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 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoints/nsf_hifigan/model filter=lfs diff=lfs merge=lfs -text
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new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..33c8d678a2ee8c6e0c194133686b39b26c1c4f17
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+K_step: 1000
+accumulate_grad_batches: 1
+audio_num_mel_bins: 128
+audio_sample_rate: 44100
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_hubert: true
+  with_spk_embed: false
+  with_wav: false
+binarizer_cls: preprocessing.SVCpre.SVCBinarizer
+binary_data_dir: data/binary/aquapre
+check_val_every_n_epoch: 10
+choose_test_manually: false
+clip_grad_norm: 1
+config_path: F:\diff-svc-main\training\config_nsf.yaml
+content_cond_steps: []
+cwt_add_f0_loss: false
+cwt_hidden_size: 128
+cwt_layers: 2
+cwt_loss: l1
+cwt_std_scale: 0.8
+datasets:
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+debug: false
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diff --git a/checkpoints/0102_xiaoma_pe/config.yaml b/checkpoints/0102_xiaoma_pe/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..69a88444205377d48573d53bb4fb500860976588
--- /dev/null
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+accumulate_grad_batches: 1
+audio_num_mel_bins: 80
+audio_sample_rate: 24000
+base_config:
+- configs/tts/lj/fs2.yaml
+binarization_args:
+  shuffle: false
+  with_align: true
+  with_f0: true
+  with_f0cwt: true
+  with_spk_embed: true
+  with_txt: true
+  with_wav: false
+binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer
+binary_data_dir: data/binary/xiaoma1022_24k_128hop
+check_val_every_n_epoch: 10
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+dict_dir: ''
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+pitch_type: frame
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+  allow_no_txt: false
+  denoise: false
+  forced_align: mfa
+  txt_processor: en
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+  use_tone: true
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diff --git a/checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt b/checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
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diff --git a/checkpoints/hubert/hubert_soft.pt b/checkpoints/hubert/hubert_soft.pt
new file mode 100644
index 0000000000000000000000000000000000000000..5ccd36b11dc124c97a0b73fa5f39eed8d1a6f27a
--- /dev/null
+++ b/checkpoints/hubert/hubert_soft.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e82e7d079df05fe3aa535f6f7d42d309bdae1d2a53324e2b2386c56721f4f649
+size 378435957
diff --git a/checkpoints/nsf_hifigan/NOTICE.txt b/checkpoints/nsf_hifigan/NOTICE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..228fc663c20c3166dc16dca0b3b94dca38a489b8
--- /dev/null
+++ b/checkpoints/nsf_hifigan/NOTICE.txt
@@ -0,0 +1,74 @@
+--- DiffSinger Community Vocoder ---
+
+ARCHITECTURE: NSF-HiFiGAN
+RELEASE DATE: 2022-12-11
+
+HYPER PARAMETERS:
+ - 44100 sample rate
+ - 128 mel bins
+ - 512 hop size
+ - 2048 window size
+ - fmin at 40Hz
+ - fmax at 16000Hz
+
+
+NOTICE:
+
+All model weights in the [DiffSinger Community Vocoder Project](https://openvpi.github.io/vocoders/), including
+model weights in this directory, are provided by the [OpenVPI Team](https://github.com/openvpi/), under the
+[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
+
+
+ACKNOWLEDGEMENTS:
+
+Training data of this vocoder is provided and permitted by the following organizations, societies and individuals:
+
+孙飒              https://www.qfssr.cn
+赤松_Akamatsu     https://www.zhibin.club
+乐威              https://www.zhibin.club
+伯添              https://space.bilibili.com/24087011
+雲宇光             https://space.bilibili.com/660675050
+橙子言             https://space.bilibili.com/318486464
+人衣大人           https://space.bilibili.com/2270344
+玖蝶              https://space.bilibili.com/676771003
+Yuuko
+白夜零BYL          https://space.bilibili.com/1605040503
+嗷天              https://space.bilibili.com/5675252
+洛泠羽            https://space.bilibili.com/347373318
+灰条纹的灰猫君      https://space.bilibili.com/2083633
+幽寂              https://space.bilibili.com/478860
+恶魔王女           https://space.bilibili.com/2475098
+AlexYHX 芮晴
+绮萱              https://y.qq.com/n/ryqq/singer/003HjD6H4aZn1K
+诗芸              https://y.qq.com/n/ryqq/singer/0005NInj142zm0
+汐蕾              https://y.qq.com/n/ryqq/singer/0023cWMH1Bq1PJ
+1262917464
+炜阳
+叶卡yolka
+幸の夏            https://space.bilibili.com/1017297686
+暮色未量           https://space.bilibili.com/272904686
+晓寞sama          https://space.bilibili.com/3463394
+没头绪的节操君
+串串BunC          https://space.bilibili.com/95817834
+落雨              https://space.bilibili.com/1292427
+长尾巴的翎艾        https://space.bilibili.com/1638666
+声闻计划           https://space.bilibili.com/392812269
+唐家大小姐         http://5sing.kugou.com/palmusic/default.html
+不伊子
+
+Training machines are provided by:
+
+花儿不哭           https://space.bilibili.com/5760446
+
+
+TERMS OF REDISTRIBUTIONS:
+
+1. Do not sell this vocoder, or charge any fees from redistributing it, as prohibited by
+   the license.
+2. Include a copy of the CC BY-NC-SA 4.0 license, or a link referring to it.
+3. Include a copy of this notice, or any other notices informing that this vocoder is
+   provided by the OpenVPI Team, that this vocoder is licensed under CC BY-NC-SA 4.0, and
+   with a complete acknowledgement list as shown above.
+4. If you fine-tuned or modified the weights, leave a notice about what has been changed.
+5. (Optional) Leave a link to the official release page of the vocoder, and tell users
+   that other versions and future updates of this vocoder can be obtained from the website.
diff --git a/checkpoints/nsf_hifigan/config.json b/checkpoints/nsf_hifigan/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..79821fb218253a51b8bcbaa2eaed539a79c78d32
--- /dev/null
+++ b/checkpoints/nsf_hifigan/config.json
@@ -0,0 +1,38 @@
+{
+    "resblock": "1",
+    "num_gpus": 4,
+    "batch_size": 10,
+    "learning_rate": 0.0002,
+    "adam_b1": 0.8,
+    "adam_b2": 0.99,
+    "lr_decay": 0.999,
+    "seed": 1234,
+
+    "upsample_rates":        [ 8, 8, 2, 2, 2],
+    "upsample_kernel_sizes": [16,16, 4, 4, 4],
+    "upsample_initial_channel": 512,
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "discriminator_periods": [3, 5, 7, 11, 17, 23, 37],
+
+    "segment_size": 16384,
+    "num_mels": 128,
+    "num_freq": 1025,
+    "n_fft"   : 2048,
+    "hop_size": 512,
+    "win_size": 2048,
+
+    "sampling_rate": 44100,
+
+    "fmin": 40,
+    "fmax": 16000,
+    "fmax_for_loss": null,
+
+    "num_workers": 16,
+
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321",
+        "world_size": 1
+    }
+}
diff --git a/checkpoints/nsf_hifigan/model b/checkpoints/nsf_hifigan/model
new file mode 100644
index 0000000000000000000000000000000000000000..6ff8d81f7fe19ab507232cdd35667f3ccba9893c
--- /dev/null
+++ b/checkpoints/nsf_hifigan/model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c576b63b7ed952161b70fad34e0562ace502ce689195520d8a2a6c051de29d6
+size 56825430
diff --git a/infer.py b/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c3022270cc8e04cd1b7f48adbef2cf961bd7c6d
--- /dev/null
+++ b/infer.py
@@ -0,0 +1,81 @@
+import io
+from pathlib import Path
+
+import numpy as np
+import soundfile
+
+from infer_tools import infer_tool
+from infer_tools import slicer
+from infer_tools.infer_tool import Svc
+from utils.hparams import hparams
+
+
+def run_clip(raw_audio_path, svc_model, key, acc, use_crepe, spk_id=0, auto_key=False, out_path=None, slice_db=-40,
+             **kwargs):
+    print(f'code version:2023-01-22')
+
+    clean_name = Path(raw_audio_path).name.split(".")[0]
+    infer_tool.format_wav(raw_audio_path)
+    wav_path = Path(raw_audio_path).with_suffix('.wav')
+    key = svc_model.evaluate_key(wav_path, key, auto_key)
+    chunks = slicer.cut(wav_path, db_thresh=slice_db)
+    audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
+
+    count = 0
+    f0_tst, f0_pred, audio = [], [], []
+    for (slice_tag, data) in audio_data:
+        print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
+        length = int(np.ceil(len(data) / audio_sr * hparams['audio_sample_rate']))
+        raw_path = io.BytesIO()
+        soundfile.write(raw_path, data, audio_sr, format="wav")
+        raw_path.seek(0)
+        if slice_tag:
+            print('jump empty segment')
+            _f0_tst, _f0_pred, _audio = (
+                np.zeros(int(np.ceil(length / hparams['hop_size']))),
+                np.zeros(int(np.ceil(length / hparams['hop_size']))),
+                np.zeros(length))
+        else:
+            _f0_tst, _f0_pred, _audio = svc_model.infer(raw_path, spk_id=spk_id, key=key, acc=acc, use_crepe=use_crepe)
+        fix_audio = np.zeros(length)
+        fix_audio[:] = np.mean(_audio)
+        fix_audio[:len(_audio)] = _audio[0 if len(_audio) < len(fix_audio) else len(_audio) - len(fix_audio):]
+        f0_tst.extend(_f0_tst)
+        f0_pred.extend(_f0_pred)
+        audio.extend(list(fix_audio))
+        count += 1
+    if out_path is None:
+        out_path = f'./results/{clean_name}_{key}key_{project_name}_{hparams["residual_channels"]}_{hparams["residual_layers"]}_{int(step / 1000)}k_{accelerate}x.{kwargs["format"]}'
+    soundfile.write(out_path, audio, hparams["audio_sample_rate"], 'PCM_16', format=out_path.split('.')[-1])
+    return np.array(f0_tst), np.array(f0_pred), audio
+
+
+if __name__ == '__main__':
+    # 工程文件夹名，训练时用的那个
+    project_name = "open-aqua"
+    model_path = f'./checkpoints/{project_name}/model_ckpt_steps_90000.ckpt'
+    config_path = f'./checkpoints/{project_name}/config.yaml'
+
+    # 支持多个wav/ogg文件，放在raw文件夹下，带扩展名
+    file_names = ["横竖撇点折-main-2key.wav"]
+    spk_id = "single"
+    # 自适应变调（仅支持单人模型）
+    auto_key = False
+    trans = [0]  # 音高调整，支持正负（半音），数量与上一行对应，不足的自动按第一个移调参数补齐
+    # 加速倍数
+    accelerate = 1
+    hubert_gpu = True
+    wav_format = 'wav'
+    step = int(model_path.split("_")[-1].split(".")[0])
+
+    # 下面不动
+    infer_tool.mkdir(["./raw", "./results"])
+    infer_tool.fill_a_to_b(trans, file_names)
+
+    model = Svc(project_name, config_path, hubert_gpu, model_path, onnx=False)
+    for f_name, tran in zip(file_names, trans):
+        if "." not in f_name:
+            f_name += ".wav"
+        audio_path = f"./raw/{f_name}"
+        run_clip(raw_audio_path=audio_path, svc_model=model, key=tran, acc=accelerate, use_crepe=False,
+                 spk_id=spk_id, auto_key=auto_key, project_name=project_name, format=wav_format)
diff --git a/infer_tools/__pycache__/f0_static.cpython-38.pyc b/infer_tools/__pycache__/f0_static.cpython-38.pyc
new file mode 100644
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diff --git a/infer_tools/__pycache__/infer_tool_beta.cpython-38.pyc b/infer_tools/__pycache__/infer_tool_beta.cpython-38.pyc
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new file mode 100644
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diff --git a/infer_tools/__pycache__/trans_key.cpython-38.pyc b/infer_tools/__pycache__/trans_key.cpython-38.pyc
new file mode 100644
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diff --git a/infer_tools/f0_static.py b/infer_tools/f0_static.py
new file mode 100644
index 0000000000000000000000000000000000000000..f57d34819e4042244d5338393f43134f4e27aa22
--- /dev/null
+++ b/infer_tools/f0_static.py
@@ -0,0 +1,116 @@
+import json
+import os
+import shutil
+from functools import reduce
+from pathlib import Path
+
+import matplotlib
+import matplotlib.pyplot as plt
+import yaml
+from pylab import xticks, np
+from tqdm import tqdm
+
+from modules.vocoders.nsf_hifigan import NsfHifiGAN
+from preprocessing.process_pipeline import get_pitch_parselmouth, get_pitch_crepe
+from utils.hparams import set_hparams, hparams
+
+head_list = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
+
+
+def compare_pitch(f0_static_dict, pitch_time_temp, trans_key=0):
+    return sum({k: v * f0_static_dict[str(k + trans_key)] for k, v in pitch_time_temp.items() if
+                str(k + trans_key) in f0_static_dict}.values())
+
+
+def f0_to_pitch(ff):
+    f0_pitch = 69 + 12 * np.log2(ff / 440)
+    return round(f0_pitch, 0)
+
+
+def pitch_to_name(pitch):
+    return f"{head_list[int(pitch % 12)]}{int(pitch / 12) - 1}"
+
+
+def get_f0(audio_path, crepe=False):
+    wav, mel = NsfHifiGAN.wav2spec(audio_path)
+    if crepe:
+        f0, pitch_coarse = get_pitch_crepe(wav, mel, hparams)
+    else:
+        f0, pitch_coarse = get_pitch_parselmouth(wav, mel, hparams)
+    return f0
+
+
+def merge_f0_dict(dict_list):
+    def sum_dict(a, b):
+        temp = dict()
+        for key in a.keys() | b.keys():
+            temp[key] = sum([d.get(key, 0) for d in (a, b)])
+        return temp
+
+    return reduce(sum_dict, dict_list)
+
+
+def collect_f0(f0):
+    pitch_num = {}
+    pitch_list = [f0_to_pitch(x) for x in f0[f0 > 0]]
+    for key in pitch_list:
+        pitch_num[key] = pitch_num.get(key, 0) + 1
+    return pitch_num
+
+
+def static_f0_time(f0):
+    if isinstance(f0, dict):
+        pitch_num = merge_f0_dict({k: collect_f0(v) for k, v in f0.items()}.values())
+    else:
+        pitch_num = collect_f0(f0)
+    static_pitch_time = {}
+    sort_key = sorted(pitch_num.keys())
+    for key in sort_key:
+        static_pitch_time[key] = round(pitch_num[key] * hparams['hop_size'] / hparams['audio_sample_rate'], 2)
+    return static_pitch_time
+
+
+def get_end_file(dir_path, end):
+    file_lists = []
+    for root, dirs, files in os.walk(dir_path):
+        files = [f for f in files if f[0] != '.']
+        dirs[:] = [d for d in dirs if d[0] != '.']
+        for f_file in files:
+            if f_file.endswith(end):
+                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
+    return file_lists
+
+
+if __name__ == "__main__":
+    # 给config文件增加f0_static统计音域
+    config_path = "F:/sovits/diff-svc-main/checkpoints/aquapre/config.yaml"
+    hparams = set_hparams(config=config_path, exp_name='', infer=True, reset=True, hparams_str='', print_hparams=False)
+    f0_dict = {}
+    # 获取batch文件夹下所有wav文件
+    wav_paths = get_end_file("F:/sovits/diff-svc-main/batch/aquapre", "wav")
+    # parselmouth获取f0
+    with tqdm(total=len(wav_paths)) as p_bar:
+        p_bar.set_description('Processing')
+        for wav_path in wav_paths:
+            f0_dict[wav_path] = get_f0(wav_path, crepe=False)
+            p_bar.update(1)
+    pitch_time = static_f0_time(f0_dict)
+    total_time = round(sum(pitch_time.values()), 2)
+    pitch_time["total_time"] = total_time
+    print(f"total time: {total_time}s")
+    shutil.copy(config_path, f"{Path(config_path).parent}\\back_{Path(config_path).name}")
+    with open(config_path, encoding='utf-8') as f:
+        _hparams = yaml.safe_load(f)
+        _hparams['f0_static'] = json.dumps(pitch_time)
+    with open(config_path, 'w', encoding='utf-8') as f:
+        yaml.safe_dump(_hparams, f)
+        print("原config文件已在原目录建立备份：back_config.yaml")
+        print("音域统计已保存至config文件，此模型可使用自动变调功能")
+    matplotlib.use('TkAgg')
+    plt.title("数据集音域统计", fontproperties='SimHei')
+    plt.xlabel("音高", fontproperties='SimHei')
+    plt.ylabel("时长(s)", fontproperties='SimHei')
+    xticks_labels = [pitch_to_name(i) for i in range(36, 96)]
+    xticks(np.linspace(36, 96, 60, endpoint=True), xticks_labels)
+    plt.plot(pitch_time.keys(), pitch_time.values(), color='dodgerblue')
+    plt.show()
diff --git a/infer_tools/f0_temp.json b/infer_tools/f0_temp.json
new file mode 100644
index 0000000000000000000000000000000000000000..5946c83dfe2a57dcc0d279a07ae70bbcac63effd
--- /dev/null
+++ b/infer_tools/f0_temp.json
@@ -0,0 +1 @@
+{"info": "temp_dict", "75ab3602becd60cdd4b9d75093d2103c": {"f0": [164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.2452392578125, 164.03901343659209, 163.34630918610935, 163.34198070707777, 163.78470908694246, 163.75670854718754, 163.72870800743263, 163.70070746767772, 163.6727069279228, 163.64470638816792, 163.616705848413, 163.5887053086581, 163.56070476890318, 163.53270422914827, 163.50470368939335, 163.47670314963844, 163.44870260988355, 163.42070207012864, 163.39270153037373, 163.36470099061881, 163.3367004508639, 163.308699911109, 163.28069937135407, 163.2526988315992, 163.22469829184428, 163.19669775208936, 163.16869721233445, 163.14069667257954, 163.11269613282462, 163.0846955930697, 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\ No newline at end of file
diff --git a/infer_tools/infer_tool.py b/infer_tools/infer_tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..1808524003615dc781046c01847a07a894405d8e
--- /dev/null
+++ b/infer_tools/infer_tool.py
@@ -0,0 +1,201 @@
+import json
+import os
+import time
+from io import BytesIO
+from pathlib import Path
+
+import librosa
+import numpy as np
+import soundfile
+import torch
+
+import utils
+from infer_tools.f0_static import compare_pitch, static_f0_time
+from modules.diff.diffusion import GaussianDiffusion
+from modules.diff.net import DiffNet
+from modules.vocoders.nsf_hifigan import NsfHifiGAN
+from preprocessing.hubertinfer import HubertEncoder
+from preprocessing.process_pipeline import File2Batch, get_pitch_parselmouth
+from utils.hparams import hparams, set_hparams
+from utils.pitch_utils import denorm_f0, norm_interp_f0
+
+
+def timeit(func):
+    def run(*args, **kwargs):
+        t = time.time()
+        res = func(*args, **kwargs)
+        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
+        return res
+
+    return run
+
+
+def format_wav(audio_path):
+    if Path(audio_path).suffix == '.wav':
+        return
+    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
+    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
+
+
+def fill_a_to_b(a, b):
+    if len(a) < len(b):
+        for _ in range(0, len(b) - len(a)):
+            a.append(a[0])
+
+
+def get_end_file(dir_path, end):
+    file_lists = []
+    for root, dirs, files in os.walk(dir_path):
+        files = [f for f in files if f[0] != '.']
+        dirs[:] = [d for d in dirs if d[0] != '.']
+        for f_file in files:
+            if f_file.endswith(end):
+                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
+    return file_lists
+
+
+def mkdir(paths: list):
+    for path in paths:
+        if not os.path.exists(path):
+            os.mkdir(path)
+
+
+class Svc:
+    def __init__(self, project_name, config_name, hubert_gpu, model_path, onnx=False):
+        self.project_name = project_name
+        self.DIFF_DECODERS = {
+            'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
+        }
+
+        self.model_path = model_path
+        self.dev = torch.device("cuda")
+
+        self._ = set_hparams(config=config_name, exp_name=self.project_name, infer=True,
+                             reset=True, hparams_str='', print_hparams=False)
+
+        hparams['hubert_gpu'] = hubert_gpu
+        self.hubert = HubertEncoder(hparams['hubert_path'], onnx=onnx)
+        self.model = GaussianDiffusion(
+            phone_encoder=self.hubert,
+            out_dims=hparams['audio_num_mel_bins'],
+            denoise_fn=self.DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
+            timesteps=hparams['timesteps'],
+            K_step=hparams['K_step'],
+            loss_type=hparams['diff_loss_type'],
+            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
+        )
+        utils.load_ckpt(self.model, self.model_path, 'model', force=True, strict=True)
+        self.model.cuda()
+        self.vocoder = NsfHifiGAN()
+
+    def infer(self, in_path, key, acc, spk_id=0, use_crepe=True, singer=False):
+        batch = self.pre(in_path, acc, spk_id, use_crepe)
+        batch['f0'] = batch['f0'] + (key / 12)
+        batch['f0'][batch['f0'] > np.log2(hparams['f0_max'])] = 0
+
+        @timeit
+        def diff_infer():
+            spk_embed = batch.get('spk_embed') if not hparams['use_spk_id'] else batch.get('spk_ids')
+            energy = batch.get('energy').cuda() if batch.get('energy') else None
+            if spk_embed is None:
+                spk_embed = torch.LongTensor([0])
+            diff_outputs = self.model(
+                hubert=batch['hubert'].cuda(), spk_embed_id=spk_embed.cuda(), mel2ph=batch['mel2ph'].cuda(),
+                f0=batch['f0'].cuda(), energy=energy, ref_mels=batch["mels"].cuda(), infer=True)
+            return diff_outputs
+
+        outputs = diff_infer()
+        batch['outputs'] = outputs['mel_out']
+        batch['mel2ph_pred'] = outputs['mel2ph']
+        batch['f0_gt'] = denorm_f0(batch['f0'], batch['uv'], hparams)
+        batch['f0_pred'] = outputs.get('f0_denorm')
+        return self.after_infer(batch, singer, in_path)
+
+    @timeit
+    def after_infer(self, prediction, singer, in_path):
+        for k, v in prediction.items():
+            if type(v) is torch.Tensor:
+                prediction[k] = v.cpu().numpy()
+
+        # remove paddings
+        mel_gt = prediction["mels"]
+        mel_gt_mask = np.abs(mel_gt).sum(-1) > 0
+
+        mel_pred = prediction["outputs"]
+        mel_pred_mask = np.abs(mel_pred).sum(-1) > 0
+        mel_pred = mel_pred[mel_pred_mask]
+        mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax'])
+
+        f0_gt = prediction.get("f0_gt")
+        f0_pred = prediction.get("f0_pred")
+        if f0_pred is not None:
+            f0_gt = f0_gt[mel_gt_mask]
+        if len(f0_pred) > len(mel_pred_mask):
+            f0_pred = f0_pred[:len(mel_pred_mask)]
+        f0_pred = f0_pred[mel_pred_mask]
+        torch.cuda.is_available() and torch.cuda.empty_cache()
+
+        if singer:
+            data_path = in_path.replace("batch", "singer_data")
+            mel_path = data_path[:-4] + "_mel.npy"
+            f0_path = data_path[:-4] + "_f0.npy"
+            np.save(mel_path, mel_pred)
+            np.save(f0_path, f0_pred)
+        wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred)
+        return f0_gt, f0_pred, wav_pred
+
+    def pre(self, wav_fn, accelerate, spk_id=0, use_crepe=True):
+        if isinstance(wav_fn, BytesIO):
+            item_name = self.project_name
+        else:
+            song_info = wav_fn.split('/')
+            item_name = song_info[-1].split('.')[-2]
+        temp_dict = {'wav_fn': wav_fn, 'spk_id': spk_id, 'id': 0}
+
+        temp_dict = File2Batch.temporary_dict2processed_input(item_name, temp_dict, self.hubert, infer=True,
+                                                              use_crepe=use_crepe)
+        hparams['pndm_speedup'] = accelerate
+        batch = File2Batch.processed_input2batch([getitem(temp_dict)])
+        return batch
+
+    def evaluate_key(self, wav_path, key, auto_key):
+        if "f0_static" in hparams.keys():
+            f0_static = json.loads(hparams['f0_static'])
+            wav, mel = self.vocoder.wav2spec(wav_path)
+            input_f0 = get_pitch_parselmouth(wav, mel, hparams)[0]
+            pitch_time_temp = static_f0_time(input_f0)
+            eval_dict = {}
+            for trans_key in range(-12, 12):
+                eval_dict[trans_key] = compare_pitch(f0_static, pitch_time_temp, trans_key=trans_key)
+            sort_key = sorted(eval_dict, key=eval_dict.get, reverse=True)[:5]
+            print(f"推荐移调:{sort_key}")
+            if auto_key:
+                print(f"自动变调已启用，您的输入key被{sort_key[0]}key覆盖，控制参数为auto_key")
+                return sort_key[0]
+        else:
+            print("config缺少f0_staic，无法使用自动变调，可通过infer_tools/data_static添加")
+        return key
+
+
+def getitem(item):
+    max_frames = hparams['max_frames']
+    spec = torch.Tensor(item['mel'])[:max_frames]
+    mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
+    f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
+    hubert = torch.Tensor(item['hubert'][:hparams['max_input_tokens']])
+    pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
+    sample = {
+        "id": item['id'],
+        "spk_id": item['spk_id'],
+        "item_name": item['item_name'],
+        "hubert": hubert,
+        "mel": spec,
+        "pitch": pitch,
+        "f0": f0,
+        "uv": uv,
+        "mel2ph": mel2ph,
+        "mel_nonpadding": spec.abs().sum(-1) > 0,
+    }
+    if hparams['use_energy_embed']:
+        sample['energy'] = item['energy']
+    return sample
diff --git a/infer_tools/infer_tool_beta.py b/infer_tools/infer_tool_beta.py
new file mode 100644
index 0000000000000000000000000000000000000000..d51cdfbf5db7ac0352fef055cbe14ad435d2e1ce
--- /dev/null
+++ b/infer_tools/infer_tool_beta.py
@@ -0,0 +1,229 @@
+import json
+import os
+import time
+from io import BytesIO
+from pathlib import Path
+
+import librosa
+import numpy as np
+import soundfile
+import torch
+
+import utils
+from infer_tools.f0_static import compare_pitch, static_f0_time
+from modules.diff.diffusion import GaussianDiffusion
+from modules.diff.net import DiffNet
+from modules.vocoders.nsf_hifigan import NsfHifiGAN
+from preprocessing.hubertinfer import HubertEncoder
+from preprocessing.process_pipeline import File2Batch, get_pitch_parselmouth
+from utils.hparams import hparams, set_hparams
+from utils.pitch_utils import denorm_f0, norm_interp_f0
+
+
+def timeit(func):
+    def run(*args, **kwargs):
+        t = time.time()
+        res = func(*args, **kwargs)
+        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
+        return res
+
+    return run
+
+
+def format_wav(audio_path):
+    if Path(audio_path).suffix == '.wav':
+        return
+    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
+    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
+
+
+def fill_a_to_b(a, b):
+    if len(a) < len(b):
+        for _ in range(0, len(b) - len(a)):
+            a.append(a[0])
+
+
+def get_end_file(dir_path, end):
+    file_lists = []
+    for root, dirs, files in os.walk(dir_path):
+        files = [f for f in files if f[0] != '.']
+        dirs[:] = [d for d in dirs if d[0] != '.']
