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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ # SoftVC VITS Singing Voice Conversion
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+
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+ ## Updates
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+ > According to incomplete statistics, it seems that training with multiple speakers may lead to **worsened leaking of voice timbre**. It is not recommended to train models with more than 5 speakers. The current suggestion is to try to train models with only a single speaker if you want to achieve a voice timbre that is more similar to the target.
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+ > Fixed the issue with unwanted staccato, improving audio quality by a decent amount.\
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+ > The 2.0 version has been moved to the 2.0 branch.\
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+ > Version 3.0 uses the code structure of FreeVC, which isn't compatible with older versions.\
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+ > Compared to [DiffSVC](https://github.com/prophesier/diff-svc) , diffsvc performs much better when the training data is of extremely high quality, but this repository may perform better on datasets with lower quality. Additionally, this repository is much faster in terms of inference speed compared to diffsvc.
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+
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+ ## Model Overview
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+ A singing voice coversion (SVC) model, using the SoftVC encoder to extract features from the input audio, sent into VITS along with the F0 to replace the original input to acheive a voice conversion effect. Additionally, changing the vocoder to [NSF HiFiGAN](https://github.com/openvpi/DiffSinger/tree/refactor/modules/nsf_hifigan) to fix the issue with unwanted staccato.
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+
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+ ## Notice
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+ + The current branch is the 32kHz version, which requires less vram during inferencing, as well as faster inferencing speeds, and datasets for said branch take up less disk space. Thus the 32 kHz branch is recommended for use.
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+ + If you want to train 48 kHz variant models, switch to the [main branch](https://github.com/innnky/so-vits-svc/tree/main).
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+
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+
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+ ## Required models
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+ + soft vc hubert:[hubert-soft-0d54a1f4.pt](https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt)
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+ + Place under `hubert`.
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+ + Pretrained models [G_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth) and [D_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth)
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+ + Place under `logs/32k`.
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+ + Pretrained models are required, because from experiments, training from scratch can be rather unpredictable to say the least, and training with a pretrained model can greatly improve training speeds.
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+ + The pretrained model includes云灏, 即霜, 辉宇·星AI, 派蒙, and 绫地宁宁, covering the common ranges of both male and female voices, and so it can be seen as a rather universal pretrained model.
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+ + The pretrained model exludes the `optimizer speaker_embedding` section, rendering it only usable for pretraining and incapable of inferencing with.
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+ ```shell
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+ # For simple downloading.
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+ # hubert
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+ wget -P hubert/ https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt
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+ # G&D pretrained models
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+ wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth
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+ wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth
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+
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+ ```
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+
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+ ## Colab notebook script for dataset creation and training.
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+ [colab training notebook](https://colab.research.google.com/drive/1rCUOOVG7-XQlVZuWRAj5IpGrMM8t07pE?usp=sharing)
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+
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+ ## Dataset preparation
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+ All that is required is that the data be put under the `dataset_raw` folder in the structure format provided below.
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+ ```shell
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+ dataset_raw
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+ ├───speaker0
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+ │ ├───xxx1-xxx1.wav
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+ │ ├───...
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+ │ └───Lxx-0xx8.wav
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+ └───speaker1
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+ ├───xx2-0xxx2.wav
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+ ├───...
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+ └───xxx7-xxx007.wav
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+ ```
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+
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+ ## Data pre-processing.
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+ 1. Resample to 32khz
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+
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+ ```shell
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+ python resample.py
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+ ```
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+ 2. Automatically sort out training set, validation set, test set, and automatically generate configuration files.
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+ ```shell
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+ python preprocess_flist_config.py
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+ # Notice.
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+ # The n_speakers value in the config will be set automatically according to the amount of speakers in the dataset.
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+ # To reserve space for additionally added speakers in the dataset, the n_speakers value will be be set to twice the actual amount.
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+ # If you want even more space for adding more data, you can edit the n_speakers value in the config after runing this step.
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+ # This can not be changed after training starts.
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+ ```
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+ 3. Generate hubert and F0 features/
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+ ```shell
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+ python preprocess_hubert_f0.py
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+ ```
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+ After running the step above, the `dataset` folder will contain all the pre-processed data, you can delete the `dataset_raw` folder after that.
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+
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+ ## Training.
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+ ```shell
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+ python train.py -c configs/config.json -m 32k
77
+ ```
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+
79
+ ## Inferencing.
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+
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+ Use [inference_main.py](inference_main.py)
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+ + Edit `model_path` to your newest checkpoint.
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+ + Place the input audio under the `raw` folder.
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+ + Change `clean_names` to the output file name.
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+ + Use `trans` to edit the pitch shifting amount (semitones).
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+ + Change `spk_list` to the speaker name.
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+
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+ ## Onnx Exporting.
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+ ### **When exporting Onnx, please make sure you re-clone the whole repository!!!**
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+ Use [onnx_export.py](onnx_export.py)
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+ + Create a new folder called `checkpoints`.
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+ + Create a project folder in `checkpoints` folder with the desired name for your project, let's use `myproject` as example. Folder structure looks like `./checkpoints/myproject`.
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+ + Rename your model to `model.pth`, rename your config file to `config.json` then move them into `myproject` folder.
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+ + Modify [onnx_export.py](onnx_export.py) where `path = "NyaruTaffy"`, change `NyaruTaffy` to your project name, here it will be `path = "myproject"`.
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+ + Run [onnx_export.py](onnx_export.py)
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+ + Once it finished, a `model.onnx` will be generated in `myproject` folder, that's the model you just exported.
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+ + Notice: if you want to export a 48K model, please follow the instruction below or use `model_onnx_48k.py` directly.
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+ + Open [model_onnx.py](model_onnx.py) and change `hps={"sampling_rate": 32000...}` to `hps={"sampling_rate": 48000}` in class `SynthesizerTrn`.
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+ + Open [nvSTFT](/vdecoder/hifigan/nvSTFT.py) and replace all `32000` with `48000`
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+ ### Onnx Model UI Support
101
+ + [MoeSS](https://github.com/NaruseMioShirakana/MoeSS)
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+ + All training function and transformation are removed, only if they are all removed you are actually using Onnx.
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+
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+ ## Gradio (WebUI)
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+ Use [sovits_gradio.py](sovits_gradio.py) to run Gradio WebUI
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+ + Create a new folder called `checkpoints`.
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+ + Create a project folder in `checkpoints` folder with the desired name for your project, let's use `myproject` as example. Folder structure looks like `./checkpoints/myproject`.
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+ + Rename your model to `model.pth`, rename your config file to `config.json` then move them into `myproject` folder.
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+ + Run [sovits_gradio.py](sovits_gradio.py)
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README.md ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ title: Sovits4
3
+ emoji: 🐨
4
+ colorFrom: gray
5
+ colorTo: pink
6
+ sdk: gradio
7
+ sdk_version: 3.18.0
8
+ app_file: app.py
9
+ pinned: false
10
+ license: mit
11
+ duplicated_from: Nogizaka46/Nogizaka46-so
12
+ ---
13
+
14
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Summertime.wav ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d8c4d9379da0be2c0456196f25a84cc4e242232d9993df4663f37b029ce9d3d2
3
+ size 3611042
app.py ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import io
2
+ import os
3
+
4
+ #os.system("wget -P hubert/ https://huggingface.co/spaces/Nogizaka46/Nogizaka46-so/resolve/main/hubert/checkpoint_best_legacy_500.pt")
5
+ import gradio as gr
6
+ import librosa
7
+ import numpy as np
8
+ import soundfile
9
+ from inference.infer_tool import Svc
10
+ import logging
11
+ import time
12
+ logging.getLogger('numba').setLevel(logging.WARNING)
13
+ logging.getLogger('markdown_it').setLevel(logging.WARNING)
14
+ logging.getLogger('urllib3').setLevel(logging.WARNING)
15
+ logging.getLogger('matplotlib').setLevel(logging.WARNING)
16
+ model = Svc("logs/44k/@github-NGZ-sovits-4.pth", "configs/config-65.json", cluster_model_path="logs/44k/kmeans_10000.pt")
17
+ #model = Svc("logs/44k/@github-NGZ-sovits-4.pth", "configs/config.json")
18
+
19
+ from matplotlib import pyplot as plt
20
+
21
+ def f0_to_pitch(ff):
22
+ f0_pitch = 69 + 12 * np.log2(ff / 160)
23
+ return f0_pitch
24
+ def compute_f0(wav_file1, wav_file2,tran):
25
+ y1, sr1 = librosa.load(wav_file1, sr=16000)
26
+ y2, sr2 = librosa.load(wav_file2, sr=16000)
27
+
28
+ # Compute the f0 using the YIN pitch estimation method
29
+ f0_1 = librosa.core.yin(y1, fmin=70, fmax=600)
30
+ f0_2 = librosa.core.yin(y2, fmin=70, fmax=600)
31
+ # 半音偏差
32
+ sum_y = []
33
+ if np.sum(wav_file1 == 0) / len(wav_file1) > 0.9:
34
+ mistake, var_take = 0, 0
35
+ else:
36
+ for i in range(min(len(f0_1), len(f0_2))):
37
+ if f0_1[i] > 0 and f0_2[i] > 0:
38
+ sum_y.append(
39
+ abs(f0_to_pitch(f0_2[i]) - (f0_to_pitch(f0_1[i]) + tran)))
40
+ num_y = 0
41
+ for x in sum_y:
42
+ num_y += x
43
+ len_y = len(sum_y) if len(sum_y) else 1
44
+ mistake = round(float(num_y / len_y), 2)
45
+ var_take = round(float(np.std(sum_y, ddof=1)), 2)
46
+ print("mistake", mistake, var_take)
47
+ return f0_1, f0_2, sr1, sr2, round(mistake / 10, 2), round(var_take / 10, 2)
48
+
49
+
50
+ def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,F0_mean_pooling):
51
+ #cluster_ratio =0
52
+ start_time = time.time()
53
+ if input_audio is None:
54
+ return "You need to upload an audio", None
55
