LVCBlock
+from modules.FastDiff.module.util import calc_diffusion_step_embedding
+
+def swish(x):
+    return x * torch.sigmoid(x)
+
+class FastDiff(nn.Module):
+    """FastDiff module."""
+
+    def __init__(self,
+                 audio_channels=1,
+                 inner_channels=32,
+                 cond_channels=80,
+                 upsample_ratios=[8, 8, 4],
+                 lvc_layers_each_block=4,
+                 lvc_kernel_size=3,
+                 kpnet_hidden_channels=64,
+                 kpnet_conv_size=3,
+                 dropout=0.0,
+                 diffusion_step_embed_dim_in=128,
+                 diffusion_step_embed_dim_mid=512,
+                 diffusion_step_embed_dim_out=512,
+                 use_weight_norm=True):
+        super().__init__()
+
+        self.diffusion_step_embed_dim_in = diffusion_step_embed_dim_in
+
+        self.audio_channels = audio_channels
+        self.cond_channels = cond_channels
+        self.lvc_block_nums = len(upsample_ratios)
+        self.first_audio_conv = nn.Conv1d(1, inner_channels,
+                                    kernel_size=7, padding=(7 - 1) // 2,
+                                    dilation=1, bias=True)
+
+        # define residual blocks
+        self.lvc_blocks = nn.ModuleList()
+        self.downsample = nn.ModuleList()
+
+        # the layer-specific fc for noise scale embedding
+        self.fc_t = nn.ModuleList()
+        self.fc_t1 = nn.Linear(diffusion_step_embed_dim_in, diffusion_step_embed_dim_mid)
+        self.fc_t2 = nn.Linear(diffusion_step_embed_dim_mid, diffusion_step_embed_dim_out)
+
+        cond_hop_length = 1
+        for n in range(self.lvc_block_nums):
+            cond_hop_length = cond_hop_length * upsample_ratios[n]
+            lvcb = TimeAware_LVCBlock(
+                in_channels=inner_channels,
+                cond_channels=cond_channels,
+                upsample_ratio=upsample_ratios[n],
+                conv_layers=lvc_layers_each_block,
+                conv_kernel_size=lvc_kernel_size,
+                cond_hop_length=cond_hop_length,
+                kpnet_hidden_channels=kpnet_hidden_channels,
+                kpnet_conv_size=kpnet_conv_size,
+                kpnet_dropout=dropout,
+                noise_scale_embed_dim_out=diffusion_step_embed_dim_out
+            )
+            self.lvc_blocks += [lvcb]
+            self.downsample.append(DiffusionDBlock(inner_channels, inner_channels, upsample_ratios[self.lvc_block_nums-n-1]))
+
+
+        # define output layers
+        self.final_conv = nn.Sequential(nn.Conv1d(inner_channels, audio_channels, kernel_size=7, padding=(7 - 1) // 2,
+                                        dilation=1, bias=True))
+
+        # apply weight norm
+        if use_weight_norm:
+            self.apply_weight_norm()
+
+    def forward(self, data):
+        """Calculate forward propagation.
+        Args:
+            x (Tensor): Input noise signal (B, 1, T).
+            c (Tensor): Local conditioning auxiliary features (B, C ,T').
+        Returns:
+            Tensor: Output tensor (B, out_channels, T)
+        """
+        audio, c, diffusion_steps = data
+
+        # embed diffusion step t
+        diffusion_step_embed = calc_diffusion_step_embedding(diffusion_steps, self.diffusion_step_embed_dim_in)
+        diffusion_step_embed = swish(self.fc_t1(diffusion_step_embed))
+        diffusion_step_embed = swish(self.fc_t2(diffusion_step_embed))
+
+        audio = self.first_audio_conv(audio)
+        downsample = []
+        for down_layer in self.downsample:
+            downsample.append(audio)
+            audio = down_layer(audio)
+
+        x = audio
+        for n, audio_down in enumerate(reversed(downsample)):
+            x = self.lvc_blocks[n]((x, audio_down, c, diffusion_step_embed))
+
+        # apply final layers
+        x = self.final_conv(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        """Remove weight normalization module from all of the layers."""
+        def _remove_weight_norm(m):
+            try:
+                logging.debug(f"Weight norm is removed from {m}.")
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(_remove_weight_norm)
+
+    def apply_weight_norm(self):
+        """Apply weight normalization module from all of the layers."""