+        for f_file in files:
+            if f_file.endswith(end):
+                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
+    return file_lists
+
+
+def mkdir(paths: list):
+    for path in paths:
+        if not os.path.exists(path):
+            os.mkdir(path)
+
+
+class Svcb:
+    def __init__(self, project_name, config_name, hubert_gpu, model_path, onnx=False):
+        self.project_name = project_name
+        self.DIFF_DECODERS = {
+            'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
+        }
+
+        self.model_path = model_path
+        self.dev = torch.device("cuda")
+
+        self._ = set_hparams(config=config_name, exp_name=self.project_name, infer=True,
+                             reset=True, hparams_str='', print_hparams=False)
+
+        self.mel_bins = hparams['audio_num_mel_bins']
+        hparams['hubert_gpu'] = hubert_gpu
+        self.hubert = HubertEncoder(hparams['hubert_path'], onnx=onnx)
+        self.model = GaussianDiffusion(
+            phone_encoder=self.hubert,
+            out_dims=self.mel_bins, denoise_fn=self.DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
+            timesteps=hparams['timesteps'],
+            K_step=hparams['K_step'],
+            loss_type=hparams['diff_loss_type'],
+            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
+        )
+        utils.load_ckpt(self.model, self.model_path, 'model', force=True, strict=True)
+        self.model.cuda()
+        self.vocoder = NsfHifiGAN()
+
+    # def process_batch_f0(batch_f0, hparams):
+    #     pitch_num = collect_f0(batch_f0)
+    #     pitch_time = {}
+    #     sort_key = sorted(pitch_num.keys())
+    #     for key in sort_key:
+    #         pitch_time[key] = round(pitch_num[key] * hparams['hop_size'] / hparams['audio_sample_rate'], 2)
+    #     return pitch_time
+
+    def infer_autokey(self, in_path, key, acc, spk_id=0, use_crepe=False):
+        batch, temp_dict = self.pre(in_path, acc, spk_id, use_crepe)
+        input_f0 = temp_dict['f0']
+        if "f0_static" in hparams.keys():
+            f0_static = json.loads(hparams['f0_static'])
+            pitch_time_temp = static_f0_time(input_f0)
+            eval_dict = {}
+            for trans_key in range(-12, 12):
+                eval_dict[trans_key] = compare_pitch(f0_static, pitch_time_temp, trans_key=trans_key)
+            sort_key = sorted(eval_dict, key=eval_dict.get, reverse=True)[:5]
+            print(f"推荐移调:{sort_key}")
+            print(f"自动变调已启用，您的输入key被{sort_key[0]}key覆盖，控制参数为auto_key")
+            if sort_key[0] > 6:
+                key = sort_key[0] + 6
+            else:
+                key = sort_key[0]
+        return key, in_path, batch
+
+    # def infer(self, in_path, key, acc, spk_id=0, use_crepe=True, singer=False):
+    #     batch = self.pre(in_path, acc, spk_id, use_crepe)
+
+    def infer(self, in_path, key, batch, singer=False):
+        batch['f0'] = batch['f0'] + (key / 12)
+        batch['f0'][batch['f0'] > np.log2(hparams['f0_max'])] = 0
+
+        @timeit
+        def diff_infer():
+            spk_embed = batch.get('spk_embed') if not hparams['use_spk_id'] else batch.get('spk_ids')
+            energy = batch.get('energy').cuda() if batch.get('energy') else None
+            if spk_embed is None:
+                spk_embed = torch.LongTensor([0])
+            diff_outputs = self.model(
+                hubert=batch['hubert'].cuda(), spk_embed_id=spk_embed.cuda(), mel2ph=batch['mel2ph'].cuda(),
+                f0=batch['f0'].cuda(), energy=energy, ref_mels=batch["mels"].cuda(), infer=True)
+            return diff_outputs
+
+        outputs = diff_infer()
+        batch['outputs'] = outputs['mel_out']
+        batch['mel2ph_pred'] = outputs['mel2ph']
+        batch['f0_gt'] = denorm_f0(batch['f0'], batch['uv'], hparams)
+        batch['f0_pred'] = outputs.get('f0_denorm')
+        return self.after_infer(batch, singer, in_path)
+
+    @timeit
+    def after_infer(self, prediction, singer, in_path):
+        for k, v in prediction.items():
+            if type(v) is torch.Tensor:
+                prediction[k] = v.cpu().numpy()
+
+        # remove paddings
+        mel_gt = prediction["mels"]
+        mel_gt_mask = np.abs(mel_gt).sum(-1) > 0
+
+        mel_pred = prediction["outputs"]
+        mel_pred_mask = np.abs(mel_pred).sum(-1) > 0
+        mel_pred = mel_pred[mel_pred_mask]
+        mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax'])
+
+        f0_gt = prediction.get("f0_gt")
+        f0_pred = prediction.get("f0_pred")
+        if f0_pred is not None:
+            f0_gt = f0_gt[mel_gt_mask]
+        if len(f0_pred) > len(mel_pred_mask):
+            f0_pred = f0_pred[:len(mel_pred_mask)]
+        f0_pred = f0_pred[mel_pred_mask]
+        torch.cuda.is_available() and torch.cuda.empty_cache()
+
+        if singer:
+            data_path = in_path.replace("batch", "singer_data")
+            mel_path = data_path[:-4] + "_mel.npy"
+            f0_path = data_path[:-4] + "_f0.npy"
+            np.save(mel_path, mel_pred)
+            np.save(f0_path, f0_pred)
+        wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred)
+        return f0_gt, f0_pred, wav_pred
+
+    def pre(self, wav_fn, accelerate, spk_id=0, use_crepe=True):
+        if isinstance(wav_fn, BytesIO):
+            item_name = self.project_name
+        else:
+            song_info = wav_fn.split('/')
+            item_name = song_info[-1].split('.')[-2]
+        temp_dict = {'wav_fn': wav_fn, 'spk_id': spk_id, 'id': 0}
+
+        temp_dict = File2Batch.temporary_dict2processed_input(item_name, temp_dict, self.hubert, infer=True,
+                                                              use_crepe=use_crepe)
+        hparams['pndm_speedup'] = accelerate
+        batch = File2Batch.processed_input2batch([getitem(temp_dict)])
+        return batch, temp_dict
+
+    def evaluate_key(self, wav_path, key, auto_key):
+        if "f0_static" in hparams.keys():
+            f0_static = json.loads(hparams['f0_static'])
+            wav, mel = self.vocoder.wav2spec(wav_path)
+            input_f0 = get_pitch_parselmouth(wav, mel, hparams)[0]
+            pitch_time_temp = static_f0_time(input_f0)
+            eval_dict = {}
+            for trans_key in range(-12, 12):
+                eval_dict[trans_key] = compare_pitch(f0_static, pitch_time_temp, trans_key=trans_key)
+            sort_key = sorted(eval_dict, key=eval_dict.get, reverse=True)[:5]
+            print(f"推荐移调:{sort_key}")
+            if auto_key:
+                print(f"自动变调已启用，您的输入key被{sort_key[0]}key覆盖，控制参数为auto_key")
+                return sort_key[0]
+        else:
+            print("config缺少f0_staic，无法使用自动变调，可通过infer_tools/data_static添加")
+        return key
+
+
+def getitem(item):
+    max_frames = hparams['max_frames']
+    spec = torch.Tensor(item['mel'])[:max_frames]
+    mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
+    f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
+    hubert = torch.Tensor(item['hubert'][:hparams['max_input_tokens']])
+    pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
+    sample = {
+        "id": item['id'],
+        "spk_id": item['spk_id'],
+        "item_name": item['item_name'],
+        "hubert": hubert,
+        "mel": spec,
+        "pitch": pitch,
+        "f0": f0,
+        "uv": uv,
+        "mel2ph": mel2ph,
+        "mel_nonpadding": spec.abs().sum(-1) > 0,
+    }
+    if hparams['use_energy_embed']:
+        sample['energy'] = item['energy']
+    return sample
diff --git a/infer_tools/slicer.py b/infer_tools/slicer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462
--- /dev/null
+++ b/infer_tools/slicer.py
@@ -0,0 +1,142 @@
+import librosa
+import torch
+import torchaudio
+
+
+class Slicer:
+    def __init__(self,
+                 sr: int,
+                 threshold: float = -40.,
+                 min_length: int = 5000,
+                 min_interval: int = 300,
+                 hop_size: int = 20,
+                 max_sil_kept: int = 5000):
+        if not min_length >= min_interval >= hop_size:
+            raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
+        if not max_sil_kept >= hop_size:
+            raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
+        min_interval = sr * min_interval / 1000
+        self.threshold = 10 ** (threshold / 20.)
+        self.hop_size = round(sr * hop_size / 1000)
+        self.win_size = min(round(min_interval), 4 * self.hop_size)
+        self.min_length = round(sr * min_length / 1000 / self.hop_size)
+        self.min_interval = round(min_interval / self.hop_size)
+        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+    def _apply_slice(self, waveform, begin, end):
+        if len(waveform.shape) > 1:
+            return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
+        else:
+            return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
+
+    # @timeit
+    def slice(self, waveform):
+        if len(waveform.shape) > 1:
+            samples = librosa.to_mono(waveform)
+        else:
+            samples = waveform
+        if samples.shape[0] <= self.min_length:
+            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
+        rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
+        sil_tags = []
+        silence_start = None
+        clip_start = 0
+        for i, rms in enumerate(rms_list):
+            # Keep looping while frame is silent.
+            if rms < self.threshold:
+                # Record start of silent frames.
+                if silence_start is None:
+                    silence_start = i
+                continue
+            # Keep looping while frame is not silent and silence start has not been recorded.
+            if silence_start is None:
+                continue
+            # Clear recorded silence start if interval is not enough or clip is too short
+            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
+            if not is_leading_silence and not need_slice_middle:
+                silence_start = None
+                continue
+            # Need slicing. Record the range of silent frames to be removed.
+            if i - silence_start <= self.max_sil_kept:
+                pos = rms_list[silence_start: i + 1].argmin() + silence_start
+                if silence_start == 0:
+                    sil_tags.append((0, pos))
+                else:
+                    sil_tags.append((pos, pos))
+                clip_start = pos
+            elif i - silence_start <= self.max_sil_kept * 2:
+                pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
+                pos += i - self.max_sil_kept
+                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                    clip_start = pos_r
+                else:
+                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
+                    clip_start = max(pos_r, pos)
+            else:
+                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                else:
+                    sil_tags.append((pos_l, pos_r))
+                clip_start = pos_r
+            silence_start = None
+        # Deal with trailing silence.
+        total_frames = rms_list.shape[0]
+        if silence_start is not None and total_frames - silence_start >= self.min_interval:
+            silence_end = min(total_frames, silence_start + self.max_sil_kept)
+            pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
+            sil_tags.append((pos, total_frames + 1))
+        # Apply and return slices.
+        if len(sil_tags) == 0:
+            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
+        else:
+            chunks = []
+            # 第一段静音并非从头开始，补上有声片段
+            if sil_tags[0][0]:
+                chunks.append(
+                    {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
+            for i in range(0, len(sil_tags)):
+                # 标识有声片段（跳过第一段）
+                if i:
+                    chunks.append({"slice": False,
+                                   "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
+                # 标识所有静音片段
+                chunks.append({"slice": True,
+                               "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
+            # 最后一段静音并非结尾，补上结尾片段
+            if sil_tags[-1][1] * self.hop_size < len(waveform):
+                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
+            chunk_dict = {}
+            for i in range(len(chunks)):
+                chunk_dict[str(i)] = chunks[i]
+            return chunk_dict
+
+
+def cut(audio_path, db_thresh=-30, min_len=5000):
+    audio, sr = librosa.load(audio_path, sr=None)
+    slicer = Slicer(
+        sr=sr,
+        threshold=db_thresh,
+        min_length=min_len
+    )
+    chunks = slicer.slice(audio)
+    return chunks
+
+
+def chunks2audio(audio_path, chunks):
+    chunks = dict(chunks)
+    audio, sr = torchaudio.load(audio_path)
+    if len(audio.shape) == 2 and audio.shape[1] >= 2:
+        audio = torch.mean(audio, dim=0).unsqueeze(0)
+    audio = audio.cpu().numpy()[0]
+    result = []
+    for k, v in chunks.items():
+        tag = v["split_time"].split(",")
+        if tag[0] != tag[1]:
+            result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
+    return result, sr
diff --git a/infer_tools/trans_key.py b/infer_tools/trans_key.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc4f30aa054ee20b228c193fa115f767cbbf7055
--- /dev/null
+++ b/infer_tools/trans_key.py
@@ -0,0 +1,67 @@
+import os
+head_list = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
+
+
+def trans_f0_seq(feature_pit, transform):
+    feature_pit = feature_pit * 2 ** (transform / 12)
+    return round(feature_pit, 1)
+
+
+def move_key(raw_data, mv_key):
+    head = raw_data[:-1]
+    body = int(raw_data[-1])
+    new_head_index = head_list.index(head) + mv_key
+    while new_head_index < 0:
+        body -= 1
+        new_head_index += 12
+    while new_head_index > 11:
+        body += 1
+        new_head_index -= 12
+    result_data = head_list[new_head_index] + str(body)
+    return result_data
+
+
+def trans_key(raw_data, key):
+    for i in raw_data:
+        note_seq_list = i["note_seq"].split(" ")
+        new_note_seq_list = []
+        for note_seq in note_seq_list:
+            if note_seq != "rest":
+                new_note_seq = move_key(note_seq, key)
+                new_note_seq_list.append(new_note_seq)
+            else:
+                new_note_seq_list.append(note_seq)
+        i["note_seq"] = " ".join(new_note_seq_list)
+
+        f0_seq_list = i["f0_seq"].split(" ")
+        f0_seq_list = [float(x) for x in f0_seq_list]
+        new_f0_seq_list = []
+        for f0_seq in f0_seq_list:
+            new_f0_seq = trans_f0_seq(f0_seq, key)
+            new_f0_seq_list.append(str(new_f0_seq))
+        i["f0_seq"] = " ".join(new_f0_seq_list)
+    return raw_data
+
+
+def trans_opencpop(raw_txt, res_txt, key):
+    if os.path.exists(raw_txt):
+        f_w = open(res_txt, "w", encoding='utf-8')
+        with open(raw_txt, "r", encoding='utf-8') as f:
+            raw_data = f.readlines()
+            for raw in raw_data:
+                raw_list = raw.split("|")
+                new_note_seq_list = []
+                for note_seq in raw_list[3].split(" "):
+                    if note_seq != "rest":
+                        note_seq = note_seq.split("/")[0] if "/" in note_seq else note_seq
+                        new_note_seq = move_key(note_seq, key)
+                        new_note_seq_list.append(new_note_seq)
+                    else:
+                        new_note_seq_list.append(note_seq)
+                raw_list[3] = " ".join(new_note_seq_list)
+                f_w.write("|".join(raw_list))
+        f_w.close()
+        print("opencpop标注文件转换完毕")
+    else:
+        print("未发现opencpop标注文件，请检查路径")
+
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diff --git a/modules/commons/common_layers.py b/modules/commons/common_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a3ee3a6d95c193ee7ceb0ec0568eeb366b5d6b0
--- /dev/null
+++ b/modules/commons/common_layers.py
@@ -0,0 +1,675 @@
+import math
+
+import torch
+import torch.nn.functional as F
+import torch.onnx.operators
+from torch import nn
+from torch.nn import Parameter
+
+import utils
+
+
+class Reshape(nn.Module):
+    def __init__(self, *args):
+        super(Reshape, self).__init__()
+        self.shape = args
+
+    def forward(self, x):
+        return x.view(self.shape)
+
+
+class Permute(nn.Module):
+    def __init__(self, *args):
+        super(Permute, self).__init__()
+        self.args = args
+
+    def forward(self, x):
+        return x.permute(self.args)
+
+
+class LinearNorm(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
+        super(LinearNorm, self).__init__()
+        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, x):
+        return self.linear_layer(x)
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
+                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
+        super(ConvNorm, self).__init__()
+        if padding is None:
+            assert (kernel_size % 2 == 1)
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(in_channels, out_channels,
+                                    kernel_size=kernel_size, stride=stride,
+                                    padding=padding, dilation=dilation,
+                                    bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, signal):
+        conv_signal = self.conv(signal)
+        return conv_signal
+
+
+def Embedding(num_embeddings, embedding_dim, padding_idx=None):
+    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
+    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
+    if padding_idx is not None:
+        nn.init.constant_(m.weight[padding_idx], 0)
+    return m
+
+
+def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
+    if not export and torch.cuda.is_available():
+        try:
+            from apex.normalization import FusedLayerNorm
+            return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
+        except ImportError:
+            pass
+    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
+
+
+def Linear(in_features, out_features, bias=True):
+    m = nn.Linear(in_features, out_features, bias)
+    nn.init.xavier_uniform_(m.weight)
+    if bias:
+        nn.init.constant_(m.bias, 0.)
+    return m
+
+
+class SinusoidalPositionalEmbedding(nn.Module):
+    """This module produces sinusoidal positional embeddings of any length.
+
+    Padding symbols are ignored.
+    """
+
+    def __init__(self, embedding_dim, padding_idx, init_size=1024):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.weights = SinusoidalPositionalEmbedding.get_embedding(
+            init_size,
+            embedding_dim,
+            padding_idx,
+        )
+        self.register_buffer('_float_tensor', torch.FloatTensor(1))
+
+    @staticmethod
+    def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
+        """Build sinusoidal embeddings.
+
+        This matches the implementation in tensor2tensor, but differs slightly
+        from the description in Section 3.5 of "Attention Is All You Need".
+        """
+        half_dim = embedding_dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
+        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+        if embedding_dim % 2 == 1:
+            # zero pad
+            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+        if padding_idx is not None:
+            emb[padding_idx, :] = 0
+        return emb
+
+    def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs):
+        """Input is expected to be of size [bsz x seqlen]."""
+        bsz, seq_len = input.shape[:2]
+        max_pos = self.padding_idx + 1 + seq_len
+        if self.weights is None or max_pos > self.weights.size(0):
+            # recompute/expand embeddings if needed
+            self.weights = SinusoidalPositionalEmbedding.get_embedding(
+                max_pos,
+                self.embedding_dim,
+                self.padding_idx,
+            )
+        self.weights = self.weights.to(self._float_tensor)
+
+        if incremental_state is not None:
+            # positions is the same for every token when decoding a single step
+            pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
+            return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
+
+        positions = utils.make_positions(input, self.padding_idx) if positions is None else positions
+        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
+
+    def max_positions(self):
+        """Maximum number of supported positions."""
+        return int(1e5)  # an arbitrary large number
+
+
+class ConvTBC(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, padding=0):
+        super(ConvTBC, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.padding = padding
+
+        self.weight = torch.nn.Parameter(torch.Tensor(
+            self.kernel_size, in_channels, out_channels))
+        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
+
+    def forward(self, input):
+        return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding)
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
+                 add_bias_kv=False, add_zero_attn=False, self_attention=False,
+                 encoder_decoder_attention=False):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim ** -0.5
+
+        self.self_attention = self_attention
+        self.encoder_decoder_attention = encoder_decoder_attention
+
+        assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \
+                                                             'value to be of the same size'
+
+        if self.qkv_same_dim:
+            self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
+        else:
+            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
+            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
+            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
+
+        if bias:
+            self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
+        else:
+            self.register_parameter('in_proj_bias', None)
+
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
+            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self.reset_parameters()
+
+        self.enable_torch_version = False
+        if hasattr(F, "multi_head_attention_forward"):
+            self.enable_torch_version = True
+        else:
+            self.enable_torch_version = False
+        self.last_attn_probs = None
+
+    def reset_parameters(self):
+        if self.qkv_same_dim:
+            nn.init.xavier_uniform_(self.in_proj_weight)
+        else:
+            nn.init.xavier_uniform_(self.k_proj_weight)
+            nn.init.xavier_uniform_(self.v_proj_weight)
+            nn.init.xavier_uniform_(self.q_proj_weight)
+
+        nn.init.xavier_uniform_(self.out_proj.weight)
+        if self.in_proj_bias is not None:
+            nn.init.constant_(self.in_proj_bias, 0.)
+            nn.init.constant_(self.out_proj.bias, 0.)
+        if self.bias_k is not None:
+            nn.init.xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            nn.init.xavier_normal_(self.bias_v)
+
+    def forward(
+            self,
+            query, key, value,
+            key_padding_mask=None,
+            incremental_state=None,
+            need_weights=True,
+            static_kv=False,
+            attn_mask=None,
+            before_softmax=False,
+            need_head_weights=False,
+            enc_dec_attn_constraint_mask=None,
+            reset_attn_weight=None
+    ):
+        """Input shape: Time x Batch x Channel
+
+        Args:
+            key_padding_mask (ByteTensor, optional): mask to exclude
+                keys that are pads, of shape `(batch, src_len)`, where
+                padding elements are indicated by 1s.
+            need_weights (bool, optional): return the attention weights,
+                averaged over heads (default: False).
+            attn_mask (ByteTensor, optional): typically used to
+                implement causal attention, where the mask prevents the
+                attention from looking forward in time (default: None).
+            before_softmax (bool, optional): return the raw attention
+                weights and values before the attention softmax.
+            need_head_weights (bool, optional): return the attention
+                weights for each head. Implies *need_weights*. Default:
+                return the average attention weights over all heads.
+        """
+        if need_head_weights:
+            need_weights = True
+
+        tgt_len, bsz, embed_dim = query.size()
+        assert embed_dim == self.embed_dim
+        assert list(query.size()) == [tgt_len, bsz, embed_dim]
+
+        if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None:
+            if self.qkv_same_dim:
+                return F.multi_head_attention_forward(query, key, value,
+                                                      self.embed_dim, self.num_heads,
+                                                      self.in_proj_weight,
+                                                      self.in_proj_bias, self.bias_k, self.bias_v,
+                                                      self.add_zero_attn, self.dropout,
+                                                      self.out_proj.weight, self.out_proj.bias,
+                                                      self.training, key_padding_mask, need_weights,
+                                                      attn_mask)
+            else:
+                return F.multi_head_attention_forward(query, key, value,
+                                                      self.embed_dim, self.num_heads,
+                                                      torch.empty([0]),
+                                                      self.in_proj_bias, self.bias_k, self.bias_v,
+                                                      self.add_zero_attn, self.dropout,
+                                                      self.out_proj.weight, self.out_proj.bias,
+                                                      self.training, key_padding_mask, need_weights,
+                                                      attn_mask, use_separate_proj_weight=True,
+                                                      q_proj_weight=self.q_proj_weight,
+                                                      k_proj_weight=self.k_proj_weight,
+                                                      v_proj_weight=self.v_proj_weight)
+
+        if incremental_state is not None:
+            print('Not implemented error.')
+            exit()
+        else:
+            saved_state = None
+
+        if self.self_attention:
+            # self-attention
+            q, k, v = self.in_proj_qkv(query)
+        elif self.encoder_decoder_attention:
+            # encoder-decoder attention
+            q = self.in_proj_q(query)
+            if key is None:
+                assert value is None
+                k = v = None
+            else:
+                k = self.in_proj_k(key)
+                v = self.in_proj_v(key)
+
+        else:
+            q = self.in_proj_q(query)
+            k = self.in_proj_k(key)
+            v = self.in_proj_v(value)
+        q *= self.scaling
+
+        if self.bias_k is not None:
+            assert self.bias_v is not None
+            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
+            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
+
+        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+        if k is not None:
+            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+        if v is not None:
+            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+
+        if saved_state is not None:
+            print('Not implemented error.')
+            exit()
+
+        src_len = k.size(1)
+
+        # This is part of a workaround to get around fork/join parallelism
+        # not supporting Optional types.
+        if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]):
+            key_padding_mask = None
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            src_len += 1
+            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
+            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
+
+        attn_weights = torch.bmm(q, k.transpose(1, 2))
+        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
+
+        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            if len(attn_mask.shape) == 2:
+                attn_mask = attn_mask.unsqueeze(0)
+            elif len(attn_mask.shape) == 3:
+                attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape(
+                    bsz * self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights + attn_mask
+
+        if enc_dec_attn_constraint_mask is not None:  # bs x head x L_kv
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.masked_fill(
+                enc_dec_attn_constraint_mask.unsqueeze(2).bool(),
+                -1e9,
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if key_padding_mask is not None:
+            # don't attend to padding symbols
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2),
+                -1e9,
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+
+        if before_softmax:
+            return attn_weights, v
+
+        attn_weights_float = utils.softmax(attn_weights, dim=-1)
+        attn_weights = attn_weights_float.type_as(attn_weights)
+        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
+
+        if reset_attn_weight is not None:
+            if reset_attn_weight:
+                self.last_attn_probs = attn_probs.detach()
+            else:
+                assert self.last_attn_probs is not None
+                attn_probs = self.last_attn_probs
+        attn = torch.bmm(attn_probs, v)
+        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+        attn = self.out_proj(attn)
+
+        if need_weights:
+            attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
+            if not need_head_weights:
+                # average attention weights over heads
+                attn_weights = attn_weights.mean(dim=0)
+        else:
+            attn_weights = None
+
+        return attn, (attn_weights, attn_logits)
+
+    def in_proj_qkv(self, query):
+        return self._in_proj(query).chunk(3, dim=-1)
+
+    def in_proj_q(self, query):
+        if self.qkv_same_dim:
+            return self._in_proj(query, end=self.embed_dim)
+        else:
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[:self.embed_dim]
+            return F.linear(query, self.q_proj_weight, bias)
+
+    def in_proj_k(self, key):
+        if self.qkv_same_dim:
+            return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
+        else:
+            weight = self.k_proj_weight
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[self.embed_dim:2 * self.embed_dim]
+            return F.linear(key, weight, bias)
+
+    def in_proj_v(self, value):
+        if self.qkv_same_dim:
+            return self._in_proj(value, start=2 * self.embed_dim)
+        else:
+            weight = self.v_proj_weight
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[2 * self.embed_dim:]
+            return F.linear(value, weight, bias)
+
+    def _in_proj(self, input, start=0, end=None):
+        weight = self.in_proj_weight
+        bias = self.in_proj_bias
+        weight = weight[start:end, :]
+        if bias is not None:
+            bias = bias[start:end]
+        return F.linear(input, weight, bias)
+
+    def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
+        return attn_weights
+
+
+class Swish(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, i):
+        result = i * torch.sigmoid(i)
+        ctx.save_for_backward(i)
+        return result
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        i = ctx.saved_variables[0]
+        sigmoid_i = torch.sigmoid(i)
+        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
+
+
+class CustomSwish(nn.Module):
+    def forward(self, input_tensor):
+        return Swish.apply(input_tensor)
+
+
+class Mish(nn.Module):
+    def forward(self, x):
+        return x * torch.tanh(F.softplus(x))
+
+
+class TransformerFFNLayer(nn.Module):
+    def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.dropout = dropout
+        self.act = act
+        if padding == 'SAME':
+            self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2)
+        elif padding == 'LEFT':
+            self.ffn_1 = nn.Sequential(
+                nn.ConstantPad1d((kernel_size - 1, 0), 0.0),
+                nn.Conv1d(hidden_size, filter_size, kernel_size)
+            )
+        self.ffn_2 = Linear(filter_size, hidden_size)
+        if self.act == 'swish':
+            self.swish_fn = CustomSwish()
+
+    def forward(self, x, incremental_state=None):
+        # x: T x B x C
+        if incremental_state is not None:
+            assert incremental_state is None, 'Nar-generation does not allow this.'
+            exit(1)
+
+        x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1)
+        x = x * self.kernel_size ** -0.5
+
+        if incremental_state is not None:
+            x = x[-1:]
+        if self.act == 'gelu':
+            x = F.gelu(x)
+        if self.act == 'relu':
+            x = F.relu(x)
+        if self.act == 'swish':
+            x = self.swish_fn(x)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = self.ffn_2(x)
+        return x
+
+
+class BatchNorm1dTBC(nn.Module):
+    def __init__(self, c):
+        super(BatchNorm1dTBC, self).__init__()
+        self.bn = nn.BatchNorm1d(c)
+
+    def forward(self, x):
+        """
+
+        :param x: [T, B, C]
+        :return: [T, B, C]
+        """
+        x = x.permute(1, 2, 0)  # [B, C, T]
+        x = self.bn(x)  # [B, C, T]
+        x = x.permute(2, 0, 1)  # [T, B, C]
+        return x
+
+
+class EncSALayer(nn.Module):
+    def __init__(self, c, num_heads, dropout, attention_dropout=0.1,
+                 relu_dropout=0.1, kernel_size=9, padding='SAME', norm='ln', act='gelu'):
+        super().__init__()
+        self.c = c
+        self.dropout = dropout
+        self.num_heads = num_heads
+        if num_heads > 0:
+            if norm == 'ln':
+                self.layer_norm1 = LayerNorm(c)
+            elif norm == 'bn':
+                self.layer_norm1 = BatchNorm1dTBC(c)
+            self.self_attn = MultiheadAttention(
+                self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False,
+            )
+        if norm == 'ln':
+            self.layer_norm2 = LayerNorm(c)
+        elif norm == 'bn':
+            self.layer_norm2 = BatchNorm1dTBC(c)
+        self.ffn = TransformerFFNLayer(
+            c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act)
+
+    def forward(self, x, encoder_padding_mask=None, **kwargs):
+        layer_norm_training = kwargs.get('layer_norm_training', None)
+        if layer_norm_training is not None:
+            self.layer_norm1.training = layer_norm_training
+            self.layer_norm2.training = layer_norm_training
+        if self.num_heads > 0:
+            residual = x
+            x = self.layer_norm1(x)
+            x, _, = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=encoder_padding_mask
+            )
+            x = F.dropout(x, self.dropout, training=self.training)
+            x = residual + x
+            x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
+
+        residual = x
+        x = self.layer_norm2(x)
+        x = self.ffn(x)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+        x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
+        return x
+
+
+class DecSALayer(nn.Module):
+    def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'):
+        super().__init__()
+        self.c = c
+        self.dropout = dropout
+        self.layer_norm1 = LayerNorm(c)
+        self.self_attn = MultiheadAttention(
+            c, num_heads, self_attention=True, dropout=attention_dropout, bias=False
+        )
+        self.layer_norm2 = LayerNorm(c)
+        self.encoder_attn = MultiheadAttention(
+            c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False,
+        )
+        self.layer_norm3 = LayerNorm(c)
+        self.ffn = TransformerFFNLayer(
+            c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act)
+
+    def forward(
+            self,
+            x,
+            encoder_out=None,
+            encoder_padding_mask=None,
+            incremental_state=None,
+            self_attn_mask=None,
+            self_attn_padding_mask=None,
+            attn_out=None,
+            reset_attn_weight=None,
+            **kwargs,
+    ):
+        layer_norm_training = kwargs.get('layer_norm_training', None)
+        if layer_norm_training is not None:
+            self.layer_norm1.training = layer_norm_training
+            self.layer_norm2.training = layer_norm_training
+            self.layer_norm3.training = layer_norm_training
+        residual = x
+        x = self.layer_norm1(x)
+        x, _ = self.self_attn(
+            query=x,
+            key=x,
+            value=x,
+            key_padding_mask=self_attn_padding_mask,
+            incremental_state=incremental_state,
+            attn_mask=self_attn_mask
+        )
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+
+        residual = x
+        x = self.layer_norm2(x)
+        if encoder_out is not None:
+            x, attn = self.encoder_attn(
+                query=x,
+                key=encoder_out,
+                value=encoder_out,
+                key_padding_mask=encoder_padding_mask,
+                incremental_state=incremental_state,
+                static_kv=True,
+                enc_dec_attn_constraint_mask=None,
+                # utils.get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'),
+                reset_attn_weight=reset_attn_weight
+            )
+            attn_logits = attn[1]
+        else:
+            assert attn_out is not None
+            x = self.encoder_attn.in_proj_v(attn_out.transpose(0, 1))
+            attn_logits = None
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+
+        residual = x
+        x = self.layer_norm3(x)
+        x = self.ffn(x, incremental_state=incremental_state)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+        # if len(attn_logits.size()) > 3:
+        #    indices = attn_logits.softmax(-1).max(-1).values.sum(-1).argmax(-1)
+        #    attn_logits = attn_logits.gather(1,
+        #        indices[:, None, None, None].repeat(1, 1, attn_logits.size(-2), attn_logits.size(-1))).squeeze(1)
+        return x, attn_logits
diff --git a/modules/commons/ssim.py b/modules/commons/ssim.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f77c95803206138dd05095a037fed5acb1c4112
--- /dev/null
+++ b/modules/commons/ssim.py
@@ -0,0 +1,84 @@
+"""
+Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim
+"""
+
+from math import exp
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+    return window
+
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
+
+    C1 = 0.01 ** 2
+    C2 = 0.03 ** 2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1)
+
+
+class SSIM(torch.nn.Module):
+    def __init__(self, window_size=11, size_average=True):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = 1
+        self.window = create_window(window_size, self.channel)
+
+    def forward(self, img1, img2):
+        (_, channel, _, _) = img1.size()
+
+        if channel == self.channel and self.window.data.type() == img1.data.type():
+            window = self.window
+        else:
+            window = create_window(self.window_size, channel)
+
+            if img1.is_cuda:
+                window = window.cuda(img1.get_device())
+            window = window.type_as(img1)
+
+            self.window = window
+            self.channel = channel
+
+        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
+
+
+window = None
+
+
+def ssim(img1, img2, window_size=11, size_average=True):
+    (_, channel, _, _) = img1.size()
+    global window
+    if window is None:
+        window = create_window(window_size, channel)
+        if img1.is_cuda:
+            window = window.cuda(img1.get_device())
+        window = window.type_as(img1)
+    return _ssim(img1, img2, window, window_size, channel, size_average)
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diff --git a/modules/diff/diffusion.py b/modules/diff/diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d632df5fff17b64f2eb9932a891611ec9447738
--- /dev/null
+++ b/modules/diff/diffusion.py
@@ -0,0 +1,312 @@
+from collections import deque
+from functools import partial
+from inspect import isfunction
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from tqdm import tqdm
+
+from modules.encoder import SvcEncoder
+from training.train_pipeline import Batch2Loss
+from utils.hparams import hparams
+
+
+def exists(x):
+    return x is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+# gaussian diffusion trainer class
+
+def extract(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
+
+
+def linear_beta_schedule(timesteps, max_beta=hparams.get('max_beta', 0.01)):
+    """
+    linear schedule
+    """
+    betas = np.linspace(1e-4, max_beta, timesteps)
+    return betas
+
+
+def cosine_beta_schedule(timesteps, s=0.008):
+    """
+    cosine schedule
+    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
+    """
+    steps = timesteps + 1
+    x = np.linspace(0, steps, steps)
+    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
+    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
+    return np.clip(betas, a_min=0, a_max=0.999)
+
+
+beta_schedule = {
+    "cosine": cosine_beta_schedule,
+    "linear": linear_beta_schedule,
+}
+
+
+class GaussianDiffusion(nn.Module):
+    def __init__(self, phone_encoder, out_dims, denoise_fn,
+                 timesteps=1000, K_step=1000, loss_type=hparams.get('diff_loss_type', 'l1'), betas=None, spec_min=None,
+                 spec_max=None):
+        super().__init__()
+        self.denoise_fn = denoise_fn
+        self.fs2 = SvcEncoder(phone_encoder, out_dims)
+        self.mel_bins = out_dims
+
+        if exists(betas):
+            betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
+        else:
+            if 'schedule_type' in hparams.keys():
+                betas = beta_schedule[hparams['schedule_type']](timesteps)
+            else:
+                betas = cosine_beta_schedule(timesteps)
+
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.K_step = K_step
+        self.loss_type = loss_type
+
+        self.noise_list = deque(maxlen=4)
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']])
+        self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']])
+
+    def q_mean_variance(self, x_start, t):
+        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, cond, clip_denoised: bool):
+        noise_pred = self.denoise_fn(x, t, cond=cond)
+        x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False):
+        """
+        Use the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778).