+ sampling_rate, audio = input_audio
56
+ duration = audio.shape[0] / sampling_rate
57
+ if duration > 70:
58
+ return "请上传小于70s的音频,需要转换长音频请本地进行转换", None , None
59
+ audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
60
+ if len(audio.shape) > 1:
61
+ audio = librosa.to_mono(audio.transpose(1, 0))
62
+ if sampling_rate != 16000:
63
+ audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
64
+ #print(audio.shape)
65
+
66
+
67
+ out_wav_path = "temp.wav"
68
+ soundfile.write(out_wav_path, audio, 16000, format="wav")
69
+
70
+ #print(slice_db, cluster_ratio, auto_f0, noise_scale, sid)
71
+ print(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale, F0_mean_pooling)
72
+ _audio = model.slice_inference(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,F0_mean_pooling=F0_mean_pooling)
73
+
74
+ soundfile.write("output.wav", _audio, 44100, format="wav")
75
+ f01, f02, sr1, sr2 , mistake ,var = compute_f0('temp.wav', 'output.wav',vc_transform)
76
+ time_step_1 = np.arange(0,len(f01) )
77
+ time_step_2 = np.arange(0,len(f02) )
78
+ plt.figure(figsize=[8, 3])
79
+ plt.plot(time_step_1 , f01, label='Input')
80
+ plt.plot(time_step_2 , f02, label='Output')
81
+
82
+ #plt.title("T0 of Input and Output")
83
+ #plt.ylabel("T0")
84
+ #plt.xlabel("Time step")
85
+
86
+ length = np.arange(0,int( duration*10) ,int( duration))
87
+ plt.xticks(np.linspace(0, len(f01),len(length)), length)
88
+ plt.legend()
89
+ plt.savefig('temp.svg')
90
+ plt.close()
91
+
92
+ used_time = round(time.time() - start_time, 2)
93
+ out_str = ("Success! total use time:{}s\n半音偏差:{}\n半音方差:{}".format(
94
+ used_time, mistake, var))
95
+ return out_str , (44100, _audio), gr.Image.update("temp.svg")
96
+
97
+
98
+ app = gr.Blocks()
99
+ with app:
100
+ with gr.Tabs():
101
+ with gr.TabItem("Basic"):
102
+ gr.Markdown(value="""
103
+ # 前言
104
+ * 此模型为sovits4.0原版(抗混响强),如果音色不像可以试试另一个模型:[https://huggingface.co/spaces/Nogizaka46/Nogizaka46-so-dev](https://huggingface.co/spaces/Nogizaka46/Nogizaka46-so-dev)
105
+ * 23-05-29修复池化功能,有bug记得反馈下。模型更新日期23-04-26.新模型使用65小时语音训练63位成员。仅供个人娱乐和非商业用途,禁止用于血腥、暴力、性相关、政治相关内容,转换长音频请本地进行转换
106
+ * 扒干声教程:[BV1sb411o7xF](https://www.bilibili.com/video/BV1sb411o7xF) [cv23095265](https://www.bilibili.com/read/cv23095265) b站传播的Ai翻唱大多数是他人翻唱或原曲混响和声少的,不是所有歌都能扒干净的,如果声音不像都是因为混响与和声扒不干净,结合自己的时间学会放弃。更多相关教程,翻唱,本地整合包在Tg群:[t.me/+vP8NK1NMLiYzMDJl](https://t.me/+vP8NK1NMLiYzMDJl)
107
+ * [Ripx,Au,UVR工具下载](https://pan.baidu.com/s/1Ne55iKqoacjKE-moK_YtGg?pwd=qsfd) 总有问制作流程,这说一下。。以冬之花为例,1.用UVR-4_HP-Vocal模型提取人声 或 vocalremover.org(这个网站处理不会损伤人声,方便二次处理,推荐),UVR-5_HP-Karaoke去除和声,2.合成,对比干声听听有几处哑音 如果有,使用RipX去除干声里造成哑音的和声 4.合成再听听,再不行就使用池化 5.使用Au调音,按喜好,添加混响,和声,回声等,这步可以增强音色,效果是很明显的。通过冬之花的练习,你已经具备处理干声的能力,轻松一天量产10首。
108
+
109
+ # 声明
110
+ * 如用此模型制作音频请标注来源:github.com/3110asuka/Nogizaka46-so 或 huggingface.co/spaces/Nogizaka46/Nogizaka46-so""")
111
+ gr.Markdown(value="""秋元真夏 AKIMOTO_MANATSU| 生田絵梨花 IKUTA_ERIKA| 生駒里奈 IKOMA_RINA| 伊藤純奈 ITO_JUNNA| 井上小百合 INOUE_SAYURI| 衛藤美彩 ETO_MISA| 川後陽菜 KAWAGO_HINA|北野日奈子 KITANO_HINAKO|齋藤飛鳥 SAITO_ASUKA|斉藤優里 SATO_YUURI|相楽伊織 SAGARA_IORI|桜井玲香 SAKURAI_REIKA|佐々木琴子 SASAKI_KOTOKO|白石麻衣 SHIRAISHI_MAI|新内眞衣 SHINUCHI_MAI|鈴木絢音 SUZUKI_AYANE|高山一実 TAKAYAMA_KAZUMI|寺田蘭世 TERADA_RANZE|西野七瀬 NISHINO_NANASE|能條愛未 NOUJO_AMI|樋口日奈 HIGUCHI_HINA|星野みなみ HOSHINO_MINAMI|堀未央奈 HORI_MIONA|松村沙友理 MATSUMURA_SAYURI|山崎怜奈 YAMAZAKI_RENA|若月佑美 WAKATSUKI_YUMI|渡辺みり愛 WATANABE_MIRIA|和田まあや WADA_MAAYA|伊藤理々杏 ITO_RIRIA|岩本蓮加 IWAMOTO_RENKA|梅澤美波 UMEZAWA_MINAMI|大園桃子 OZONO_MOMOKO|久保史緒里 KUBO_SHIORI|阪口珠美 SAKAGUCHI_TAMAMI|佐藤楓 SATO_KAEDE|中村麗乃 NAKAMURA_RENO|向井葉月 MUKAI_HAZUKI|山下美月 YAMASHITA_MIZUKI|与田祐希 YODA_YUUKI|遠藤さくら ENDO_SAKURA|賀喜遥香 KAKI_HARUKA|掛橋沙耶香 KAKEHASHI_SAYAKA|金川紗耶 KANAGAWA_SAYA|北川悠理 KITAGAWA_YURI|柴田柚菜 SHIBATA_YUNA|田村真佑 TAMURA_MAYU|筒井あやめ TSUTSUI_AYAME|早川聖来 HAYAKAWA_SEIRA|矢久保美緒 YAKUBO_MIO|黒見明香 HARUKA_KUROMI|佐藤璃果 RIKA_SATO|林瑠奈 RUNA_HAYASHI|松尾美佑 MIYU_MATSUO|弓木奈於 NAO_YUMIKI|五百城茉央 IOKI_MAO|池田瑛紗 IKEDA_TERESA|一ノ瀬美空 ICHINOSE_MIKU|井上和 INOUE_NAGI|小川彩 OGAWA_AYA|奥田いろは OKUDA_IROHA|川﨑桜 KAWASAKI_SAKURA|菅原咲月 SUGAWARA_SATSUKI|冨里奈央 TOMISATO_NAO|中西アルノ NAKANISHI_ARUNO""")
112
+ spks = list(model.spk2id.keys())
113
+ sid = gr.Dropdown(label="音色", choices=spks, value="IKUTA_ERIKA")
114
+ vc_input3 = gr.Audio(label="上传音频<70s无BGM无混响的干声",value="没什么「你的名字。」干声素材12s.mp3")
115
+ #vc_transform = gr.Number(label="变调(整数,可以正负,半音数量,升高八度就是12)一般填写6或-6之内", value=0)
116
+ vc_transform = gr.Slider(label="变调(整数,可以正负,半音数量,升高八度就是12)一般填写6或-6之内",
117
+ maximum=16, minimum=-16, step=1, value=0)
118
+ cluster_ratio = gr.Number(label="聚类模型混合比例,0-1之间,默认为0不启用聚类,能提升音色相似度,但会导致咬字下降(如果使用建议0.5左右)", value=0)
119
+ auto_f0 = gr.Checkbox(label="自动f0预测,配合聚类模型f0预测效果更好,会导致变调功能失效(仅限转换语音,歌声不要勾选此项会究极跑调)", value=False)
120
+ slice_db = gr.Slider(label="切片阈值(较嘈杂时-30,保留呼吸声时-50,一般默认-40)",
121
+ maximum=-30, minimum=-70, step=1, value=-40)
122
+ noise_scale = gr.Number(label="noise_scale 建议不要动,会影响音质,玄学参数", value=0.4)
123
+ F0_mean_pooling = gr.Checkbox(label="是否对F0使用均值滤波器(池化),对部分哑音有改善(和声混响造成的哑音无效)。注意,会导致推理速度下降,默认关闭", value=False)
124
+ vc_submit = gr.Button("转换", variant="primary")
125
+ vc_output1 = gr.Textbox(label="音高平均偏差半音数量,体现转换音频的跑调情况(一般小于0.5)")
126
+ vc_output2 = gr.Audio(label="Output Audio")
127
+ f0_image = gr.Image(label="f0曲线,蓝色为输入音高,橙色为合成音频的音高(代码有误差)")
128
+ vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,F0_mean_pooling],
129
+ [vc_output1, vc_output2, f0_image])
130
+
131
+ app.launch()
cluster/__init__.py ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import torch
3
+ from sklearn.cluster import KMeans
4
+
5
+ def get_cluster_model(ckpt_path):
6
+ checkpoint = torch.load(ckpt_path)
7
+ kmeans_dict = {}
8
+ for spk, ckpt in checkpoint.items():
9
+ km = KMeans(ckpt["n_features_in_"])
10
+ km.__dict__["n_features_in_"] = ckpt["n_features_in_"]
11
+ km.__dict__["_n_threads"] = ckpt["_n_threads"]
12
+ km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"]
13
+ kmeans_dict[spk] = km
14
+ return kmeans_dict
15
+
16
+ def get_cluster_result(model, x, speaker):
17
+ """
18
+ x: np.array [t, 256]
19
+ return cluster class result
20
+ """
21
+ return model[speaker].predict(x)
22
+
23
+ def get_cluster_center_result(model, x,speaker):
24
+ """x: np.array [t, 256]"""
25
+ predict = model[speaker].predict(x)
26
+ return model[speaker].cluster_centers_[predict]
27
+
28
+ def get_center(model, x,speaker):
29
+ return model[speaker].cluster_centers_[x]
cluster/train_cluster.py ADDED
@@ -0,0 +1,89 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ from glob import glob
3
+ from pathlib import Path
4
+ import torch
5
+ import logging
6
+ import argparse
7
+ import torch
8
+ import numpy as np
9
+ from sklearn.cluster import KMeans, MiniBatchKMeans
10
+ import tqdm
11
+ logging.basicConfig(level=logging.INFO)
12
+ logger = logging.getLogger(__name__)
13
+ import time
14
+ import random
15
+
16
+ def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False):
17
+
18
+ logger.info(f"Loading features from {in_dir}")
19
+ features = []
20
+ nums = 0
21
+ for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
22
+ features.append(torch.load(path).squeeze(0).numpy().T)
23
+ # print(features[-1].shape)
24
+ features = np.concatenate(features, axis=0)
25
+ print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
26
+ features = features.astype(np.float32)
27
+ logger.info(f"Clustering features of shape: {features.shape}")
28
+ t = time.time()
29
+ if use_minibatch:
30
+ kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
31
+ else:
32
+ kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
33
+ print(time.time()-t, "s")
34
+
35
+ x = {
36
+ "n_features_in_": kmeans.n_features_in_,
37
+ "_n_threads": kmeans._n_threads,
38
+ "cluster_centers_": kmeans.cluster_centers_,
39
+ }
40
+ print("end")
41
+
42
+ return x
43
+
44
+
45
+ if __name__ == "__main__":
46
+
47
+ parser = argparse.ArgumentParser()
48
+ parser.add_argument('--dataset', type=Path, default="./dataset/44k",
49
+ help='path of training data directory')
50
+ parser.add_argument('--output', type=Path, default="logs/44k",
51
+ help='path of model output directory')
52
+
53
+ args = parser.parse_args()
54
+
55
+ checkpoint_dir = args.output
56
+ dataset = args.dataset
57
+ n_clusters = 10000
58
+
59
+ ckpt = {}
60
+ for spk in os.listdir(dataset):
61
+ if os.path.isdir(dataset/spk):
62
+ print(f"train kmeans for {spk}...")
63
+ in_dir = dataset/spk
64
+ x = train_cluster(in_dir, n_clusters, verbose=False)
65
+ ckpt[spk] = x
66
+
67
+ checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
68
+ checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
69
+ torch.save(
70
+ ckpt,
71
+ checkpoint_path,
72
+ )
73
+
74
+
75
+ # import cluster
76
+ # for spk in tqdm.tqdm(os.listdir("dataset")):
77
+ # if os.path.isdir(f"dataset/{spk}"):
78
+ # print(f"start kmeans inference for {spk}...")
79
+ # for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)):
80
+ # mel_path = feature_path.replace(".discrete.npy",".mel.npy")
81
+ # mel_spectrogram = np.load(mel_path)
82
+ # feature_len = mel_spectrogram.shape[-1]
83
+ # c = np.load(feature_path)
84
+ # c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy()
85
+ # feature = c.T
86
+ # feature_class = cluster.get_cluster_result(feature, spk)
87
+ # np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class)
88
+
89
+
configs/config-65.json ADDED
@@ -0,0 +1,156 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "eval_interval": 1600,
5
+ "seed": 1234,
6
+ "epochs": 10000,
7
+ "learning_rate": 0.0001,
8
+ "betas": [
9
+ 0.8,
10
+ 0.99
11
+ ],
12
+ "eps": 1e-09,
13
+ "batch_size": 1,
14
+ "fp16_run": false,
15
+ "lr_decay": 0.999875,
16
+ "segment_size": 10240,
17
+ "init_lr_ratio": 1,
18
+ "warmup_epochs": 0,
19
+ "c_mel": 45,
20
+ "c_kl": 1.0,
21
+ "use_sr": true,
22
+ "max_speclen": 512,
23
+ "port": "8001",
24
+ "keep_ckpts": 53,
25
+ "all_in_mem": false
26
+ },
27
+ "data": {
28
+ "training_files": "filelists/train.txt",
29
+ "validation_files": "filelists/val.txt",
30
+ "max_wav_value": 32768.0,
31
+ "sampling_rate": 44100,
32
+ "filter_length": 2048,
33
+ "hop_length": 512,
34
+ "win_length": 2048,
35
+ "n_mel_channels": 80,
36
+ "mel_fmin": 0.0,
37
+ "mel_fmax": 22050
38
+ },
39
+ "model": {
40
+ "inter_channels": 192,
41
+ "hidden_channels": 192,
42
+ "filter_channels": 768,
43
+ "n_heads": 2,
44
+ "n_layers": 6,
45
+ "kernel_size": 3,
46
+ "p_dropout": 0.1,
47
+ "resblock": "1",
48
+ "resblock_kernel_sizes": [
49
+ 3,
50
+ 7,
51
+ 11
52
+ ],
53
+ "resblock_dilation_sizes": [
54
+ [
55
+ 1,
56
+ 3,
57
+ 5
58
+ ],
59
+ [
60
+ 1,
61
+ 3,
62
+ 5
63
+ ],
64
+ [
65
+ 1,
66
+ 3,
67
+ 5
68
+ ]
69
+ ],
70
+ "upsample_rates": [
71
+ 8,
72
+ 8,
73
+ 2,
74
+ 2,
75
+ 2
76
+ ],
77
+ "upsample_initial_channel": 512,
78
+ "upsample_kernel_sizes": [
79
+ 16,
80
+ 16,
81
+ 4,
82
+ 4,
83
+ 4
84
+ ],
85
+ "n_layers_q": 3,
86
+ "use_spectral_norm": false,
87
+ "gin_channels": 256,
88
+ "ssl_dim": 256,
89
+ "n_speakers": 63
90
+ },
91
+ "spk": {
92
+ "AKIMOTO_MANATSU": 0,
93
+ "ENDO_SAKURA": 1,
94
+ "ETO_MISA": 2,
95
+ "HARUKA_KUROMI": 3,
96
+ "HAYAKAWA_SEIRA": 4,
97
+ "HIGUCHI_HINA": 5,
98
+ "HORI_MIONA": 6,
99
+ "HOSHINO_MINAMI": 7,
100
+ "ICHINOSE_MIKU": 8,
101
+ "IKEDA_TERESA": 9,
102
+ "IKUTA_ERIKA": 10,
103
+ "INOUE_NAGI": 11,
104
+ "INOUE_SAYURI": 12,
105
+ "IOKI_MAO": 13,
106
+ "ITO_JUNNA": 14,
107
+ "ITO_RIRIA": 15,
108
+ "IWAMOTO_RENKA": 16,
109
+ "KAKEHASHI_SAYAKA": 17,
110
+ "KAKI_HARUKA": 18,
111
+ "KANAGAWA_SAYA": 19,
112
+ "KAWAGO_HINA": 20,
113
+ "KAWASAKI_SAKURA": 21,
114
+ "KITAGAWA_YURI": 22,
115
+ "KITANO_HINAKO": 23,
116
+ "KUBO_SHIORI": 24,
117
+ "MATSUMURA_SAYURI": 25,
118
+ "MIYU_MATSUO": 26,
119
+ "MUKAI_HAZUKI": 27,
120
+ "NAKAMURA_RENO": 28,
121
+ "NAKANISHI_ARUNO": 29,
122
+ "NAO_YUMIKI": 30,
123
+ "NISHINO_NANASE": 31,
124
+ "NOUJO_AMI": 32,
125
+ "OGAWA_AYA": 33,
126
+ "OKUDA_IROHA": 34,
127
+ "OZONO_MOMOKO": 35,
128
+ "RIKA_SATO": 36,
129
+ "RUNA_HAYASHI": 37,
130
+ "SAGARA_IORI": 38,
131
+ "SAITO_ASUKA": 39,
132
+ "SAKAGUCHI_TAMAMI": 40,
133
+ "SAKURAI_REIKA": 41,
134
+ "SASAKI_KOTOKO": 42,
135
+ "SATO_KAEDE": 43,
136
+ "SATO_YUURI": 44,
137
+ "SHIBATA_YUNA": 45,
138
+ "SHINUCHI_MAI": 46,
139
+ "SHIRAISHI_MAI": 47,
140
+ "SUGAWARA_SATSUKI": 48,
141
+ "SUZUKI_AYANE": 49,
142
+ "TAKAYAMA_KAZUMI": 50,
143
+ "TAMURA_MAYU": 51,
144
+ "TERADA_RANZE": 52,
145
+ "TOMISATO_NAO": 53,
146
+ "TSUTSUI_AYAME": 54,
147
+ "UMEZAWA_MINAMI": 55,
148
+ "WADA_MAAYA": 56,
149
+ "WAKATSUKI_YUMI": 57,
150
+ "WATANABE_MIRIA": 58,
151
+ "YAKUBO_MIO": 59,
152
+ "YAMASHITA_MIZUKI": 60,
153
+ "YAMAZAKI_RENA": 61,
154
+ "YODA_YUUKI": 62
155
+ }
156
+ }
configs/config.json ADDED
@@ -0,0 +1,156 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "train": {
3
+ "log_interval": 200,
4
+ "eval_interval": 1600,
5
+ "seed": 1234,
6
+ "epochs": 10000,
7
+ "learning_rate": 0.0001,
8
+ "betas": [
9
+ 0.8,
10
+ 0.99
11
+ ],
12
+ "eps": 1e-09,
13
+ "batch_size": 2,
14
+ "fp16_run": false,
15
+ "lr_decay": 0.999875,
16
+ "segment_size": 10240,
17
+ "init_lr_ratio": 1,
18
+ "warmup_epochs": 0,
19
+ "c_mel": 45,
20
+ "c_kl": 1.0,
21
+ "use_sr": true,
22
+ "max_speclen": 512,
23
+ "port": "8001",
24
+ "keep_ckpts": 53,
25
+ "all_in_mem": false
26
+ },
27
+ "data": {
28
+ "training_files": "filelists/train.txt",
29
+ "validation_files": "filelists/val.txt",
30
+ "max_wav_value": 32768.0,
31
+ "sampling_rate": 44100,
32
+ "filter_length": 2048,
33
+ "hop_length": 512,
34
+ "win_length": 2048,
35
+ "n_mel_channels": 80,
36
+ "mel_fmin": 0.0,
37
+ "mel_fmax": 22050
38
+ },
39
+ "model": {
40
+ "inter_channels": 192,
41
+ "hidden_channels": 192,
42
+ "filter_channels": 768,
43
+ "n_heads": 2,
44
+ "n_layers": 6,
45
+ "kernel_size": 3,
46
+ "p_dropout": 0.1,
47
+ "resblock": "1",
48
+ "resblock_kernel_sizes": [
49
+ 3,
50
+ 7,
51
+ 11
52
+ ],
53
+ "resblock_dilation_sizes": [
54
+ [
55
+ 1,
56
+ 3,
57
+ 5
58
+ ],
59
+ [
60
+ 1,
61
+ 3,
62
+ 5
63
+ ],
64
+ [
65
+ 1,
66
+ 3,
67
+ 5
68
+ ]
69
+ ],
70
+ "upsample_rates": [
71
+ 8,
72
+ 8,
73
+ 2,
74
+ 2,
75
+ 2
76
+ ],
77
+ "upsample_initial_channel": 512,
78
+ "upsample_kernel_sizes": [
79
+ 16,
80
+ 16,
81
+ 4,
82
+ 4,
83
+ 4
84
+ ],
85
+ "n_layers_q": 3,
86
+ "use_spectral_norm": false,
87
+ "gin_channels": 256,
88
+ "ssl_dim": 256,
89
+ "n_speakers": 63
90
+ },
91
+ "spk": {
92
+ "AKIMOTO_MANATSU": 0,
93
+ "ENDO_SAKURA": 1,
94
+ "ETO_MISA": 2,
95
+ "HARUKA_KUROMI": 3,
96
+ "HAYAKAWA_SEIRA": 4,
97
+ "HIGUCHI_HINA": 5,
98
+ "HORI_MIONA": 6,
99
+ "HOSHINO_MINAMI": 7,
100
+ "ICHINOSE_MIKU": 8,
101
+ "IKEDA_TERESA": 9,
102
+ "IKUTA_ERIKA": 10,
103
+ "INOUE_NAGI": 11,
104
+ "INOUE_SAYURI": 12,
105
+ "IOKI_MAO": 13,
106
+ "ITO_JUNNA": 14,
107
+ "ITO_RIRIA": 15,
108
+ "IWAMOTO_RENKA": 16,
109
+ "KAKEHASHI_SAYAKA": 17,
110
+ "KAKI_HARUKA": 18,
111
+ "KANAGAWA_SAYA": 19,
112
+ "KAWAGO_HINA": 20,
113
+ "KAWASAKI_SAKURA": 21,
114
+ "KITAGAWA_YURI": 22,
115
+ "KITANO_HINAKO": 23,
116
+ "KUBO_SHIORI": 24,
117
+ "MATSUMURA_SAYURI": 25,
118