+        def _apply_weight_norm(m):
+            if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
+                torch.nn.utils.weight_norm(m)
+                logging.debug(f"Weight norm is applied to {m}.")
+
+        self.apply(_apply_weight_norm)
+

modules/FastDiff/module/WaveNet.py

@@ -0,0 +1,189 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from modules.FastDiff.module.util import calc_noise_scale_embedding
+def swish(x):
+    return x * torch.sigmoid(x)
+
+
+# dilated conv layer with kaiming_normal initialization
+# from https://github.com/ksw0306/FloWaveNet/blob/master/modules.py
+class Conv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1):
+        super(Conv, self).__init__()
+        self.padding = dilation * (kernel_size - 1) // 2
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation, padding=self.padding)
+        self.conv = nn.utils.weight_norm(self.conv)
+        nn.init.kaiming_normal_(self.conv.weight)
+
+    def forward(self, x):
+        out = self.conv(x)
+        return out
+
+
+# conv1x1 layer with zero initialization
+# from https://github.com/ksw0306/FloWaveNet/blob/master/modules.py but the scale parameter is removed
+class ZeroConv1d(nn.Module):
+    def __init__(self, in_channel, out_channel):
+        super(ZeroConv1d, self).__init__()
+        self.conv = nn.Conv1d(in_channel, out_channel, kernel_size=1, padding=0)
+        self.conv.weight.data.zero_()
+        self.conv.bias.data.zero_()
+
+    def forward(self, x):
+        out = self.conv(x)
+        return out
+
+
+# every residual block (named residual layer in paper)
+# contains one noncausal dilated conv
+class Residual_block(nn.Module):
+    def __init__(self, res_channels, skip_channels, dilation, 
+                 noise_scale_embed_dim_out, multiband=True):
+        super(Residual_block, self).__init__()
+        self.res_channels = res_channels
+
+        # the layer-specific fc for noise scale embedding
+        self.fc_t = nn.Linear(noise_scale_embed_dim_out, self.res_channels)
+
+        # dilated conv layer
+        self.dilated_conv_layer = Conv(self.res_channels, 2 * self.res_channels, kernel_size=3, dilation=dilation)
+
+        # add mel spectrogram upsampler and conditioner conv1x1 layer
+        self.upsample_conv2d = torch.nn.ModuleList()
+        if multiband is True:
+             params = 8
+        else:
+             params = 16
+        for s in [params, params]:  #######  Very  Important!!!!!  #######
+            conv_trans2d = torch.nn.ConvTranspose2d(1, 1, (3, 2 * s), padding=(1, s // 2), stride=(1, s))
+            conv_trans2d = torch.nn.utils.weight_norm(conv_trans2d)
+            torch.nn.init.kaiming_normal_(conv_trans2d.weight)
+            self.upsample_conv2d.append(conv_trans2d)
+        self.mel_conv = Conv(80, 2 * self.res_channels, kernel_size=1)  # 80 is mel bands
+
+        # residual conv1x1 layer, connect to next residual layer
+        self.res_conv = nn.Conv1d(res_channels, res_channels, kernel_size=1)
+        self.res_conv = nn.utils.weight_norm(self.res_conv)
+        nn.init.kaiming_normal_(self.res_conv.weight)
+
+        # skip conv1x1 layer, add to all skip outputs through skip connections
+        self.skip_conv = nn.Conv1d(res_channels, skip_channels, kernel_size=1)
+        self.skip_conv = nn.utils.weight_norm(self.skip_conv)
+        nn.init.kaiming_normal_(self.skip_conv.weight)
+
+    def forward(self, input_data):
+        x, mel_spec, noise_scale_embed = input_data
+        h = x
+        B, C, L = x.shape   # B, res_channels, L
+        assert C == self.res_channels
+
+        # add in noise scale embedding
+        part_t = self.fc_t(noise_scale_embed)
+        part_t = part_t.view([B, self.res_channels, 1])
+        h += part_t
+
+        # dilated conv layer
+        h = self.dilated_conv_layer(h)
+
+        # add mel spectrogram as (local) conditioner
+        assert mel_spec is not None
+
+        # Upsample spectrogram to size of audio
+        mel_spec = torch.unsqueeze(mel_spec, dim=1)  # (B, 1, 80, T')
+        mel_spec = F.leaky_relu(self.upsample_conv2d[0](mel_spec), 0.4)
+        mel_spec = F.leaky_relu(self.upsample_conv2d[1](mel_spec), 0.4)
+        mel_spec = torch.squeeze(mel_spec, dim=1)