+        """
+
+        def get_x_pred(x, noise_t, t):
+            a_t = extract(self.alphas_cumprod, t, x.shape)
+            a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape)
+            a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
+
+            x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (
+                    a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
+            x_pred = x + x_delta
+
+            return x_pred
+
+        noise_list = self.noise_list
+        noise_pred = self.denoise_fn(x, t, cond=cond)
+
+        if len(noise_list) == 0:
+            x_pred = get_x_pred(x, noise_pred, t)
+            noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond)
+            noise_pred_prime = (noise_pred + noise_pred_prev) / 2
+        elif len(noise_list) == 1:
+            noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2
+        elif len(noise_list) == 2:
+            noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12
+        elif len(noise_list) >= 3:
+            noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24
+
+        x_prev = get_x_pred(x, noise_pred_prime, t)
+        noise_list.append(noise_pred)
+
+        return x_prev
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (
+                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def p_losses(self, x_start, t, cond, noise=None, nonpadding=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        x_recon = self.denoise_fn(x_noisy, t, cond)
+
+        if self.loss_type == 'l1':
+            if nonpadding is not None:
+                loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean()
+            else:
+                # print('are you sure w/o nonpadding?')
+                loss = (noise - x_recon).abs().mean()
+
+        elif self.loss_type == 'l2':
+            loss = F.mse_loss(noise, x_recon)
+        else:
+            raise NotImplementedError()
+
+        return loss
+
+    def forward(self, hubert, mel2ph=None, spk_embed=None,
+                ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs):
+        '''
+            conditioning diffusion, use fastspeech2 encoder output as the condition
+        '''
+        ret = self.fs2(hubert, mel2ph, spk_embed, None, f0, uv, energy,
+                       skip_decoder=True, infer=infer, **kwargs)
+        cond = ret['decoder_inp'].transpose(1, 2)
+        b, *_, device = *hubert.shape, hubert.device
+
+        if not infer:
+            Batch2Loss.module4(
+                self.p_losses,
+                self.norm_spec(ref_mels), cond, ret, self.K_step, b, device
+            )
+        else:
+            if 'use_gt_mel' in kwargs.keys() and kwargs['use_gt_mel']:
+                t = kwargs['add_noise_step']
+                print('===>using ground truth mel as start, please make sure parameter "key==0" !')
+                fs2_mels = ref_mels
+                fs2_mels = self.norm_spec(fs2_mels)
+                fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :]
+                x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long())
+            else:
+                t = self.K_step
+                shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
+                x = torch.randn(shape, device=device)
+            if hparams.get('pndm_speedup') and hparams['pndm_speedup'] > 1:
+                self.noise_list = deque(maxlen=4)
+                iteration_interval = hparams['pndm_speedup']
+                for i in tqdm(reversed(range(0, t, iteration_interval)), desc='sample time step',
+                              total=t // iteration_interval):
+                    x = self.p_sample_plms(x, torch.full((b,), i, device=device, dtype=torch.long), iteration_interval,
+                                           cond)
+            else:
+                for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
+                    x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
+            x = x[:, 0].transpose(1, 2)
+            if mel2ph is not None:  # for singing
+                ret['mel_out'] = self.denorm_spec(x) * ((mel2ph > 0).float()[:, :, None])
+            else:
+                ret['mel_out'] = self.denorm_spec(x)
+        return ret
+
+    def norm_spec(self, x):
+        return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
+
+    def denorm_spec(self, x):
+        return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
+
+    def out2mel(self, x):
+        return x
+
+
+class OfflineGaussianDiffusion(GaussianDiffusion):
+    def forward(self, txt_tokens, mel2ph=None, spk_embed=None,
+                ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs):
+        b, *_, device = *txt_tokens.shape, txt_tokens.device
+
+        ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy,
+                       skip_decoder=True, infer=True, **kwargs)
+        cond = ret['decoder_inp'].transpose(1, 2)
+        fs2_mels = ref_mels[1]
+        ref_mels = ref_mels[0]
+
+        if not infer:
+            t = torch.randint(0, self.K_step, (b,), device=device).long()
+            x = ref_mels
+            x = self.norm_spec(x)
+            x = x.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
+            ret['diff_loss'] = self.p_losses(x, t, cond)
+        else:
+            t = self.K_step
+            fs2_mels = self.norm_spec(fs2_mels)
+            fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :]
+
+            x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long())
+
+            if hparams.get('gaussian_start') is not None and hparams['gaussian_start']:
+                print('===> gaussion start.')
+                shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2])
+                x = torch.randn(shape, device=device)
+            for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
+                x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
+            x = x[:, 0].transpose(1, 2)
+            ret['mel_out'] = self.denorm_spec(x)
+
+        return ret
diff --git a/modules/diff/net.py b/modules/diff/net.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c5aff1f6d5dba67d42d41f0bfe1845ba79f444b
--- /dev/null
+++ b/modules/diff/net.py
@@ -0,0 +1,135 @@
+import math
+from math import sqrt
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from modules.commons.common_layers import Mish
+from utils.hparams import hparams
+
+Linear = nn.Linear
+ConvTranspose2d = nn.ConvTranspose2d
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
+
+    def override(self, attrs):
+        if isinstance(attrs, dict):
+            self.__dict__.update(**attrs)
+        elif isinstance(attrs, (list, tuple, set)):
+            for attr in attrs:
+                self.override(attr)
+        elif attrs is not None:
+            raise NotImplementedError
+        return self
+
+
+class SinusoidalPosEmb(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
+        emb = x[:, None] * emb[None, :]
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+def Conv1d(*args, **kwargs):
+    layer = nn.Conv1d(*args, **kwargs)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+@torch.jit.script
+def silu(x):
+    return x * torch.sigmoid(x)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, encoder_hidden, residual_channels, dilation):
+        super().__init__()
+        self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
+        self.diffusion_projection = Linear(residual_channels, residual_channels)
+        self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1)
+        self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
+
+    def forward(self, x, conditioner, diffusion_step):
+        diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
+        conditioner = self.conditioner_projection(conditioner)
+        y = x + diffusion_step
+
+        y = self.dilated_conv(y) + conditioner
+
+        gate, filter = torch.chunk(y, 2, dim=1)
+        # Using torch.split instead of torch.chunk to avoid using onnx::Slice
+        # gate, filter = torch.split(y, torch.div(y.shape[1], 2), dim=1)
+
+        y = torch.sigmoid(gate) * torch.tanh(filter)
+
+        y = self.output_projection(y)
+        residual, skip = torch.chunk(y, 2, dim=1)
+        # Using torch.split instead of torch.chunk to avoid using onnx::Slice
+        # residual, skip = torch.split(y, torch.div(y.shape[1], 2), dim=1)
+
+        return (x + residual) / sqrt(2.0), skip
+
+
+class DiffNet(nn.Module):
+    def __init__(self, in_dims=80):
+        super().__init__()
+        self.params = params = AttrDict(
+            # Model params
+            encoder_hidden=hparams['hidden_size'],
+            residual_layers=hparams['residual_layers'],
+            residual_channels=hparams['residual_channels'],
+            dilation_cycle_length=hparams['dilation_cycle_length'],
+        )
+        self.input_projection = Conv1d(in_dims, params.residual_channels, 1)
+        self.diffusion_embedding = SinusoidalPosEmb(params.residual_channels)
+        dim = params.residual_channels
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, dim * 4),
+            Mish(),
+            nn.Linear(dim * 4, dim)
+        )
+        self.residual_layers = nn.ModuleList([
+            ResidualBlock(params.encoder_hidden, params.residual_channels, 2 ** (i % params.dilation_cycle_length))
+            for i in range(params.residual_layers)
+        ])
+        self.skip_projection = Conv1d(params.residual_channels, params.residual_channels, 1)
+        self.output_projection = Conv1d(params.residual_channels, in_dims, 1)
+        nn.init.zeros_(self.output_projection.weight)
+
+    def forward(self, spec, diffusion_step, cond):
+        """
+
+        :param spec: [B, 1, M, T]
+        :param diffusion_step: [B, 1]
+        :param cond: [B, M, T]
+        :return:
+        """
+        x = spec[:, 0]
+        x = self.input_projection(x)  # x [B, residual_channel, T]
+
+        x = F.relu(x)
+        diffusion_step = self.diffusion_embedding(diffusion_step)
+        diffusion_step = self.mlp(diffusion_step)
+        skip = []
+        for layer_id, layer in enumerate(self.residual_layers):
+            x, skip_connection = layer(x, cond, diffusion_step)
+            skip.append(skip_connection)
+
+        x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers))
+        x = self.skip_projection(x)
+        x = F.relu(x)
+        x = self.output_projection(x)  # [B, 80, T]
+        return x[:, None, :, :]
diff --git a/modules/encoder.py b/modules/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..4690e8024862ab97d68f0b62d66982c20da19ef9
--- /dev/null
+++ b/modules/encoder.py
@@ -0,0 +1,208 @@
+import torch
+
+from modules.commons.common_layers import *
+from modules.commons.common_layers import Embedding
+from modules.commons.common_layers import SinusoidalPositionalEmbedding
+from utils.hparams import hparams
+from utils.pitch_utils import f0_to_coarse, denorm_f0
+
+
+class LayerNorm(torch.nn.LayerNorm):
+    """Layer normalization module.
+    :param int nout: output dim size
+    :param int dim: dimension to be normalized
+    """
+
+    def __init__(self, nout, dim=-1):
+        """Construct an LayerNorm object."""
+        super(LayerNorm, self).__init__(nout, eps=1e-12)
+        self.dim = dim
+
+    def forward(self, x):
+        """Apply layer normalization.
+        :param torch.Tensor x: input tensor
+        :return: layer normalized tensor
+        :rtype torch.Tensor
+        """
+        if self.dim == -1:
+            return super(LayerNorm, self).forward(x)
+        return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
+
+
+class PitchPredictor(torch.nn.Module):
+    def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5,
+                 dropout_rate=0.1, padding='SAME'):
+        """Initilize pitch predictor module.
+        Args:
+            idim (int): Input dimension.
+            n_layers (int, optional): Number of convolutional layers.
+            n_chans (int, optional): Number of channels of convolutional layers.
+            kernel_size (int, optional): Kernel size of convolutional layers.
+            dropout_rate (float, optional): Dropout rate.
+        """
+        super(PitchPredictor, self).__init__()
+        self.conv = torch.nn.ModuleList()
+        self.kernel_size = kernel_size
+        self.padding = padding
+        for idx in range(n_layers):
+            in_chans = idim if idx == 0 else n_chans
+            self.conv += [torch.nn.Sequential(
+                torch.nn.ConstantPad1d(((kernel_size - 1) // 2, (kernel_size - 1) // 2)
+                                       if padding == 'SAME'
+                                       else (kernel_size - 1, 0), 0),
+                torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=0),
+                torch.nn.ReLU(),
+                LayerNorm(n_chans, dim=1),
+                torch.nn.Dropout(dropout_rate)
+            )]
+        self.linear = torch.nn.Linear(n_chans, odim)
+        self.embed_positions = SinusoidalPositionalEmbedding(idim, 0, init_size=4096)
+        self.pos_embed_alpha = nn.Parameter(torch.Tensor([1]))
+
+    def forward(self, xs):
+        """
+
+        :param xs: [B, T, H]
+        :return: [B, T, H]
+        """
+        positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0])
+        xs = xs + positions
+        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
+        for f in self.conv:
+            xs = f(xs)  # (B, C, Tmax)
+        # NOTE: calculate in log domain
+        xs = self.linear(xs.transpose(1, -1))  # (B, Tmax, H)
+        return xs
+
+
+class SvcEncoder(nn.Module):
+    def __init__(self, dictionary, out_dims=None):
+        super().__init__()
+        # self.dictionary = dictionary
+        self.padding_idx = 0
+        self.hidden_size = hparams['hidden_size']
+        self.out_dims = out_dims
+        if out_dims is None:
+            self.out_dims = hparams['audio_num_mel_bins']
+        self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
+        predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
+        if hparams['use_pitch_embed']:
+            self.pitch_embed = Embedding(300, self.hidden_size, self.padding_idx)
+            self.pitch_predictor = PitchPredictor(
+                self.hidden_size,
+                n_chans=predictor_hidden,
+                n_layers=hparams['predictor_layers'],
+                dropout_rate=hparams['predictor_dropout'],
+                odim=2 if hparams['pitch_type'] == 'frame' else 1,
+                padding=hparams['ffn_padding'], kernel_size=hparams['predictor_kernel'])
+        if hparams['use_energy_embed']:
+            self.energy_embed = Embedding(256, self.hidden_size, self.padding_idx)
+        if hparams['use_spk_id']:
+            self.spk_embed_proj = Embedding(hparams['num_spk'], self.hidden_size)
+            if hparams['use_split_spk_id']:
+                self.spk_embed_f0 = Embedding(hparams['num_spk'], self.hidden_size)
+                self.spk_embed_dur = Embedding(hparams['num_spk'], self.hidden_size)
+        elif hparams['use_spk_embed']:
+            self.spk_embed_proj = Linear(256, self.hidden_size, bias=True)
+
+    def forward(self, hubert, mel2ph=None, spk_embed=None,
+                ref_mels=None, f0=None, uv=None, energy=None, skip_decoder=True,
+                spk_embed_dur_id=None, spk_embed_f0_id=None, infer=False, **kwargs):
+        ret = {}
+        encoder_out = hubert
+        src_nonpadding = (hubert != 0).any(-1)[:, :, None]
+
+        # add ref style embed
+        # Not implemented
+        # variance encoder
+        var_embed = 0
+
+        # encoder_out_dur denotes encoder outputs for duration predictor
+        # in speech adaptation, duration predictor use old speaker embedding
+        if hparams['use_spk_embed']:
+            spk_embed_dur = spk_embed_f0 = spk_embed = self.spk_embed_proj(spk_embed)[:, None, :]
+        elif hparams['use_spk_id']:
+            spk_embed_id = spk_embed
+            if spk_embed_dur_id is None:
+                spk_embed_dur_id = spk_embed_id
+            if spk_embed_f0_id is None:
+                spk_embed_f0_id = spk_embed_id
+            spk_embed_0 = self.spk_embed_proj(spk_embed_id.to(hubert.device))[:, None, :]
+            spk_embed_1 = self.spk_embed_proj(torch.LongTensor([0]).to(hubert.device))[:, None, :]
+            spk_embed_2 = self.spk_embed_proj(torch.LongTensor([0]).to(hubert.device))[:, None, :]
+            spk_embed = 1 * spk_embed_0 + 0 * spk_embed_1 + 0 * spk_embed_2
+            spk_embed_dur = spk_embed_f0 = spk_embed
+            if hparams['use_split_spk_id']:
+                spk_embed_dur = self.spk_embed_dur(spk_embed_dur_id)[:, None, :]
+                spk_embed_f0 = self.spk_embed_f0(spk_embed_f0_id)[:, None, :]
+        else:
+            spk_embed_dur = spk_embed_f0 = spk_embed = 0
+
+        ret['mel2ph'] = mel2ph
+
+        decoder_inp = F.pad(encoder_out, [0, 0, 1, 0])
+
+        mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]])
+        decoder_inp_origin = decoder_inp = torch.gather(decoder_inp, 1, mel2ph_)  # [B, T, H]
+
+        tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
+
+        # add pitch and energy embed
+        pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding
+        if hparams['use_pitch_embed']:
+            pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding
+            decoder_inp = decoder_inp + self.add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph)
+        if hparams['use_energy_embed']:
+            decoder_inp = decoder_inp + self.add_energy(pitch_inp, energy, ret)
+
+        ret['decoder_inp'] = decoder_inp = (decoder_inp + spk_embed) * tgt_nonpadding
+        return ret
+
+    def add_dur(self, dur_input, mel2ph, hubert, ret):
+        src_padding = (hubert == 0).all(-1)
+        dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
+        if mel2ph is None:
+            dur, xs = self.dur_predictor.inference(dur_input, src_padding)
+            ret['dur'] = xs
+            ret['dur_choice'] = dur
+            mel2ph = self.length_regulator(dur, src_padding).detach()
+        else:
+            ret['dur'] = self.dur_predictor(dur_input, src_padding)
+        ret['mel2ph'] = mel2ph
+        return mel2ph
+
+    def run_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
+        x = decoder_inp  # [B, T, H]
+        x = self.mel_out(x)
+        return x * tgt_nonpadding
+
+    def out2mel(self, out):
+        return out
+
+    def add_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
+        decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+
+        pitch_padding = (mel2ph == 0)
+        ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding)
+        if pitch_padding is not None:
+            f0[pitch_padding] = 0
+
+        pitch = f0_to_coarse(f0_denorm, hparams)  # start from 0
+        ret['pitch_pred'] = pitch.unsqueeze(-1)
+        pitch_embedding = self.pitch_embed(pitch)
+        return pitch_embedding
+
+    def add_energy(self, decoder_inp, energy, ret):
+        decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+        ret['energy_pred'] = energy  # energy_pred = self.energy_predictor(decoder_inp)[:, :, 0]
+        energy = torch.clamp(energy * 256 // 4, max=255).long()  # energy_to_coarse
+        energy_embedding = self.energy_embed(energy)
+        return energy_embedding
+
+    @staticmethod
+    def mel_norm(x):
+        return (x + 5.5) / (6.3 / 2) - 1
+
+    @staticmethod
+    def mel_denorm(x):
+        return (x + 1) * (6.3 / 2) - 5.5
diff --git a/modules/hubert/__pycache__/cn_hubert.cpython-38.pyc b/modules/hubert/__pycache__/cn_hubert.cpython-38.pyc
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diff --git a/modules/hubert/__pycache__/hubert_onnx.cpython-38.pyc b/modules/hubert/__pycache__/hubert_onnx.cpython-38.pyc
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diff --git a/modules/hubert/cn_hubert.py b/modules/hubert/cn_hubert.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba1c34bc8ce8c3c638b846f2da1da0ca27a52121
--- /dev/null
+++ b/modules/hubert/cn_hubert.py
@@ -0,0 +1,40 @@
+import librosa
+import torch
+import torch.nn as nn
+
+
+def load_cn_model(ch_hubert_path):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    from fairseq import checkpoint_utils
+    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+        [ch_hubert_path],
+        suffix="",
+    )
+    model = models[0]
+    model = model.to(device)
+    model.eval()
+    return model
+
+
+def get_cn_hubert_units(con_model, audio_path, dev):
+    audio, sampling_rate = librosa.load(audio_path)
+    if len(audio.shape) > 1:
+        audio = librosa.to_mono(audio.transpose(1, 0))
+    if sampling_rate != 16000:
+        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+
+    feats = torch.from_numpy(audio).float()
+    if feats.dim() == 2:  # double channels
+        feats = feats.mean(-1)
+    assert feats.dim() == 1, feats.dim()
+    feats = feats.view(1, -1)
+    padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+    inputs = {
+        "source": feats.to(dev),
+        "padding_mask": padding_mask.to(dev),
+        "output_layer": 9,  # layer 9
+    }
+    with torch.no_grad():
+        logits = con_model.extract_features(**inputs)
+        feats = con_model.final_proj(logits[0])
+    return feats
diff --git a/modules/hubert/hubert_model.py b/modules/hubert/hubert_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b494c3c0e5ea2746f202af07f4b6a895aee5529f
--- /dev/null
+++ b/modules/hubert/hubert_model.py
@@ -0,0 +1,243 @@
+import copy
+import random
+from typing import Optional, Tuple
+
+import librosa
+import torch
+import torch.nn as nn
+import torch.nn.functional as t_func
+from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
+
+
+class Hubert(nn.Module):
+    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
+        super().__init__()
+        self._mask = mask
+        self.feature_extractor = FeatureExtractor()
+        self.feature_projection = FeatureProjection()
+        self.positional_embedding = PositionalConvEmbedding()
+        self.norm = nn.LayerNorm(768)
+        self.dropout = nn.Dropout(0.1)
+        self.encoder = TransformerEncoder(
+            nn.TransformerEncoderLayer(
+                768, 12, 3072, activation="gelu", batch_first=True
+            ),
+            12,
+        )
+        self.proj = nn.Linear(768, 256)
+
+        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
+        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
+
+    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        mask = None
+        if self.training and self._mask:
+            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
+            x[mask] = self.masked_spec_embed.to(x.dtype)
+        return x, mask
+
+    def encode(
+            self, x: torch.Tensor, layer: Optional[int] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        x = self.feature_extractor(x)
+        x = self.feature_projection(x.transpose(1, 2))
+        x, mask = self.mask(x)
+        x = x + self.positional_embedding(x)
+        x = self.dropout(self.norm(x))
+        x = self.encoder(x, output_layer=layer)
+        return x, mask
+
+    def logits(self, x: torch.Tensor) -> torch.Tensor:
+        logits = torch.cosine_similarity(
+            x.unsqueeze(2),
+            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
+            dim=-1,
+        )
+        return logits / 0.1
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        x, mask = self.encode(x)
+        x = self.proj(x)
+        logits = self.logits(x)
+        return logits, mask
+
+
+class HubertSoft(Hubert):
+    def __init__(self):
+        super().__init__()
+
+    # @torch.inference_mode()
+    def units(self, wav: torch.Tensor) -> torch.Tensor:
+        wav = torch.nn.functional.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
+        x, _ = self.encode(wav)
+        return self.proj(x)
+
+    def forward(self, wav: torch.Tensor):
+        return self.units(wav)
+
+
+class FeatureExtractor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
+        self.norm0 = nn.GroupNorm(512, 512)
+        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
+        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
+        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = t_func.gelu(self.norm0(self.conv0(x)))
+        x = t_func.gelu(self.conv1(x))
+        x = t_func.gelu(self.conv2(x))
+        x = t_func.gelu(self.conv3(x))
+        x = t_func.gelu(self.conv4(x))
+        x = t_func.gelu(self.conv5(x))
+        x = t_func.gelu(self.conv6(x))
+        return x
+
+
+class FeatureProjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.norm = nn.LayerNorm(512)
+        self.projection = nn.Linear(512, 768)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm(x)
+        x = self.projection(x)
+        x = self.dropout(x)
+        return x
+
+
+class PositionalConvEmbedding(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            768,
+            768,
+            kernel_size=128,
+            padding=128 // 2,
+            groups=16,
+        )
+        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x.transpose(1, 2))
+        x = t_func.gelu(x[:, :, :-1])
+        return x.transpose(1, 2)
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
+    ) -> None:
+        super(TransformerEncoder, self).__init__()
+        self.layers = nn.ModuleList(
+            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
+        )
+        self.num_layers = num_layers
+
+    def forward(
+            self,
+            src: torch.Tensor,
+            mask: torch.Tensor = None,
+            src_key_padding_mask: torch.Tensor = None,
+            output_layer: Optional[int] = None,
+    ) -> torch.Tensor:
+        output = src
+        for layer in self.layers[:output_layer]:
+            output = layer(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+        return output
+
+
+def _compute_mask(
+        shape: Tuple[int, int],
+        mask_prob: float,
+        mask_length: int,
+        device: torch.device,
+        min_masks: int = 0,
+) -> torch.Tensor:
+    batch_size, sequence_length = shape
+
+    if mask_length < 1:
+        raise ValueError("`mask_length` has to be bigger than 0.")
+
+    if mask_length > sequence_length:
+        raise ValueError(
+            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
+        )
+
+    # compute number of masked spans in batch
+    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
+    num_masked_spans = max(num_masked_spans, min_masks)
+
+    # make sure num masked indices <= sequence_length
+    if num_masked_spans * mask_length > sequence_length:
+        num_masked_spans = sequence_length // mask_length
+
+    # SpecAugment mask to fill
+    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
+
+    # uniform distribution to sample from, make sure that offset samples are < sequence_length
+    uniform_dist = torch.ones(
+        (batch_size, sequence_length - (mask_length - 1)), device=device
+    )
+
+    # get random indices to mask
+    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
+
+    # expand masked indices to masked spans
+    mask_indices = (
+        mask_indices.unsqueeze(dim=-1)
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    offsets = (
+        torch.arange(mask_length, device=device)[None, None, :]
+        .expand((batch_size, num_masked_spans, mask_length))
+        .reshape(batch_size, num_masked_spans * mask_length)
+    )
+    mask_idxs = mask_indices + offsets
+
+    # scatter indices to mask
+    mask = mask.scatter(1, mask_idxs, True)
+
+    return mask
+
+
+def hubert_soft(
+        path: str
+) -> HubertSoft:
+    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
+    Args:
+        path (str): path of a pretrained model
+    """
+    dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    hubert = HubertSoft()
+    checkpoint = torch.load(path)
+    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
+    hubert.load_state_dict(checkpoint)
+    hubert.eval().to(dev)
+    return hubert
+
+
+def get_units(hbt_soft, raw_wav_path, dev=torch.device('cuda')):
+    wav, sr = librosa.load(raw_wav_path, sr=None)
+    assert (sr >= 16000)
+    if len(wav.shape) > 1:
+        wav = librosa.to_mono(wav)
+    if sr != 16000:
+        wav16 = librosa.resample(wav, sr, 16000)
+    else:
+        wav16 = wav
+    dev = torch.device("cuda" if (dev == torch.device('cuda') and torch.cuda.is_available()) else "cpu")
+    torch.cuda.is_available() and torch.cuda.empty_cache()
+    with torch.inference_mode():
+        units = hbt_soft.units(torch.FloatTensor(wav16.astype(float)).unsqueeze(0).unsqueeze(0).to(dev))
+        return units
diff --git a/modules/hubert/hubert_onnx.py b/modules/hubert/hubert_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd5dbb08031dba5c00539727729890a0843c40cd
--- /dev/null
+++ b/modules/hubert/hubert_onnx.py
@@ -0,0 +1,19 @@
+import time
+
+import torch
+import torchaudio
+
+
+def get_onnx_units(hbt_soft, raw_wav_path):
+    source, sr = torchaudio.load(raw_wav_path)
+    source = torchaudio.functional.resample(source, sr, 16000)
+    if len(source.shape) == 2 and source.shape[1] >= 2:
+        source = torch.mean(source, dim=0).unsqueeze(0)
+    source = source.unsqueeze(0)
+    # 使用ONNX Runtime进行推理
+    start = time.time()
+    units = hbt_soft.run(output_names=["units"],
+                         input_feed={"wav": source.numpy()})[0]
+    use_time = time.time() - start
+    print("hubert_onnx_session.run time:{}".format(use_time))
+    return units
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diff --git a/modules/nsf_hifigan/env.py b/modules/nsf_hifigan/env.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056
--- /dev/null
+++ b/modules/nsf_hifigan/env.py
@@ -0,0 +1,15 @@
+import os
+import shutil
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
+
+
+def build_env(config, config_name, path):
+    t_path = os.path.join(path, config_name)
+    if config != t_path:
+        os.makedirs(path, exist_ok=True)
+        shutil.copyfile(config, os.path.join(path, config_name))
diff --git a/modules/nsf_hifigan/models.py b/modules/nsf_hifigan/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ce1c2fd77dd009d6e432568ab1db0bb8b7a6410
--- /dev/null
+++ b/modules/nsf_hifigan/models.py
@@ -0,0 +1,558 @@
+import json
+import os
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+
+from .env import AttrDict
+from .utils import init_weights, get_padding
+
+LRELU_SLOPE = 0.1
+
+
+def load_model(model_path, device='cuda'):
+    config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
+    with open(config_file) as f:
+        data = f.read()
+
+    global h
+    json_config = json.loads(data)
+    h = AttrDict(json_config)
+
+    generator = Generator(h).to(device)
+
+    cp_dict = torch.load(model_path)
+    generator.load_state_dict(cp_dict['generator'])
+    generator.eval()
+    generator.remove_weight_norm()
+    del cp_dict
+    return generator, h
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.h = h
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.h = h
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, h):
+        super(Generator, self).__init__()
+        self.h = h
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
+        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)),
+                                k, u, padding=(k - u) // 2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+
+    def forward(self, x):
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0,
+                 flag_for_pulse=False):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+
+    def _f02sine(self, f0_values):
+        """ f0_values: (batchsize, length, dim)
+            where dim indicates fundamental tone and overtones
+        """
+        # convert to F0 in rad. The interger part n can be ignored
+        # because 2 * np.pi * n doesn't affect phase
+        rad_values = (f0_values / self.sampling_rate) % 1
+
+        # initial phase noise (no noise for fundamental component)
+        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
+                              device=f0_values.device)
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+        if not self.flag_for_pulse:
+            # for normal case
+
+            # To prevent torch.cumsum numerical overflow,
+            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
+            # Buffer tmp_over_one_idx indicates the time step to add -1.