+ "MIYU_MATSUO": 26,
119
+ "MUKAI_HAZUKI": 27,
120
+ "NAKAMURA_RENO": 28,
121
+ "NAKANISHI_ARUNO": 29,
122
+ "NAO_YUMIKI": 30,
123
+ "NISHINO_NANASE": 31,
124
+ "NOUJO_AMI": 32,
125
+ "OGAWA_AYA": 33,
126
+ "OKUDA_IROHA": 34,
127
+ "OZONO_MOMOKO": 35,
128
+ "RIKA_SATO": 36,
129
+ "RUNA_HAYASHI": 37,
130
+ "SAGARA_IORI": 38,
131
+ "SAITO_ASUKA": 39,
132
+ "SAKAGUCHI_TAMAMI": 40,
133
+ "SAKURAI_REIKA": 41,
134
+ "SASAKI_KOTOKO": 42,
135
+ "SATO_KAEDE": 43,
136
+ "SATO_YUURI": 44,
137
+ "SHIBATA_YUNA": 45,
138
+ "SHINUCHI_MAI": 46,
139
+ "SHIRAISHI_MAI": 47,
140
+ "SUGAWARA_SATSUKI": 48,
141
+ "SUZUKI_AYANE": 49,
142
+ "TAKAYAMA_KAZUMI": 50,
143
+ "TAMURA_MAYU": 51,
144
+ "TERADA_RANZE": 52,
145
+ "TOMISATO_NAO": 53,
146
+ "TSUTSUI_AYAME": 54,
147
+ "UMEZAWA_MINAMI": 55,
148
+ "WADA_MAAYA": 56,
149
+ "WAKATSUKI_YUMI": 57,
150
+ "WATANABE_MIRIA": 58,
151
+ "YAKUBO_MIO": 59,
152
+ "YAMASHITA_MIZUKI": 60,
153
+ "YAMAZAKI_RENA": 61,
154
+ "YODA_YUUKI": 62
155
+ }
156
+ }
cvec/checkpoint_best_legacy_500.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:294a2e8c98136070a999e040ec98dfa5a99b88a7938181c56cc2ab0e2f6ce0e8
3
+ size 48501067
data_utils.py ADDED
@@ -0,0 +1,155 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import time
2
+ import os
3
+ import random
4
+ import numpy as np
5
+ import torch
6
+ import torch.utils.data
7
+
8
+ import modules.commons as commons
9
+ import utils
10
+ from modules.mel_processing import spectrogram_torch, spec_to_mel_torch
11
+ from utils import load_wav_to_torch, load_filepaths_and_text
12
+
13
+ # import h5py
14
+
15
+
16
+ """Multi speaker version"""
17
+
18
+
19
+ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
20
+ """
21
+ 1) loads audio, speaker_id, text pairs
22
+ 2) normalizes text and converts them to sequences of integers
23
+ 3) computes spectrograms from audio files.
24
+ """
25
+
26
+ def __init__(self, audiopaths, hparams, all_in_mem: bool = False):
27
+ self.audiopaths = load_filepaths_and_text(audiopaths)
28
+ self.max_wav_value = hparams.data.max_wav_value
29
+ self.sampling_rate = hparams.data.sampling_rate
30
+ self.filter_length = hparams.data.filter_length
31
+ self.hop_length = hparams.data.hop_length
32
+ self.win_length = hparams.data.win_length
33
+ self.sampling_rate = hparams.data.sampling_rate
34
+ self.use_sr = hparams.train.use_sr
35
+ self.spec_len = hparams.train.max_speclen
36
+ self.spk_map = hparams.spk
37
+
38
+ random.seed(1234)
39
+ random.shuffle(self.audiopaths)
40
+
41
+ self.all_in_mem = all_in_mem
42
+ if self.all_in_mem:
43
+ self.cache = [self.get_audio(p[0]) for p in self.audiopaths]
44
+
45
+ def get_audio(self, filename):
46
+ filename = filename.replace("\\", "/")
47
+ audio, sampling_rate = load_wav_to_torch(filename)
48
+ if sampling_rate != self.sampling_rate:
49
+ raise ValueError("{} SR doesn't match target {} SR".format(
50
+ sampling_rate, self.sampling_rate))
51
+ audio_norm = audio / self.max_wav_value
52
+ audio_norm = audio_norm.unsqueeze(0)
53
+ spec_filename = filename.replace(".wav", ".spec.pt")
54
+
55
+ # Ideally, all data generated after Mar 25 should have .spec.pt
56
+ if os.path.exists(spec_filename):
57
+ spec = torch.load(spec_filename)
58
+ else:
59
+ spec = spectrogram_torch(audio_norm, self.filter_length,
60
+ self.sampling_rate, self.hop_length, self.win_length,
61
+ center=False)
62
+ spec = torch.squeeze(spec, 0)
63
+ torch.save(spec, spec_filename)
64
+
65
+ spk = filename.split("/")[-2]
66
+ spk = torch.LongTensor([self.spk_map[spk]])
67
+
68
+ f0 = np.load(filename + ".f0.npy")
69
+ f0, uv = utils.interpolate_f0(f0)
70
+ f0 = torch.FloatTensor(f0)
71
+ uv = torch.FloatTensor(uv)
72
+
73
+ c = torch.load(filename+ ".soft.pt")
74
+ c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
75
+
76
+
77
+ lmin = min(c.size(-1), spec.size(-1))
78
+ assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
79
+ assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
80
+ spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
81
+ audio_norm = audio_norm[:, :lmin * self.hop_length]
82
+
83
+ return c, f0, spec, audio_norm, spk, uv
84
+
85
+ def random_slice(self, c, f0, spec, audio_norm, spk, uv):
86
+ # if spec.shape[1] < 30:
87
+ # print("skip too short audio:", filename)
88
+ # return None
89
+ if spec.shape[1] > 800:
90
+ start = random.randint(0, spec.shape[1]-800)
91
+ end = start + 790
92
+ spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
93
+ audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
94
+
95
+ return c, f0, spec, audio_norm, spk, uv
96
+
97
+ def __getitem__(self, index):
98
+ if self.all_in_mem:
99
+ return self.random_slice(*self.cache[index])
100
+ else:
101
+ return self.random_slice(*self.get_audio(self.audiopaths[index][0]))
102
+
103
+ def __len__(self):
104
+ return len(self.audiopaths)
105
+
106
+
107
+ class TextAudioCollate:
108
+
109
+ def __call__(self, batch):
110
+ batch = [b for b in batch if b is not None]
111
+
112
+ input_lengths, ids_sorted_decreasing = torch.sort(
113
+ torch.LongTensor([x[0].shape[1] for x in batch]),
114
+ dim=0, descending=True)
115
+
116
+ max_c_len = max([x[0].size(1) for x in batch])
117
+ max_wav_len = max([x[3].size(1) for x in batch])
118
+
119
+ lengths = torch.LongTensor(len(batch))
120
+
121
+ c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
122
+ f0_padded = torch.FloatTensor(len(batch), max_c_len)
123
+ spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
124
+ wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
125
+ spkids = torch.LongTensor(len(batch), 1)
126
+ uv_padded = torch.FloatTensor(len(batch), max_c_len)
127
+
128
+ c_padded.zero_()
129
+ spec_padded.zero_()
130
+ f0_padded.zero_()
131
+ wav_padded.zero_()
132
+ uv_padded.zero_()
133
+
134
+ for i in range(len(ids_sorted_decreasing)):
135
+ row = batch[ids_sorted_decreasing[i]]
136
+
137
+ c = row[0]
138
+ c_padded[i, :, :c.size(1)] = c
139
+ lengths[i] = c.size(1)
140
+
141
+ f0 = row[1]
142
+ f0_padded[i, :f0.size(0)] = f0
143
+
144
+ spec = row[2]
145
+ spec_padded[i, :, :spec.size(1)] = spec
146
+
147
+ wav = row[3]
148
+ wav_padded[i, :, :wav.size(1)] = wav
149
+
150
+ spkids[i, 0] = row[4]
151
+
152
+ uv = row[5]
153
+ uv_padded[i, :uv.size(0)] = uv
154
+
155
+ return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded
filelists/test.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ ./dataset/44k/taffy/000562.wav
2
+ ./dataset/44k/nyaru/000011.wav
3
+ ./dataset/44k/nyaru/000008.wav
4
+ ./dataset/44k/taffy/000563.wav
filelists/train.txt ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ./dataset/44k/taffy/000549.wav
2
+ ./dataset/44k/nyaru/000004.wav
3
+ ./dataset/44k/nyaru/000006.wav
4
+ ./dataset/44k/taffy/000551.wav
5
+ ./dataset/44k/nyaru/000009.wav
6
+ ./dataset/44k/taffy/000561.wav
7
+ ./dataset/44k/nyaru/000001.wav
8
+ ./dataset/44k/taffy/000553.wav
9
+ ./dataset/44k/nyaru/000002.wav
10
+ ./dataset/44k/taffy/000560.wav
11
+ ./dataset/44k/taffy/000557.wav
12
+ ./dataset/44k/nyaru/000005.wav
13
+ ./dataset/44k/taffy/000554.wav
14
+ ./dataset/44k/taffy/000550.wav
15
+ ./dataset/44k/taffy/000559.wav
filelists/val.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ ./dataset/44k/nyaru/000003.wav
2
+ ./dataset/44k/nyaru/000007.wav
3
+ ./dataset/44k/taffy/000558.wav
4
+ ./dataset/44k/taffy/000556.wav
flask_api.py ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import io
2
+ import logging
3
+
4
+ import soundfile
5
+ import torch
6
+ import torchaudio
7
+ from flask import Flask, request, send_file
8
+ from flask_cors import CORS
9
+
10
+ from inference.infer_tool import Svc, RealTimeVC
11
+
12
+ app = Flask(__name__)
13
+
14
+ CORS(app)
15
+
16
+ logging.getLogger('numba').setLevel(logging.WARNING)
17
+
18
+
19
+ @app.route("/voiceChangeModel", methods=["POST"])
20
+ def voice_change_model():
21
+ request_form = request.form
22
+ wave_file = request.files.get("sample", None)
23
+ # 变调信息
24
+ f_pitch_change = float(request_form.get("fPitchChange", 0))
25
+ # DAW所需的采样率
26
+ daw_sample = int(float(request_form.get("sampleRate", 0)))
27
+ speaker_id = int(float(request_form.get("sSpeakId", 0)))
28
+ # http获得wav文件并转换
29
+ input_wav_path = io.BytesIO(wave_file.read())
30
+
31
+ # 模型推理
32
+ if raw_infer:
33
+ # out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
34
+ out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0,
35
+ auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
36
+ tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample)
37
+ else:
38
+ out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0,
39
+ auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
40
+ tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample)
41
+ # 返回音频
42
+ out_wav_path = io.BytesIO()
43
+ soundfile.write(out_wav_path, tar_audio.cpu().numpy(), daw_sample, format="wav")
44
+ out_wav_path.seek(0)
45
+ return send_file(out_wav_path, download_name="temp.wav", as_attachment=True)
46
+
47
+
48
+ if __name__ == '__main__':
49
+ # 启用则为直接切片合成,False为交叉淡化方式
50
+ # vst插件调整0.3-0.5s切片时间可以降低延迟,直接切片方法会有连接处爆音、交叉淡化会有轻微重叠声音
51
+ # 自行选择能接受的方法,或将vst最大切片时间调整为1s,此处设为Ture,延迟大音质稳定一些
52
+ raw_infer = True
53
+ # 每个模型和config是唯一对应的
54
+ model_name = "logs/32k/G_174000-Copy1.pth"
55
+ config_name = "configs/config.json"
56
+ cluster_model_path = "logs/44k/kmeans_10000.pt"
57
+ svc_model = Svc(model_name, config_name, cluster_model_path=cluster_model_path)
58
+ svc = RealTimeVC()
59
+ # 此处与vst插件对应,不建议更改
60
+ app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)
hubert/__init__.py ADDED
File without changes
hubert/checkpoint_best_legacy_500.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:60d936ec5a566776fc392e69ad8b630d14eb588111233fe313436e200a7b187b
3
+ size 1330114945
hubert/hubert_model.py ADDED
@@ -0,0 +1,222 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import random
3
+ from typing import Optional, Tuple
4
+
5
+ import torch
6
+ import torch.nn as nn
7
+ import torch.nn.functional as t_func
8
+ from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
9
+
10
+
11
+ class Hubert(nn.Module):
12
+ def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
13
+ super().__init__()
14
+ self._mask = mask
15
+ self.feature_extractor = FeatureExtractor()
16
+ self.feature_projection = FeatureProjection()
17
+ self.positional_embedding = PositionalConvEmbedding()
18
+ self.norm = nn.LayerNorm(768)
19
+ self.dropout = nn.Dropout(0.1)
20
+ self.encoder = TransformerEncoder(
21
+ nn.TransformerEncoderLayer(
22
+ 768, 12, 3072, activation="gelu", batch_first=True
23
+ ),
24
+ 12,
25
+ )
26
+ self.proj = nn.Linear(768, 256)
27
+
28
+ self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
29
+ self.label_embedding = nn.Embedding(num_label_embeddings, 256)
30
+
31
+ def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
32
+ mask = None
33
+ if self.training and self._mask:
34
+ mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
35
+ x[mask] = self.masked_spec_embed.to(x.dtype)
36
+ return x, mask
37
+
38
+ def encode(
39
+ self, x: torch.Tensor, layer: Optional[int] = None
40
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
41
+ x = self.feature_extractor(x)
42
+ x = self.feature_projection(x.transpose(1, 2))
43
+ x, mask = self.mask(x)
44
+ x = x + self.positional_embedding(x)
45
+ x = self.dropout(self.norm(x))
46
+ x = self.encoder(x, output_layer=layer)
47
+ return x, mask
48
+
49
+ def logits(self, x: torch.Tensor) -> torch.Tensor:
50
+ logits = torch.cosine_similarity(
51
+ x.unsqueeze(2),
52
+ self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
53
+ dim=-1,
54
+ )
55
+ return logits / 0.1
56
+
57
+ def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
58
+ x, mask = self.encode(x)
59
+ x = self.proj(x)
60
+ logits = self.logits(x)
61
+ return logits, mask
62
+
63
+
64
+ class HubertSoft(Hubert):
65
+ def __init__(self):
66
+ super().__init__()
67
+
68
+ @torch.inference_mode()
69
+ def units(self, wav: torch.Tensor) -> torch.Tensor:
70
+ wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
71
+ x, _ = self.encode(wav)
72
+ return self.proj(x)
73
+
74
+
75
+ class FeatureExtractor(nn.Module):
76
+ def __init__(self):
77
+ super().__init__()
78
+ self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
79
+ self.norm0 = nn.GroupNorm(512, 512)
80
+ self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
81
+ self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
82
+ self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
83
+ self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
84
+ self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
85
+ self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
86
+
87
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
88
+ x = t_func.gelu(self.norm0(self.conv0(x)))
89
+ x = t_func.gelu(self.conv1(x))
90
+ x = t_func.gelu(self.conv2(x))
91
+ x = t_func.gelu(self.conv3(x))
92
+ x = t_func.gelu(self.conv4(x))
93
+ x = t_func.gelu(self.conv5(x))
94
+ x = t_func.gelu(self.conv6(x))
95
+ return x
96
+
97
+
98
+ class FeatureProjection(nn.Module):
99
+ def __init__(self):
100
+ super().__init__()
101
+ self.norm = nn.LayerNorm(512)
102
+ self.projection = nn.Linear(512, 768)
103
+ self.dropout = nn.Dropout(0.1)
104
+
105
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
106
+ x = self.norm(x)
107
+ x = self.projection(x)
108
+ x = self.dropout(x)
109
+ return x
110
+
111
+
112
+ class PositionalConvEmbedding(nn.Module):
113
+ def __init__(self):
114
+ super().__init__()
115
+ self.conv = nn.Conv1d(
116
+ 768,
117
+ 768,
118
+ kernel_size=128,
119
+ padding=128 // 2,
120
+ groups=16,
121
+ )
122
+ self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
123
+
124
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
125
+ x = self.conv(x.transpose(1, 2))
126
+ x = t_func.gelu(x[:, :, :-1])
127
+ return x.transpose(1, 2)
128
+
129
+
130
+ class TransformerEncoder(nn.Module):
131
+ def __init__(
132
+ self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
133
+ ) -> None:
134
+ super(TransformerEncoder, self).__init__()
135
+ self.layers = nn.ModuleList(
136
+ [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
137
+ )
138
+ self.num_layers = num_layers
139
+
140
+ def forward(
141
+ self,
142
+ src: torch.Tensor,
143
+ mask: torch.Tensor = None,
144
+ src_key_padding_mask: torch.Tensor = None,
145
+ output_layer: Optional[int] = None,
146
+ ) -> torch.Tensor:
147
+ output = src
148
+ for layer in self.layers[:output_layer]:
149
+ output = layer(
150
+ output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
151
+ )
152
+ return output
153
+
154
+
155
+ def _compute_mask(
156
+ shape: Tuple[int, int],
157
+ mask_prob: float,
158
+ mask_length: int,
159
+ device: torch.device,
160
+ min_masks: int = 0,
161
+ ) -> torch.Tensor:
162
+ batch_size, sequence_length = shape
163
+
164
+ if mask_length < 1:
165
+ raise ValueError("`mask_length` has to be bigger than 0.")