+
+        assert(mel_spec.size(2) >= L)
+        if mel_spec.size(2) > L:
+            mel_spec = mel_spec[:, :, :L]
+
+        mel_spec = self.mel_conv(mel_spec)
+        h += mel_spec
+
+        # gated-tanh nonlinearity
+        out = torch.tanh(h[:,:self.res_channels,:]) * torch.sigmoid(h[:,self.res_channels:,:])
+
+        # residual and skip outputs
+        res = self.res_conv(out)
+        assert x.shape == res.shape
+        skip = self.skip_conv(out)
+
+        return (x + res) * math.sqrt(0.5), skip  # normalize for training stability
+
+
+class Residual_group(nn.Module):
+    def __init__(self, res_channels, skip_channels, num_res_layers, dilation_cycle, 
+                 noise_scale_embed_dim_in, 
+                 noise_scale_embed_dim_mid,
+                 noise_scale_embed_dim_out, multiband):
+        super(Residual_group, self).__init__()
+        self.num_res_layers = num_res_layers
+        self.noise_scale_embed_dim_in = noise_scale_embed_dim_in
+
+        # the shared two fc layers for noise scale embedding
+        self.fc_t1 = nn.Linear(noise_scale_embed_dim_in, noise_scale_embed_dim_mid)
+        self.fc_t2 = nn.Linear(noise_scale_embed_dim_mid, noise_scale_embed_dim_out)
+
+        # stack all residual blocks with dilations 1, 2, ... , 512, ... , 1, 2, ..., 512
+        self.residual_blocks = nn.ModuleList()
+        for n in range(self.num_res_layers):
+            self.residual_blocks.append(Residual_block(res_channels, skip_channels, 
+                                                       dilation=2 ** (n % dilation_cycle),
+                                                       noise_scale_embed_dim_out=noise_scale_embed_dim_out, multiband=multiband))
+
+    def forward(self, input_data):
+        x, mel_spectrogram, noise_scales = input_data
+
+        # embed noise scale
+        noise_scale_embed = calc_noise_scale_embedding(noise_scales, self.noise_scale_embed_dim_in)
+        noise_scale_embed = swish(self.fc_t1(noise_scale_embed))
+        noise_scale_embed = swish(self.fc_t2(noise_scale_embed))
+
+        # pass all residual layers
+        h = x
+        skip = 0
+        for n in range(self.num_res_layers):
+            h, skip_n = self.residual_blocks[n]((h, mel_spectrogram, noise_scale_embed))  # use the output from last residual layer
+            skip += skip_n  # accumulate all skip outputs
+
+        return skip * math.sqrt(1.0 / self.num_res_layers)  # normalize for training stability
+
+
+class WaveNet_vocoder(nn.Module):
+    def __init__(self, in_channels, res_channels, skip_channels, out_channels, 
+                 num_res_layers, dilation_cycle, 
+                 noise_scale_embed_dim_in, 
+                 noise_scale_embed_dim_mid,
+                 noise_scale_embed_dim_out, multiband):
+        super(WaveNet_vocoder, self).__init__()
+
+        # initial conv1x1 with relu
+        self.init_conv = nn.Sequential(Conv(in_channels, res_channels, kernel_size=1), nn.ReLU())
+        
+        # all residual layers
+        self.residual_layer = Residual_group(res_channels=res_channels, 
+                                             skip_channels=skip_channels, 
+                                             num_res_layers=num_res_layers, 
+                                             dilation_cycle=dilation_cycle,
+                                             noise_scale_embed_dim_in=noise_scale_embed_dim_in,
+                                             noise_scale_embed_dim_mid=noise_scale_embed_dim_mid,
+                                             noise_scale_embed_dim_out=noise_scale_embed_dim_out, multiband=multiband)
+        
+        # final conv1x1 -> relu -> zeroconv1x1
+        self.final_conv = nn.Sequential(Conv(skip_channels, skip_channels, kernel_size=1),
+                                        nn.ReLU(),
+                                        ZeroConv1d(skip_channels, out_channels))
+
+    def forward(self, input_data):
+        audio, mel_spectrogram, noise_scales = input_data  # b x band x T, b x 80 x T', b x 1
+        x = audio
+        x = self.init_conv(x)
+        x = self.residual_layer((x, mel_spectrogram, noise_scales))
+        x = self.final_conv(x)
+
+        return x
+