+            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
+            tmp_over_one = torch.cumsum(rad_values, 1) % 1
+            tmp_over_one_idx = (tmp_over_one[:, 1:, :] -
+                                tmp_over_one[:, :-1, :]) < 0
+            cumsum_shift = torch.zeros_like(rad_values)
+            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
+                              * 2 * np.pi)
+        else:
+            # If necessary, make sure that the first time step of every
+            # voiced segments is sin(pi) or cos(0)
+            # This is used for pulse-train generation
+
+            # identify the last time step in unvoiced segments
+            uv = self._f02uv(f0_values)
+            uv_1 = torch.roll(uv, shifts=-1, dims=1)
+            uv_1[:, -1, :] = 1
+            u_loc = (uv < 1) * (uv_1 > 0)
+
+            # get the instantanouse phase
+            tmp_cumsum = torch.cumsum(rad_values, dim=1)
+            # different batch needs to be processed differently
+            for idx in range(f0_values.shape[0]):
+                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+                # stores the accumulation of i.phase within
+                # each voiced segments
+                tmp_cumsum[idx, :, :] = 0
+                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+            # rad_values - tmp_cumsum: remove the accumulation of i.phase
+            # within the previous voiced segment.
+            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+            # get the sines
+            sines = torch.cos(i_phase * 2 * np.pi)
+        return sines
+
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
+                                 device=f0.device)
+            # fundamental component
+            f0_buf[:, :, 0] = f0[:, :, 0]
+            for idx in np.arange(self.harmonic_num):
+                # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+            # generate sine waveforms
+            sine_waves = self._f02sine(f0_buf) * self.sine_amp
+
+            # generate uv signal
+            # uv = torch.ones(f0.shape)
+            # uv = uv * (f0 > self.voiced_threshold)
+            uv = self._f02uv(f0)
+
+            # noise: for unvoiced should be similar to sine_amp
+            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+            # .       for voiced regions is self.noise_std
+            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+
+            # first: set the unvoiced part to 0 by uv
+            # then: additive noise
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
+                                 sine_amp, add_noise_std, voiced_threshod)
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        # source for harmonic branch
+        sine_wavs, uv, _ = self.l_sin_gen(x)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        # source for noise branch, in the same shape as uv
+        noise = torch.randn_like(uv) * self.sine_amp / 3
+        return sine_merge, noise, uv
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, h):
+        super(Generator, self).__init__()
+        self.h = h
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h.upsample_rates))
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=h.sampling_rate,
+            harmonic_num=8)
+        self.noise_convs = nn.ModuleList()
+        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
+        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            c_cur = h.upsample_initial_channel // (2 ** (i + 1))
+            self.ups.append(weight_norm(
+                ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)),
+                                k, u, padding=(k - u) // 2)))
+            if i + 1 < len(h.upsample_rates):  #
+                stride_f0 = np.prod(h.upsample_rates[i + 1:])
+                self.noise_convs.append(Conv1d(
+                    1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
+            else:
+                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+
+    def forward(self, x, f0):
+        # print(1,x.shape,f0.shape,f0[:, None].shape)
+        f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
+        # print(2,f0.shape)
+        har_source, noi_source, uv = self.m_source(f0)
+        har_source = har_source.transpose(1, 2)
+        x = self.conv_pre(x)
+        # print(124,x.shape,har_source.shape)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            # print(3,x.shape)
+            x = self.ups[i](x)
+            x_source = self.noise_convs[i](har_source)
+            # print(4,x_source.shape,har_source.shape,x.shape)
+            x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, periods=None):
+        super(MultiPeriodDiscriminator, self).__init__()
+        self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
+        self.discriminators = nn.ModuleList()
+        for period in self.periods:
+            self.discriminators.append(DiscriminatorP(period))
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
+            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
+            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
+            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiScaleDiscriminator(torch.nn.Module):
+    def __init__(self):
+        super(MultiScaleDiscriminator, self).__init__()
+        self.discriminators = nn.ModuleList([
+            DiscriminatorS(use_spectral_norm=True),
+            DiscriminatorS(),
+            DiscriminatorS(),
+        ])
+        self.meanpools = nn.ModuleList([
+            AvgPool1d(4, 2, padding=2),
+            AvgPool1d(4, 2, padding=2)
+        ])
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            if i != 0:
+                y = self.meanpools[i - 1](y)
+                y_hat = self.meanpools[i - 1](y_hat)
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            fmap_rs.append(fmap_r)
+            y_d_gs.append(y_d_g)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+def feature_loss(fmap_r, fmap_g):
+    loss = 0
+    for dr, dg in zip(fmap_r, fmap_g):
+        for rl, gl in zip(dr, dg):
+            loss += torch.mean(torch.abs(rl - gl))
+
+    return loss * 2
+
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+    loss = 0
+    r_losses = []
+    g_losses = []
+    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+        r_loss = torch.mean((1 - dr) ** 2)
+        g_loss = torch.mean(dg ** 2)
+        loss += (r_loss + g_loss)
+        r_losses.append(r_loss.item())
+        g_losses.append(g_loss.item())
+
+    return loss, r_losses, g_losses
+
+
+def generator_loss(disc_outputs):
+    loss = 0
+    gen_losses = []
+    for dg in disc_outputs:
+        l = torch.mean((1 - dg) ** 2)
+        gen_losses.append(l)
+        loss += l
+
+    return loss, gen_losses
diff --git a/modules/nsf_hifigan/nvSTFT.py b/modules/nsf_hifigan/nvSTFT.py
new file mode 100644
index 0000000000000000000000000000000000000000..45d235f2c537de8c71efd3a81ecafb0c92212f36
--- /dev/null
+++ b/modules/nsf_hifigan/nvSTFT.py
@@ -0,0 +1,120 @@
+import os
+
+os.environ["LRU_CACHE_CAPACITY"] = "3"
+import torch
+import torch.utils.data
+import numpy as np
+import librosa
+from librosa.filters import mel as librosa_mel_fn
+import soundfile as sf
+
+
+def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
+    sampling_rate = None
+    try:
+        data, sampling_rate = sf.read(full_path, always_2d=True)  # than soundfile.
+    except Exception as ex:
+        print(f"'{full_path}' failed to load.\nException:")
+        print(ex)
+        if return_empty_on_exception:
+            return [], sampling_rate or target_sr or 48000
+        else:
+            raise Exception(ex)
+
+    if len(data.shape) > 1:
+        data = data[:, 0]
+        assert len(
+            data) > 2  # check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
+
+    if np.issubdtype(data.dtype, np.integer):  # if audio data is type int
+        max_mag = -np.iinfo(data.dtype).min  # maximum magnitude = min possible value of intXX
+    else:  # if audio data is type fp32
+        max_mag = max(np.amax(data), -np.amin(data))
+        max_mag = (2 ** 31) + 1 if max_mag > (2 ** 15) else ((
+                                                                         2 ** 15) + 1 if max_mag > 1.01 else 1.0)  # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
+
+    data = torch.FloatTensor(data.astype(np.float32)) / max_mag
+
+    if (torch.isinf(data) | torch.isnan(
+            data)).any() and return_empty_on_exception:  # resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
+        return [], sampling_rate or target_sr or 48000
+    if target_sr is not None and sampling_rate != target_sr:
+        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
+        sampling_rate = target_sr
+
+    return data, sampling_rate
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    return np.exp(x) / C
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    return torch.exp(x) / C
+
+
+class STFT():
+    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025,
+                 clip_val=1e-5):
+        self.target_sr = sr
+
+        self.n_mels = n_mels
+        self.n_fft = n_fft
+        self.win_size = win_size
+        self.hop_length = hop_length
+        self.fmin = fmin
+        self.fmax = fmax
+        self.clip_val = clip_val
+        self.mel_basis = {}
+        self.hann_window = {}
+
+    def get_mel(self, y, center=False):
+        sampling_rate = self.target_sr
+        n_mels = self.n_mels
+        n_fft = self.n_fft
+        win_size = self.win_size
+        hop_length = self.hop_length
+        fmin = self.fmin
+        fmax = self.fmax
+        clip_val = self.clip_val
+
+        if torch.min(y) < -1.:
+            print('min value is ', torch.min(y))
+        if torch.max(y) > 1.:
+            print('max value is ', torch.max(y))
+
+        if fmax not in self.mel_basis:
+            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
+            self.mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
+            self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
+
+        y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)),
+                                    mode='reflect')
+        y = y.squeeze(1)
+
+        spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
+                          center=center, pad_mode='reflect', normalized=False, onesided=True)
+        # print(111,spec)
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+        # print(222,spec)
+        spec = torch.matmul(self.mel_basis[str(fmax) + '_' + str(y.device)], spec)
+        # print(333,spec)
+        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
+        # print(444,spec)
+        return spec
+
+    def __call__(self, audiopath):
+        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
+        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
+        return spect
+
+
+stft = STFT()
diff --git a/modules/nsf_hifigan/utils.py b/modules/nsf_hifigan/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8691fb71c84263bfe3e44f35290c254609174811
--- /dev/null
+++ b/modules/nsf_hifigan/utils.py
@@ -0,0 +1,69 @@
+import glob
+import os
+
+import matplotlib
+import torch
+from torch.nn.utils import weight_norm
+
+matplotlib.use("Agg")
+import matplotlib.pylab as plt
+
+
+def plot_spectrogram(spectrogram):
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                   interpolation='none')
+    plt.colorbar(im, ax=ax)
+
+    fig.canvas.draw()
+    plt.close()
+
+    return fig
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def apply_weight_norm(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        weight_norm(m)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def load_checkpoint(filepath, device):
+    assert os.path.isfile(filepath)
+    print("Loading '{}'".format(filepath))
+    checkpoint_dict = torch.load(filepath, map_location=device)
+    print("Complete.")
+    return checkpoint_dict
+
+
+def save_checkpoint(filepath, obj):
+    print("Saving checkpoint to {}".format(filepath))
+    torch.save(obj, filepath)
+    print("Complete.")
+
+
+def del_old_checkpoints(cp_dir, prefix, n_models=2):
+    pattern = os.path.join(cp_dir, prefix + '????????')
+    cp_list = glob.glob(pattern)  # get checkpoint paths
+    cp_list = sorted(cp_list)  # sort by iter
+    if len(cp_list) > n_models:  # if more than n_models models are found
+        for cp in cp_list[:-n_models]:  # delete the oldest models other than lastest n_models
+            open(cp, 'w').close()  # empty file contents
+            os.unlink(cp)  # delete file (move to trash when using Colab)
+
+
+def scan_checkpoint(cp_dir, prefix):
+    pattern = os.path.join(cp_dir, prefix + '????????')
+    cp_list = glob.glob(pattern)
+    if len(cp_list) == 0:
+        return None
+    return sorted(cp_list)[-1]
diff --git a/modules/vocoders/__init__.py b/modules/vocoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b4cbcab246907e9fc1b96b62c10d15f9a53a1b4
--- /dev/null
+++ b/modules/vocoders/__init__.py
@@ -0,0 +1 @@
+from modules.vocoders import nsf_hifigan
diff --git a/modules/vocoders/__pycache__/__init__.cpython-310.pyc b/modules/vocoders/__pycache__/__init__.cpython-310.pyc
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diff --git a/modules/vocoders/__pycache__/__init__.cpython-38.pyc b/modules/vocoders/__pycache__/__init__.cpython-38.pyc
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diff --git a/modules/vocoders/__pycache__/nsf_hifigan.cpython-310.pyc b/modules/vocoders/__pycache__/nsf_hifigan.cpython-310.pyc
new file mode 100644
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diff --git a/modules/vocoders/__pycache__/nsf_hifigan.cpython-38.pyc b/modules/vocoders/__pycache__/nsf_hifigan.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..795c2c2105477d0621cc3f5ac441818982babb18
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diff --git a/modules/vocoders/nsf_hifigan.py b/modules/vocoders/nsf_hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..4528f5a64402aee40e89ef3840799751dd63998b
--- /dev/null
+++ b/modules/vocoders/nsf_hifigan.py
@@ -0,0 +1,77 @@
+import os
+
+import torch
+
+from modules.nsf_hifigan.models import load_model
+from modules.nsf_hifigan.nvSTFT import load_wav_to_torch, STFT
+from utils.hparams import hparams
+
+nsf_hifigan = None
+
+
+def register_vocoder(cls):
+    global nsf_hifigan
+    nsf_hifigan = cls
+    return cls
+
+
+@register_vocoder
+class NsfHifiGAN():
+    def __init__(self, device=None):
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = device
+        model_path = hparams['vocoder_ckpt']
+        if os.path.exists(model_path):
+            print('| Load HifiGAN: ', model_path)
+            self.model, self.h = load_model(model_path, device=self.device)
+        else:
+            print('Error: HifiGAN model file is not found!')
+
+    def spec2wav(self, mel, **kwargs):
+        if self.h.sampling_rate != hparams['audio_sample_rate']:
+            print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=',
+                  self.h.sampling_rate, '(vocoder)')
+        if self.h.num_mels != hparams['audio_num_mel_bins']:
+            print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=',
+                  self.h.num_mels, '(vocoder)')
+        if self.h.n_fft != hparams['fft_size']:
+            print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.h.n_fft, '(vocoder)')
+        if self.h.win_size != hparams['win_size']:
+            print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.h.win_size,
+                  '(vocoder)')
+        if self.h.hop_size != hparams['hop_size']:
+            print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.h.hop_size,
+                  '(vocoder)')
+        if self.h.fmin != hparams['fmin']:
+            print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.h.fmin, '(vocoder)')
+        if self.h.fmax != hparams['fmax']:
+            print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.h.fmax, '(vocoder)')
+        with torch.no_grad():
+            c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(self.device)
+            # log10 to log mel
+            c = 2.30259 * c
+            f0 = kwargs.get('f0')
+            f0 = torch.FloatTensor(f0[None, :]).to(self.device)
+            y = self.model(c, f0).view(-1)
+        wav_out = y.cpu().numpy()
+        return wav_out
+
+    @staticmethod
+    def wav2spec(inp_path, device=None):
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        sampling_rate = hparams['audio_sample_rate']
+        num_mels = hparams['audio_num_mel_bins']
+        n_fft = hparams['fft_size']
+        win_size = hparams['win_size']
+        hop_size = hparams['hop_size']
+        fmin = hparams['fmin']
+        fmax = hparams['fmax']
+        stft = STFT(sampling_rate, num_mels, n_fft, win_size, hop_size, fmin, fmax)
+        with torch.no_grad():
+            wav_torch, _ = load_wav_to_torch(inp_path, target_sr=stft.target_sr)
+            mel_torch = stft.get_mel(wav_torch.unsqueeze(0).to(device)).squeeze(0).T
+            # log mel to log10 mel
+            mel_torch = 0.434294 * mel_torch
+            return wav_torch.cpu().numpy(), mel_torch.cpu().numpy()
diff --git a/pre_hubert.py b/pre_hubert.py
new file mode 100644
index 0000000000000000000000000000000000000000..eec24c444797ed6eca0ea877914a2b729da96b21
--- /dev/null
+++ b/pre_hubert.py
@@ -0,0 +1,20 @@
+import os
+from pathlib import Path
+
+import numpy as np
+from tqdm import tqdm
+
+from infer_tools import infer_tool
+from preprocessing.hubertinfer import HubertEncoder
+
+# hubert_mode可选——"soft_hubert"、"cn_hubert"
+hubert_model = HubertEncoder(hubert_mode='cn_hubert')
+# 自动搜索batch文件夹下所有wav文件，可自行更改路径
+wav_paths = infer_tool.get_end_file("./batch", "wav")
+with tqdm(total=len(wav_paths)) as p_bar:
+    p_bar.set_description('Processing')
+    for wav_path in wav_paths:
+        npy_path = Path(wav_path).with_suffix(".npy")
+        if not os.path.exists(npy_path):
+            np.save(str(npy_path), hubert_model.encode(wav_path))
+        p_bar.update(1)
diff --git a/preprocessing/__pycache__/hubertinfer.cpython-310.pyc b/preprocessing/__pycache__/hubertinfer.cpython-310.pyc
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diff --git a/preprocessing/__pycache__/hubertinfer.cpython-38.pyc b/preprocessing/__pycache__/hubertinfer.cpython-38.pyc
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diff --git a/preprocessing/__pycache__/process_pipeline.cpython-38.pyc b/preprocessing/__pycache__/process_pipeline.cpython-38.pyc
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diff --git a/preprocessing/hubertinfer.py b/preprocessing/hubertinfer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5625bf6c2d5d3f441b66837419309d954ae5ad2f
--- /dev/null
+++ b/preprocessing/hubertinfer.py
@@ -0,0 +1,53 @@
+import os.path
+from io import BytesIO
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+import torch
+
+from modules.hubert.cn_hubert import load_cn_model, get_cn_hubert_units
+from modules.hubert.hubert_model import hubert_soft, get_units
+from modules.hubert.hubert_onnx import get_onnx_units
+from utils.hparams import hparams
+
+
+class HubertEncoder:
+    def __init__(self, pt_path='checkpoints/hubert/hubert_soft.pt', hubert_mode='', onnx=False):
+        self.hubert_mode = hubert_mode
+        self.onnx = onnx
+        if 'use_cn_hubert' not in hparams.keys():
+            hparams['use_cn_hubert'] = False
+        if hparams['use_cn_hubert'] or self.hubert_mode == 'cn_hubert':
+            pt_path = "checkpoints/cn_hubert/chinese-hubert-base-fairseq-ckpt.pt"
+            self.dev = torch.device("cuda")
+            self.hbt_model = load_cn_model(pt_path)
+        else:
+            if onnx:
+                self.hbt_model = ort.InferenceSession("onnx/hubert_soft.onnx",
+                                                      providers=['CUDAExecutionProvider', 'CPUExecutionProvider', ])
+            else:
+                pt_path = list(Path(pt_path).parent.rglob('*.pt'))[0]
+                if 'hubert_gpu' in hparams.keys():
+                    self.use_gpu = hparams['hubert_gpu']
+                else:
+                    self.use_gpu = True
+                self.dev = torch.device("cuda" if self.use_gpu and torch.cuda.is_available() else "cpu")
+                self.hbt_model = hubert_soft(str(pt_path)).to(self.dev)
+        print(f"| load 'model' from '{pt_path}'")
+
+    def encode(self, wav_path):
+        if isinstance(wav_path, BytesIO):
+            npy_path = ""
+            wav_path.seek(0)
+        else:
+            npy_path = Path(wav_path).with_suffix('.npy')
+        if os.path.exists(npy_path):
+            units = np.load(str(npy_path))
+        elif self.onnx:
+            units = get_onnx_units(self.hbt_model, wav_path).squeeze(0)
+        elif hparams['use_cn_hubert'] or self.hubert_mode == 'cn_hubert':
+            units = get_cn_hubert_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0]
+        else:
+            units = get_units(self.hbt_model, wav_path, self.dev).cpu().numpy()[0]
+        return units  # [T,256]
diff --git a/preprocessing/process_pipeline.py b/preprocessing/process_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..928256a07d07f09cc93cad739bafc43e2c60ce37
--- /dev/null
+++ b/preprocessing/process_pipeline.py
@@ -0,0 +1,247 @@
+import hashlib
+import json
+import os
+import time
+import traceback
+import warnings
+from pathlib import Path
+
+import numpy as np
+import parselmouth
+import resampy
+import torch
+import torchcrepe
+
+import utils
+from modules.vocoders.nsf_hifigan import nsf_hifigan
+from utils.hparams import hparams
+from utils.pitch_utils import f0_to_coarse
+
+warnings.filterwarnings("ignore")
+
+
+class BinarizationError(Exception):
+    pass
+
+
+def get_md5(content):
+    return hashlib.new("md5", content).hexdigest()
+
+
+def read_temp(file_name):
+    if not os.path.exists(file_name):
+        with open(file_name, "w") as f:
+            f.write(json.dumps({"info": "temp_dict"}))
+        return {}
+    else:
+        try:
+            with open(file_name, "r") as f:
+                data = f.read()
+            data_dict = json.loads(data)
+            if os.path.getsize(file_name) > 50 * 1024 * 1024:
+                f_name = file_name.split("/")[-1]
+                print(f"clean {f_name}")
+                for wav_hash in list(data_dict.keys()):
+                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
+                        del data_dict[wav_hash]
+        except Exception as e:
+            print(e)
+            print(f"{file_name} error,auto rebuild file")
+            data_dict = {"info": "temp_dict"}
+        return data_dict
+
+
+def write_temp(file_name, data):
+    with open(file_name, "w") as f:
+        f.write(json.dumps(data))
+
+
+f0_dict = read_temp("./infer_tools/f0_temp.json")
+
+
+def get_pitch_parselmouth(wav_data, mel, hparams):
+    """
+
+    :param wav_data: [T]
+    :param mel: [T, 80]
+    :param hparams:
+    :return:
+    """
+    time_step = hparams['hop_size'] / hparams['audio_sample_rate']
+    f0_min = hparams['f0_min']
+    f0_max = hparams['f0_max']
+
+    f0 = parselmouth.Sound(wav_data, hparams['audio_sample_rate']).to_pitch_ac(
+        time_step=time_step, voicing_threshold=0.6,
+        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
+
+    pad_size = (int(len(wav_data) // hparams['hop_size']) - len(f0) + 1) // 2
+    f0 = np.pad(f0, [[pad_size, len(mel) - len(f0) - pad_size]], mode='constant')
+    pitch_coarse = f0_to_coarse(f0, hparams)
+    return f0, pitch_coarse
+
+
+def get_pitch_crepe(wav_data, mel, hparams, threshold=0.05):
+    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda")
+    # crepe只支持16khz采样率，需要重采样
+    wav16k = resampy.resample(wav_data, hparams['audio_sample_rate'], 16000)
+    wav16k_torch = torch.FloatTensor(wav16k).unsqueeze(0).to(device)
+
+    # 频率范围
+    f0_min = hparams['f0_min']
+    f0_max = hparams['f0_max']
+
+    # 重采样后按照hopsize=80,也就是5ms一帧分析f0
+    f0, pd = torchcrepe.predict(wav16k_torch, 16000, 80, f0_min, f0_max, pad=True, model='full', batch_size=1024,
+                                device=device, return_periodicity=True)
+
+    # 滤波，去掉静音，设置uv阈值，参考原仓库readme
+    pd = torchcrepe.filter.median(pd, 3)
+    pd = torchcrepe.threshold.Silence(-60.)(pd, wav16k_torch, 16000, 80)
+    f0 = torchcrepe.threshold.At(threshold)(f0, pd)
+    f0 = torchcrepe.filter.mean(f0, 3)
+
+    # 将nan频率（uv部分）转换为0频率
+    f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)
+
+    # 去掉0频率，并线性插值
+    nzindex = torch.nonzero(f0[0]).squeeze()
+    f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy()
+    time_org = 0.005 * nzindex.cpu().numpy()
+    time_frame = np.arange(len(mel)) * hparams['hop_size'] / hparams['audio_sample_rate']
+    if f0.shape[0] == 0:
+        f0 = torch.FloatTensor(time_frame.shape[0]).fill_(0)
+        print('f0 all zero!')
+    else:
+        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
+    pitch_coarse = f0_to_coarse(f0, hparams)
+    return f0, pitch_coarse
+
+
+class File2Batch:
+    '''
+        pipeline: file -> temporary_dict -> processed_input -> batch
+    '''
+
+    @staticmethod
+    def file2temporary_dict(raw_data_dir, ds_id):
+        '''
+            read from file, store data in temporary dicts
+        '''
+        raw_data_dir = Path(raw_data_dir)
+        utterance_labels = []
+        utterance_labels.extend(list(raw_data_dir.rglob(f"*.wav")))
+        utterance_labels.extend(list(raw_data_dir.rglob(f"*.ogg")))
+
+        all_temp_dict = {}
+        for utterance_label in utterance_labels:
+            item_name = str(utterance_label)
+            temp_dict = {'wav_fn': str(utterance_label), 'spk_id': ds_id}
+            all_temp_dict[item_name] = temp_dict
+        return all_temp_dict
+
+    @staticmethod
+    def temporary_dict2processed_input(item_name, temp_dict, encoder, infer=False, **kwargs):
+        '''
+            process data in temporary_dicts
+        '''
+
+        def get_pitch(wav, mel):
+            # get ground truth f0 by self.get_pitch_algorithm
+            global f0_dict
+            use_crepe = hparams['use_crepe'] if not infer else kwargs['use_crepe']
+            if use_crepe:
+                md5 = get_md5(wav)
+                if infer and md5 in f0_dict.keys():
+                    print("load temp crepe f0")
+                    gt_f0 = np.array(f0_dict[md5]["f0"])
+                    coarse_f0 = np.array(f0_dict[md5]["coarse"])
+                else:
+                    torch.cuda.is_available() and torch.cuda.empty_cache()
+                    gt_f0, coarse_f0 = get_pitch_crepe(wav, mel, hparams, threshold=0.05)
+                if infer:
+                    f0_dict[md5] = {"f0": gt_f0.tolist(), "coarse": coarse_f0.tolist(), "time": int(time.time())}
+                    write_temp("./infer_tools/f0_temp.json", f0_dict)
+            else:
+                gt_f0, coarse_f0 = get_pitch_parselmouth(wav, mel, hparams)
+            if sum(gt_f0) == 0:
+                raise BinarizationError("Empty **gt** f0")
+            processed_input['f0'] = gt_f0
+            processed_input['pitch'] = coarse_f0
+
+        def get_align(mel, phone_encoded):
+            mel2ph = np.zeros([mel.shape[0]], int)
+            start_frame = 0
+            ph_durs = mel.shape[0] / phone_encoded.shape[0]
+            for i_ph in range(phone_encoded.shape[0]):
+                end_frame = int(i_ph * ph_durs + ph_durs + 0.5)
+                mel2ph[start_frame:end_frame + 1] = i_ph + 1
+                start_frame = end_frame + 1
+
+            processed_input['mel2ph'] = mel2ph
+
+        wav, mel = nsf_hifigan.wav2spec(temp_dict['wav_fn'])
+        processed_input = {
+            'item_name': item_name, 'mel': mel,
+            'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]
+        }
+        processed_input = {**temp_dict, **processed_input,
+                           'spec_min': np.min(mel, axis=0),
+                           'spec_max': np.max(mel, axis=0)}  # merge two dicts
+        try:
+            get_pitch(wav, mel)
+            try:
+                hubert_encoded = processed_input['hubert'] = encoder.encode(temp_dict['wav_fn'])
+            except:
+                traceback.print_exc()
+                raise Exception(f"hubert encode error")
+            get_align(mel, hubert_encoded)
+        except Exception as e:
+            print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {temp_dict['wav_fn']}")
+            return None
+        if hparams['use_energy_embed']:
+            max_frames = hparams['max_frames']
+            spec = torch.Tensor(processed_input['mel'])[:max_frames]
+            processed_input['energy'] = (spec.exp() ** 2).sum(-1).sqrt()
+        return processed_input
+
+    @staticmethod
+    def processed_input2batch(samples):
+        '''
+            Args:
+                samples: one batch of processed_input
+            NOTE:
+                the batch size is controlled by hparams['max_sentences']
+        '''
+        if len(samples) == 0:
+            return {}
+        id = torch.LongTensor([s['id'] for s in samples])
+        item_names = [s['item_name'] for s in samples]
+        hubert = utils.collate_2d([s['hubert'] for s in samples], 0.0)
+        f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
+        pitch = utils.collate_1d([s['pitch'] for s in samples])
+        uv = utils.collate_1d([s['uv'] for s in samples])
+        mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
+            if samples[0]['mel2ph'] is not None else None
+        mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
+        mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
+
+        batch = {
+            'id': id,
+            'item_name': item_names,
+            'nsamples': len(samples),
+            'hubert': hubert,
+            'mels': mels,
+            'mel_lengths': mel_lengths,
+            'mel2ph': mel2ph,
+            'pitch': pitch,
+            'f0': f0,
+            'uv': uv,
+        }
+        if hparams['use_energy_embed']:
+            batch['energy'] = utils.collate_1d([s['energy'] for s in samples], 0.0)
+        if hparams['use_spk_id']:
+            spk_ids = torch.LongTensor([s['spk_id'] for s in samples])
+            batch['spk_ids'] = spk_ids
+        return batch
diff --git a/preprocessing/svc_binarizer.py b/preprocessing/svc_binarizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cc35c00b46e7168188e49f79f01b7ac60e4e368
--- /dev/null
+++ b/preprocessing/svc_binarizer.py
@@ -0,0 +1,224 @@
+import json
+import logging
+import os
+import random
+from copy import deepcopy
+
+import numpy as np
+import yaml
+from resemblyzer import VoiceEncoder
+from tqdm import tqdm
+
+from infer_tools.f0_static import static_f0_time
+from modules.vocoders.nsf_hifigan import NsfHifiGAN
+from preprocessing.hubertinfer import HubertEncoder
+from preprocessing.process_pipeline import File2Batch
+from preprocessing.process_pipeline import get_pitch_parselmouth, get_pitch_crepe
+from utils.hparams import hparams
+from utils.hparams import set_hparams
+from utils.indexed_datasets import IndexedDatasetBuilder
+
+os.environ["OMP_NUM_THREADS"] = "1"
+BASE_ITEM_ATTRIBUTES = ['wav_fn', 'spk_id']
+
+
+class SvcBinarizer:
+    '''
+        Base class for data processing.
+        1. *process* and *process_data_split*:
+            process entire data, generate the train-test split (support parallel processing);
+        2. *process_item*:
+            process singe piece of data;
+        3. *get_pitch*:
+            infer the pitch using some algorithm;
+        4. *get_align*:
+            get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263).
+        5. phoneme encoder, voice encoder, etc.
+
+        Subclasses should define:
+        1. *load_metadata*:
+            how to read multiple datasets from files;
+        2. *train_item_names*, *valid_item_names*, *test_item_names*:
+            how to split the dataset;
+        3. load_ph_set:
+            the phoneme set.
+    '''
+
+    def __init__(self, data_dir=None, item_attributes=None):
+        self.spk_map = None
+        self.vocoder = NsfHifiGAN()
+        self.phone_encoder = HubertEncoder(pt_path=hparams['hubert_path'])
+        if item_attributes is None:
+            item_attributes = BASE_ITEM_ATTRIBUTES
+        if data_dir is None:
+            data_dir = hparams['raw_data_dir']
+        if 'speakers' not in hparams:
+            speakers = hparams['datasets']
+            hparams['speakers'] = hparams['datasets']
+        else:
+            speakers = hparams['speakers']
+        assert isinstance(speakers, list), 'Speakers must be a list'
+        assert len(speakers) == len(set(speakers)), 'Speakers cannot contain duplicate names'
+
+        self.raw_data_dirs = data_dir if isinstance(data_dir, list) else [data_dir]
+        assert len(speakers) == len(self.raw_data_dirs), \
+            'Number of raw data dirs must equal number of speaker names!'