166
+
167
+ if mask_length > sequence_length:
168
+ raise ValueError(
169
+ f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
170
+ )
171
+
172
+ # compute number of masked spans in batch
173
+ num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
174
+ num_masked_spans = max(num_masked_spans, min_masks)
175
+
176
+ # make sure num masked indices <= sequence_length
177
+ if num_masked_spans * mask_length > sequence_length:
178
+ num_masked_spans = sequence_length // mask_length
179
+
180
+ # SpecAugment mask to fill
181
+ mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
182
+
183
+ # uniform distribution to sample from, make sure that offset samples are < sequence_length
184
+ uniform_dist = torch.ones(
185
+ (batch_size, sequence_length - (mask_length - 1)), device=device
186
+ )
187
+
188
+ # get random indices to mask
189
+ mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
190
+
191
+ # expand masked indices to masked spans
192
+ mask_indices = (
193
+ mask_indices.unsqueeze(dim=-1)
194
+ .expand((batch_size, num_masked_spans, mask_length))
195
+ .reshape(batch_size, num_masked_spans * mask_length)
196
+ )
197
+ offsets = (
198
+ torch.arange(mask_length, device=device)[None, None, :]
199
+ .expand((batch_size, num_masked_spans, mask_length))
200
+ .reshape(batch_size, num_masked_spans * mask_length)
201
+ )
202
+ mask_idxs = mask_indices + offsets
203
+
204
+ # scatter indices to mask
205
+ mask = mask.scatter(1, mask_idxs, True)
206
+
207
+ return mask
208
+
209
+
210
+ def hubert_soft(
211
+ path: str,
212
+ ) -> HubertSoft:
213
+ r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
214
+ Args:
215
+ path (str): path of a pretrained model
216
+ """
217
+ hubert = HubertSoft()
218
+ checkpoint = torch.load(path)
219
+ consume_prefix_in_state_dict_if_present(checkpoint, "module.")
220
+ hubert.load_state_dict(checkpoint)
221
+ hubert.eval()
222
+ return hubert
hubert/hubert_model_onnx.py ADDED
@@ -0,0 +1,217 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import random
3
+ from typing import Optional, Tuple
4
+
5
+ import torch
6
+ import torch.nn as nn
7
+ import torch.nn.functional as t_func
8
+ from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
9
+
10
+
11
+ class Hubert(nn.Module):
12
+ def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
13
+ super().__init__()
14
+ self._mask = mask
15
+ self.feature_extractor = FeatureExtractor()
16
+ self.feature_projection = FeatureProjection()
17
+ self.positional_embedding = PositionalConvEmbedding()
18
+ self.norm = nn.LayerNorm(768)
19
+ self.dropout = nn.Dropout(0.1)
20
+ self.encoder = TransformerEncoder(
21
+ nn.TransformerEncoderLayer(
22
+ 768, 12, 3072, activation="gelu", batch_first=True
23
+ ),
24
+ 12,
25
+ )
26
+ self.proj = nn.Linear(768, 256)
27
+
28
+ self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
29
+ self.label_embedding = nn.Embedding(num_label_embeddings, 256)
30
+
31
+ def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
32
+ mask = None
33
+ if self.training and self._mask:
34
+ mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
35
+ x[mask] = self.masked_spec_embed.to(x.dtype)
36
+ return x, mask
37
+
38
+ def encode(
39
+ self, x: torch.Tensor, layer: Optional[int] = None
40
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
41
+ x = self.feature_extractor(x)
42
+ x = self.feature_projection(x.transpose(1, 2))
43
+ x, mask = self.mask(x)
44
+ x = x + self.positional_embedding(x)
45
+ x = self.dropout(self.norm(x))
46
+ x = self.encoder(x, output_layer=layer)
47
+ return x, mask
48
+
49
+ def logits(self, x: torch.Tensor) -> torch.Tensor:
50
+ logits = torch.cosine_similarity(
51
+ x.unsqueeze(2),
52
+ self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
53
+ dim=-1,
54
+ )
55
+ return logits / 0.1
56
+
57
+
58
+ class HubertSoft(Hubert):
59
+ def __init__(self):
60
+ super().__init__()
61
+
62
+ def units(self, wav: torch.Tensor) -> torch.Tensor:
63
+ wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
64
+ x, _ = self.encode(wav)
65
+ return self.proj(x)
66
+
67
+ def forward(self, x):
68
+ return self.units(x)
69
+
70
+ class FeatureExtractor(nn.Module):
71
+ def __init__(self):
72
+ super().__init__()
73
+ self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
74
+ self.norm0 = nn.GroupNorm(512, 512)
75
+ self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
76
+ self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
77
+ self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
78
+ self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
79
+ self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
80
+ self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
81
+
82
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
83
+ x = t_func.gelu(self.norm0(self.conv0(x)))
84
+ x = t_func.gelu(self.conv1(x))
85
+ x = t_func.gelu(self.conv2(x))
86
+ x = t_func.gelu(self.conv3(x))
87
+ x = t_func.gelu(self.conv4(x))
88
+ x = t_func.gelu(self.conv5(x))
89
+ x = t_func.gelu(self.conv6(x))
90
+ return x
91
+
92
+
93
+ class FeatureProjection(nn.Module):
94
+ def __init__(self):
95
+ super().__init__()
96
+ self.norm = nn.LayerNorm(512)
97
+ self.projection = nn.Linear(512, 768)
98
+ self.dropout = nn.Dropout(0.1)
99
+
100
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
101
+ x = self.norm(x)
102
+ x = self.projection(x)
103
+ x = self.dropout(x)
104
+ return x
105
+
106
+
107
+ class PositionalConvEmbedding(nn.Module):
108
+ def __init__(self):
109
+ super().__init__()
110
+ self.conv = nn.Conv1d(
111
+ 768,
112
+ 768,
113
+ kernel_size=128,
114
+ padding=128 // 2,
115
+ groups=16,
116
+ )
117
+ self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
118
+
119
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
120
+ x = self.conv(x.transpose(1, 2))
121
+ x = t_func.gelu(x[:, :, :-1])
122
+ return x.transpose(1, 2)
123
+
124
+
125
+ class TransformerEncoder(nn.Module):
126
+ def __init__(
127
+ self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
128
+ ) -> None:
129
+ super(TransformerEncoder, self).__init__()
130
+ self.layers = nn.ModuleList(
131
+ [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
132
+ )
133
+ self.num_layers = num_layers
134
+
135
+ def forward(
136
+ self,
137
+ src: torch.Tensor,
138
+ mask: torch.Tensor = None,
139
+ src_key_padding_mask: torch.Tensor = None,
140
+ output_layer: Optional[int] = None,
141
+ ) -> torch.Tensor:
142
+ output = src
143
+ for layer in self.layers[:output_layer]:
144
+ output = layer(
145
+ output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
146
+ )
147
+ return output
148
+
149
+
150
+ def _compute_mask(
151
+ shape: Tuple[int, int],
152
+ mask_prob: float,
153
+ mask_length: int,
154
+ device: torch.device,
155
+ min_masks: int = 0,
156
+ ) -> torch.Tensor:
157
+ batch_size, sequence_length = shape
158
+
159
+ if mask_length < 1:
160
+ raise ValueError("`mask_length` has to be bigger than 0.")
161
+
162
+ if mask_length > sequence_length:
163
+ raise ValueError(
164
+ f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
165
+ )
166
+
167
+ # compute number of masked spans in batch
168
+ num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
169
+ num_masked_spans = max(num_masked_spans, min_masks)
170
+
171
+ # make sure num masked indices <= sequence_length
172
+ if num_masked_spans * mask_length > sequence_length:
173
+ num_masked_spans = sequence_length // mask_length
174
+
175
+ # SpecAugment mask to fill
176
+ mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
177
+
178
+ # uniform distribution to sample from, make sure that offset samples are < sequence_length
179
+ uniform_dist = torch.ones(
180
+ (batch_size, sequence_length - (mask_length - 1)), device=device
181
+ )
182
+
183
+ # get random indices to mask
184
+ mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
185
+
186
+ # expand masked indices to masked spans
187
+ mask_indices = (
188
+ mask_indices.unsqueeze(dim=-1)
189
+ .expand((batch_size, num_masked_spans, mask_length))
190
+ .reshape(batch_size, num_masked_spans * mask_length)
191
+ )
192
+ offsets = (
193
+ torch.arange(mask_length, device=device)[None, None, :]
194
+ .expand((batch_size, num_masked_spans, mask_length))
195
+ .reshape(batch_size, num_masked_spans * mask_length)
196
+ )
197
+ mask_idxs = mask_indices + offsets
198
+
199
+ # scatter indices to mask
200
+ mask = mask.scatter(1, mask_idxs, True)
201
+
202
+ return mask
203
+
204
+
205
+ def hubert_soft(
206
+ path: str,
207
+ ) -> HubertSoft:
208
+ r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
209
+ Args:
210
+ path (str): path of a pretrained model
211
+ """
212
+ hubert = HubertSoft()
213
+ checkpoint = torch.load(path)
214
+ consume_prefix_in_state_dict_if_present(checkpoint, "module.")
215
+ hubert.load_state_dict(checkpoint)
216
+ hubert.eval()
217
+ return hubert
hubert/put_hubert_ckpt_here ADDED
File without changes
inference/__init__.py ADDED
File without changes
inference/chunks_temp.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"info": "temp_dict"}
inference/infer_tool.py ADDED
@@ -0,0 +1,354 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import hashlib
2
+ import io
3
+ import json
4
+ import logging
5
+ import os
6
+ import time
7
+ from pathlib import Path
8
+ from inference import slicer
9
+ import gc
10
+
11
+ import librosa
12
+ import numpy as np
13
+ # import onnxruntime
14
+ import parselmouth
15
+ import soundfile
16
+ import torch
17
+ import torchaudio
18
+
19
+ import cluster
20
+ from hubert import hubert_model
21
+ import utils
22
+ from models import SynthesizerTrn
23
+
24
+ logging.getLogger('matplotlib').setLevel(logging.WARNING)
25
+
26
+
27
+ def read_temp(file_name):
28
+ if not os.path.exists(file_name):
29
+ with open(file_name, "w") as f:
30
+ f.write(json.dumps({"info": "temp_dict"}))
31
+ return {}
32
+ else:
33
+ try:
34
+ with open(file_name, "r") as f:
35
+ data = f.read()
36
+ data_dict = json.loads(data)
37
+ if os.path.getsize(file_name) > 50 * 1024 * 1024:
38
+ f_name = file_name.replace("\\", "/").split("/")[-1]
39
+ print(f"clean {f_name}")
40
+ for wav_hash in list(data_dict.keys()):
41
+ if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
42
+ del data_dict[wav_hash]
43
+ except Exception as e:
44
+ print(e)
45
+ print(f"{file_name} error,auto rebuild file")
46
+ data_dict = {"info": "temp_dict"}
47
+ return data_dict
48
+
49
+
50
+ def write_temp(file_name, data):
51
+ with open(file_name, "w") as f:
52
+ f.write(json.dumps(data))
53
+
54
+
55
+ def timeit(func):
56
+ def run(*args, **kwargs):
57
+ t = time.time()
58
+ res = func(*args, **kwargs)
59
+ print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
60
+ return res
61
+
62
+ return run
63
+
64
+
65
+ def format_wav(audio_path):
66
+ if Path(audio_path).suffix == '.wav':
67
+ return
68
+ raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
69
+ soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
70
+
71
+
72
+ def get_end_file(dir_path, end):
73
+ file_lists = []
74
+ for root, dirs, files in os.walk(dir_path):
75
+ files = [f for f in files if f[0] != '.']