+        self.speakers = speakers
+        self.binarization_args = hparams['binarization_args']
+
+        self.items = {}
+        # every item in self.items has some attributes
+        self.item_attributes = item_attributes
+
+        # load each dataset
+        for ds_id, data_dir in enumerate(self.raw_data_dirs):
+            self.load_meta_data(data_dir, ds_id)
+            if ds_id == 0:
+                # check program correctness
+                assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
+        self.item_names = sorted(list(self.items.keys()))
+
+        if self.binarization_args['shuffle']:
+            random.seed(hparams['seed'])
+            random.shuffle(self.item_names)
+
+        # set default get_pitch algorithm
+        if hparams['use_crepe']:
+            self.get_pitch_algorithm = get_pitch_crepe
+        else:
+            self.get_pitch_algorithm = get_pitch_parselmouth
+        print('spkers: ', set(self.speakers))
+        self._train_item_names, self._test_item_names = self.split_train_test_set(self.item_names)
+
+    @staticmethod
+    def split_train_test_set(item_names):
+        auto_test = item_names[-5:]
+        item_names = set(deepcopy(item_names))
+        if hparams['choose_test_manually']:
+            prefixes = set([str(pr) for pr in hparams['test_prefixes']])
+            test_item_names = set()
+            # Add prefixes that specified speaker index and matches exactly item name to test set
+            for prefix in deepcopy(prefixes):
+                if prefix in item_names:
+                    test_item_names.add(prefix)
+                    prefixes.remove(prefix)
+            # Add prefixes that exactly matches item name without speaker id to test set
+            for prefix in deepcopy(prefixes):
+                for name in item_names:
+                    if name.split(':')[-1] == prefix:
+                        test_item_names.add(name)
+                        prefixes.remove(prefix)
+            # Add names with one of the remaining prefixes to test set
+            for prefix in deepcopy(prefixes):
+                for name in item_names:
+                    if name.startswith(prefix):
+                        test_item_names.add(name)
+                        prefixes.remove(prefix)
+            for prefix in prefixes:
+                for name in item_names:
+                    if name.split(':')[-1].startswith(prefix):
+                        test_item_names.add(name)
+            test_item_names = sorted(list(test_item_names))
+        else:
+            test_item_names = auto_test
+        train_item_names = [x for x in item_names if x not in set(test_item_names)]
+        logging.info("train {}".format(len(train_item_names)))
+        logging.info("test {}".format(len(test_item_names)))
+        return train_item_names, test_item_names
+
+    @property
+    def train_item_names(self):
+        return self._train_item_names
+
+    @property
+    def valid_item_names(self):
+        return self._test_item_names
+
+    @property
+    def test_item_names(self):
+        return self._test_item_names
+
+    def load_meta_data(self, raw_data_dir, ds_id):
+        self.items.update(File2Batch.file2temporary_dict(raw_data_dir, ds_id))
+
+    @staticmethod
+    def build_spk_map():
+        spk_map = {x: i for i, x in enumerate(hparams['speakers'])}
+        assert len(spk_map) <= hparams['num_spk'], 'Actual number of speakers should be smaller than num_spk!'
+        return spk_map
+
+    def item_name2spk_id(self, item_name):
+        return self.spk_map[self.items[item_name]['spk_id']]
+
+    def meta_data_iterator(self, prefix):
+        if prefix == 'valid':
+            item_names = self.valid_item_names
+        elif prefix == 'test':
+            item_names = self.test_item_names
+        else:
+            item_names = self.train_item_names
+        for item_name in item_names:
+            meta_data = self.items[item_name]
+            yield item_name, meta_data
+
+    def process(self):
+        os.makedirs(hparams['binary_data_dir'], exist_ok=True)
+        self.spk_map = self.build_spk_map()
+        print("| spk_map: ", self.spk_map)
+        spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json"
+        json.dump(self.spk_map, open(spk_map_fn, 'w', encoding='utf-8'))
+        self.process_data_split('valid')
+        self.process_data_split('test')
+        self.process_data_split('train')
+
+    def process_data_split(self, prefix):
+        data_dir = hparams['binary_data_dir']
+        args = []
+        builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
+        lengths = []
+        total_sec = 0
+        if self.binarization_args['with_spk_embed']:
+            voice_encoder = VoiceEncoder().cuda()
+        for item_name, meta_data in self.meta_data_iterator(prefix):
+            args.append([item_name, meta_data, self.binarization_args])
+        spec_min = []
+        spec_max = []
+        f0_dict = {}
+        # code for single cpu processing
+        for i in tqdm(reversed(range(len(args))), total=len(args)):
+            a = args[i]
+            item = self.process_item(*a)
+            if item is None:
+                continue
+            item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \
+                if self.binarization_args['with_spk_embed'] else None
+            spec_min.append(item['spec_min'])
+            spec_max.append(item['spec_max'])
+            f0_dict[item['wav_fn']] = item['f0']
+            builder.add_item(item)
+            lengths.append(item['len'])
+            total_sec += item['sec']
+        if prefix == 'train':
+            spec_max = np.max(spec_max, 0)
+            spec_min = np.min(spec_min, 0)
+            pitch_time = static_f0_time(f0_dict)
+            with open(hparams['config_path'], encoding='utf-8') as f:
+                _hparams = yaml.safe_load(f)
+                _hparams['spec_max'] = spec_max.tolist()
+                _hparams['spec_min'] = spec_min.tolist()
+                if self.speakers == 1:
+                    _hparams['f0_static'] = json.dumps(pitch_time)
+            with open(hparams['config_path'], 'w', encoding='utf-8') as f:
+                yaml.safe_dump(_hparams, f)
+        builder.finalize()
+        np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
+        print(f"| {prefix} total duration: {total_sec:.3f}s")
+
+    def process_item(self, item_name, meta_data, binarization_args):
+        from preprocessing.process_pipeline import File2Batch
+        return File2Batch.temporary_dict2processed_input(item_name, meta_data, self.phone_encoder)
+
+
+if __name__ == "__main__":
+    set_hparams()
+    SvcBinarizer().process()
diff --git a/run.py b/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..0935dee5ffc8670b6a68a9466179bd926c6de8c6
--- /dev/null
+++ b/run.py
@@ -0,0 +1,17 @@
+import importlib
+
+from utils.hparams import set_hparams, hparams
+
+set_hparams(print_hparams=False)
+
+
+def run_task():
+    assert hparams['task_cls'] != ''
+    pkg = ".".join(hparams["task_cls"].split(".")[:-1])
+    cls_name = hparams["task_cls"].split(".")[-1]
+    task_cls = getattr(importlib.import_module(pkg), cls_name)
+    task_cls.start()
+
+
+if __name__ == '__main__':
+    run_task()
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diff --git a/training/base_task.py b/training/base_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..796db8688c8f6b48320eb59d7576b6d5412b82d4
--- /dev/null
+++ b/training/base_task.py
@@ -0,0 +1,334 @@
+import logging
+import os
+import random
+import shutil
+import sys
+
+import matplotlib
+import numpy as np
+import torch.distributed as dist
+import torch.utils.data
+from pytorch_lightning.loggers import TensorBoardLogger
+from torch import nn
+
+import utils
+from utils.hparams import hparams, set_hparams
+from utils.pl_utils import LatestModelCheckpoint, BaseTrainer, data_loader, DDP
+
+matplotlib.use('Agg')
+torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system'))
+
+log_format = '%(asctime)s %(message)s'
+logging.basicConfig(stream=sys.stdout, level=logging.INFO,
+                    format=log_format, datefmt='%m/%d %I:%M:%S %p')
+
+
+class BaseTask(nn.Module):
+    '''
+        Base class for training tasks.
+        1. *load_ckpt*:
+            load checkpoint;
+        2. *training_step*:
+            record and log the loss;
+        3. *optimizer_step*:
+            run backwards step;
+        4. *start*:
+            load training configs, backup code, log to tensorboard, start training;
+        5. *configure_ddp* and *init_ddp_connection*:
+            start parallel training.
+
+        Subclasses should define:
+        1. *build_model*, *build_optimizer*, *build_scheduler*:
+            how to build the model, the optimizer and the training scheduler;
+        2. *_training_step*:
+            one training step of the model;
+        3. *validation_end* and *_validation_end*:
+            postprocess the validation output.
+    '''
+
+    def __init__(self, *args, **kwargs):
+        # dataset configs
+        super(BaseTask, self).__init__(*args, **kwargs)
+        self.current_epoch = 0
+        self.global_step = 0
+        self.loaded_optimizer_states_dict = {}
+        self.trainer = None
+        self.logger = None
+        self.on_gpu = False
+        self.use_dp = False
+        self.use_ddp = False
+        self.example_input_array = None
+
+        self.max_tokens = hparams['max_tokens']
+        self.max_sentences = hparams['max_sentences']
+        self.max_eval_tokens = hparams['max_eval_tokens']
+        if self.max_eval_tokens == -1:
+            hparams['max_eval_tokens'] = self.max_eval_tokens = self.max_tokens
+        self.max_eval_sentences = hparams['max_eval_sentences']
+        if self.max_eval_sentences == -1:
+            hparams['max_eval_sentences'] = self.max_eval_sentences = self.max_sentences
+
+        self.model = None
+        self.training_losses_meter = None
+
+    ###########
+    # Training, validation and testing
+    ###########
+    def build_model(self):
+        raise NotImplementedError
+
+    def load_ckpt(self, ckpt_base_dir, current_model_name=None, model_name='model', force=True, strict=True):
+        # This function is updated on 2021.12.13
+        if current_model_name is None:
+            current_model_name = model_name
+        utils.load_ckpt(self.__getattr__(current_model_name), ckpt_base_dir, current_model_name, force, strict)
+
+    def on_epoch_start(self):
+        self.training_losses_meter = {'total_loss': utils.AvgrageMeter()}
+
+    def _training_step(self, sample, batch_idx, optimizer_idx):
+        """
+
+        :param sample:
+        :param batch_idx:
+        :return: total loss: torch.Tensor, loss_log: dict
+        """
+        raise NotImplementedError
+
+    def training_step(self, sample, batch_idx, optimizer_idx=-1):
+        loss_ret = self._training_step(sample, batch_idx, optimizer_idx)
+        self.opt_idx = optimizer_idx
+        if loss_ret is None:
+            return {'loss': None}
+        total_loss, log_outputs = loss_ret
+        log_outputs = utils.tensors_to_scalars(log_outputs)
+        for k, v in log_outputs.items():
+            if k not in self.training_losses_meter:
+                self.training_losses_meter[k] = utils.AvgrageMeter()
+            if not np.isnan(v):
+                self.training_losses_meter[k].update(v)
+        self.training_losses_meter['total_loss'].update(total_loss.item())
+
+        try:
+            log_outputs['lr'] = self.scheduler.get_lr()
+            if isinstance(log_outputs['lr'], list):
+                log_outputs['lr'] = log_outputs['lr'][0]
+        except:
+            pass
+
+        # log_outputs['all_loss'] = total_loss.item()
+        progress_bar_log = log_outputs
+        tb_log = {f'tr/{k}': v for k, v in log_outputs.items()}
+        return {
+            'loss': total_loss,
+            'progress_bar': progress_bar_log,
+            'log': tb_log
+        }
+
+    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx):
+        optimizer.step()
+        optimizer.zero_grad()
+        if self.scheduler is not None:
+            self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
+
+    def on_epoch_end(self):
+        loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()}
+        print(f"\n==============\n "
+              f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}"
+              f"\n==============\n")
+
+    def validation_step(self, sample, batch_idx):
+        """
+
+        :param sample:
+        :param batch_idx:
+        :return: output: dict
+        """
+        raise NotImplementedError
+
+    def _validation_end(self, outputs):
+        """
+
+        :param outputs:
+        :return: loss_output: dict
+        """
+        raise NotImplementedError
+
+    def validation_end(self, outputs):
+        loss_output = self._validation_end(outputs)
+        print(f"\n==============\n "
+              f"valid results: {loss_output}"
+              f"\n==============\n")
+        return {
+            'log': {f'val/{k}': v for k, v in loss_output.items()},
+            'val_loss': loss_output['total_loss']
+        }
+
+    def build_scheduler(self, optimizer):
+        raise NotImplementedError
+
+    def build_optimizer(self, model):
+        raise NotImplementedError
+
+    def configure_optimizers(self):
+        optm = self.build_optimizer(self.model)
+        self.scheduler = self.build_scheduler(optm)
+        return [optm]
+
+    def test_start(self):
+        pass
+
+    def test_step(self, sample, batch_idx):
+        return self.validation_step(sample, batch_idx)
+
+    def test_end(self, outputs):
+        return self.validation_end(outputs)
+
+    ###########
+    # Running configuration
+    ###########
+
+    @classmethod
+    def start(cls):
+        set_hparams()
+        os.environ['MASTER_PORT'] = str(random.randint(15000, 30000))
+        random.seed(hparams['seed'])
+        np.random.seed(hparams['seed'])
+        task = cls()
+        work_dir = hparams['work_dir']
+        trainer = BaseTrainer(checkpoint_callback=LatestModelCheckpoint(
+            filepath=work_dir,
+            verbose=True,
+            monitor='val_loss',
+            mode='min',
+            num_ckpt_keep=hparams['num_ckpt_keep'],
+            save_best=hparams['save_best'],
+            period=1 if hparams['save_ckpt'] else 100000
+        ),
+            logger=TensorBoardLogger(
+                save_dir=work_dir,
+                name='lightning_logs',
+                version='lastest'
+            ),
+            gradient_clip_val=hparams['clip_grad_norm'],
+            val_check_interval=hparams['val_check_interval'],
+            row_log_interval=hparams['log_interval'],
+            max_updates=hparams['max_updates'],
+            num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams[
+                'validate'] else 10000,
+            accumulate_grad_batches=hparams['accumulate_grad_batches'])
+        if not hparams['infer']:  # train
+            # Copy spk_map.json to work dir
+            spk_map = os.path.join(work_dir, 'spk_map.json')
+            spk_map_orig = os.path.join(hparams['binary_data_dir'], 'spk_map.json')
+            if not os.path.exists(spk_map) and os.path.exists(spk_map_orig):
+                shutil.copy(spk_map_orig, spk_map)
+                print(f"| Copied spk map to {spk_map}.")
+            trainer.checkpoint_callback.task = task
+            trainer.fit(task)
+        else:
+            trainer.test(task)
+
+    @staticmethod
+    def configure_ddp(model, device_ids):
+        model = DDP(
+            model,
+            device_ids=device_ids,
+            find_unused_parameters=True
+        )
+        if dist.get_rank() != 0 and not hparams['debug']:
+            sys.stdout = open(os.devnull, "w")
+            sys.stderr = open(os.devnull, "w")
+        random.seed(hparams['seed'])
+        np.random.seed(hparams['seed'])
+        return model
+
+    @staticmethod
+    def training_end(self, *args, **kwargs):
+        return None
+
+    def init_ddp_connection(self, proc_rank, world_size):
+        set_hparams(print_hparams=False)
+        # guarantees unique ports across jobs from same grid search
+        default_port = 12910
+        # if user gave a port number, use that one instead
+        try:
+            default_port = os.environ['MASTER_PORT']
+        except Exception:
+            os.environ['MASTER_PORT'] = str(default_port)
+
+        # figure out the root node addr
+        root_node = '127.0.0.2'
+        root_node = self.trainer.resolve_root_node_address(root_node)
+        os.environ['MASTER_ADDR'] = root_node
+        dist.init_process_group('nccl', rank=proc_rank, world_size=world_size)
+
+    @data_loader
+    def train_dataloader(self):
+        return None
+
+    @data_loader
+    def test_dataloader(self):
+        return None
+
+    @data_loader
+    def val_dataloader(self):
+        return None
+
+    def on_load_checkpoint(self, checkpoint):
+        pass
+
+    def on_save_checkpoint(self, checkpoint):
+        pass
+
+    def on_sanity_check_start(self):
+        pass
+
+    def on_train_start(self):
+        pass
+
+    def on_train_end(self):
+        pass
+
+    def on_batch_start(self, batch):
+        pass
+
+    def on_batch_end(self):
+        pass
+
+    def on_pre_performance_check(self):
+        pass
+
+    def on_post_performance_check(self):
+        pass
+
+    def on_before_zero_grad(self, optimizer):
+        pass
+
+    def on_after_backward(self):
+        pass
+
+    @staticmethod
+    def backward(loss, optimizer):
+        loss.backward()
+
+    def grad_norm(self, norm_type):
+        results = {}
+        total_norm = 0
+        for name, p in self.named_parameters():
+            if p.requires_grad:
+                try:
+                    param_norm = p.grad.data.norm(norm_type)
+                    total_norm += param_norm ** norm_type
+                    norm = param_norm ** (1 / norm_type)
+
+                    grad = round(norm.data.cpu().numpy().flatten()[0], 3)
+                    results['grad_{}_norm_{}'.format(norm_type, name)] = grad
+                except Exception:
+                    # this param had no grad
+                    pass
+
+        total_norm = total_norm ** (1. / norm_type)
+        grad = round(total_norm.data.cpu().numpy().flatten()[0], 3)
+        results['grad_{}_norm_total'.format(norm_type)] = grad
+        return results
diff --git a/training/svc_task.py b/training/svc_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f79f535ebc5cc845287a91626cabb3e287a1136
--- /dev/null
+++ b/training/svc_task.py
@@ -0,0 +1,481 @@
+import os
+from multiprocessing.pool import Pool
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.distributions
+import torch.nn.functional as F
+import torch.optim
+import torch.utils.data
+from tqdm import tqdm
+
+import utils
+from modules.commons.ssim import ssim
+from modules.diff.diffusion import GaussianDiffusion
+from modules.diff.net import DiffNet
+from modules.vocoders.nsf_hifigan import NsfHifiGAN, nsf_hifigan
+from preprocessing.hubertinfer import HubertEncoder
+from preprocessing.process_pipeline import get_pitch_parselmouth
+from training.base_task import BaseTask
+from utils import audio
+from utils.hparams import hparams
+from utils.pitch_utils import denorm_f0
+from utils.pl_utils import data_loader
+from utils.plot import spec_to_figure, f0_to_figure
+from utils.svc_utils import SvcDataset
+
+matplotlib.use('Agg')
+DIFF_DECODERS = {
+    'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins'])
+}
+
+
+class SvcTask(BaseTask):
+    def __init__(self):
+        super(SvcTask, self).__init__()
+        self.vocoder = NsfHifiGAN()
+        self.phone_encoder = HubertEncoder(hparams['hubert_path'])
+        self.saving_result_pool = None
+        self.saving_results_futures = None
+        self.stats = {}
+        self.dataset_cls = SvcDataset
+        self.mse_loss_fn = torch.nn.MSELoss()
+        mel_losses = hparams['mel_loss'].split("|")
+        self.loss_and_lambda = {}
+        for i, l in enumerate(mel_losses):
+            if l == '':
+                continue
+            if ':' in l:
+                l, lbd = l.split(":")
+                lbd = float(lbd)
+            else:
+                lbd = 1.0
+            self.loss_and_lambda[l] = lbd
+        print("| Mel losses:", self.loss_and_lambda)
+
+    def build_dataloader(self, dataset, shuffle, max_tokens=None, max_sentences=None,
+                         required_batch_size_multiple=-1, endless=False, batch_by_size=True):
+        devices_cnt = torch.cuda.device_count()
+        if devices_cnt == 0:
+            devices_cnt = 1
+        if required_batch_size_multiple == -1:
+            required_batch_size_multiple = devices_cnt
+
+        def shuffle_batches(batches):
+            np.random.shuffle(batches)
+            return batches
+
+        if max_tokens is not None:
+            max_tokens *= devices_cnt
+        if max_sentences is not None:
+            max_sentences *= devices_cnt
+        indices = dataset.ordered_indices()
+        if batch_by_size:
+            batch_sampler = utils.batch_by_size(
+                indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
+                required_batch_size_multiple=required_batch_size_multiple,
+            )
+        else:
+            batch_sampler = []
+            for i in range(0, len(indices), max_sentences):
+                batch_sampler.append(indices[i:i + max_sentences])
+
+        if shuffle:
+            batches = shuffle_batches(list(batch_sampler))
+            if endless:
+                batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))]
+        else:
+            batches = batch_sampler
+            if endless:
+                batches = [b for _ in range(1000) for b in batches]
+        num_workers = dataset.num_workers
+        if self.trainer.use_ddp:
+            num_replicas = dist.get_world_size()
+            rank = dist.get_rank()
+            batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0]
+        return torch.utils.data.DataLoader(dataset,
+                                           collate_fn=dataset.collater,
+                                           batch_sampler=batches,
+                                           num_workers=num_workers,
+                                           pin_memory=False)
+
+    def test_start(self):
+        self.saving_result_pool = Pool(8)
+        self.saving_results_futures = []
+        self.vocoder = nsf_hifigan
+
+    def test_end(self, outputs):
+        self.saving_result_pool.close()
+        [f.get() for f in tqdm(self.saving_results_futures)]
+        self.saving_result_pool.join()
+        return {}
+
+    @data_loader
+    def train_dataloader(self):
+        train_dataset = self.dataset_cls(hparams['train_set_name'], shuffle=True)
+        return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences,
+                                     endless=hparams['endless_ds'])
+
+    @data_loader
+    def val_dataloader(self):
+        valid_dataset = self.dataset_cls(hparams['valid_set_name'], shuffle=False)
+        return self.build_dataloader(valid_dataset, False, self.max_eval_tokens, self.max_eval_sentences)
+
+    @data_loader
+    def test_dataloader(self):
+        test_dataset = self.dataset_cls(hparams['test_set_name'], shuffle=False)
+        return self.build_dataloader(test_dataset, False, self.max_eval_tokens,
+                                     self.max_eval_sentences, batch_by_size=False)
+
+    def build_model(self):
+        self.build_tts_model()
+        if hparams['load_ckpt'] != '':
+            self.load_ckpt(hparams['load_ckpt'], strict=True)
+        utils.print_arch(self.model)
+        return self.model
+
+    def build_tts_model(self):
+        mel_bins = hparams['audio_num_mel_bins']
+        self.model = GaussianDiffusion(
+            phone_encoder=self.phone_encoder,
+            out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
+            timesteps=hparams['timesteps'],
+            K_step=hparams['K_step'],
+            loss_type=hparams['diff_loss_type'],
+            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
+        )
+
+    def build_optimizer(self, model):
+        self.optimizer = optimizer = torch.optim.AdamW(
+            filter(lambda p: p.requires_grad, model.parameters()),
+            lr=hparams['lr'],
+            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
+            weight_decay=hparams['weight_decay'])
+        return optimizer
+
+    @staticmethod
+    def run_model(model, sample, return_output=False, infer=False):
+        '''
+            steps:
+            1. run the full model, calc the main loss
+            2. calculate loss for dur_predictor, pitch_predictor, energy_predictor
+        '''
+        hubert = sample['hubert']  # [B, T_t,H]
+        target = sample['mels']  # [B, T_s, 80]
+        mel2ph = sample['mel2ph']  # [B, T_s]
+        f0 = sample['f0']
+        uv = sample['uv']
+        energy = sample.get('energy')
+
+        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
+        output = model(hubert, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer)
+
+        losses = {}
+        if 'diff_loss' in output:
+            losses['mel'] = output['diff_loss']
+        if not return_output:
+            return losses
+        else:
+            return losses, output
+
+    def build_scheduler(self, optimizer):
+        return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5)
+
+    def _training_step(self, sample, batch_idx, _):
+        log_outputs = self.run_model(self.model, sample)
+        total_loss = sum([v for v in log_outputs.values() if isinstance(v, torch.Tensor) and v.requires_grad])
+        log_outputs['batch_size'] = sample['hubert'].size()[0]
+        log_outputs['lr'] = self.scheduler.get_lr()[0]
+        return total_loss, log_outputs
+
+    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx):
+        if optimizer is None:
+            return
+        optimizer.step()
+        optimizer.zero_grad()
+        if self.scheduler is not None:
+            self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
+
+    def validation_step(self, sample, batch_idx):
+        outputs = {}
+        hubert = sample['hubert']  # [B, T_t]
+        energy = sample.get('energy')
+        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
+        mel2ph = sample['mel2ph']
+
+        outputs['losses'] = {}
+
+        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
+
+        outputs['total_loss'] = sum(outputs['losses'].values())
+        outputs['nsamples'] = sample['nsamples']
+        outputs = utils.tensors_to_scalars(outputs)
+        if batch_idx < hparams['num_valid_plots']:
+            model_out = self.model(
+                hubert, spk_embed=spk_embed, mel2ph=mel2ph, f0=sample['f0'], uv=sample['uv'], energy=energy,
+                ref_mels=None, infer=True
+            )
+
+            gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
+            pred_f0 = model_out.get('f0_denorm')
+            self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
+            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
+            if hparams['use_pitch_embed']:
+                self.plot_pitch(batch_idx, sample, model_out)
+        return outputs
+
+    def _validation_end(self, outputs):
+        all_losses_meter = {
+            'total_loss': utils.AvgrageMeter(),
+        }
+        for output in outputs:
+            n = output['nsamples']
+            for k, v in output['losses'].items():
+                if k not in all_losses_meter:
+                    all_losses_meter[k] = utils.AvgrageMeter()
+                all_losses_meter[k].update(v, n)
+            all_losses_meter['total_loss'].update(output['total_loss'], n)
+        return {k: round(v.avg, 4) for k, v in all_losses_meter.items()}
+
+    ############
+    # losses
+    ############
+    def add_mel_loss(self, mel_out, target, losses, postfix='', mel_mix_loss=None):
+        if mel_mix_loss is None:
+            for loss_name, lbd in self.loss_and_lambda.items():
+                if 'l1' == loss_name:
+                    l = self.l1_loss(mel_out, target)
+                elif 'mse' == loss_name:
+                    raise NotImplementedError
+                elif 'ssim' == loss_name:
+                    l = self.ssim_loss(mel_out, target)
+                elif 'gdl' == loss_name:
+                    raise NotImplementedError
+                losses[f'{loss_name}{postfix}'] = l * lbd
+        else:
+            raise NotImplementedError
+
+    def l1_loss(self, decoder_output, target):
+        # decoder_output : B x T x n_mel
+        # target : B x T x n_mel
+        l1_loss = F.l1_loss(decoder_output, target, reduction='none')
+        weights = self.weights_nonzero_speech(target)
+        l1_loss = (l1_loss * weights).sum() / weights.sum()
+        return l1_loss
+
+    def ssim_loss(self, decoder_output, target, bias=6.0):
+        # decoder_output : B x T x n_mel
+        # target : B x T x n_mel
+        assert decoder_output.shape == target.shape
+        weights = self.weights_nonzero_speech(target)
+        decoder_output = decoder_output[:, None] + bias
+        target = target[:, None] + bias
+        ssim_loss = 1 - ssim(decoder_output, target, size_average=False)
+        ssim_loss = (ssim_loss * weights).sum() / weights.sum()
+        return ssim_loss
+
+    def add_pitch_loss(self, output, sample, losses):
+        if hparams['pitch_type'] == 'ph':
+            nonpadding = (sample['txt_tokens'] != 0).float()
+            pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss
+            losses['f0'] = (pitch_loss_fn(output['pitch_pred'][:, :, 0], sample['f0'],
+                                          reduction='none') * nonpadding).sum() \
+                           / nonpadding.sum() * hparams['lambda_f0']
+            return
+        mel2ph = sample['mel2ph']  # [B, T_s]
+        f0 = sample['f0']
+        uv = sample['uv']
+        nonpadding = (mel2ph != 0).float()
+        if hparams['pitch_type'] == 'frame':
+            self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding)
+
+    @staticmethod
+    def add_f0_loss(p_pred, f0, uv, losses, nonpadding):
+        assert p_pred[..., 0].shape == f0.shape
+        if hparams['use_uv']:
+            assert p_pred[..., 1].shape == uv.shape
+            losses['uv'] = (F.binary_cross_entropy_with_logits(
+                p_pred[:, :, 1], uv, reduction='none') * nonpadding).sum() \
+                           / nonpadding.sum() * hparams['lambda_uv']
+            nonpadding = nonpadding * (uv == 0).float()
+
+        f0_pred = p_pred[:, :, 0]
+        if hparams['pitch_loss'] in ['l1', 'l2']:
+            pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss
+            losses['f0'] = (pitch_loss_fn(f0_pred, f0, reduction='none') * nonpadding).sum() \
+                           / nonpadding.sum() * hparams['lambda_f0']
+        elif hparams['pitch_loss'] == 'ssim':
+            return NotImplementedError
+
+    @staticmethod
+    def add_energy_loss(energy_pred, energy, losses):
+        nonpadding = (energy != 0).float()
+        loss = (F.mse_loss(energy_pred, energy, reduction='none') * nonpadding).sum() / nonpadding.sum()
+        loss = loss * hparams['lambda_energy']
+        losses['e'] = loss
+
+    ############
+    # validation plots
+    ############
+    def plot_mel(self, batch_idx, spec, spec_out, name=None):
+        spec_cat = torch.cat([spec, spec_out], -1)
+        name = f'mel_{batch_idx}' if name is None else name
+        vmin = hparams['mel_vmin']
+        vmax = hparams['mel_vmax']
+        self.logger.experiment.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step)
+
+    def plot_pitch(self, batch_idx, sample, model_out):
+        f0 = sample['f0']
+        if hparams['pitch_type'] == 'ph':
+            mel2ph = sample['mel2ph']
+            f0 = self.expand_f0_ph(f0, mel2ph)
+            f0_pred = self.expand_f0_ph(model_out['pitch_pred'][:, :, 0], mel2ph)
+            self.logger.experiment.add_figure(
+                f'f0_{batch_idx}', f0_to_figure(f0[0], None, f0_pred[0]), self.global_step)
+            return
+        f0 = denorm_f0(f0, sample['uv'], hparams)
+        if hparams['pitch_type'] == 'frame':
+            pitch_pred = denorm_f0(model_out['pitch_pred'][:, :, 0], sample['uv'], hparams)
+            self.logger.experiment.add_figure(
+                f'f0_{batch_idx}', f0_to_figure(f0[0], None, pitch_pred[0]), self.global_step)
+
+    def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None):
+        gt_wav = gt_wav[0].cpu().numpy()
+        wav_out = wav_out[0].cpu().numpy()
+        gt_f0 = gt_f0[0].cpu().numpy()
+        f0 = f0[0].cpu().numpy()
+        if is_mel:
+            gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0)
+            wav_out = self.vocoder.spec2wav(wav_out, f0=f0)
+        self.logger.experiment.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'],
+                                         global_step=self.global_step)
+        self.logger.experiment.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'],
+                                         global_step=self.global_step)
+
+    ############
+    # infer
+    ############
+    def test_step(self, sample, batch_idx):
+        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
+        hubert = sample['hubert']
+        ref_mels = None
+        mel2ph = sample['mel2ph']
+        f0 = sample['f0']
+        uv = sample['uv']
+        outputs = self.model(hubert, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels,
+                             infer=True)
+        sample['outputs'] = self.model.out2mel(outputs['mel_out'])
+        sample['mel2ph_pred'] = outputs['mel2ph']
+        sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams)
+        sample['f0_pred'] = outputs.get('f0_denorm')
+        return self.after_infer(sample)
+
+    def after_infer(self, predictions):
+        if self.saving_result_pool is None and not hparams['profile_infer']:
+            self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16))
+            self.saving_results_futures = []
+        predictions = utils.unpack_dict_to_list(predictions)
+        t = tqdm(predictions)
+        for num_predictions, prediction in enumerate(t):
+            for k, v in prediction.items():
+                if type(v) is torch.Tensor:
+                    prediction[k] = v.cpu().numpy()
+
+            item_name = prediction.get('item_name')
+
+            # remove paddings
+            mel_gt = prediction["mels"]
+            mel_gt_mask = np.abs(mel_gt).sum(-1) > 0
+            mel_gt = mel_gt[mel_gt_mask]
+            mel_pred = prediction["outputs"]
+            mel_pred_mask = np.abs(mel_pred).sum(-1) > 0
+            mel_pred = mel_pred[mel_pred_mask]
+            mel_gt = np.clip(mel_gt, hparams['mel_vmin'], hparams['mel_vmax'])
+            mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax'])
+
+            f0_gt = prediction.get("f0")
+            f0_pred = f0_gt
+            if f0_pred is not None:
+                f0_gt = f0_gt[mel_gt_mask]
+                if len(f0_pred) > len(mel_pred_mask):
+                    f0_pred = f0_pred[:len(mel_pred_mask)]
+                f0_pred = f0_pred[mel_pred_mask]
+            gen_dir = os.path.join(hparams['work_dir'],
+                                   f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}')
+            wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred)
+            if not hparams['profile_infer']:
+                os.makedirs(gen_dir, exist_ok=True)
+                os.makedirs(f'{gen_dir}/wavs', exist_ok=True)
+                os.makedirs(f'{gen_dir}/plot', exist_ok=True)
+                os.makedirs(os.path.join(hparams['work_dir'], 'P_mels_npy'), exist_ok=True)
+                os.makedirs(os.path.join(hparams['work_dir'], 'G_mels_npy'), exist_ok=True)
+                self.saving_results_futures.append(
+                    self.saving_result_pool.apply_async(self.save_result, args=[
+                        wav_pred, mel_pred, 'P', item_name, gen_dir]))
+
+                if mel_gt is not None and hparams['save_gt']:
+                    wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt)
+                    self.saving_results_futures.append(
+                        self.saving_result_pool.apply_async(self.save_result, args=[
+                            wav_gt, mel_gt, 'G', item_name, gen_dir]))
+                    if hparams['save_f0']:
+                        import matplotlib.pyplot as plt
+                        f0_pred_ = f0_pred
+                        f0_gt_, _ = get_pitch_parselmouth(wav_gt, mel_gt, hparams)
+                        fig = plt.figure()
+                        plt.plot(f0_pred_, label=r'$f0_P$')
+                        plt.plot(f0_gt_, label=r'$f0_G$')
+                        plt.legend()
+                        plt.tight_layout()
+                        plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png')
+                        plt.close(fig)
+
+                t.set_description(
+                    f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
+            else:
+                if 'gen_wav_time' not in self.stats:
+                    self.stats['gen_wav_time'] = 0
+                self.stats['gen_wav_time'] += len(wav_pred) / hparams['audio_sample_rate']
+                print('gen_wav_time: ', self.stats['gen_wav_time'])
+
+        return {}
+
+    @staticmethod
+    def save_result(wav_out, mel, prefix, item_name, gen_dir):
+        item_name = item_name.replace('/', '-')
+        base_fn = f'[{item_name}][{prefix}]'
+        base_fn += ('-' + hparams['exp_name'])
+        np.save(os.path.join(hparams['work_dir'], f'{prefix}_mels_npy', item_name), mel)
+        audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', 24000,  # hparams['audio_sample_rate'],
+                       norm=hparams['out_wav_norm'])
+        fig = plt.figure(figsize=(14, 10))
+        spec_vmin = hparams['mel_vmin']
+        spec_vmax = hparams['mel_vmax']
+        heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax)
+        fig.colorbar(heatmap)
+        f0, _ = get_pitch_parselmouth(wav_out, mel, hparams)
+        f0 = (f0 - 100) / (800 - 100) * 80 * (f0 > 0)
+        plt.plot(f0, c='white', linewidth=1, alpha=0.6)
+        plt.tight_layout()
+        plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png', dpi=1000)
+        plt.close(fig)
+
+    ##############
+    # utils
+    ##############
+    @staticmethod
+    def expand_f0_ph(f0, mel2ph):
+        f0 = denorm_f0(f0, None, hparams)
+        f0 = F.pad(f0, [1, 0])
+        f0 = torch.gather(f0, 1, mel2ph)  # [B, T_mel]
+        return f0
+
+    @staticmethod
+    def weights_nonzero_speech(target):
+        # target : B x T x mel
+        # Assign weight 1.0 to all labels except for padding (id=0).