76
+ dirs[:] = [d for d in dirs if d[0] != '.']
77
+ for f_file in files:
78
+ if f_file.endswith(end):
79
+ file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
80
+ return file_lists
81
+
82
+
83
+ def get_md5(content):
84
+ return hashlib.new("md5", content).hexdigest()
85
+
86
+ def fill_a_to_b(a, b):
87
+ if len(a) < len(b):
88
+ for _ in range(0, len(b) - len(a)):
89
+ a.append(a[0])
90
+
91
+ def mkdir(paths: list):
92
+ for path in paths:
93
+ if not os.path.exists(path):
94
+ os.mkdir(path)
95
+
96
+ def pad_array(arr, target_length):
97
+ current_length = arr.shape[0]
98
+ if current_length >= target_length:
99
+ return arr
100
+ else:
101
+ pad_width = target_length - current_length
102
+ pad_left = pad_width // 2
103
+ pad_right = pad_width - pad_left
104
+ padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
105
+ return padded_arr
106
+
107
+ def split_list_by_n(list_collection, n, pre=0):
108
+ for i in range(0, len(list_collection), n):
109
+ yield list_collection[i-pre if i-pre>=0 else i: i + n]
110
+
111
+
112
+ class F0FilterException(Exception):
113
+ pass
114
+
115
+ class Svc(object):
116
+ def __init__(self, net_g_path, config_path,
117
+ device=None,
118
+ cluster_model_path="logs/44k/kmeans_10000.pt",
119
+ nsf_hifigan_enhance = False
120
+ ):
121
+ self.net_g_path = net_g_path
122
+ if device is None:
123
+ self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
124
+ else:
125
+ self.dev = torch.device(device)
126
+ self.net_g_ms = None
127
+ self.hps_ms = utils.get_hparams_from_file(config_path)
128
+ self.target_sample = self.hps_ms.data.sampling_rate
129
+ self.hop_size = self.hps_ms.data.hop_length
130
+ self.spk2id = self.hps_ms.spk
131
+ self.nsf_hifigan_enhance = nsf_hifigan_enhance
132
+ # load hubert
133
+ self.hubert_model = utils.get_hubert_model().to(self.dev)
134
+ self.load_model()
135
+ if os.path.exists(cluster_model_path):
136
+ self.cluster_model = cluster.get_cluster_model(cluster_model_path)
137
+ if self.nsf_hifigan_enhance:
138
+ from modules.enhancer import Enhancer
139
+ self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev)
140
+
141
+ def load_model(self):
142
+ # get model configuration
143
+ self.net_g_ms = SynthesizerTrn(
144
+ self.hps_ms.data.filter_length // 2 + 1,
145
+ self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
146
+ **self.hps_ms.model)
147
+ _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
148
+ if "half" in self.net_g_path and torch.cuda.is_available():
149
+ _ = self.net_g_ms.half().eval().to(self.dev)
150
+ else:
151
+ _ = self.net_g_ms.eval().to(self.dev)
152
+
153
+
154
+
155
+ def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling,cr_threshold=0.05):
156
+
157
+ wav, sr = librosa.load(in_path, sr=self.target_sample)
158
+
159
+ if F0_mean_pooling == True:
160
+ f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev,cr_threshold = cr_threshold)
161
+ if f0_filter and sum(f0) == 0:
162
+ raise F0FilterException("No voice detected")
163
+ f0 = torch.FloatTensor(list(f0))
164
+ uv = torch.FloatTensor(list(uv))
165
+ if F0_mean_pooling == False:
166
+ f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
167
+ if f0_filter and sum(f0) == 0:
168
+ raise F0FilterException("No voice detected")
169
+ f0, uv = utils.interpolate_f0(f0)
170
+ f0 = torch.FloatTensor(f0)
171
+ uv = torch.FloatTensor(uv)
172
+
173
+ f0 = f0 * 2 ** (tran / 12)
174
+ f0 = f0.unsqueeze(0).to(self.dev)
175
+ uv = uv.unsqueeze(0).to(self.dev)
176
+
177
+ wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
178
+ wav16k = torch.from_numpy(wav16k).to(self.dev)
179
+ c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
180
+ c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
181
+
182
+ if cluster_infer_ratio !=0:
183
+ cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
184
+ cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
185
+ c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
186
+
187
+ c = c.unsqueeze(0)
188
+ return c, f0, uv
189
+
190
+ def infer(self, speaker, tran, raw_path,
191
+ cluster_infer_ratio=0,
192
+ auto_predict_f0=False,
193
+ noice_scale=0.4,
194
+ f0_filter=False,
195
+ F0_mean_pooling=False,
196
+ enhancer_adaptive_key = 0,
197
+ cr_threshold = 0.05
198
+ ):
199
+
200
+ speaker_id = self.spk2id.__dict__.get(speaker)
201
+ if not speaker_id and type(speaker) is int:
202
+ if len(self.spk2id.__dict__) >= speaker:
203
+ speaker_id = speaker
204
+ sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
205
+ c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling,cr_threshold=cr_threshold)
206
+ if "half" in self.net_g_path and torch.cuda.is_available():
207
+ c = c.half()
208
+ with torch.no_grad():
209
+ start = time.time()
210
+ audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
211
+ if self.nsf_hifigan_enhance:
212
+ audio, _ = self.enhancer.enhance(
213
+ audio[None,:],
214
+ self.target_sample,
215
+ f0[:,:,None],
216
+ self.hps_ms.data.hop_length,
217
+ adaptive_key = enhancer_adaptive_key)
218
+ use_time = time.time() - start
219
+ print("vits use time:{}".format(use_time))
220
+ return audio, audio.shape[-1]
221
+
222
+ def clear_empty(self):
223
+ # clean up vram
224
+ torch.cuda.empty_cache()
225
+
226
+ def unload_model(self):
227
+ # unload model
228
+ self.net_g_ms = self.net_g_ms.to("cpu")
229
+ del self.net_g_ms
230
+ if hasattr(self,"enhancer"):
231
+ self.enhancer.enhancer = self.enhancer.enhancer.to("cpu")
232
+ del self.enhancer.enhancer
233
+ del self.enhancer
234
+ gc.collect()
235
+
236
+ def slice_inference(self,
237
+ raw_audio_path,
238
+ spk,
239
+ tran,
240
+ slice_db,
241
+ cluster_infer_ratio,
242
+ auto_predict_f0,
243
+ noice_scale,
244
+ pad_seconds=0.5,
245
+ clip_seconds=0,
246
+ lg_num=0,
247
+ lgr_num =0.75,
248
+ F0_mean_pooling = False,
249
+ enhancer_adaptive_key = 0,
250
+ cr_threshold = 0.05
251
+ ):
252
+ wav_path = raw_audio_path
253
+ chunks = slicer.cut(wav_path, db_thresh=slice_db)
254
+ audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
255
+ per_size = int(clip_seconds*audio_sr)
256
+ lg_size = int(lg_num*audio_sr)
257
+ lg_size_r = int(lg_size*lgr_num)
258
+ lg_size_c_l = (lg_size-lg_size_r)//2
259
+ lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
260
+ lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
261
+
262
+ audio = []
263
+ for (slice_tag, data) in audio_data:
264
+ print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
265
+ # padd
266
+ length = int(np.ceil(len(data) / audio_sr * self.target_sample))
267
+ if slice_tag:
268
+ print('jump empty segment')
269
+ _audio = np.zeros(length)
270
+ audio.extend(list(pad_array(_audio, length)))
271
+ continue
272
+ if per_size != 0:
273
+ datas = split_list_by_n(data, per_size,lg_size)
274
+ else:
275
+ datas = [data]
276
+ for k,dat in enumerate(datas):
277
+ per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
278
+ if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
279
+ # padd
280
+ pad_len = int(audio_sr * pad_seconds)
281
+ dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
282
+ raw_path = io.BytesIO()
283
+ soundfile.write(raw_path, dat, audio_sr, format="wav")
284
+ raw_path.seek(0)
285
+ out_audio, out_sr = self.infer(spk, tran, raw_path,
286
+ cluster_infer_ratio=cluster_infer_ratio,
287
+ auto_predict_f0=auto_predict_f0,
288
+ noice_scale=noice_scale,
289
+ F0_mean_pooling = F0_mean_pooling,
290
+ enhancer_adaptive_key = enhancer_adaptive_key,
291
+ cr_threshold = cr_threshold
292
+ )
293
+ _audio = out_audio.cpu().numpy()
294
+ pad_len = int(self.target_sample * pad_seconds)
295
+ _audio = _audio[pad_len:-pad_len]
296
+ _audio = pad_array(_audio, per_length)
297
+ if lg_size!=0 and k!=0:
298
+ lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
299
+ lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr_num != 1 else _audio[0:lg_size]
300
+ lg_pre = lg1*(1-lg)+lg2*lg
301
+ audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
302
+ audio.extend(lg_pre)
303
+ _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
304
+ audio.extend(list(_audio))
305
+ return np.array(audio)
306
+
307
+ class RealTimeVC:
308
+ def __init__(self):
309
+ self.last_chunk = None
310
+ self.last_o = None
311
+ self.chunk_len = 16000 # chunk length
312
+ self.pre_len = 3840 # cross fade length, multiples of 640
313
+
314
+ # Input and output are 1-dimensional numpy waveform arrays
315
+
316
+ def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
317
+ cluster_infer_ratio=0,
318
+ auto_predict_f0=False,
319
+ noice_scale=0.4,
320
+ f0_filter=False):
321
+
322
+ import maad
323
+ audio, sr = torchaudio.load(input_wav_path)
324
+ audio = audio.cpu().numpy()[0]
325
+ temp_wav = io.BytesIO()
326
+ if self.last_chunk is None:
327
+ input_wav_path.seek(0)
328
+
329
+ audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
330
+ cluster_infer_ratio=cluster_infer_ratio,
331
+ auto_predict_f0=auto_predict_f0,
332
+ noice_scale=noice_scale,
333
+ f0_filter=f0_filter)
334
+
335
+ audio = audio.cpu().numpy()
336
+ self.last_chunk = audio[-self.pre_len:]
337
+ self.last_o = audio
338
+ return audio[-self.chunk_len:]
339
+ else:
340
+ audio = np.concatenate([self.last_chunk, audio])
341
+ soundfile.write(temp_wav, audio, sr, format="wav")
342
+ temp_wav.seek(0)
343
+
344
+ audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
345
+ cluster_infer_ratio=cluster_infer_ratio,
346
+ auto_predict_f0=auto_predict_f0,
347
+ noice_scale=noice_scale,
348
+ f0_filter=f0_filter)
349
+
350
+ audio = audio.cpu().numpy()
351
+ ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
352
+ self.last_chunk = audio[-self.pre_len:]
353
+ self.last_o = audio
354
+ return ret[self.chunk_len:2 * self.chunk_len]
inference/infer_tool_grad.py ADDED
@@ -0,0 +1,160 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import hashlib
2
+ import json
3
+ import logging
4
+ import os
5
+ import time
6
+ from pathlib import Path
7
+ import io
8
+ import librosa
9
+ import maad
10
+ import numpy as np
11
+ from inference import slicer
12
+ import parselmouth
13
+ import soundfile
14
+ import torch
15
+ import torchaudio
16
+
17
+ from hubert import hubert_model
18
+ import utils
19
+ from models import SynthesizerTrn
20
+ logging.getLogger('numba').setLevel(logging.WARNING)
21
+ logging.getLogger('matplotlib').setLevel(logging.WARNING)
22
+
23
+ def resize2d_f0(x, target_len):
24
+ source = np.array(x)
25
+ source[source < 0.001] = np.nan
26
+ target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
27
+ source)
28
+ res = np.nan_to_num(target)
29
+ return res
30
+
31
+ def get_f0(x, p_len,f0_up_key=0):
32
+
33
+ time_step = 160 / 16000 * 1000
34
+ f0_min = 50
35
+ f0_max = 1100
36
+ f0_mel_min = 1127 * np.log(1 + f0_min / 700)
37
+ f0_mel_max = 1127 * np.log(1 + f0_max / 700)
38
+
39
+ f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
40
+ time_step=time_step / 1000, voicing_threshold=0.6,
41
+ pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
42
+
43
+ pad_size=(p_len - len(f0) + 1) // 2
44
+ if(pad_size>0 or p_len - len(f0) - pad_size>0):
45
+ f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
46
+
47
+ f0 *= pow(2, f0_up_key / 12)
48
+ f0_mel = 1127 * np.log(1 + f0 / 700)
49
+ f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
50
+ f0_mel[f0_mel <= 1] = 1
51
+ f0_mel[f0_mel > 255] = 255
52
+ f0_coarse = np.rint(f0_mel).astype(np.int)
53
+ return f0_coarse, f0
54
+
55
+ def clean_pitch(input_pitch):
56
+ num_nan = np.sum(input_pitch == 1)
57
+ if num_nan / len(input_pitch) > 0.9:
58
+ input_pitch[input_pitch != 1] = 1
59
+ return input_pitch
60
+
61
+
62
+ def plt_pitch(input_pitch):
63
+ input_pitch = input_pitch.astype(float)
64
+ input_pitch[input_pitch == 1] = np.nan
65
+ return input_pitch
66
+
67
+
68
+ def f0_to_pitch(ff):
69
+ f0_pitch = 69 + 12 * np.log2(ff / 440)
70
+ return f0_pitch
71
+
72
+
73
+ def fill_a_to_b(a, b):
74
+ if len(a) < len(b):
75
+ for _ in range(0, len(b) - len(a)):
76
+ a.append(a[0])
77
+
78
+
79
+ def mkdir(paths: list):
80
+ for path in paths:
81
+ if not os.path.exists(path):
82
+ os.mkdir(path)
83
+
84
+
85
+ class VitsSvc(object):
86
+ def __init__(self):
87
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
88
+ self.SVCVITS = None
89
+ self.hps = None
90
+ self.speakers = None
91
+ self.hubert_soft = utils.get_hubert_model()
92
+
93
+ def set_device(self, device):
94
+ self.device = torch.device(device)
95
+ self.hubert_soft.to(self.device)
96
+ if self.SVCVITS != None:
97
+ self.SVCVITS.to(self.device)
98
+
99
+ def loadCheckpoint(self, path):
100
+ self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
101
+ self.SVCVITS = SynthesizerTrn(
102
+ self.hps.data.filter_length // 2 + 1,
103
+ self.hps.train.segment_size // self.hps.data.hop_length,
104
+ **self.hps.model)
105
+ _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.SVCVITS, None)
106
+ _ = self.SVCVITS.eval().to(self.device)
107
+ self.speakers = self.hps.spk
108
+
109
+ def get_units(self, source, sr):
110
+ source = source.unsqueeze(0).to(self.device)
111
+ with torch.inference_mode():
112
+ units = self.hubert_soft.units(source)
113
+ return units
114
+
115
+
116
+ def get_unit_pitch(self, in_path, tran):
117
+ source, sr = torchaudio.load(in_path)
118
+ source = torchaudio.functional.resample(source, sr, 16000)
119
+ if len(source.shape) == 2 and source.shape[1] >= 2:
120
+ source = torch.mean(source, dim=0).unsqueeze(0)
121
+ soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
122
+ f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
123
+ return soft, f0
124
+
125
+ def infer(self, speaker_id, tran, raw_path):
126
+ speaker_id = self.speakers[speaker_id]
127
+ sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
128
+ soft, pitch = self.get_unit_pitch(raw_path, tran)
129
+ f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device)
130
+ stn_tst = torch.FloatTensor(soft)
131
+ with torch.no_grad():
132
+ x_tst = stn_tst.unsqueeze(0).to(self.device)
133
+ x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
134
+ audio = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
135
+ return audio, audio.shape[-1]
136
+
137
+ def inference(self,srcaudio,chara,tran,slice_db):
138
+ sampling_rate, audio = srcaudio
139
+ audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
140
+ if len(audio.shape) > 1:
141
+ audio = librosa.to_mono(audio.transpose(1, 0))
142
+ if sampling_rate != 16000:
143
+ audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
144
+ soundfile.write("tmpwav.wav", audio, 16000, format="wav")
145
+ chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db)
146
+ audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks)
147
+ audio = []
148
+ for (slice_tag, data) in audio_data:
149
+ length = int(np.ceil(len(data) / audio_sr * self.hps.data.sampling_rate))
150
+ raw_path = io.BytesIO()
151
+ soundfile.write(raw_path, data, audio_sr, format="wav")
152
+ raw_path.seek(0)
153
+ if slice_tag:
154
+ _audio = np.zeros(length)
155
+ else:
156
+ out_audio, out_sr = self.infer(chara, tran, raw_path)
157
+ _audio = out_audio.cpu().numpy()
158
+ audio.extend(list(_audio))
159
+ audio = (np.array(audio) * 32768.0).astype('int16')
160
+ return (self.hps.data.sampling_rate,audio)
inference/slicer.py ADDED
@@ -0,0 +1,142 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import librosa
2
+ import torch
3
+ import torchaudio
4
+
5
+
6
+ class Slicer:
7
+ def __init__(self,
8
+ sr: int,
9
+ threshold: float = -40.,
10
+ min_length: int = 5000,
11
+ min_interval: int = 300,
12
+ hop_size: int = 20,
13
+ max_sil_kept: int = 5000):
14
+ if not min_length >= min_interval >= hop_size:
15
+ raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
16
+ if not max_sil_kept >= hop_size:
17
+ raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
18
+ min_interval = sr * min_interval / 1000
19
+ self.threshold = 10 ** (threshold / 20.)