+        dim = target.size(-1)
+        return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim)
diff --git a/training/train_pipeline.py b/training/train_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..7904a7593e942456f2e98ca06f2796cb78ac4daa
--- /dev/null
+++ b/training/train_pipeline.py
@@ -0,0 +1,227 @@
+import torch
+from torch.nn import functional as F
+
+from utils.hparams import hparams
+from utils.pitch_utils import f0_to_coarse, denorm_f0
+
+
+class Batch2Loss:
+    '''
+        pipeline: batch -> insert1 -> module1 -> insert2 -> module2 -> insert3 -> module3 -> insert4 -> module4 -> loss
+    '''
+
+    @staticmethod
+    def insert1(pitch_midi, midi_dur, is_slur,  # variables
+                midi_embed, midi_dur_layer, is_slur_embed):  # modules
+        '''
+            add embeddings for midi, midi_dur, slur
+        '''
+        midi_embedding = midi_embed(pitch_midi)
+        midi_dur_embedding, slur_embedding = 0, 0
+        if midi_dur is not None:
+            midi_dur_embedding = midi_dur_layer(midi_dur[:, :, None])  # [B, T, 1] -> [B, T, H]
+        if is_slur is not None:
+            slur_embedding = is_slur_embed(is_slur)
+        return midi_embedding, midi_dur_embedding, slur_embedding
+
+    @staticmethod
+    def module1(fs2_encoder,  # modules
+                txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding):  # variables
+        '''
+            get *encoder_out* == fs2_encoder(*txt_tokens*, some embeddings)
+        '''
+        encoder_out = fs2_encoder(txt_tokens, midi_embedding, midi_dur_embedding, slur_embedding)
+        return encoder_out
+
+    @staticmethod
+    def insert2(encoder_out, spk_embed_id, spk_embed_dur_id, spk_embed_f0_id, src_nonpadding,  # variables
+                spk_embed_proj):  # modules
+        '''
+            1. add embeddings for pspk, spk_dur, sk_f0
+            2. get *dur_inp* ~= *encoder_out* + *spk_embed_dur*
+        '''
+        # add ref style embed
+        # Not implemented
+        # variance encoder
+        var_embed = 0
+
+        # encoder_out_dur denotes encoder outputs for duration predictor
+        # in speech adaptation, duration predictor use old speaker embedding
+        if hparams['use_spk_embed']:
+            spk_embed_dur = spk_embed_f0 = spk_embed = spk_embed_proj(spk_embed_id)[:, None, :]
+        elif hparams['use_spk_id']:
+            if spk_embed_dur_id is None:
+                spk_embed_dur_id = spk_embed_id
+            if spk_embed_f0_id is None:
+                spk_embed_f0_id = spk_embed_id
+            spk_embed = spk_embed_proj(spk_embed_id)[:, None, :]
+            spk_embed_dur = spk_embed_f0 = spk_embed
+            if hparams['use_split_spk_id']:
+                spk_embed_dur = spk_embed_dur(spk_embed_dur_id)[:, None, :]
+                spk_embed_f0 = spk_embed_f0(spk_embed_f0_id)[:, None, :]
+        else:
+            spk_embed_dur = spk_embed_f0 = spk_embed = 0
+
+        # add dur
+        dur_inp = (encoder_out + var_embed + spk_embed_dur) * src_nonpadding
+        return var_embed, spk_embed, spk_embed_dur, spk_embed_f0, dur_inp
+
+    @staticmethod
+    def module2(dur_predictor, length_regulator,  # modules
+                dur_input, mel2ph, txt_tokens, all_vowel_tokens, ret, midi_dur=None):  # variables
+        '''
+            1. get *dur* ~= dur_predictor(*dur_inp*)
+            2. (mel2ph is None): get *mel2ph* ~= length_regulater(*dur*)
+        '''
+        src_padding = (txt_tokens == 0)
+        dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
+
+        if mel2ph is None:
+            dur, xs = dur_predictor.inference(dur_input, src_padding)
+            ret['dur'] = xs
+            dur = xs.squeeze(-1).exp() - 1.0
+            for i in range(len(dur)):
+                for j in range(len(dur[i])):
+                    if txt_tokens[i, j] in all_vowel_tokens:
+                        if j < len(dur[i]) - 1 and txt_tokens[i, j + 1] not in all_vowel_tokens:
+                            dur[i, j] = midi_dur[i, j] - dur[i, j + 1]
+                            if dur[i, j] < 0:
+                                dur[i, j] = 0
+                                dur[i, j + 1] = midi_dur[i, j]
+                        else:
+                            dur[i, j] = midi_dur[i, j]
+            dur[:, 0] = dur[:, 0] + 0.5
+            dur_acc = F.pad(torch.round(torch.cumsum(dur, axis=1)), (1, 0))
+            dur = torch.clamp(dur_acc[:, 1:] - dur_acc[:, :-1], min=0).long()
+            ret['dur_choice'] = dur
+            mel2ph = length_regulator(dur, src_padding).detach()
+        else:
+            ret['dur'] = dur_predictor(dur_input, src_padding)
+        ret['mel2ph'] = mel2ph
+
+        return mel2ph
+
+    @staticmethod
+    def insert3(encoder_out, mel2ph, var_embed, spk_embed_f0, src_nonpadding, tgt_nonpadding):  # variables
+        '''
+            1. get *decoder_inp* ~= gather *encoder_out* according to *mel2ph*
+            2. get *pitch_inp* ~= *decoder_inp* + *spk_embed_f0*
+            3. get *pitch_inp_ph* ~= *encoder_out* + *spk_embed_f0*
+        '''
+        decoder_inp = F.pad(encoder_out, [0, 0, 1, 0])
+        mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]])
+        decoder_inp = decoder_inp_origin = torch.gather(decoder_inp, 1, mel2ph_)  # [B, T, H]
+
+        pitch_inp = (decoder_inp_origin + var_embed + spk_embed_f0) * tgt_nonpadding
+        pitch_inp_ph = (encoder_out + var_embed + spk_embed_f0) * src_nonpadding
+        return decoder_inp, pitch_inp, pitch_inp_ph
+
+    @staticmethod
+    def module3(pitch_predictor, pitch_embed, energy_predictor, energy_embed,  # modules
+                pitch_inp, pitch_inp_ph, f0, uv, energy, mel2ph, is_training, ret):  # variables
+        '''
+            1. get *ret['pitch_pred']*, *ret['energy_pred']* ~= pitch_predictor(*pitch_inp*), energy_predictor(*pitch_inp*)
+            2. get *pitch_embedding* ~= pitch_embed(f0_to_coarse(denorm_f0(*f0* or *pitch_pred*))
+            3. get *energy_embedding* ~= energy_embed(energy_to_coarse(*energy* or *energy_pred*))
+        '''
+
+        def add_pitch(decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
+            if hparams['pitch_type'] == 'ph':
+                pitch_pred_inp = encoder_out.detach() + hparams['predictor_grad'] * (encoder_out - encoder_out.detach())
+                pitch_padding = (encoder_out.sum().abs() == 0)
+                ret['pitch_pred'] = pitch_pred = pitch_predictor(pitch_pred_inp)
+                if f0 is None:
+                    f0 = pitch_pred[:, :, 0]
+                ret['f0_denorm'] = f0_denorm = denorm_f0(f0, None, hparams, pitch_padding=pitch_padding)
+                pitch = f0_to_coarse(f0_denorm)  # start from 0 [B, T_txt]
+                pitch = F.pad(pitch, [1, 0])
+                pitch = torch.gather(pitch, 1, mel2ph)  # [B, T_mel]
+                pitch_embedding = pitch_embed(pitch)
+                return pitch_embedding
+
+            decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+
+            pitch_padding = (mel2ph == 0)
+
+            if hparams['pitch_ar']:
+                ret['pitch_pred'] = pitch_pred = pitch_predictor(decoder_inp, f0 if is_training else None)
+                if f0 is None:
+                    f0 = pitch_pred[:, :, 0]
+            else:
+                ret['pitch_pred'] = pitch_pred = pitch_predictor(decoder_inp)
+                if f0 is None:
+                    f0 = pitch_pred[:, :, 0]
+                if hparams['use_uv'] and uv is None:
+                    uv = pitch_pred[:, :, 1] > 0
+            ret['f0_denorm'] = f0_denorm = denorm_f0(f0, uv, hparams, pitch_padding=pitch_padding)
+            if pitch_padding is not None:
+                f0[pitch_padding] = 0
+
+            pitch = f0_to_coarse(f0_denorm)  # start from 0
+            pitch_embedding = pitch_embed(pitch)
+            return pitch_embedding
+
+        def add_energy(decoder_inp, energy, ret):
+            decoder_inp = decoder_inp.detach() + hparams['predictor_grad'] * (decoder_inp - decoder_inp.detach())
+            ret['energy_pred'] = energy_pred = energy_predictor(decoder_inp)[:, :, 0]
+            if energy is None:
+                energy = energy_pred
+            energy = torch.clamp(energy * 256 // 4, max=255).long()  # energy_to_coarse
+            energy_embedding = energy_embed(energy)
+            return energy_embedding
+
+        # add pitch and energy embed
+        nframes = mel2ph.size(1)
+
+        pitch_embedding = 0
+        if hparams['use_pitch_embed']:
+            if f0 is not None:
+                delta_l = nframes - f0.size(1)
+                if delta_l > 0:
+                    f0 = torch.cat((f0, torch.FloatTensor([[x[-1]] * delta_l for x in f0]).to(f0.device)), 1)
+                f0 = f0[:, :nframes]
+            if uv is not None:
+                delta_l = nframes - uv.size(1)
+                if delta_l > 0:
+                    uv = torch.cat((uv, torch.FloatTensor([[x[-1]] * delta_l for x in uv]).to(uv.device)), 1)
+                uv = uv[:, :nframes]
+            pitch_embedding = add_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out=pitch_inp_ph)
+
+        energy_embedding = 0
+        if hparams['use_energy_embed']:
+            if energy is not None:
+                delta_l = nframes - energy.size(1)
+                if delta_l > 0:
+                    energy = torch.cat(
+                        (energy, torch.FloatTensor([[x[-1]] * delta_l for x in energy]).to(energy.device)), 1)
+                energy = energy[:, :nframes]
+            energy_embedding = add_energy(pitch_inp, energy, ret)
+
+        return pitch_embedding, energy_embedding
+
+    @staticmethod
+    def insert4(decoder_inp, pitch_embedding, energy_embedding, spk_embed, ret, tgt_nonpadding):
+        '''
+            *decoder_inp* ~= *decoder_inp* + embeddings for spk, pitch, energy
+        '''
+        ret['decoder_inp'] = decoder_inp = (
+                                                   decoder_inp + pitch_embedding + energy_embedding + spk_embed) * tgt_nonpadding
+        return decoder_inp
+
+    @staticmethod
+    def module4(diff_main_loss,  # modules
+                norm_spec, decoder_inp_t, ret, K_step, batch_size, device):  # variables
+        '''
+            training diffusion using spec as input and decoder_inp as condition.
+            
+            Args:
+                norm_spec: (normalized) spec
+                decoder_inp_t: (transposed) decoder_inp
+            Returns:
+                ret['diff_loss']
+        '''
+        t = torch.randint(0, K_step, (batch_size,), device=device).long()
+        norm_spec = norm_spec.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
+        ret['diff_loss'] = diff_main_loss(norm_spec, t, cond=decoder_inp_t)
+        # nonpadding = (mel2ph != 0).float()
+        # ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding)
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..edd05b1cbcf86d489ce395ab90e50587c7bef4c6
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1,250 @@
+import glob
+import logging
+import re
+import time
+from collections import defaultdict
+import os
+import sys
+import shutil
+import types
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.distributed as dist
+from torch import nn
+
+
+def tensors_to_scalars(metrics):
+    new_metrics = {}
+    for k, v in metrics.items():
+        if isinstance(v, torch.Tensor):
+            v = v.item()
+        if type(v) is dict:
+            v = tensors_to_scalars(v)
+        new_metrics[k] = v
+    return new_metrics
+
+
+class AvgrageMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.avg = 0
+        self.sum = 0
+        self.cnt = 0
+
+    def update(self, val, n=1):
+        self.sum += val * n
+        self.cnt += n
+        self.avg = self.sum / self.cnt
+
+
+def collate_1d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1):
+    """Convert a list of 1d tensors into a padded 2d tensor."""
+    size = max(v.size(0) for v in values) if max_len is None else max_len
+    res = values[0].new(len(values), size).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        if shift_right:
+            dst[1:] = src[:-1]
+            dst[0] = shift_id
+        else:
+            dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
+    return res
+
+
+def collate_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None):
+    """Convert a list of 2d tensors into a padded 3d tensor."""
+    size = max(v.size(0) for v in values) if max_len is None else max_len
+    res = values[0].new(len(values), size, values[0].shape[1]).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        if shift_right:
+            dst[1:] = src[:-1]
+        else:
+            dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
+    return res
+
+
+def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+    if len(batch) == 0:
+        return 0
+    if len(batch) == max_sentences:
+        return 1
+    if num_tokens > max_tokens:
+        return 1
+    return 0
+
+
+def batch_by_size(
+        indices, num_tokens_fn, max_tokens=None, max_sentences=None,
+        required_batch_size_multiple=1, distributed=False
+):
+    """
+    Yield mini-batches of indices bucketed by size. Batches may contain
+    sequences of different lengths.
+
+    Args:
+        indices (List[int]): ordered list of dataset indices
+        num_tokens_fn (callable): function that returns the number of tokens at
+            a given index
+        max_tokens (int, optional): max number of tokens in each batch
+            (default: None).
+        max_sentences (int, optional): max number of sentences in each
+            batch (default: None).
+        required_batch_size_multiple (int, optional): require batch size to
+            be a multiple of N (default: 1).
+    """
+    max_tokens = max_tokens if max_tokens is not None else sys.maxsize
+    max_sentences = max_sentences if max_sentences is not None else sys.maxsize
+    bsz_mult = required_batch_size_multiple
+
+    if isinstance(indices, types.GeneratorType):
+        indices = np.fromiter(indices, dtype=np.int64, count=-1)
+
+    sample_len = 0
+    sample_lens = []
+    batch = []
+    batches = []
+    for i in range(len(indices)):
+        idx = indices[i]
+        num_tokens = num_tokens_fn(idx)
+        sample_lens.append(num_tokens)
+        sample_len = max(sample_len, num_tokens)
+        assert sample_len <= max_tokens, (
+            "sentence at index {} of size {} exceeds max_tokens "
+            "limit of {}!".format(idx, sample_len, max_tokens)
+        )
+        num_tokens = (len(batch) + 1) * sample_len
+
+        if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+            mod_len = max(
+                bsz_mult * (len(batch) // bsz_mult),
+                len(batch) % bsz_mult,
+            )
+            batches.append(batch[:mod_len])
+            batch = batch[mod_len:]
+            sample_lens = sample_lens[mod_len:]
+            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
+        batch.append(idx)
+    if len(batch) > 0:
+        batches.append(batch)
+    return batches
+
+
+def make_positions(tensor, padding_idx):
+    """Replace non-padding symbols with their position numbers.
+
+    Position numbers begin at padding_idx+1. Padding symbols are ignored.
+    """
+    # The series of casts and type-conversions here are carefully
+    # balanced to both work with ONNX export and XLA. In particular XLA
+    # prefers ints, cumsum defaults to output longs, and ONNX doesn't know
+    # how to handle the dtype kwarg in cumsum.
+    mask = tensor.ne(padding_idx).int()
+    return (
+                   torch.cumsum(mask, dim=1).type_as(mask) * mask
+           ).long() + padding_idx
+
+
+def softmax(x, dim):
+    return F.softmax(x, dim=dim, dtype=torch.float32)
+
+
+def unpack_dict_to_list(samples):
+    samples_ = []
+    bsz = samples.get('outputs').size(0)
+    for i in range(bsz):
+        res = {}
+        for k, v in samples.items():
+            try:
+                res[k] = v[i]
+            except:
+                pass
+        samples_.append(res)
+    return samples_
+
+
+def load_ckpt(cur_model, ckpt_base_dir, prefix_in_ckpt='model', force=True, strict=True):
+    if os.path.isfile(ckpt_base_dir):
+        base_dir = os.path.dirname(ckpt_base_dir)
+        checkpoint_path = [ckpt_base_dir]
+    else:
+        base_dir = ckpt_base_dir
+        checkpoint_path = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
+        lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x.replace('\\','/'))[0]))
+    if len(checkpoint_path) > 0:
+        checkpoint_path = checkpoint_path[-1]
+        state_dict = torch.load(checkpoint_path, map_location="cpu")["state_dict"]
+        state_dict = {k[len(prefix_in_ckpt) + 1:]: v for k, v in state_dict.items()
+                      if k.startswith(f'{prefix_in_ckpt}.')}
+        if not strict:
+            cur_model_state_dict = cur_model.state_dict()
+            unmatched_keys = []
+            for key, param in state_dict.items():
+                if key in cur_model_state_dict:
+                    new_param = cur_model_state_dict[key]
+                    if new_param.shape != param.shape:
+                        unmatched_keys.append(key)
+                        print("| Unmatched keys: ", key, new_param.shape, param.shape)
+            for key in unmatched_keys:
+                del state_dict[key]
+        cur_model.load_state_dict(state_dict, strict=strict)
+        print(f"| load '{prefix_in_ckpt}' from '{checkpoint_path}'.")
+    else:
+        e_msg = f"| ckpt not found in {base_dir}."
+        if force:
+            assert False, e_msg
+        else:
+            print(e_msg)
+
+
+def remove_padding(x, padding_idx=0):
+    if x is None:
+        return None
+    assert len(x.shape) in [1, 2]
+    if len(x.shape) == 2:  # [T, H]
+        return x[np.abs(x).sum(-1) != padding_idx]
+    elif len(x.shape) == 1:  # [T]
+        return x[x != padding_idx]
+
+
+class Timer:
+    timer_map = {}
+
+    def __init__(self, name, print_time=False):
+        if name not in Timer.timer_map:
+            Timer.timer_map[name] = 0
+        self.name = name
+        self.print_time = print_time
+
+    def __enter__(self):
+        self.t = time.time()
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        Timer.timer_map[self.name] += time.time() - self.t
+        if self.print_time:
+            print(self.name, Timer.timer_map[self.name])
+
+
+def print_arch(model, model_name='model'):
+    #print(f"| {model_name} Arch: ", model)
+    num_params(model, model_name=model_name)
+
+
+def num_params(model, print_out=True, model_name="model"):
+    parameters = filter(lambda p: p.requires_grad, model.parameters())
+    parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
+    if print_out:
+        print(f'| {model_name} Trainable Parameters: %.3fM' % parameters)
+    return parameters
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diff --git a/utils/audio.py b/utils/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..aba7ab926cf793d085bbdc70c97f376001183fe1
--- /dev/null
+++ b/utils/audio.py
@@ -0,0 +1,56 @@
+import subprocess
+import matplotlib
+
+matplotlib.use('Agg')
+import librosa
+import librosa.filters
+import numpy as np
+from scipy import signal
+from scipy.io import wavfile
+
+
+def save_wav(wav, path, sr, norm=False):
+    if norm:
+        wav = wav / np.abs(wav).max()
+    wav *= 32767
+    # proposed by @dsmiller
+    wavfile.write(path, sr, wav.astype(np.int16))
+
+
+def get_hop_size(hparams):
+    hop_size = hparams['hop_size']
+    if hop_size is None:
+        assert hparams['frame_shift_ms'] is not None
+        hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate'])
+    return hop_size
+
+
+###########################################################################################
+def _stft(y, hparams):
+    return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams),
+                        win_length=hparams['win_size'], pad_mode='constant')
+
+
+def _istft(y, hparams):
+    return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size'])
+
+
+def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
+    '''compute right padding (final frame) or both sides padding (first and final frames)
+    '''
+    assert pad_sides in (1, 2)
+    # return int(fsize // 2)
+    pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
+    if pad_sides == 1:
+        return 0, pad
+    else:
+        return pad // 2, pad // 2 + pad % 2
+
+
+# Conversions
+def amp_to_db(x):
+    return 20 * np.log10(np.maximum(1e-5, x))
+
+
+def normalize(S, hparams):
+    return (S - hparams['min_level_db']) / -hparams['min_level_db']
diff --git a/utils/hparams.py b/utils/hparams.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2a341e3b61d6e2549c93b35698298efee469936
--- /dev/null
+++ b/utils/hparams.py
@@ -0,0 +1,136 @@
+import argparse
+import os
+
+import yaml
+
+global_print_hparams = True
+hparams = {}
+
+
+class Args:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            self.__setattr__(k, v)
+
+
+def override_config(old_config: dict, new_config: dict):
+    for k, v in new_config.items():
+        if isinstance(v, dict) and k in old_config:
+            override_config(old_config[k], new_config[k])
+        else:
+            old_config[k] = v
+
+
+def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True, reset=True,
+                infer=True):
+    '''
+        Load hparams from multiple sources:
+        1. config chain (i.e. first load base_config, then load config);
+        2. if reset == True, load from the (auto-saved) complete config file ('config.yaml')
+           which contains all settings and do not rely on base_config;
+        3. load from argument --hparams or hparams_str, as temporary modification.
+    '''
+    if config == '':
+        parser = argparse.ArgumentParser(description='neural music')
+        parser.add_argument('--config', type=str, default='',
+                            help='location of the data corpus')
+        parser.add_argument('--exp_name', type=str, default='', help='exp_name')
+        parser.add_argument('--hparams', type=str, default='',
+                            help='location of the data corpus')
+        parser.add_argument('--infer', action='store_true', help='infer')
+        parser.add_argument('--validate', action='store_true', help='validate')
+        parser.add_argument('--reset', action='store_true', help='reset hparams')
+        parser.add_argument('--debug', action='store_true', help='debug')
+        args, unknown = parser.parse_known_args()
+    else:
+        args = Args(config=config, exp_name=exp_name, hparams=hparams_str,
+                    infer=infer, validate=False, reset=reset, debug=False)
+    args_work_dir = ''
+    if args.exp_name != '':
+        args.work_dir = args.exp_name
+        args_work_dir = f'checkpoints/{args.work_dir}'
+
+    config_chains = []
+    loaded_config = set()
+
+    def load_config(config_fn):  # deep first
+        with open(config_fn, encoding='utf-8') as f:
+            hparams_ = yaml.safe_load(f)
+        loaded_config.add(config_fn)
+        if 'base_config' in hparams_:
+            ret_hparams = {}
+            if not isinstance(hparams_['base_config'], list):
+                hparams_['base_config'] = [hparams_['base_config']]
+            for c in hparams_['base_config']:
+                if c not in loaded_config:
+                    if c.startswith('.'):
+                        c = f'{os.path.dirname(config_fn)}/{c}'
+                        c = os.path.normpath(c)
+                    override_config(ret_hparams, load_config(c))
+            override_config(ret_hparams, hparams_)
+        else:
+            ret_hparams = hparams_
+        config_chains.append(config_fn)
+        return ret_hparams
+
+    global hparams
+    assert args.config != '' or args_work_dir != ''
+    saved_hparams = {}
+    if args_work_dir != 'checkpoints/':
+        ckpt_config_path = f'{args_work_dir}/config.yaml'
+        if os.path.exists(ckpt_config_path):
+            try:
+                with open(ckpt_config_path, encoding='utf-8') as f:
+                    saved_hparams.update(yaml.safe_load(f))
+            except:
+                pass
+        if args.config == '':
+            args.config = ckpt_config_path
+
+    hparams_ = {}
+
+    hparams_.update(load_config(args.config))
+
+    if not args.reset:
+        hparams_.update(saved_hparams)
+    hparams_['work_dir'] = args_work_dir
+
+    if args.hparams != "":
+        for new_hparam in args.hparams.split(","):
+            k, v = new_hparam.split("=")
+            if k not in hparams_:
+                hparams_[k] = eval(v)
+            if v in ['True', 'False'] or type(hparams_[k]) == bool:
+                hparams_[k] = eval(v)
+            else:
+                hparams_[k] = type(hparams_[k])(v)
+
+    if args_work_dir != '' and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer:
+        os.makedirs(hparams_['work_dir'], exist_ok=True)
+        with open(ckpt_config_path, 'w', encoding='utf-8') as f:
+            temp_haparams = hparams_
+            if 'base_config' in temp_haparams.keys():
+                del temp_haparams['base_config']
+            yaml.safe_dump(temp_haparams, f)
+
+    hparams_['infer'] = args.infer
+    hparams_['debug'] = args.debug
+    hparams_['validate'] = args.validate
+    global global_print_hparams
+    if global_hparams:
+        hparams.clear()
+        hparams.update(hparams_)
+
+    if print_hparams and global_print_hparams and global_hparams:
+        print('| Hparams chains: ', config_chains)
+        print('| Hparams: ')
+        for i, (k, v) in enumerate(sorted(hparams_.items())):
+            print(f"\033[;33;m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "")
+        print("")
+        global_print_hparams = False
+    # print(hparams_.keys())
+    if hparams.get('exp_name') is None:
+        hparams['exp_name'] = args.exp_name
+    if hparams_.get('exp_name') is None:
+        hparams_['exp_name'] = args.exp_name
+    return hparams_
diff --git a/utils/indexed_datasets.py b/utils/indexed_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..d135b36b25fd8dc2b78ae4761b77e4258f71b747
--- /dev/null
+++ b/utils/indexed_datasets.py
@@ -0,0 +1,73 @@
+import pickle
+from copy import deepcopy
+
+import numpy as np
+
+
+class IndexedDataset:
+    def __init__(self, path, num_cache=1):
+        super().__init__()
+        self.path = path
+        self.data_file = None
+        self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets']
+        self.data_file = open(f"{path}.data", 'rb', buffering=-1)
+        self.cache = []
+        self.num_cache = num_cache
+
+    def check_index(self, i):
+        if i < 0 or i >= len(self.data_offsets) - 1:
+            raise IndexError('index out of range')
+
+    def __del__(self):
+        if self.data_file:
+            self.data_file.close()
+
+    def __getitem__(self, i):
+        self.check_index(i)
+        if self.num_cache > 0:
+            for c in self.cache:
+                if c[0] == i:
+                    return c[1]
+        self.data_file.seek(self.data_offsets[i])
+        b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i])
+        item = pickle.loads(b)
+        if self.num_cache > 0:
+            self.cache = [(i, deepcopy(item))] + self.cache[:-1]
+        return item
+
+    def __len__(self):
+        return len(self.data_offsets) - 1
+
+
+class IndexedDatasetBuilder:
+    def __init__(self, path):
+        self.path = path
+        self.out_file = open(f"{path}.data", 'wb')
+        self.byte_offsets = [0]
+
+    def add_item(self, item):
+        s = pickle.dumps(item)
+        bytes = self.out_file.write(s)
+        self.byte_offsets.append(self.byte_offsets[-1] + bytes)
+
+    def finalize(self):
+        self.out_file.close()
+        np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets})
+
+
+if __name__ == "__main__":
+    import random
+    from tqdm import tqdm
+
+    ds_path = '/tmp/indexed_ds_example'
+    size = 100
+    items = [{"a": np.random.normal(size=[10000, 10]),
+              "b": np.random.normal(size=[10000, 10])} for i in range(size)]
+    builder = IndexedDatasetBuilder(ds_path)
+    for i in tqdm(range(size)):
+        builder.add_item(items[i])
+    builder.finalize()
+    ds = IndexedDataset(ds_path)
+    for i in tqdm(range(10000)):
+        idx = random.randint(0, size - 1)
+        assert (ds[idx]['a'] == items[idx]['a']).all()
diff --git a/utils/pitch_utils.py b/utils/pitch_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1767810e600c8f82e821ff4fc0a164daddaf7af4
--- /dev/null
+++ b/utils/pitch_utils.py
@@ -0,0 +1,76 @@
+#########
+# world
+##########
+import librosa
+import numpy as np
+import torch
+
+# gamma = 0
+# mcepInput = 3  # 0 for dB, 3 for magnitude
+# alpha = 0.45
+# en_floor = 10 ** (-80 / 20)
+# FFT_SIZE = 2048
+
+
+
+
+def f0_to_coarse(f0,hparams):
+    f0_bin = hparams['f0_bin']
+    f0_max = hparams['f0_max']
+    f0_min = hparams['f0_min']
+    is_torch = isinstance(f0, torch.Tensor)
+    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+    f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
+    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
+
+    f0_mel[f0_mel <= 1] = 1
+    f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
+    f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(int)
+    assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
+    return f0_coarse
+
+
+def norm_f0(f0, uv, hparams):
+    is_torch = isinstance(f0, torch.Tensor)
+    if hparams['pitch_norm'] == 'standard':
+        f0 = (f0 - hparams['f0_mean']) / hparams['f0_std']
+    if hparams['pitch_norm'] == 'log':
+        f0 = torch.log2(f0) if is_torch else np.log2(f0)
+    if uv is not None and hparams['use_uv']:
+        f0[uv > 0] = 0
+    return f0
+
+
+def norm_interp_f0(f0, hparams):
+    is_torch = isinstance(f0, torch.Tensor)
+    if is_torch:
+        device = f0.device
+        f0 = f0.data.cpu().numpy()
+    uv = f0 == 0
+    f0 = norm_f0(f0, uv, hparams)
+    if sum(uv) == len(f0):
+        f0[uv] = 0
+    elif sum(uv) > 0:
+        f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
+    uv = torch.FloatTensor(uv)
+    f0 = torch.FloatTensor(f0)
+    if is_torch:
+        f0 = f0.to(device)
+    return f0, uv
+
+
+def denorm_f0(f0, uv, hparams, pitch_padding=None, min=None, max=None):
+    if hparams['pitch_norm'] == 'standard':
+        f0 = f0 * hparams['f0_std'] + hparams['f0_mean']
+    if hparams['pitch_norm'] == 'log':
+        f0 = 2 ** f0
+    if min is not None:
+        f0 = f0.clamp(min=min)
+    if max is not None:
+        f0 = f0.clamp(max=max)
+    if uv is not None and hparams['use_uv']:
+        f0[uv > 0] = 0
+    if pitch_padding is not None:
+        f0[pitch_padding] = 0
+    return f0
diff --git a/utils/pl_utils.py b/utils/pl_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e93c77270ae9360ebe597fa6b309eb57dfdf40df
--- /dev/null
+++ b/utils/pl_utils.py
@@ -0,0 +1,1625 @@
+import matplotlib
+from torch.nn import DataParallel
+from torch.nn.parallel import DistributedDataParallel
+
+matplotlib.use('Agg')
+import glob
+import itertools
+import subprocess
+import threading
+import traceback
+
+from pytorch_lightning.callbacks import GradientAccumulationScheduler
+from pytorch_lightning.callbacks import ModelCheckpoint
+
+from functools import wraps
+from torch.cuda._utils import _get_device_index
+import numpy as np
+import torch.optim
+import torch.utils.data
+import copy
+import logging
+import os
+import re
+import sys
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import tqdm
+from torch.optim.optimizer import Optimizer
+
+
+def get_a_var(obj):  # pragma: no cover
+    if isinstance(obj, torch.Tensor):
+        return obj
+
+    if isinstance(obj, list) or isinstance(obj, tuple):
+        for result in map(get_a_var, obj):
+            if isinstance(result, torch.Tensor):
+                return result
+    if isinstance(obj, dict):
+        for result in map(get_a_var, obj.items()):
+            if isinstance(result, torch.Tensor):
+                return result
+    return None
+
+
+def data_loader(fn):
+    """
+    Decorator to make any fx with this use the lazy property
+    :param fn:
+    :return:
+    """
+
+    wraps(fn)
+    attr_name = '_lazy_' + fn.__name__
+
+    def _get_data_loader(self):
+        try:
+            value = getattr(self, attr_name)
+        except AttributeError:
+            try:
+                value = fn(self)  # Lazy evaluation, done only once.
+                if (
+                        value is not None and
+                        not isinstance(value, list) and
+                        fn.__name__ in ['test_dataloader', 'val_dataloader']
+                ):
+                    value = [value]
+            except AttributeError as e:
+                # Guard against AttributeError suppression. (Issue #142)
+                traceback.print_exc()
+                error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e)
+                raise RuntimeError(error) from e
+            setattr(self, attr_name, value)  # Memoize evaluation.
+        return value
+
+    return _get_data_loader
+
+
+def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):  # pragma: no cover
+    r"""Applies each `module` in :attr:`modules` in parallel on arguments
+    contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
+    on each of :attr:`devices`.
+
+    Args:
+        modules (Module): modules to be parallelized
+        inputs (tensor): inputs to the modules
+        devices (list of int or torch.device): CUDA devices
+
+    :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and
+    :attr:`devices` (if given) should all have same length. Moreover, each
+    element of :attr:`inputs` can either be a single object as the only argument
+    to a module, or a collection of positional arguments.
+    """
+    assert len(modules) == len(inputs)
+    if kwargs_tup is not None:
+        assert len(modules) == len(kwargs_tup)
+    else:
+        kwargs_tup = ({},) * len(modules)
+    if devices is not None:
+        assert len(modules) == len(devices)
+    else:
+        devices = [None] * len(modules)
+    devices = list(map(lambda x: _get_device_index(x, True), devices))
+    lock = threading.Lock()
+    results = {}
+    grad_enabled = torch.is_grad_enabled()
+
+    def _worker(i, module, input, kwargs, device=None):
+        torch.set_grad_enabled(grad_enabled)
+        if device is None:
+            device = get_a_var(input).get_device()
+        try:
+            with torch.cuda.device(device):
+                # this also avoids accidental slicing of `input` if it is a Tensor
+                if not isinstance(input, (list, tuple)):
+                    input = (input,)
+
+                # ---------------
+                # CHANGE
+                if module.training:
+                    output = module.training_step(*input, **kwargs)
+
+                elif module.testing:
+                    output = module.test_step(*input, **kwargs)
+
+                else:
+                    output = module.validation_step(*input, **kwargs)
+                # ---------------
+
+            with lock:
+                results[i] = output
+        except Exception as e:
+            with lock:
+                results[i] = e
+
+    # make sure each module knows what training state it's in...
+    # fixes weird bug where copies are out of sync
+    root_m = modules[0]
+    for m in modules[1:]:
+        m.training = root_m.training
+        m.testing = root_m.testing
+
+    if len(modules) > 1:
+        threads = [threading.Thread(target=_worker,
+                                    args=(i, module, input, kwargs, device))
+                   for i, (module, input, kwargs, device) in
+                   enumerate(zip(modules, inputs, kwargs_tup, devices))]
+
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+    else:
+        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])
+
+    outputs = []
+    for i in range(len(inputs)):
+        output = results[i]
+        if isinstance(output, Exception):
+            raise output
+        outputs.append(output)
+    return outputs
+
+
+def _find_tensors(obj):  # pragma: no cover
+    r"""
+    Recursively find all tensors contained in the specified object.
+    """
+    if isinstance(obj, torch.Tensor):
+        return [obj]
+    if isinstance(obj, (list, tuple)):
+        return itertools.chain(*map(_find_tensors, obj))
+    if isinstance(obj, dict):
+        return itertools.chain(*map(_find_tensors, obj.values()))
+    return []
+
+
+class DDP(DistributedDataParallel):
+    """
+    Override the forward call in lightning so it goes to training and validation step respectively
+    """
+
+    def parallel_apply(self, replicas, inputs, kwargs):
+        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
+
+    def forward(self, *inputs, **kwargs):  # pragma: no cover
+        self._sync_params()
+        if self.device_ids:
+            inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+            if len(self.device_ids) == 1:
+                # --------------
+                # LIGHTNING MOD
+                # --------------
+                # normal
+                # output = self.module(*inputs[0], **kwargs[0])
+                # lightning
+                if self.module.training:
+                    output = self.module.training_step(*inputs[0], **kwargs[0])
+                elif self.module.testing:
+                    output = self.module.test_step(*inputs[0], **kwargs[0])
+                else:
+                    output = self.module.validation_step(*inputs[0], **kwargs[0])
+            else:
+                outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)
+                output = self.gather(outputs, self.output_device)
+        else:
+            # normal
+            output = self.module(*inputs, **kwargs)
+
+        if torch.is_grad_enabled():
+            # We'll return the output object verbatim since it is a freeform
+            # object. We need to find any tensors in this object, though,
+            # because we need to figure out which parameters were used during
+            # this forward pass, to ensure we short circuit reduction for any
+            # unused parameters. Only if `find_unused_parameters` is set.
+            if self.find_unused_parameters:
+                self.reducer.prepare_for_backward(list(_find_tensors(output)))
+            else:
+                self.reducer.prepare_for_backward([])
+        return output
+
+
+class DP(DataParallel):
+    """
+    Override the forward call in lightning so it goes to training and validation step respectively
+    """
+
+    def forward(self, *inputs, **kwargs):
+        if not self.device_ids:
+            return self.module(*inputs, **kwargs)
+
+        for t in itertools.chain(self.module.parameters(), self.module.buffers()):
+            if t.device != self.src_device_obj:
+                raise RuntimeError("module must have its parameters and buffers "
+                                   "on device {} (device_ids[0]) but found one of "
+                                   "them on device: {}".format(self.src_device_obj, t.device))
+
+        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+        if len(self.device_ids) == 1:
+            # lightning
+            if self.module.training:
+                return self.module.training_step(*inputs[0], **kwargs[0])
+            elif self.module.testing:
+                return self.module.test_step(*inputs[0], **kwargs[0])
+            else:
+                return self.module.validation_step(*inputs[0], **kwargs[0])
+
+        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
+        outputs = self.parallel_apply(replicas, inputs, kwargs)
+        return self.gather(outputs, self.output_device)
+
+    def parallel_apply(self, replicas, inputs, kwargs):
+        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
+
+
+class GradientAccumulationScheduler:
+    def __init__(self, scheduling: dict):
+        if scheduling == {}:  # empty dict error
+            raise TypeError("Empty dict cannot be interpreted correct")
+
+        for key in scheduling.keys():
+            if not isinstance(key, int) or not isinstance(scheduling[key], int):
+                raise TypeError("All epoches and accumulation factor must be integers")
+
+        minimal_epoch = min(scheduling.keys())
+        if minimal_epoch < 1:
+            msg = f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct"
+            raise IndexError(msg)
+        elif minimal_epoch != 1:  # if user didnt define first epoch accumulation factor
+            scheduling.update({1: 1})
+
+        self.scheduling = scheduling
+        self.epochs = sorted(scheduling.keys())
+
+    def on_epoch_begin(self, epoch, trainer):
+        epoch += 1  # indexing epochs from 1
+        for i in reversed(range(len(self.epochs))):
+            if epoch >= self.epochs[i]:
+                trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])
+                break
+
+
+class LatestModelCheckpoint(ModelCheckpoint):
+    def __init__(self, filepath, monitor='val_loss', verbose=0, num_ckpt_keep=5,
+                 save_weights_only=False, mode='auto', period=1, prefix='model', save_best=True):
+        super(ModelCheckpoint, self).__init__()
+        self.monitor = monitor
+        self.verbose = verbose
+        self.filepath = filepath
+        os.makedirs(filepath, exist_ok=True)
+        self.num_ckpt_keep = num_ckpt_keep
+        self.save_best = save_best
+        self.save_weights_only = save_weights_only
+        self.period = period
+        self.epochs_since_last_check = 0
+        self.prefix = prefix
+        self.best_k_models = {}
+        # {filename: monitor}
+        self.kth_best_model = ''
+        self.save_top_k = 1
+        self.task = None
+        if mode == 'min':
+            self.monitor_op = np.less
+            self.best = np.Inf
+            self.mode = 'min'
+        elif mode == 'max':
+            self.monitor_op = np.greater
+            self.best = -np.Inf
+            self.mode = 'max'
+        else:
+            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
+                self.monitor_op = np.greater
+                self.best = -np.Inf
+                self.mode = 'max'
+            else:
+                self.monitor_op = np.less
+                self.best = np.Inf
+                self.mode = 'min'
+        if os.path.exists(f'{self.filepath}/best_valid.npy'):
+            self.best = np.load(f'{self.filepath}/best_valid.npy')[0]
+
+    def get_all_ckpts(self):
+        return sorted(glob.glob(f'{self.filepath}/{self.prefix}_ckpt_steps_*.ckpt'),
+                      key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0]))
+
+    def on_epoch_end(self, epoch, logs=None):
+        logs = logs or {}
+        self.epochs_since_last_check += 1
+        best_filepath = f'{self.filepath}/{self.prefix}_ckpt_best.pt'
+        if self.epochs_since_last_check >= self.period:
+            self.epochs_since_last_check = 0
+            filepath = f'{self.filepath}/{self.prefix}_ckpt_steps_{self.task.global_step}.ckpt'
+            if self.verbose > 0:
+                logging.info(f'Epoch {epoch:05d}@{self.task.global_step}: saving model to {filepath}')
+            self._save_model(filepath)
+            for old_ckpt in self.get_all_ckpts()[self.num_ckpt_keep:]:
+                # TODO: test filesystem calls
+                os.remove(old_ckpt)
+                # subprocess.check_call(f'del "{old_ckpt}"', shell=True)
+                if self.verbose > 0:
+                    logging.info(f'Delete ckpt: {os.path.basename(old_ckpt)}')
+            current = logs.get(self.monitor)
+            if current is not None and self.save_best:
+                if self.monitor_op(current, self.best):
+                    self.best = current
+                    if self.verbose > 0:
+                        logging.info(
+                            f'Epoch {epoch:05d}@{self.task.global_step}: {self.monitor} reached'
+                            f' {current:0.5f} (best {self.best:0.5f}), saving model to'
+                            f' {best_filepath} as top 1')
+                    self._save_model(best_filepath)
+                    np.save(f'{self.filepath}/best_valid.npy', [self.best])
+    
+    def _save_model(self,path):
+        return self.save_function(path)
+
+
+
+class BaseTrainer:
+    def __init__(
+            self,
+            logger=True,
+            checkpoint_callback=True,
+            default_save_path=None,
+            gradient_clip_val=0,
+            process_position=0,
+            gpus=-1,
+            log_gpu_memory=None,
+            show_progress_bar=True,
+            track_grad_norm=-1,
+            check_val_every_n_epoch=1,
+            accumulate_grad_batches=1,
+            max_updates=1000,
+            min_epochs=1,
+            val_check_interval=1.0,
+            log_save_interval=100,
+            row_log_interval=10,
+            print_nan_grads=False,
+            weights_summary='full',
+            num_sanity_val_steps=5,
+            resume_from_checkpoint=None,
+    ):
+        self.log_gpu_memory = log_gpu_memory
+        self.gradient_clip_val = gradient_clip_val
+        self.check_val_every_n_epoch = check_val_every_n_epoch
+        self.track_grad_norm = track_grad_norm
+        self.on_gpu = True if (gpus and torch.cuda.is_available()) else False
+        self.process_position = process_position
+        self.weights_summary = weights_summary
+        self.max_updates = max_updates
+        self.min_epochs = min_epochs
+        self.num_sanity_val_steps = num_sanity_val_steps
+        self.print_nan_grads = print_nan_grads
+        self.resume_from_checkpoint = resume_from_checkpoint
+        self.default_save_path = default_save_path
+
+        # training bookeeping
+        self.total_batch_idx = 0
+        self.running_loss = []
+        self.avg_loss = 0
+        self.batch_idx = 0
+        self.tqdm_metrics = {}
+        self.callback_metrics = {}
+        self.num_val_batches = 0
+        self.num_training_batches = 0
+        self.num_test_batches = 0
+        self.get_train_dataloader = None
+        self.get_test_dataloaders = None
+        self.get_val_dataloaders = None
+        self.is_iterable_train_dataloader = False
+
+        # training state
+        self.model = None
+        self.testing = False
+        self.disable_validation = False
+        self.lr_schedulers = []
+        self.optimizers = None
+        self.global_step = 0
+        self.current_epoch = 0
+        self.total_batches = 0
+
+        # configure checkpoint callback
+        self.checkpoint_callback = checkpoint_callback
+        self.checkpoint_callback.save_function = self.save_checkpoint
+        self.weights_save_path = self.checkpoint_callback.filepath
+
+        # accumulated grads
+        self.configure_accumulated_gradients(accumulate_grad_batches)
+
+        # allow int, string and gpu list
+        self.data_parallel_device_ids = [
+            int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != '']
+        if len(self.data_parallel_device_ids) == 0:
+            self.root_gpu = None
+            self.on_gpu = False
+        else:
+            self.root_gpu = self.data_parallel_device_ids[0]
+            self.on_gpu = True
+
+        # distributed backend choice
+        self.use_ddp = False
+        self.use_dp = False
+        self.single_gpu = False
+        self.distributed_backend = 'ddp' if self.num_gpus > 0 else 'dp'
+        self.set_distributed_mode(self.distributed_backend)
+
+        self.proc_rank = 0
+        self.world_size = 1
+        self.node_rank = 0
+
+        # can't init progress bar here because starting a new process
+        # means the progress_bar won't survive pickling
+        self.show_progress_bar = show_progress_bar
+
+        # logging
+        self.log_save_interval = log_save_interval
+        self.val_check_interval = val_check_interval
+        self.logger = logger
+        self.logger.rank = 0
+        self.row_log_interval = row_log_interval
+
+    @property
+    def num_gpus(self):
+        gpus = self.data_parallel_device_ids
+        if gpus is None:
+            return 0
+        else:
+            return len(gpus)
+
+    @property
+    def data_parallel(self):
+        return self.use_dp or self.use_ddp
+
+    def get_model(self):
+        is_dp_module = isinstance(self.model, (DDP, DP))
+        model = self.model.module if is_dp_module else self.model
+        return model
+
+    # -----------------------------
+    # MODEL TRAINING
+    # -----------------------------
+    def fit(self, model):
+        if self.use_ddp:
+            mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,))
+        else:
+            model.svc_model = model.build_model()
+            if not self.testing:
+                self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())
+            if self.use_dp:
+                model.cuda(self.root_gpu)
+                model = DP(model, device_ids=self.data_parallel_device_ids)
+            elif self.single_gpu:
+                model.cuda(self.root_gpu)
+            self.run_pretrain_routine(model)
+        return 1
+
+    def init_optimizers(self, optimizers):
+
+        # single optimizer
+        if isinstance(optimizers, Optimizer):
+            return [optimizers], []
+
+        # two lists
+        elif len(optimizers) == 2 and isinstance(optimizers[0], list):
+            optimizers, lr_schedulers = optimizers
+            return optimizers, lr_schedulers
+
+        # single list or tuple
+        elif isinstance(optimizers, list) or isinstance(optimizers, tuple):
+            return optimizers, []
+
+    def run_pretrain_routine(self, model):
+        """Sanity check a few things before starting actual training.
+
+        :param model:
+        """
+        ref_model = model
+        if self.data_parallel:
+            ref_model = model.module
+
+        # give model convenience properties
+        ref_model.trainer = self
+
+        # set local properties on the model
+        self.copy_trainer_model_properties(ref_model)
+
+        # link up experiment object
+        if self.logger is not None:
+            ref_model.logger = self.logger
+            self.logger.save()
+
+        if self.use_ddp:
+            dist.barrier()
+
+        # set up checkpoint callback
+        # self.configure_checkpoint_callback()
+
+        # transfer data loaders from model
+        self.get_dataloaders(ref_model)
+
+        # track model now.
+        # if cluster resets state, the model will update with the saved weights
+        self.model = model
+
+        # restore training and model before hpc call
+        self.restore_weights(model)
+
+        # when testing requested only run test and return
+        if self.testing:
+            self.run_evaluation(test=True)
+            return
+
+        # check if we should run validation during training
+        self.disable_validation = self.num_val_batches == 0
+
+        # run tiny validation (if validation defined)
+        # to make sure program won't crash during val
+        ref_model.on_sanity_check_start()
+        ref_model.on_train_start()
+        if not self.disable_validation and self.num_sanity_val_steps > 0:
+            # init progress bars for validation sanity check
+            pbar = tqdm.tqdm(desc='Validation sanity check',
+                             total=self.num_sanity_val_steps * len(self.get_val_dataloaders()),
+                             leave=False, position=2 * self.process_position,
+                             disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch')
+            self.main_progress_bar = pbar
+            # dummy validation progress bar
+            self.val_progress_bar = tqdm.tqdm(disable=True)
+
+            self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, self.testing)
+
+            # close progress bars
+            self.main_progress_bar.close()
+            self.val_progress_bar.close()
+
+        # init progress bar
+        pbar = tqdm.tqdm(leave=True, position=2 * self.process_position,
+                         disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch',
+                         file=sys.stdout)
+        self.main_progress_bar = pbar
+
+        # clear cache before training
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+
+        # CORE TRAINING LOOP
+        self.train()
+
+    def test(self, model):
+        self.testing = True
+        self.fit(model)
+
+    @property
+    def training_tqdm_dict(self):
+        tqdm_dict = {
+            'step': '{}'.format(self.global_step),
+        }
+        tqdm_dict.update(self.tqdm_metrics)
+        return tqdm_dict
+
+    # --------------------
+    # restore ckpt
+    # --------------------
+    def restore_weights(self, model):
+        """
+        To restore weights we have two cases.
+        First, attempt to restore hpc weights. If successful, don't restore
+        other weights.
+
+        Otherwise, try to restore actual weights
+        :param model:
+        :return:
+        """
+        # clear cache before restore
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+
+        if self.resume_from_checkpoint is not None:
+            self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu)
+        else:
+            # restore weights if same exp version
+            self.restore_state_if_checkpoint_exists(model)
+
+        # wait for all models to restore weights
+        if self.use_ddp:
+            # wait for all processes to catch up
+            dist.barrier()
+
+        # clear cache after restore
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+
+    def restore_state_if_checkpoint_exists(self, model):
+        did_restore = False
+
+        # do nothing if there's not dir or callback
+        no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback)
+        if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):
+            return did_restore
+
+        # restore trainer state and model if there is a weight for this experiment
+        last_steps = -1
+        last_ckpt_name = None
+
+        # find last epoch
+        checkpoints = os.listdir(self.checkpoint_callback.filepath)
+        for name in checkpoints:
+            if '.ckpt' in name and not name.endswith('part'):
+                if 'steps_' in name:
+                    steps = name.split('steps_')[1]
+                    steps = int(re.sub('[^0-9]', '', steps))
+
+                    if steps > last_steps:
+                        last_steps = steps
+                        last_ckpt_name = name
+
+        # restore last checkpoint
+        if last_ckpt_name is not None:
+            last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)
+            self.restore(last_ckpt_path, self.on_gpu)
+            logging.info(f'model and trainer restored from checkpoint: {last_ckpt_path}')
+            did_restore = True
+
+        return did_restore
+
+    def restore(self, checkpoint_path, on_gpu):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+
+        # load model state
+        model = self.get_model()
+
+        # load the state_dict on the model automatically
+        model.load_state_dict(checkpoint['state_dict'], strict=False)
+        if on_gpu:
+            model.cuda(self.root_gpu)
+        # load training state (affects trainer only)
+        self.restore_training_state(checkpoint)
+        model.global_step = self.global_step
+        del checkpoint
+
+        try:
+            if dist.is_initialized() and dist.get_rank() > 0:
+                return
+        except Exception as e:
+            print(e)
+            return
+
+    def restore_training_state(self, checkpoint):
+        """
+        Restore trainer state.
+        Model will get its change to update
+        :param checkpoint:
+        :return:
+        """
+        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
+            # return allowing checkpoints with meta information (global_step, etc)
+            self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']
+
+        self.global_step = checkpoint['global_step']
+        self.current_epoch = checkpoint['epoch']
+
+        if self.testing:
+            return
+
+        # restore the optimizers
+        optimizer_states = checkpoint['optimizer_states']
+        for optimizer, opt_state in zip(self.optimizers, optimizer_states):
+            if optimizer is None:
+                return
+            optimizer.load_state_dict(opt_state)
+
+            # move optimizer to GPU 1 weight at a time
+            # avoids OOM
+            if self.root_gpu is not None:
+                for state in optimizer.state.values():
+                    for k, v in state.items():
+                        if isinstance(v, torch.Tensor):
+                            state[k] = v.cuda(self.root_gpu)
+
+        # restore the lr schedulers
+        lr_schedulers = checkpoint['lr_schedulers']
+        for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
+            scheduler.load_state_dict(lrs_state)
+
+    # --------------------
+    # MODEL SAVE CHECKPOINT
+    # --------------------
+    def _atomic_save(self, checkpoint, filepath):
+        """Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.
+
+        This will create a temporary checkpoint with a suffix of ``.part``, then copy it to the final location once
+        saving is finished.
+
+        Args:
+            checkpoint (object): The object to save.
+                Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save``
+                accepts.
+            filepath (str|pathlib.Path): The path to which the checkpoint will be saved.
+                This points to the file that the checkpoint will be stored in.
+        """
+        tmp_path = str(filepath) + ".part"
+        torch.save(checkpoint, tmp_path)
+        os.replace(tmp_path, filepath)
+
+    def save_checkpoint(self, filepath):
+        checkpoint = self.dump_checkpoint()
+        self._atomic_save(checkpoint, filepath)
+
+    def dump_checkpoint(self):
+
+        checkpoint = {
+            'epoch': self.current_epoch,
+            'global_step': self.global_step
+        }
+
+        if self.checkpoint_callback is not None and self.checkpoint_callback is not False:
+            checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best
+
+        # save optimizers
+        optimizer_states = []
+        for i, optimizer in enumerate(self.optimizers):
+            if optimizer is not None:
+                optimizer_states.append(optimizer.state_dict())
+
+        checkpoint['optimizer_states'] = optimizer_states
+
+        # save lr schedulers
+        lr_schedulers = []
+        for i, scheduler in enumerate(self.lr_schedulers):
+            lr_schedulers.append(scheduler.state_dict())
+
+        checkpoint['lr_schedulers'] = lr_schedulers
+
+        # add the hparams and state_dict from the model
+        model = self.get_model()
+        checkpoint['state_dict'] = model.state_dict()
+        # give the model a chance to add a few things
+        model.on_save_checkpoint(checkpoint)
+
+        return checkpoint
+
+    def copy_trainer_model_properties(self, model):
+        if isinstance(model, DP):
+            ref_model = model.module
+        elif isinstance(model, DDP):
+            ref_model = model.module
+        else:
+            ref_model = model
+
+        for m in [model, ref_model]:
+            m.trainer = self
+            m.on_gpu = self.on_gpu
+            m.use_dp = self.use_dp
+            m.use_ddp = self.use_ddp
+            m.testing = self.testing
+            m.single_gpu = self.single_gpu
+
+    def transfer_batch_to_gpu(self, batch, gpu_id):
+        # base case: object can be directly moved using `cuda` or `to`
+        if callable(getattr(batch, 'cuda', None)):
+            return batch.cuda(gpu_id, non_blocking=True)
+
+        elif callable(getattr(batch, 'to', None)):
+            return batch.to(torch.device('cuda', gpu_id), non_blocking=True)
+
+        # when list
+        elif isinstance(batch, list):
+            for i, x in enumerate(batch):
+                batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
+            return batch
+
+        # when tuple
+        elif isinstance(batch, tuple):
+            batch = list(batch)
+            for i, x in enumerate(batch):
+                batch[i] = self.transfer_batch_to_gpu(x, gpu_id)
+            return tuple(batch)
+
+        # when dict
+        elif isinstance(batch, dict):
+            for k, v in batch.items():
+                batch[k] = self.transfer_batch_to_gpu(v, gpu_id)
+
+            return batch
+
+        # nothing matches, return the value as is without transform
+        return batch
+
+    def set_distributed_mode(self, distributed_backend):
+        # skip for CPU
+        if self.num_gpus == 0:
+            return
+
+        # single GPU case
+        # in single gpu case we allow ddp so we can train on multiple
+        # nodes, 1 gpu per node
+        elif self.num_gpus == 1:
+            self.single_gpu = True
+            self.use_dp = False
+            self.use_ddp = False
+            self.root_gpu = 0
+            self.data_parallel_device_ids = [0]
+        else:
+            if distributed_backend is not None:
+                self.use_dp = distributed_backend == 'dp'
+                self.use_ddp = distributed_backend == 'ddp'
+            elif distributed_backend is None:
+                self.use_dp = True
+                self.use_ddp = False
+
+        logging.info(f'gpu available: {torch.cuda.is_available()}, used: {self.on_gpu}')
+
+    def ddp_train(self, gpu_idx, model):
+        """
+        Entry point into a DP thread
+        :param gpu_idx:
+        :param model:
+        :param cluster_obj:
+        :return:
+        """
+        # otherwise default to node rank 0
+        self.node_rank = 0
+
+        # show progressbar only on progress_rank 0
+        self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_idx == 0
+
+        # determine which process we are and world size
+        if self.use_ddp:
+            self.proc_rank = self.node_rank * self.num_gpus + gpu_idx
+            self.world_size = self.num_gpus
+
+        # let the exp know the rank to avoid overwriting logs
+        if self.logger is not None:
+            self.logger.rank = self.proc_rank
+
+        # set up server using proc 0's ip address
+        # try to init for 20 times at max in case ports are taken
+        # where to store ip_table
+        model.trainer = self
+        model.init_ddp_connection(self.proc_rank, self.world_size)
+
+        # CHOOSE OPTIMIZER
+        # allow for lr schedulers as well
+        model.svc_model = model.build_model()
+        if not self.testing:
+            self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())
+
+        # MODEL
+        # copy model to each gpu
+        if self.distributed_backend == 'ddp':
+            torch.cuda.set_device(gpu_idx)
+        model.cuda(gpu_idx)
+
+        # set model properties before going into wrapper
+        self.copy_trainer_model_properties(model)
+
+        # override root GPU
+        self.root_gpu = gpu_idx
+
+        if self.distributed_backend == 'ddp':
+            device_ids = [gpu_idx]
+        else:
+            device_ids = None
+
+        # allow user to configure ddp
+        model = model.configure_ddp(model, device_ids)
+
+        # continue training routine
+        self.run_pretrain_routine(model)
+
+    def resolve_root_node_address(self, root_node):
+        if '[' in root_node:
+            name = root_node.split('[')[0]
+            number = root_node.split(',')[0]
+            if '-' in number:
+                number = number.split('-')[0]
+
+            number = re.sub('[^0-9]', '', number)
+            root_node = name + number
+
+        return root_node
+
+    def log_metrics(self, metrics, grad_norm_dic, step=None):
+        """Logs the metric dict passed in.