20
+ self.hop_size = round(sr * hop_size / 1000)
21
+ self.win_size = min(round(min_interval), 4 * self.hop_size)
22
+ self.min_length = round(sr * min_length / 1000 / self.hop_size)
23
+ self.min_interval = round(min_interval / self.hop_size)
24
+ self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
25
+
26
+ def _apply_slice(self, waveform, begin, end):
27
+ if len(waveform.shape) > 1:
28
+ return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
29
+ else:
30
+ return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
31
+
32
+ # @timeit
33
+ def slice(self, waveform):
34
+ if len(waveform.shape) > 1:
35
+ samples = librosa.to_mono(waveform)
36
+ else:
37
+ samples = waveform
38
+ if samples.shape[0] <= self.min_length:
39
+ return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
40
+ rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
41
+ sil_tags = []
42
+ silence_start = None
43
+ clip_start = 0
44
+ for i, rms in enumerate(rms_list):
45
+ # Keep looping while frame is silent.
46
+ if rms < self.threshold:
47
+ # Record start of silent frames.
48
+ if silence_start is None:
49
+ silence_start = i
50
+ continue
51
+ # Keep looping while frame is not silent and silence start has not been recorded.
52
+ if silence_start is None:
53
+ continue
54
+ # Clear recorded silence start if interval is not enough or clip is too short
55
+ is_leading_silence = silence_start == 0 and i > self.max_sil_kept
56
+ need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
57
+ if not is_leading_silence and not need_slice_middle:
58
+ silence_start = None
59
+ continue
60
+ # Need slicing. Record the range of silent frames to be removed.
61
+ if i - silence_start <= self.max_sil_kept:
62
+ pos = rms_list[silence_start: i + 1].argmin() + silence_start
63
+ if silence_start == 0:
64
+ sil_tags.append((0, pos))
65
+ else:
66
+ sil_tags.append((pos, pos))
67
+ clip_start = pos
68
+ elif i - silence_start <= self.max_sil_kept * 2:
69
+ pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
70
+ pos += i - self.max_sil_kept
71
+ pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
72
+ pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
73
+ if silence_start == 0:
74
+ sil_tags.append((0, pos_r))
75
+ clip_start = pos_r
76
+ else:
77
+ sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
78
+ clip_start = max(pos_r, pos)
79
+ else:
80
+ pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
81
+ pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
82
+ if silence_start == 0:
83
+ sil_tags.append((0, pos_r))
84
+ else:
85
+ sil_tags.append((pos_l, pos_r))
86
+ clip_start = pos_r
87
+ silence_start = None
88
+ # Deal with trailing silence.
89
+ total_frames = rms_list.shape[0]
90
+ if silence_start is not None and total_frames - silence_start >= self.min_interval:
91
+ silence_end = min(total_frames, silence_start + self.max_sil_kept)
92
+ pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
93
+ sil_tags.append((pos, total_frames + 1))
94
+ # Apply and return slices.
95
+ if len(sil_tags) == 0:
96
+ return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
97
+ else:
98
+ chunks = []
99
+ # 第一段静音并非从头开始,补上有声片段
100
+ if sil_tags[0][0]:
101
+ chunks.append(
102
+ {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
103
+ for i in range(0, len(sil_tags)):
104
+ # 标识有声片段(跳过第一段)
105
+ if i:
106
+ chunks.append({"slice": False,
107
+ "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
108
+ # 标识所有静音片段
109
+ chunks.append({"slice": True,
110
+ "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
111
+ # 最后一段静音并非结尾,补上结尾片段
112
+ if sil_tags[-1][1] * self.hop_size < len(waveform):
113
+ chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
114
+ chunk_dict = {}
115
+ for i in range(len(chunks)):
116
+ chunk_dict[str(i)] = chunks[i]
117
+ return chunk_dict
118
+
119
+
120
+ def cut(audio_path, db_thresh=-30, min_len=5000):
121
+ audio, sr = librosa.load(audio_path, sr=None)
122
+ slicer = Slicer(
123
+ sr=sr,
124
+ threshold=db_thresh,
125
+ min_length=min_len
126
+ )
127
+ chunks = slicer.slice(audio)
128
+ return chunks
129
+
130
+
131
+ def chunks2audio(audio_path, chunks):
132
+ chunks = dict(chunks)
133
+ audio, sr = torchaudio.load(audio_path)
134
+ if len(audio.shape) == 2 and audio.shape[1] >= 2:
135
+ audio = torch.mean(audio, dim=0).unsqueeze(0)
136
+ audio = audio.cpu().numpy()[0]
137
+ result = []
138
+ for k, v in chunks.items():
139
+ tag = v["split_time"].split(",")
140
+ if tag[0] != tag[1]:
141
+ result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
142
+ return result, sr
inference_main.py ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import io
2
+ import logging
3
+ import time
4
+ from pathlib import Path
5
+
6
+ import librosa
7
+ import matplotlib.pyplot as plt
8
+ import numpy as np
9
+ import soundfile
10
+
11
+ from inference import infer_tool
12
+ from inference import slicer
13
+ from inference.infer_tool import Svc
14
+
15
+ logging.getLogger('numba').setLevel(logging.WARNING)
16
+ chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
17
+
18
+
19
+
20
+ def main():
21
+ import argparse
22
+
23
+ parser = argparse.ArgumentParser(description='sovits4 inference')
24
+
25
+ # 一定要设置的部分
26
+ parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
27
+ parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
28
+ parser.add_argument('-cl', '--clip', type=float, default=0, help='音频强制切片,默认0为自动切片,单位为秒/s')
29
+ parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表,放在raw文件夹下')
30
+ parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整,支持正负(半音)')
31
+ parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
32
+
33
+ # 可选项部分
34
+ parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False,help='语音转换自动预测音高,转换歌声时不要打开这个会严重跑调')
35
+ parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径,如果没有训练聚类则随便填')
36
+ parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比,范围0-1,若没有训练聚类模型则默认0即可')
37
+ parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='两段音频切片的交叉淡入长度,如果强制切片后出现人声不连贯可调整该数值,如果连贯建议采用默认值0,单位为秒')
38
+ parser.add_argument('-fmp', '--f0_mean_pooling', type=bool, default=False, help='是否对F0使用均值滤波器(池化),对部分哑音有改善。注意,启动该选项会导致推理速度下降,默认关闭')
39
+ parser.add_argument('-eh', '--enhance', type=bool, default=False, help='是否使用NSF_HIFIGAN增强器,该选项对部分训练集少的模型有一定的音质增强效果,但是对训练好的模型有反面效果,默认关闭')
40
+
41
+ # 不用动的部分
42
+ parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='默认-40,嘈杂的音频可以-30,干声保留呼吸可以-50')
43
+ parser.add_argument('-d', '--device', type=str, default=None, help='推理设备,None则为自动选择cpu和gpu')
44
+ parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='噪音级别,会影响咬字和音质,较为玄学')
45
+ parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='推理音频pad秒数,由于未知原因开头结尾会有异响,pad一小段静音段后就不会出现')
46
+ parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式')
47
+ parser.add_argument('-lgr', '--linear_gradient_retain', type=float, default=0.75, help='自动音频切片后,需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例,范围0-1,左开右闭')
48
+ parser.add_argument('-eak', '--enhancer_adaptive_key', type=int, default=0, help='使增强器适应更高的音域(单位为半音数)|默认为0')
49
+
50
+ args = parser.parse_args()
51
+
52
+ clean_names = args.clean_names
53
+ trans = args.trans
54
+ spk_list = args.spk_list
55
+ slice_db = args.slice_db
56
+ wav_format = args.wav_format
57
+ auto_predict_f0 = args.auto_predict_f0
58
+ cluster_infer_ratio = args.cluster_infer_ratio
59
+ noice_scale = args.noice_scale
60
+ pad_seconds = args.pad_seconds
61
+ clip = args.clip
62
+ lg = args.linear_gradient
63
+ lgr = args.linear_gradient_retain
64
+ F0_mean_pooling = args.f0_mean_pooling
65
+ enhance = args.enhance
66
+ enhancer_adaptive_key = args.enhancer_adaptive_key
67
+
68
+ svc_model = Svc(args.model_path, args.config_path, args.device, args.cluster_model_path,enhance)
69
+ infer_tool.mkdir(["raw", "results"])
70
+
71
+ infer_tool.fill_a_to_b(trans, clean_names)
72
+ for clean_name, tran in zip(clean_names, trans):
73
+ raw_audio_path = f"raw/{clean_name}"
74
+ if "." not in raw_audio_path:
75
+ raw_audio_path += ".wav"
76
+ infer_tool.format_wav(raw_audio_path)
77
+ wav_path = Path(raw_audio_path).with_suffix('.wav')
78
+ chunks = slicer.cut(wav_path, db_thresh=slice_db)
79
+ audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
80
+ per_size = int(clip*audio_sr)
81
+ lg_size = int(lg*audio_sr)
82
+ lg_size_r = int(lg_size*lgr)
83
+ lg_size_c_l = (lg_size-lg_size_r)//2
84
+ lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
85
+ lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
86
+
87
+ for spk in spk_list:
88
+ audio = []
89
+ for (slice_tag, data) in audio_data:
90
+ print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
91
+
92
+ length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
93
+ if slice_tag:
94
+ print('jump empty segment')
95
+ _audio = np.zeros(length)
96
+ audio.extend(list(infer_tool.pad_array(_audio, length)))
97
+ continue
98
+ if per_size != 0:
99
+ datas = infer_tool.split_list_by_n(data, per_size,lg_size)
100
+ else:
101
+ datas = [data]
102
+ for k,dat in enumerate(datas):
103
+ per_length = int(np.ceil(len(dat) / audio_sr * svc_model.target_sample)) if clip!=0 else length
104
+ if clip!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
105
+ # padd
106
+ pad_len = int(audio_sr * pad_seconds)
107
+ dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
108
+ raw_path = io.BytesIO()
109
+ soundfile.write(raw_path, dat, audio_sr, format="wav")
110
+ raw_path.seek(0)
111
+ out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
112
+ cluster_infer_ratio=cluster_infer_ratio,
113
+ auto_predict_f0=auto_predict_f0,
114
+ noice_scale=noice_scale,
115
+ F0_mean_pooling = F0_mean_pooling,
116
+ enhancer_adaptive_key = enhancer_adaptive_key
117
+ )
118
+ _audio = out_audio.cpu().numpy()
119
+ pad_len = int(svc_model.target_sample * pad_seconds)
120
+ _audio = _audio[pad_len:-pad_len]
121
+ _audio = infer_tool.pad_array(_audio, per_length)
122
+ if lg_size!=0 and k!=0:
123
+ lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr != 1 else audio[-lg_size:]
124
+ lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r] if lgr != 1 else _audio[0:lg_size]
125
+ lg_pre = lg1*(1-lg)+lg2*lg
126
+ audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr != 1 else audio[0:-lg_size]
127
+ audio.extend(lg_pre)
128
+ _audio = _audio[lg_size_c_l+lg_size_r:] if lgr != 1 else _audio[lg_size:]
129
+ audio.extend(list(_audio))
130
+ key = "auto" if auto_predict_f0 else f"{tran}key"
131
+ cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
132
+ res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}'
133
+ soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
134
+ svc_model.clear_empty()
135
+
136
+ if __name__ == '__main__':
137
+ main()
logs/44k/@github-NGZ-sovits-4.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:24a1ea783dc220617a656b02316cee960865c53035f23a61baf8d32d89dbd75d
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+ size 542372059
logs/44k/kmeans_10000.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ oid sha256:187624c91dae1f1d9c207f36af938e001b44942cb9e36e0ccdaa998a27c868d9
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+ size 971278521
logs/44k/put_pretrained_model_here ADDED
File without changes
models.py ADDED
@@ -0,0 +1,420 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import torch
4
+ from torch import nn
5
+ from torch.nn import functional as F
6
+
7
+ import modules.attentions as attentions
8
+ import modules.commons as commons
9
+ import modules.modules as modules
10
+
11
+ from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
12
+ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
13
+
14
+ import utils
15
+ from modules.commons import init_weights, get_padding
16
+ from vdecoder.hifigan.models import Generator
17
+ from utils import f0_to_coarse
18
+
19
+ class ResidualCouplingBlock(nn.Module):
20
+ def __init__(self,
21
+ channels,
22
+ hidden_channels,
23
+ kernel_size,
24
+ dilation_rate,
25
+ n_layers,
26
+ n_flows=4,
27
+ gin_channels=0):
28
+ super().__init__()
29
+ self.channels = channels
30
+ self.hidden_channels = hidden_channels
31
+ self.kernel_size = kernel_size
32
+ self.dilation_rate = dilation_rate
33
+ self.n_layers = n_layers
34
+ self.n_flows = n_flows
35
+ self.gin_channels = gin_channels
36
+
37
+ self.flows = nn.ModuleList()
38
+ for i in range(n_flows):
39
+ self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
40
+ self.flows.append(modules.Flip())
41
+
42
+ def forward(self, x, x_mask, g=None, reverse=False):
43
+ if not reverse:
44
+ for flow in self.flows:
45
+ x, _ = flow(x, x_mask, g=g, reverse=reverse)
46
+ else:
47
+ for flow in reversed(self.flows):
48
+ x = flow(x, x_mask, g=g, reverse=reverse)
49
+ return x
50
+
51
+
52
+ class Encoder(nn.Module):
53
+ def __init__(self,
54
+ in_channels,
55
+ out_channels,
56
+ hidden_channels,
57
+ kernel_size,
58
+ dilation_rate,
59
+ n_layers,
60
+ gin_channels=0):
61
+ super().__init__()
62
+ self.in_channels = in_channels
63
+ self.out_channels = out_channels
64
+ self.hidden_channels = hidden_channels
65
+ self.kernel_size = kernel_size
66
+ self.dilation_rate = dilation_rate
67
+ self.n_layers = n_layers
68
+ self.gin_channels = gin_channels
69
+
70
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
71
+ self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
72
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
73
+
74
+ def forward(self, x, x_lengths, g=None):
75
+ # print(x.shape,x_lengths.shape)
76
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
77
+ x = self.pre(x) * x_mask
78
+ x = self.enc(x, x_mask, g=g)
79
+ stats = self.proj(x) * x_mask
80
+ m, logs = torch.split(stats, self.out_channels, dim=1)
81
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
82
+ return z, m, logs, x_mask
83
+
84
+
85
+ class TextEncoder(nn.Module):
86
+ def __init__(self,
87
+ out_channels,
88
+ hidden_channels,
89
+ kernel_size,
90
+ n_layers,
91
+ gin_channels=0,
92
+ filter_channels=None,
93
+ n_heads=None,
94
+ p_dropout=None):
95
+ super().__init__()
96
+ self.out_channels = out_channels
97
+ self.hidden_channels = hidden_channels
98
+ self.kernel_size = kernel_size