+
+        :param metrics:
+        :param grad_norm_dic:
+        """
+        # added metrics by Lightning for convenience
+        metrics['epoch'] = self.current_epoch
+
+        # add norms
+        metrics.update(grad_norm_dic)
+
+        # turn all tensors to scalars
+        scalar_metrics = self.metrics_to_scalars(metrics)
+
+        step = step if step is not None else self.global_step
+        # log actual metrics
+        if self.proc_rank == 0 and self.logger is not None:
+            self.logger.log_metrics(scalar_metrics, step=step)
+            self.logger.save()
+
+    def add_tqdm_metrics(self, metrics):
+        for k, v in metrics.items():
+            if type(v) is torch.Tensor:
+                v = v.item()
+
+            self.tqdm_metrics[k] = v
+
+    def metrics_to_scalars(self, metrics):
+        new_metrics = {}
+        for k, v in metrics.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+
+            if type(v) is dict:
+                v = self.metrics_to_scalars(v)
+
+            new_metrics[k] = v
+
+        return new_metrics
+
+    def process_output(self, output, train=False):
+        """Reduces output according to the training mode.
+
+        Separates loss from logging and tqdm metrics
+        :param output:
+        :return:
+        """
+        # ---------------
+        # EXTRACT CALLBACK KEYS
+        # ---------------
+        # all keys not progress_bar or log are candidates for callbacks
+        callback_metrics = {}
+        for k, v in output.items():
+            if k not in ['progress_bar', 'log', 'hiddens']:
+                callback_metrics[k] = v
+
+        if train and self.use_dp:
+            num_gpus = self.num_gpus
+            callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus)
+
+        for k, v in callback_metrics.items():
+            if isinstance(v, torch.Tensor):
+                callback_metrics[k] = v.item()
+
+        # ---------------
+        # EXTRACT PROGRESS BAR KEYS
+        # ---------------
+        try:
+            progress_output = output['progress_bar']
+
+            # reduce progress metrics for tqdm when using dp
+            if train and self.use_dp:
+                num_gpus = self.num_gpus
+                progress_output = self.reduce_distributed_output(progress_output, num_gpus)
+
+            progress_bar_metrics = progress_output
+        except Exception:
+            progress_bar_metrics = {}
+
+        # ---------------
+        # EXTRACT LOGGING KEYS
+        # ---------------
+        # extract metrics to log to experiment
+        try:
+            log_output = output['log']
+
+            # reduce progress metrics for tqdm when using dp
+            if train and self.use_dp:
+                num_gpus = self.num_gpus
+                log_output = self.reduce_distributed_output(log_output, num_gpus)
+
+            log_metrics = log_output
+        except Exception:
+            log_metrics = {}
+
+        # ---------------
+        # EXTRACT LOSS
+        # ---------------
+        # if output dict doesn't have the keyword loss
+        # then assume the output=loss if scalar
+        loss = None
+        if train:
+            try:
+                loss = output['loss']
+            except Exception:
+                if type(output) is torch.Tensor:
+                    loss = output
+                else:
+                    raise RuntimeError(
+                        'No `loss` value in the dictionary returned from `model.training_step()`.'
+                    )
+
+            # when using dp need to reduce the loss
+            if self.use_dp:
+                loss = self.reduce_distributed_output(loss, self.num_gpus)
+
+        # ---------------
+        # EXTRACT HIDDEN
+        # ---------------
+        hiddens = output.get('hiddens')
+
+        # use every metric passed in as a candidate for callback
+        callback_metrics.update(progress_bar_metrics)
+        callback_metrics.update(log_metrics)
+
+        # convert tensors to numpy
+        for k, v in callback_metrics.items():
+            if isinstance(v, torch.Tensor):
+                callback_metrics[k] = v.item()
+
+        return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens
+
+    def reduce_distributed_output(self, output, num_gpus):
+        if num_gpus <= 1:
+            return output
+
+        # when using DP, we get one output per gpu
+        # average outputs and return
+        if type(output) is torch.Tensor:
+            return output.mean()
+
+        for k, v in output.items():
+            # recurse on nested dics
+            if isinstance(output[k], dict):
+                output[k] = self.reduce_distributed_output(output[k], num_gpus)
+
+            # do nothing when there's a scalar
+            elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:
+                pass
+
+            # reduce only metrics that have the same number of gpus
+            elif output[k].size(0) == num_gpus:
+                reduced = torch.mean(output[k])
+                output[k] = reduced
+        return output
+
+    def clip_gradients(self):
+        if self.gradient_clip_val > 0:
+            model = self.get_model()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), self.gradient_clip_val)
+
+    def print_nan_gradients(self):
+        model = self.get_model()
+        for param in model.parameters():
+            if (param.grad is not None) and torch.isnan(param.grad.float()).any():
+                logging.info(param, param.grad)
+
+    def configure_accumulated_gradients(self, accumulate_grad_batches):
+        self.accumulate_grad_batches = None
+
+        if isinstance(accumulate_grad_batches, dict):
+            self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)
+        elif isinstance(accumulate_grad_batches, int):
+            schedule = {1: accumulate_grad_batches}
+            self.accumulation_scheduler = GradientAccumulationScheduler(schedule)
+        else:
+            raise TypeError("Gradient accumulation supports only int and dict types")
+
+    def get_dataloaders(self, model):
+        if not self.testing:
+            self.init_train_dataloader(model)
+            self.init_val_dataloader(model)
+        else:
+            self.init_test_dataloader(model)
+
+        if self.use_ddp:
+            dist.barrier()
+            if not self.testing:
+                self.get_train_dataloader()
+                self.get_val_dataloaders()
+            else:
+                self.get_test_dataloaders()
+
+    def init_train_dataloader(self, model):
+        self.fisrt_epoch = True
+        self.get_train_dataloader = model.train_dataloader
+        if isinstance(self.get_train_dataloader(), torch.utils.data.DataLoader):
+            self.num_training_batches = len(self.get_train_dataloader())
+            self.num_training_batches = int(self.num_training_batches)
+        else:
+            self.num_training_batches = float('inf')
+            self.is_iterable_train_dataloader = True
+        if isinstance(self.val_check_interval, int):
+            self.val_check_batch = self.val_check_interval
+        else:
+            self._percent_range_check('val_check_interval')
+            self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
+            self.val_check_batch = max(1, self.val_check_batch)
+
+    def init_val_dataloader(self, model):
+        self.get_val_dataloaders = model.val_dataloader
+        self.num_val_batches = 0
+        if self.get_val_dataloaders() is not None:
+            if isinstance(self.get_val_dataloaders()[0], torch.utils.data.DataLoader):
+                self.num_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders())
+                self.num_val_batches = int(self.num_val_batches)
+            else:
+                self.num_val_batches = float('inf')
+
+    def init_test_dataloader(self, model):
+        self.get_test_dataloaders = model.test_dataloader
+        if self.get_test_dataloaders() is not None:
+            if isinstance(self.get_test_dataloaders()[0], torch.utils.data.DataLoader):
+                self.num_test_batches = sum(len(dataloader) for dataloader in self.get_test_dataloaders())
+                self.num_test_batches = int(self.num_test_batches)
+            else:
+                self.num_test_batches = float('inf')
+
+    def evaluate(self, model, dataloaders, max_batches, test=False):
+        """Run evaluation code.
+
+        :param model: PT model
+        :param dataloaders: list of PT dataloaders
+        :param max_batches: Scalar
+        :param test: boolean
+        :return:
+        """
+        # enable eval mode
+        model.zero_grad()
+        model.eval()
+
+        # copy properties for forward overrides
+        self.copy_trainer_model_properties(model)
+
+        # disable gradients to save memory
+        torch.set_grad_enabled(False)
+
+        if test:
+            self.get_model().test_start()
+        # bookkeeping
+        outputs = []
+
+        # run training
+        for dataloader_idx, dataloader in enumerate(dataloaders):
+            dl_outputs = []
+            for batch_idx, batch in enumerate(dataloader):
+
+                if batch is None:  # pragma: no cover
+                    continue
+
+                # stop short when on fast_dev_run (sets max_batch=1)
+                if batch_idx >= max_batches:
+                    break
+
+                # -----------------
+                # RUN EVALUATION STEP
+                # -----------------
+                output = self.evaluation_forward(model,
+                                                 batch,
+                                                 batch_idx,
+                                                 dataloader_idx,
+                                                 test)
+
+                # track outputs for collation
+                dl_outputs.append(output)
+
+                # batch done
+                if test:
+                    self.test_progress_bar.update(1)
+                else:
+                    self.val_progress_bar.update(1)
+            outputs.append(dl_outputs)
+
+        # with a single dataloader don't pass an array
+        if len(dataloaders) == 1:
+            outputs = outputs[0]
+
+        # give model a chance to do something with the outputs (and method defined)
+        model = self.get_model()
+        if test:
+            eval_results_ = model.test_end(outputs)
+        else:
+            eval_results_ = model.validation_end(outputs)
+        eval_results = eval_results_
+
+        # enable train mode again
+        model.train()
+
+        # enable gradients to save memory
+        torch.set_grad_enabled(True)
+
+        return eval_results
+
+    def run_evaluation(self, test=False):
+        # when testing make sure user defined a test step
+        model = self.get_model()
+        model.on_pre_performance_check()
+
+        # select dataloaders
+        if test:
+            dataloaders = self.get_test_dataloaders()
+            max_batches = self.num_test_batches
+        else:
+            # val
+            dataloaders = self.get_val_dataloaders()
+            max_batches = self.num_val_batches
+
+        # init validation or test progress bar
+        # main progress bar will already be closed when testing so initial position is free
+        position = 2 * self.process_position + (not test)
+        desc = 'Testing' if test else 'Validating'
+        pbar = tqdm.tqdm(desc=desc, total=max_batches, leave=test, position=position,
+                         disable=not self.show_progress_bar, dynamic_ncols=True,
+                         unit='batch', file=sys.stdout)
+        setattr(self, f'{"test" if test else "val"}_progress_bar', pbar)
+
+        # run evaluation
+        eval_results = self.evaluate(self.model,
+                                     dataloaders,
+                                     max_batches,
+                                     test)
+        if eval_results is not None:
+            _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(
+                eval_results)
+
+            # add metrics to prog bar
+            self.add_tqdm_metrics(prog_bar_metrics)
+
+            # log metrics
+            self.log_metrics(log_metrics, {})
+
+            # track metrics for callbacks
+            self.callback_metrics.update(callback_metrics)
+
+        # hook
+        model.on_post_performance_check()
+
+        # add model specific metrics
+        tqdm_metrics = self.training_tqdm_dict
+        if not test:
+            self.main_progress_bar.set_postfix(**tqdm_metrics)
+
+        # close progress bar
+        if test:
+            self.test_progress_bar.close()
+        else:
+            self.val_progress_bar.close()
+
+        # model checkpointing
+        if self.proc_rank == 0 and self.checkpoint_callback is not None and not test:
+            self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch,
+                                                  logs=self.callback_metrics)
+
+    def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):
+        # make dataloader_idx arg in validation_step optional
+        args = [batch, batch_idx]
+        # print(batch)
+        if test and len(self.get_test_dataloaders()) > 1:
+            args.append(dataloader_idx)
+
+        elif not test and len(self.get_val_dataloaders()) > 1:
+            args.append(dataloader_idx)
+
+        # handle DP, DDP forward
+        if self.use_ddp or self.use_dp:
+            output = model(*args)
+            return output
+
+        # single GPU
+        if self.single_gpu:
+            # for single GPU put inputs on gpu manually
+            root_gpu = 0
+            if isinstance(self.data_parallel_device_ids, list):
+                root_gpu = self.data_parallel_device_ids[0]
+            batch = self.transfer_batch_to_gpu(batch, root_gpu)
+            args[0] = batch
+
+        # CPU
+        if test:
+            output = model.test_step(*args)
+        else:
+            output = model.validation_step(*args)
+
+        return output
+
+    def train(self):
+        model = self.get_model()
+        # run all epochs
+        for epoch in range(self.current_epoch, 1000000):
+            # set seed for distributed sampler (enables shuffling for each epoch)
+            if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):
+                self.get_train_dataloader().sampler.set_epoch(epoch)
+
+            # get model
+            model = self.get_model()
+
+            # update training progress in trainer and model
+            model.current_epoch = epoch
+            self.current_epoch = epoch
+
+            total_val_batches = 0
+            if not self.disable_validation:
+                # val can be checked multiple times in epoch
+                is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
+                val_checks_per_epoch = self.num_training_batches // self.val_check_batch
+                val_checks_per_epoch = val_checks_per_epoch if is_val_epoch else 0
+                total_val_batches = self.num_val_batches * val_checks_per_epoch
+
+            # total batches includes multiple val checks
+            self.total_batches = self.num_training_batches + total_val_batches
+            self.batch_loss_value = 0  # accumulated grads
+
+            if self.is_iterable_train_dataloader:
+                # for iterable train loader, the progress bar never ends
+                num_iterations = None
+            else:
+                num_iterations = self.total_batches
+
+            # reset progress bar
+            # .reset() doesn't work on disabled progress bar so we should check
+            desc = f'Epoch {epoch + 1}' if not self.is_iterable_train_dataloader else ''
+            self.main_progress_bar.set_description(desc)
+
+            # changing gradient according accumulation_scheduler
+            self.accumulation_scheduler.on_epoch_begin(epoch, self)
+
+            # -----------------
+            # RUN TNG EPOCH
+            # -----------------
+            self.run_training_epoch()
+
+            # update LR schedulers
+            if self.lr_schedulers is not None:
+                for lr_scheduler in self.lr_schedulers:
+                    lr_scheduler.step(epoch=self.current_epoch)
+
+        self.main_progress_bar.close()
+
+        model.on_train_end()
+
+        if self.logger is not None:
+            self.logger.finalize("success")
+
+    def run_training_epoch(self):
+        # before epoch hook
+        if self.is_function_implemented('on_epoch_start'):
+            model = self.get_model()
+            model.on_epoch_start()
+
+        # run epoch
+        for batch_idx, batch in enumerate(self.get_train_dataloader()):
+            # stop epoch if we limited the number of training batches
+            if batch_idx >= self.num_training_batches:
+                break
+
+            self.batch_idx = batch_idx
+
+            model = self.get_model()
+            model.global_step = self.global_step
+
+            # ---------------
+            # RUN TRAIN STEP
+            # ---------------
+            output = self.run_training_batch(batch, batch_idx)
+            batch_result, grad_norm_dic, batch_step_metrics = output
+
+            # when returning -1 from train_step, we end epoch early
+            early_stop_epoch = batch_result == -1
+
+            # ---------------
+            # RUN VAL STEP
+            # ---------------
+            should_check_val = (
+                    not self.disable_validation and self.global_step % self.val_check_batch == 0 and not self.fisrt_epoch)
+            self.fisrt_epoch = False
+
+            if should_check_val:
+                self.run_evaluation(test=self.testing)
+
+            # when logs should be saved
+            should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or early_stop_epoch
+            if should_save_log:
+                if self.proc_rank == 0 and self.logger is not None:
+                    self.logger.save()
+
+            # when metrics should be logged
+            should_log_metrics = batch_idx % self.row_log_interval == 0 or early_stop_epoch
+            if should_log_metrics:
+                # logs user requested information to logger
+                self.log_metrics(batch_step_metrics, grad_norm_dic)
+
+            self.global_step += 1
+            self.total_batch_idx += 1
+
+            # end epoch early
+            # stop when the flag is changed or we've gone past the amount
+            # requested in the batches
+            if early_stop_epoch:
+                break
+            if self.global_step > self.max_updates:
+                print("| Training end..")
+                exit()
+
+        # epoch end hook
+        if self.is_function_implemented('on_epoch_end'):
+            model = self.get_model()
+            model.on_epoch_end()
+
+    def run_training_batch(self, batch, batch_idx):
+        # track grad norms
+        grad_norm_dic = {}
+
+        # track all metrics for callbacks
+        all_callback_metrics = []
+
+        # track metrics to log
+        all_log_metrics = []
+
+        if batch is None:
+            return 0, grad_norm_dic, {}
+
+        # hook
+        if self.is_function_implemented('on_batch_start'):
+            model_ref = self.get_model()
+            response = model_ref.on_batch_start(batch)
+
+            if response == -1:
+                return -1, grad_norm_dic, {}
+
+        splits = [batch]
+        self.hiddens = None
+        for split_idx, split_batch in enumerate(splits):
+            self.split_idx = split_idx
+
+            # call training_step once per optimizer
+            for opt_idx, optimizer in enumerate(self.optimizers):
+                if optimizer is None:
+                    continue
+                # make sure only the gradients of the current optimizer's paramaters are calculated
+                # in the training step to prevent dangling gradients in multiple-optimizer setup.
+                if len(self.optimizers) > 1:
+                    for param in self.get_model().parameters():
+                        param.requires_grad = False
+                    for group in optimizer.param_groups:
+                        for param in group['params']:
+                            param.requires_grad = True
+
+                # wrap the forward step in a closure so second order methods work
+                def optimizer_closure():
+                    # forward pass
+                    output = self.training_forward(
+                        split_batch, batch_idx, opt_idx, self.hiddens)
+
+                    closure_loss = output[0]
+                    progress_bar_metrics = output[1]
+                    log_metrics = output[2]
+                    callback_metrics = output[3]
+                    self.hiddens = output[4]
+                    if closure_loss is None:
+                        return None
+
+                    # accumulate loss
+                    # (if accumulate_grad_batches = 1 no effect)
+                    closure_loss = closure_loss / self.accumulate_grad_batches
+
+                    # backward pass
+                    model_ref = self.get_model()
+                    if closure_loss.requires_grad:
+                        model_ref.backward(closure_loss, optimizer)
+
+                    # track metrics for callbacks
+                    all_callback_metrics.append(callback_metrics)
+
+                    # track progress bar metrics
+                    self.add_tqdm_metrics(progress_bar_metrics)
+                    all_log_metrics.append(log_metrics)
+
+                    # insert after step hook
+                    if self.is_function_implemented('on_after_backward'):
+                        model_ref = self.get_model()
+                        model_ref.on_after_backward()
+
+                    return closure_loss
+
+                # calculate loss
+                loss = optimizer_closure()
+                if loss is None:
+                    continue
+
+                # nan grads
+                if self.print_nan_grads:
+                    self.print_nan_gradients()
+
+                # track total loss for logging (avoid mem leaks)
+                self.batch_loss_value += loss.item()
+
+                # gradient update with accumulated gradients
+                if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:
+
+                    # track gradient norms when requested
+                    if batch_idx % self.row_log_interval == 0:
+                        if self.track_grad_norm > 0:
+                            model = self.get_model()
+                            grad_norm_dic = model.grad_norm(
+                                self.track_grad_norm)
+
+                    # clip gradients
+                    self.clip_gradients()
+
+                    # calls .step(), .zero_grad()
+                    # override function to modify this behavior
+                    model = self.get_model()
+                    model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx)
+
+                    # calculate running loss for display
+                    self.running_loss.append(self.batch_loss_value)
+                    self.batch_loss_value = 0
+                    self.avg_loss = np.mean(self.running_loss[-100:])
+
+        # activate batch end hook
+        if self.is_function_implemented('on_batch_end'):
+            model = self.get_model()
+            model.on_batch_end()
+
+        # update progress bar
+        self.main_progress_bar.update(1)
+        self.main_progress_bar.set_postfix(**self.training_tqdm_dict)
+
+        # collapse all metrics into one dict
+        all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()}
+
+        # track all metrics for callbacks
+        self.callback_metrics.update({k: v for d in all_callback_metrics for k, v in d.items()})
+
+        return 0, grad_norm_dic, all_log_metrics
+
+    def training_forward(self, batch, batch_idx, opt_idx, hiddens):
+        """
+        Handle forward for each training case (distributed, single gpu, etc...)
+        :param batch:
+        :param batch_idx:
+        :return:
+        """
+        # ---------------
+        # FORWARD
+        # ---------------
+        # enable not needing to add opt_idx to training_step
+        args = [batch, batch_idx, opt_idx]
+
+        # distributed forward
+        if self.use_ddp or self.use_dp:
+            output = self.model(*args)
+        # single GPU forward
+        elif self.single_gpu:
+            gpu_id = 0
+            if isinstance(self.data_parallel_device_ids, list):
+                gpu_id = self.data_parallel_device_ids[0]
+            batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id)
+            args[0] = batch
+            output = self.model.training_step(*args)
+        # CPU forward
+        else:
+            output = self.model.training_step(*args)
+
+        # allow any mode to define training_end
+        model_ref = self.get_model()
+        output_ = model_ref.training_end(output)
+        if output_ is not None:
+            output = output_
+
+        # format and reduce outputs accordingly
+        output = self.process_output(output, train=True)
+
+        return output
+
+    # ---------------
+    # Utils
+    # ---------------
+    def is_function_implemented(self, f_name):
+        model = self.get_model()
+        f_op = getattr(model, f_name, None)
+        return callable(f_op)
+
+    def _percent_range_check(self, name):
+        value = getattr(self, name)
+        msg = f"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}."
+        if name == "val_check_interval":
+            msg += " If you want to disable validation set `val_percent_check` to 0.0 instead."
+
+        if not 0. <= value <= 1.:
+            raise ValueError(msg)
diff --git a/utils/plot.py b/utils/plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdca62a8cd80869c707890cd9febd39966cd3658
--- /dev/null
+++ b/utils/plot.py
@@ -0,0 +1,56 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+LINE_COLORS = ['w', 'r', 'y', 'cyan', 'm', 'b', 'lime']
+
+
+def spec_to_figure(spec, vmin=None, vmax=None):
+    if isinstance(spec, torch.Tensor):
+        spec = spec.cpu().numpy()
+    fig = plt.figure(figsize=(12, 6))
+    plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
+    return fig
+
+
+def spec_f0_to_figure(spec, f0s, figsize=None):
+    max_y = spec.shape[1]
+    if isinstance(spec, torch.Tensor):
+        spec = spec.detach().cpu().numpy()
+        f0s = {k: f0.detach().cpu().numpy() for k, f0 in f0s.items()}
+    f0s = {k: f0 / 10 for k, f0 in f0s.items()}
+    fig = plt.figure(figsize=(12, 6) if figsize is None else figsize)
+    plt.pcolor(spec.T)
+    for i, (k, f0) in enumerate(f0s.items()):
+        plt.plot(f0.clip(0, max_y), label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.8)
+    plt.legend()
+    return fig
+
+
+def dur_to_figure(dur_gt, dur_pred, txt):
+    dur_gt = dur_gt.long().cpu().numpy()
+    dur_pred = dur_pred.long().cpu().numpy()
+    dur_gt = np.cumsum(dur_gt)
+    dur_pred = np.cumsum(dur_pred)
+    fig = plt.figure(figsize=(12, 6))
+    for i in range(len(dur_gt)):
+        shift = (i % 8) + 1
+        plt.text(dur_gt[i], shift, txt[i])
+        plt.text(dur_pred[i], 10 + shift, txt[i])
+        plt.vlines(dur_gt[i], 0, 10, colors='b')  # blue is gt
+        plt.vlines(dur_pred[i], 10, 20, colors='r')  # red is pred
+    return fig
+
+
+def f0_to_figure(f0_gt, f0_cwt=None, f0_pred=None):
+    fig = plt.figure()
+    f0_gt = f0_gt.cpu().numpy()
+    plt.plot(f0_gt, color='r', label='gt')
+    if f0_cwt is not None:
+        f0_cwt = f0_cwt.cpu().numpy()
+        plt.plot(f0_cwt, color='b', label='cwt')
+    if f0_pred is not None:
+        f0_pred = f0_pred.cpu().numpy()
+        plt.plot(f0_pred, color='green', label='pred')
+    plt.legend()
+    return fig
diff --git a/utils/svc_utils.py b/utils/svc_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fea0c1ee280fa7c8927c3d692a482fd50f6660a
--- /dev/null
+++ b/utils/svc_utils.py
@@ -0,0 +1,141 @@
+import glob
+import importlib
+import os
+
+import matplotlib
+import numpy as np
+import torch
+import torch.distributions
+import torch.optim
+import torch.optim
+import torch.utils.data
+
+from preprocessing.process_pipeline import File2Batch
+from utils.hparams import hparams
+from utils.indexed_datasets import IndexedDataset
+from utils.pitch_utils import norm_interp_f0
+
+matplotlib.use('Agg')
+
+
+class SvcDataset(torch.utils.data.Dataset):
+    def __init__(self, prefix, shuffle=False):
+        super().__init__()
+        self.hparams = hparams
+        self.shuffle = shuffle
+        self.sort_by_len = hparams['sort_by_len']
+        self.sizes = None
+        self.data_dir = hparams['binary_data_dir']
+        self.prefix = prefix
+        self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
+        self.indexed_ds = None
+        # self.name2spk_id={}
+
+        # pitch stats
+        f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy'
+        if os.path.exists(f0_stats_fn):
+            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn)
+            hparams['f0_mean'] = float(hparams['f0_mean'])
+            hparams['f0_std'] = float(hparams['f0_std'])
+        else:
+            hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None
+
+        if prefix == 'test':
+            if hparams['test_input_dir'] != '':
+                self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir'])
+            else:
+                if hparams['num_test_samples'] > 0:
+                    self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids']
+                    self.sizes = [self.sizes[i] for i in self.avail_idxs]
+
+    @property
+    def _sizes(self):
+        return self.sizes
+
+    def _get_item(self, index):
+        if hasattr(self, 'avail_idxs') and self.avail_idxs is not None:
+            index = self.avail_idxs[index]
+        if self.indexed_ds is None:
+            self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
+        return self.indexed_ds[index]
+
+    def __getitem__(self, index):
+        item = self._get_item(index)
+        max_frames = hparams['max_frames']
+        spec = torch.Tensor(item['mel'])[:max_frames]
+        # energy = (spec.exp() ** 2).sum(-1).sqrt()
+        mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
+        f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
+        hubert = torch.Tensor(item['hubert'][:hparams['max_input_tokens']])
+        pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
+        sample = {
+            "id": index,
+            "item_name": item['item_name'],
+            "hubert": hubert,
+            "mel": spec,
+            "pitch": pitch,
+            "f0": f0,
+            "uv": uv,
+            "mel2ph": mel2ph,
+            "mel_nonpadding": spec.abs().sum(-1) > 0,
+        }
+        if hparams['use_energy_embed']:
+            sample['energy'] = item['energy']
+        if hparams['use_spk_embed']:
+            sample["spk_embed"] = torch.Tensor(item['spk_embed'])
+        if hparams['use_spk_id']:
+            sample["spk_id"] = item['spk_id']
+        return sample
+
+    @staticmethod
+    def collater(samples):
+        return File2Batch.processed_input2batch(samples)
+
+    @staticmethod
+    def load_test_inputs(test_input_dir):
+        inp_wav_paths = glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3')
+        sizes = []
+        items = []
+
+        binarizer_cls = hparams.get("binarizer_cls", 'basics.base_binarizer.BaseBinarizer')
+        pkg = ".".join(binarizer_cls.split(".")[:-1])
+        cls_name = binarizer_cls.split(".")[-1]
+        binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
+        from preprocessing.hubertinfer import HubertEncoder
+        for wav_fn in inp_wav_paths:
+            item_name = os.path.basename(wav_fn)
+            wav_fn = wav_fn
+            encoder = HubertEncoder(hparams['hubert_path'])
+            item = binarizer_cls.process_item(item_name, {'wav_fn': wav_fn}, encoder)
+            print(item)
+            items.append(item)
+            sizes.append(item['len'])
+        return items, sizes
+
+    def __len__(self):
+        return len(self._sizes)
+
+    def num_tokens(self, index):
+        return self.size(index)
+
+    def size(self, index):
+        """Return an example's size as a float or tuple. This value is used when
+        filtering a dataset with ``--max-positions``."""
+        size = min(self._sizes[index], hparams['max_frames'])
+        return size
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        if self.shuffle:
+            indices = np.random.permutation(len(self))
+            if self.sort_by_len:
+                indices = indices[np.argsort(np.array(self._sizes)[indices], kind='mergesort')]
+                # 先random, 然后稳定排序, 保证排序后同长度的数据顺序是依照random permutation的 (被其随机打乱).
+        else:
+            indices = np.arange(len(self))
+        return indices
+
+    @property
+    def num_workers(self):
+        return int(os.getenv('NUM_WORKERS', hparams['ds_workers']))