99
+ self.n_layers = n_layers
100
+ self.gin_channels = gin_channels
101
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
102
+ self.f0_emb = nn.Embedding(256, hidden_channels)
103
+
104
+ self.enc_ = attentions.Encoder(
105
+ hidden_channels,
106
+ filter_channels,
107
+ n_heads,
108
+ n_layers,
109
+ kernel_size,
110
+ p_dropout)
111
+
112
+ def forward(self, x, x_mask, f0=None, noice_scale=1):
113
+ x = x + self.f0_emb(f0).transpose(1,2)
114
+ x = self.enc_(x * x_mask, x_mask)
115
+ stats = self.proj(x) * x_mask
116
+ m, logs = torch.split(stats, self.out_channels, dim=1)
117
+ z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
118
+
119
+ return z, m, logs, x_mask
120
+
121
+
122
+
123
+ class DiscriminatorP(torch.nn.Module):
124
+ def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
125
+ super(DiscriminatorP, self).__init__()
126
+ self.period = period
127
+ self.use_spectral_norm = use_spectral_norm
128
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
129
+ self.convs = nn.ModuleList([
130
+ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
131
+ norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
132
+ norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
133
+ norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
134
+ norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
135
+ ])
136
+ self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
137
+
138
+ def forward(self, x):
139
+ fmap = []
140
+
141
+ # 1d to 2d
142
+ b, c, t = x.shape
143
+ if t % self.period != 0: # pad first
144
+ n_pad = self.period - (t % self.period)
145
+ x = F.pad(x, (0, n_pad), "reflect")
146
+ t = t + n_pad
147
+ x = x.view(b, c, t // self.period, self.period)
148
+
149
+ for l in self.convs:
150
+ x = l(x)
151
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
152
+ fmap.append(x)
153
+ x = self.conv_post(x)
154
+ fmap.append(x)
155
+ x = torch.flatten(x, 1, -1)
156
+
157
+ return x, fmap
158
+
159
+
160
+ class DiscriminatorS(torch.nn.Module):
161
+ def __init__(self, use_spectral_norm=False):
162
+ super(DiscriminatorS, self).__init__()
163
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
164
+ self.convs = nn.ModuleList([
165
+ norm_f(Conv1d(1, 16, 15, 1, padding=7)),
166
+ norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
167
+ norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
168
+ norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
169
+ norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
170
+ norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
171
+ ])
172
+ self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
173
+
174
+ def forward(self, x):
175
+ fmap = []
176
+
177
+ for l in self.convs:
178
+ x = l(x)
179
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
180
+ fmap.append(x)
181
+ x = self.conv_post(x)
182
+ fmap.append(x)
183
+ x = torch.flatten(x, 1, -1)
184
+
185
+ return x, fmap
186
+
187
+
188
+ class MultiPeriodDiscriminator(torch.nn.Module):
189
+ def __init__(self, use_spectral_norm=False):
190
+ super(MultiPeriodDiscriminator, self).__init__()
191
+ periods = [2,3,5,7,11]
192
+
193
+ discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
194
+ discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
195
+ self.discriminators = nn.ModuleList(discs)
196
+
197
+ def forward(self, y, y_hat):
198
+ y_d_rs = []
199
+ y_d_gs = []
200
+ fmap_rs = []
201
+ fmap_gs = []
202
+ for i, d in enumerate(self.discriminators):
203
+ y_d_r, fmap_r = d(y)
204
+ y_d_g, fmap_g = d(y_hat)
205
+ y_d_rs.append(y_d_r)
206
+ y_d_gs.append(y_d_g)
207
+ fmap_rs.append(fmap_r)
208
+ fmap_gs.append(fmap_g)
209
+
210
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
211
+
212
+
213
+ class SpeakerEncoder(torch.nn.Module):
214
+ def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
215
+ super(SpeakerEncoder, self).__init__()
216
+ self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
217
+ self.linear = nn.Linear(model_hidden_size, model_embedding_size)
218
+ self.relu = nn.ReLU()
219
+
220
+ def forward(self, mels):
221
+ self.lstm.flatten_parameters()
222
+ _, (hidden, _) = self.lstm(mels)
223
+ embeds_raw = self.relu(self.linear(hidden[-1]))
224
+ return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
225
+
226
+ def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
227
+ mel_slices = []
228
+ for i in range(0, total_frames-partial_frames, partial_hop):
229
+ mel_range = torch.arange(i, i+partial_frames)
230
+ mel_slices.append(mel_range)
231
+
232
+ return mel_slices
233
+
234
+ def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
235
+ mel_len = mel.size(1)
236
+ last_mel = mel[:,-partial_frames:]
237
+
238
+ if mel_len > partial_frames:
239
+ mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
240
+ mels = list(mel[:,s] for s in mel_slices)
241
+ mels.append(last_mel)
242
+ mels = torch.stack(tuple(mels), 0).squeeze(1)
243
+
244
+ with torch.no_grad():
245
+ partial_embeds = self(mels)
246
+ embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
247
+ #embed = embed / torch.linalg.norm(embed, 2)
248
+ else:
249
+ with torch.no_grad():
250
+ embed = self(last_mel)
251
+
252
+ return embed
253
+
254
+ class F0Decoder(nn.Module):
255
+ def __init__(self,
256
+ out_channels,
257
+ hidden_channels,
258
+ filter_channels,
259
+ n_heads,
260
+ n_layers,
261
+ kernel_size,
262
+ p_dropout,
263
+ spk_channels=0):
264
+ super().__init__()
265
+ self.out_channels = out_channels
266
+ self.hidden_channels = hidden_channels
267
+ self.filter_channels = filter_channels
268
+ self.n_heads = n_heads
269
+ self.n_layers = n_layers
270
+ self.kernel_size = kernel_size
271
+ self.p_dropout = p_dropout
272
+ self.spk_channels = spk_channels
273
+
274
+ self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
275
+ self.decoder = attentions.FFT(
276
+ hidden_channels,
277
+ filter_channels,
278
+ n_heads,
279
+ n_layers,
280
+ kernel_size,
281
+ p_dropout)
282
+ self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
283
+ self.f0_prenet = nn.Conv1d(1, hidden_channels , 3, padding=1)
284
+ self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
285
+
286
+ def forward(self, x, norm_f0, x_mask, spk_emb=None):
287
+ x = torch.detach(x)
288
+ if (spk_emb is not None):
289
+ x = x + self.cond(spk_emb)
290
+ x += self.f0_prenet(norm_f0)
291
+ x = self.prenet(x) * x_mask
292
+ x = self.decoder(x * x_mask, x_mask)
293
+ x = self.proj(x) * x_mask
294
+ return x
295
+
296
+
297
+ class SynthesizerTrn(nn.Module):
298
+ """
299
+ Synthesizer for Training
300
+ """
301
+
302
+ def __init__(self,
303
+ spec_channels,
304
+ segment_size,
305
+ inter_channels,
306
+ hidden_channels,
307
+ filter_channels,
308
+ n_heads,
309
+ n_layers,
310
+ kernel_size,
311
+ p_dropout,
312
+ resblock,
313
+ resblock_kernel_sizes,
314
+ resblock_dilation_sizes,
315
+ upsample_rates,
316
+ upsample_initial_channel,
317
+ upsample_kernel_sizes,
318
+ gin_channels,
319
+ ssl_dim,
320
+ n_speakers,
321
+ sampling_rate=44100,
322
+ **kwargs):
323
+
324
+ super().__init__()
325
+ self.spec_channels = spec_channels
326
+ self.inter_channels = inter_channels
327
+ self.hidden_channels = hidden_channels
328
+ self.filter_channels = filter_channels
329
+ self.n_heads = n_heads
330
+ self.n_layers = n_layers
331
+ self.kernel_size = kernel_size
332
+ self.p_dropout = p_dropout
333
+ self.resblock = resblock
334
+ self.resblock_kernel_sizes = resblock_kernel_sizes
335
+ self.resblock_dilation_sizes = resblock_dilation_sizes
336
+ self.upsample_rates = upsample_rates
337
+ self.upsample_initial_channel = upsample_initial_channel
338
+ self.upsample_kernel_sizes = upsample_kernel_sizes
339
+ self.segment_size = segment_size
340
+ self.gin_channels = gin_channels
341
+ self.ssl_dim = ssl_dim
342
+ self.emb_g = nn.Embedding(n_speakers, gin_channels)
343
+
344
+ self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
345
+
346
+ self.enc_p = TextEncoder(
347
+ inter_channels,
348
+ hidden_channels,
349
+ filter_channels=filter_channels,
350
+ n_heads=n_heads,
351
+ n_layers=n_layers,
352
+ kernel_size=kernel_size,
353
+ p_dropout=p_dropout
354
+ )
355
+ hps = {
356
+ "sampling_rate": sampling_rate,
357
+ "inter_channels": inter_channels,
358
+ "resblock": resblock,
359
+ "resblock_kernel_sizes": resblock_kernel_sizes,
360
+ "resblock_dilation_sizes": resblock_dilation_sizes,
361
+ "upsample_rates": upsample_rates,
362
+ "upsample_initial_channel": upsample_initial_channel,
363
+ "upsample_kernel_sizes": upsample_kernel_sizes,
364
+ "gin_channels": gin_channels,
365
+ }
366
+ self.dec = Generator(h=hps)
367
+ self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
368
+ self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
369
+ self.f0_decoder = F0Decoder(
370
+ 1,
371
+ hidden_channels,
372
+ filter_channels,
373
+ n_heads,
374
+ n_layers,
375
+ kernel_size,
376
+ p_dropout,
377
+ spk_channels=gin_channels
378
+ )
379
+ self.emb_uv = nn.Embedding(2, hidden_channels)
380
+
381
+ def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
382
+ g = self.emb_g(g).transpose(1,2)
383
+ # ssl prenet
384
+ x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
385
+ x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
386
+
387
+ # f0 predict
388
+ lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
389
+ norm_lf0 = utils.normalize_f0(lf0, x_mask, uv)
390
+ pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
391
+
392
+ # encoder
393
+ z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
394
+ z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
395
+
396
+ # flow
397
+ z_p = self.flow(z, spec_mask, g=g)
398
+ z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
399
+
400
+ # nsf decoder
401
+ o = self.dec(z_slice, g=g, f0=pitch_slice)
402
+
403
+ return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0
404
+
405
+ def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False):
406
+ c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
407
+ g = self.emb_g(g).transpose(1,2)
408
+ x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
409
+ x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
410
+
411
+ if predict_f0:
412
+ lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
413
+ norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
414
+ pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
415
+ f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
416
+
417
+ z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale)
418
+ z = self.flow(z_p, c_mask, g=g, reverse=True)
419
+ o = self.dec(z * c_mask, g=g, f0=f0)
420
+ return o
modules/__init__.py ADDED
File without changes
modules/attentions.py ADDED
@@ -0,0 +1,349 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import numpy as np
4
+ import torch
5
+ from torch import nn
6
+ from torch.nn import functional as F
7
+
8
+ import modules.commons as commons
9
+ import modules.modules as modules
10
+ from modules.modules import LayerNorm
11
+
12
+
13
+ class FFT(nn.Module):
14
+ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
15
+ proximal_bias=False, proximal_init=True, **kwargs):
16
+ super().__init__()
17
+ self.hidden_channels = hidden_channels
18
+ self.filter_channels = filter_channels
19
+ self.n_heads = n_heads
20
+ self.n_layers = n_layers
21
+ self.kernel_size = kernel_size
22
+ self.p_dropout = p_dropout
23
+ self.proximal_bias = proximal_bias
24
+ self.proximal_init = proximal_init
25
+
26
+ self.drop = nn.Dropout(p_dropout)
27
+ self.self_attn_layers = nn.ModuleList()
28
+ self.norm_layers_0 = nn.ModuleList()
29
+ self.ffn_layers = nn.ModuleList()
30
+ self.norm_layers_1 = nn.ModuleList()
31
+ for i in range(self.n_layers):
32
+ self.self_attn_layers.append(
33
+ MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
34
+ proximal_init=proximal_init))
35
+ self.norm_layers_0.append(LayerNorm(hidden_channels))
36
+ self.ffn_layers.append(
37
+ FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
38
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
39
+
40
+ def forward(self, x, x_mask):
41
+ """
42
+ x: decoder input
43
+ h: encoder output
44
+ """
45
+ self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
46
+ x = x * x_mask
47
+ for i in range(self.n_layers):
48
+ y = self.self_attn_layers[i](x, x, self_attn_mask)
49
+ y = self.drop(y)
50
+ x = self.norm_layers_0[i](x + y)
51
+
52
+ y = self.ffn_layers[i](x, x_mask)
53
+ y = self.drop(y)
54
+ x = self.norm_layers_1[i](x + y)
55
+ x = x * x_mask
56
+ return x
57
+
58
+
59
+ class Encoder(nn.Module):
60
+ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
61
+ super().__init__()
62
+ self.hidden_channels = hidden_channels
63
+ self.filter_channels = filter_channels
64
+ self.n_heads = n_heads
65
+ self.n_layers = n_layers
66
+ self.kernel_size = kernel_size
67
+ self.p_dropout = p_dropout
68
+ self.window_size = window_size
69
+
70
+ self.drop = nn.Dropout(p_dropout)
71
+ self.attn_layers = nn.ModuleList()
72
+ self.norm_layers_1 = nn.ModuleList()
73
+ self.ffn_layers = nn.ModuleList()
74
+ self.norm_layers_2 = nn.ModuleList()
75
+ for i in range(self.n_layers):
76
+ self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
77
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
78
+ self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
79
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
80
+
81
+ def forward(self, x, x_mask):
82
+ attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
83
+ x = x * x_mask
84
+ for i in range(self.n_layers):
85
+ y = self.attn_layers[i](x, x, attn_mask)
86
+ y = self.drop(y)
87
+ x = self.norm_layers_1[i](x + y)
88
+
89
+ y = self.ffn_layers[i](x, x_mask)
90
+ y = self.drop(y)
91
+ x = self.norm_layers_2[i](x + y)
92
+ x = x * x_mask
93
+ return x
94
+
95
+
96
+ class Decoder(nn.Module):
97
+ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
98
+ super().__init__()
99
+ self.hidden_channels = hidden_channels
100
+ self.filter_channels = filter_channels
101
+ self.n_heads = n_heads
102
+ self.n_layers = n_layers
103
+ self.kernel_size = kernel_size
104
+ self.p_dropout = p_dropout
105
+ self.proximal_bias = proximal_bias
106
+ self.proximal_init = proximal_init
107
+
108
+ self.drop = nn.Dropout(p_dropout)
109
+ self.self_attn_layers = nn.ModuleList()
110
+ self.norm_layers_0 = nn.ModuleList()
111
+ self.encdec_attn_layers = nn.ModuleList()
112
+ self.norm_layers_1 = nn.ModuleList()
113
+ self.ffn_layers = nn.ModuleList()
114
+ self.norm_layers_2 = nn.ModuleList()
115
+ for i in range(self.n_layers):
116
+ self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
117
+ self.norm_layers_0.append(LayerNorm(hidden_channels))
118
+ self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
119
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
120
+ self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
121
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
122
+
123
+ def forward(self, x, x_mask, h, h_mask):
124
+ """
125
+ x: decoder input
126
+ h: encoder output
127
+ """
128
+ self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
129
+ encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
130
+ x = x * x_mask
131
+ for i in range(self.n_layers):
132
+ y = self.self_attn_layers[i](x, x, self_attn_mask)
133
+ y = self.drop(y)
134
+ x = self.norm_layers_0[i](x + y)
135
+
136
+ y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
137
+ y = self.drop(y)
138
+ x = self.norm_layers_1[i](x + y)
139
+
140
+ y = self.ffn_layers[i](x, x_mask)
141
+ y = self.drop(y)
142
+ x = self.norm_layers_2[i](x + y)
143
+ x = x * x_mask
144
+ return x
145
+
146
+
147
+ class MultiHeadAttention(nn.Module):
148
+ def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
149
+ super().__init__()
150
+ assert channels % n_heads == 0
151
+
152
+ self.channels = channels
153
+ self.out_channels = out_channels
154
+ self.n_heads = n_heads
155
+ self.p_dropout = p_dropout
156
+ self.window_size = window_size
157
+ self.heads_share = heads_share
158
+ self.block_length = block_length
159
+ self.proximal_bias = proximal_bias
160
+ self.proximal_init = proximal_init
161
+ self.attn = None
162
+
163
+ self.k_channels = channels // n_heads
164
+ self.conv_q = nn.Conv1d(channels, channels, 1)
165
+ self.conv_k = nn.Conv1d(channels, channels, 1)
166
+ self.conv_v = nn.Conv1d(channels, channels, 1)
167
+ self.conv_o = nn.Conv1d(channels, out_channels, 1)
168
+ self.drop = nn.Dropout(p_dropout)
169
+
170
+ if window_size is not None:
171
+ n_heads_rel = 1 if heads_share else n_heads
172
+ rel_stddev = self.k_channels**-0.5
173
+ self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
174
+ self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
175
+
176
+ nn.init.xavier_uniform_(self.conv_q.weight)
177
+ nn.init.xavier_uniform_(self.conv_k.weight)
178
+ nn.init.xavier_uniform_(self.conv_v.weight)
179
+ if proximal_init:
180
+ with torch.no_grad():
181
+ self.conv_k.weight.copy_(self.conv_q.weight)
182
+ self.conv_k.bias.copy_(self.conv_q.bias)
183
+
184
+ def forward(self, x, c, attn_mask=None):
185
+ q = self.conv_q(x)
186
+ k = self.conv_k(c)
187
+ v = self.conv_v(c)
188
+
189
+ x, self.attn = self.attention(q, k, v, mask=attn_mask)
190
+
191
+ x = self.conv_o(x)
192
+ return x
193
+
194
+ def attention(self, query, key, value, mask=None):
195
+ # reshape [b, d, t] -> [b, n_h, t, d_k]
196
+ b, d, t_s, t_t = (*key.size(), query.size(2))
197
+ query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
198
+ key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
199
+ value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
200
+
201
+ scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
202
+ if self.window_size is not None:
203
+ assert t_s == t_t, "Relative attention is only available for self-attention."
204
+ key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
205
+ rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
206
+ scores_local = self._relative_position_to_absolute_position(rel_logits)
207
+ scores = scores + scores_local
208
+ if self.proximal_bias:
209
+ assert t_s == t_t, "Proximal bias is only available for self-attention."
210
+ scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
211
+ if mask is not None:
212
+ scores = scores.masked_fill(mask == 0, -1e4)
213
+ if self.block_length is not None:
214
+ assert t_s == t_t, "Local attention is only available for self-attention."
215
+ block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
216
+ scores = scores.masked_fill(block_mask == 0, -1e4)
217
+ p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
218
+ p_attn = self.drop(p_attn)
219
+ output = torch.matmul(p_attn, value)
220
+ if self.window_size is not None:
221
+ relative_weights = self._absolute_position_to_relative_position(p_attn)
222
+ value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
223
+ output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
224
+ output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
225
+ return output, p_attn
226
+
227
+ def _matmul_with_relative_values(self, x, y):
228
+ """
229
+ x: [b, h, l, m]
230
+ y: [h or 1, m, d]
231
+ ret: [b, h, l, d]
232
+ """
233
+ ret = torch.matmul(x, y.unsqueeze(0))
234
+ return ret
235
+
236
+ def _matmul_with_relative_keys(self, x, y):
237
+ """
238
+ x: [b, h, l, d]
239
+ y: [h or 1, m, d]
240
+ ret: [b, h, l, m]
241
+ """
242
+ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
243
+ return ret
244
+
245
+ def _get_relative_embeddings(self, relative_embeddings, length):
246
+ max_relative_position = 2 * self.window_size + 1
247
+ # Pad first before slice to avoid using cond ops.
248
+ pad_length = max(length - (self.window_size + 1), 0)
249
+ slice_start_position = max((self.window_size + 1) - length, 0)
250
+ slice_end_position = slice_start_position + 2 * length - 1
251
+ if pad_length > 0:
252
+ padded_relative_embeddings = F.pad(
253
+ relative_embeddings,
254
+ commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
255
+ else:
256
+ padded_relative_embeddings = relative_embeddings
257
+ used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
258
+ return used_relative_embeddings
259
+
260
+ def _relative_position_to_absolute_position(self, x):
261
+ """
262
+ x: [b, h, l, 2*l-1]
263
+ ret: [b, h, l, l]
264
+ """
265
+ batch, heads, length, _ = x.size()
266
+ # Concat columns of pad to shift from relative to absolute indexing.
267
+ x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
268
+
269
+ # Concat extra elements so to add up to shape (len+1, 2*len-1).
270
+ x_flat = x.view([batch, heads, length * 2 * length])
271
+ x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
272
+
273
+ # Reshape and slice out the padded elements.
274
+ x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
275
+ return x_final
276
+
277
+ def _absolute_position_to_relative_position(self, x):
278
+ """
279
+ x: [b, h, l, l]
280
+ ret: [b, h, l, 2*l-1]
281
+ """
282
+ batch, heads, length, _ = x.size()
283
+ # padd along column
284
+ x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
285
+ x_flat = x.view([batch, heads, length**2 + length*(length -1)])
286
+ # add 0's in the beginning that will skew the elements after reshape
287
+ x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
288
+ x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
289
+ return x_final
290
+
291
+ def _attention_bias_proximal(self, length):
292
+ """Bias for self-attention to encourage attention to close positions.
293
+ Args:
294
+ length: an integer scalar.
295
+ Returns:
296
+ a Tensor with shape [1, 1, length, length]
297
+ """
298
+ r = torch.arange(length, dtype=torch.float32)
299
+ diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
300
+ return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
301
+
302
+
303
+ class FFN(nn.Module):
304
+ def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
305
+ super().__init__()
306
+ self.in_channels = in_channels
307
+ self.out_channels = out_channels
308
+ self.filter_channels = filter_channels
309
+ self.kernel_size = kernel_size
310
+ self.p_dropout = p_dropout
311
+ self.activation = activation
312
+ self.causal = causal
313
+
314
+ if causal:
315
+ self.padding = self._causal_padding
316
+ else:
317
+ self.padding = self._same_padding
318
+
319
+ self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
320
+ self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
321
+ self.drop = nn.Dropout(p_dropout)
322
+
323
+ def forward(self, x, x_mask):
324
+ x = self.conv_1(self.padding(x * x_mask))
325
+ if self.activation == "gelu":
326
+ x = x * torch.sigmoid(1.702 * x)
327
+ else:
328
+ x = torch.relu(x)
329
+ x = self.drop(x)
330
+ x = self.conv_2(self.padding(x * x_mask))
331
+ return x * x_mask
332
+
333
+ def _causal_padding(self, x):
334
+ if self.kernel_size == 1:
335
+ return x
336
+ pad_l = self.kernel_size - 1
337
+ pad_r = 0
338
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
339
+ x = F.pad(x, commons.convert_pad_shape(padding))
340
+ return x
341
+
342
+ def _same_padding(self, x):
343
+ if self.kernel_size == 1:
344
+ return x
345
+ pad_l = (self.kernel_size - 1) // 2
346
+ pad_r = self.kernel_size // 2
347
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
348
+ x = F.pad(x, commons.convert_pad_shape(padding))
349
+ return x
modules/commons.py ADDED
@@ -0,0 +1,188 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import numpy as np
3
+ import torch
4
+ from torch import nn
5
+ from torch.nn import functional as F
6
+
7
+ def slice_pitch_segments(x, ids_str, segment_size=4):
8
+ ret = torch.zeros_like(x[:, :segment_size])
9
+ for i in range(x.size(0)):
10
+ idx_str = ids_str[i]
11
+ idx_end = idx_str + segment_size
12
+ ret[i] = x[i, idx_str:idx_end]
13
+ return ret
14
+
15
+ def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
16
+ b, d, t = x.size()
17
+ if x_lengths is None:
18
+ x_lengths = t
19
+ ids_str_max = x_lengths - segment_size + 1
20
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
21
+ ret = slice_segments(x, ids_str, segment_size)
22
+ ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
23
+ return ret, ret_pitch, ids_str
24
+
25
+ def init_weights(m, mean=0.0, std=0.01):
26
+ classname = m.__class__.__name__
27
+ if classname.find("Conv") != -1:
28
+ m.weight.data.normal_(mean, std)
29
+
30
+
31
+ def get_padding(kernel_size, dilation=1):
32
+ return int((kernel_size*dilation - dilation)/2)
33
+
34
+
35
+ def convert_pad_shape(pad_shape):
36
+ l = pad_shape[::-1]
37
+ pad_shape = [item for sublist in l for item in sublist]
38
+ return pad_shape
39
+
40
+
41
+ def intersperse(lst, item):
42
+ result = [item] * (len(lst) * 2 + 1)
43
+ result[1::2] = lst
44
+ return result
45
+
46
+
47
+ def kl_divergence(m_p, logs_p, m_q, logs_q):
48
+ """KL(P||Q)"""
49
+ kl = (logs_q - logs_p) - 0.5
50
+ kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
51
+ return kl
52
+
53
+
54
+ def rand_gumbel(shape):
55
+ """Sample from the Gumbel distribution, protect from overflows."""
56
+ uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
57
+ return -torch.log(-torch.log(uniform_samples))
58
+
59
+
60
+ def rand_gumbel_like(x):
61
+ g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
62
+ return g
63
+
64
+
65
+ def slice_segments(x, ids_str, segment_size=4):
66
+ ret = torch.zeros_like(x[:, :, :segment_size])
67
+ for i in range(x.size(0)):
68
+ idx_str = ids_str[i]
69
+ idx_end = idx_str + segment_size
70
+ ret[i] = x[i, :, idx_str:idx_end]
71
+ return ret
72
+
73
+
74
+ def rand_slice_segments(x, x_lengths=None, segment_size=4):
75
+ b, d, t = x.size()
76
+ if x_lengths is None:
77
+ x_lengths = t
78
+ ids_str_max = x_lengths - segment_size + 1
79
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
80
+ ret = slice_segments(x, ids_str, segment_size)
81
+ return ret, ids_str
82
+
83
+
84
+ def rand_spec_segments(x, x_lengths=None, segment_size=4):
85
+ b, d, t = x.size()
86
+ if x_lengths is None:
87
+ x_lengths = t
88
+ ids_str_max = x_lengths - segment_size
89
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
90
+ ret = slice_segments(x, ids_str, segment_size)
91
+ return ret, ids_str
92
+
93
+
94
+ def get_timing_signal_1d(
95
+ length, channels, min_timescale=1.0, max_timescale=1.0e4):
96
+ position = torch.arange(length, dtype=torch.float)
97
+ num_timescales = channels // 2
98
+ log_timescale_increment = (
99
+ math.log(float(max_timescale) / float(min_timescale)) /
100
+ (num_timescales - 1))
101
+ inv_timescales = min_timescale * torch.exp(
102
+ torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
103
+ scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
104
+ signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
105
+ signal = F.pad(signal, [0, 0, 0, channels % 2])
106
+ signal = signal.view(1, channels, length)
107
+ return signal
108
+
109
+
110
+ def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
111
+ b, channels, length = x.size()
112
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
113
+ return x + signal.to(dtype=x.dtype, device=x.device)
114
+
115
+
116
+ def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
117
+ b, channels, length = x.size()
118
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
119
+ return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
120
+
121
+
122
+ def subsequent_mask(length):
123
+ mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
124
+ return mask
125
+
126
+
127
+ @torch.jit.script
128
+ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
129
+ n_channels_int = n_channels[0]
130
+ in_act = input_a + input_b
131
+ t_act = torch.tanh(in_act[:, :n_channels_int, :])
132
+ s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
133
+ acts = t_act * s_act
134
+ return acts
135
+
136
+
137
+ def convert_pad_shape(pad_shape):
138
+ l = pad_shape[::-1]
139
+ pad_shape = [item for sublist in l for item in sublist]
140
+ return pad_shape
141
+
142
+
143
+ def shift_1d(x):
144
+ x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
145
+ return x
146
+
147
+
148
+ def sequence_mask(length, max_length=None):
149
+ if max_length is None:
150
+ max_length = length.max()
151
+ x = torch.arange(max_length, dtype=length.dtype, device=length.device)
152
+ return x.unsqueeze(0) < length.unsqueeze(1)
153
+
154
+
155
+ def generate_path(duration, mask):
156
+ """
157
+ duration: [b, 1, t_x]
158
+ mask: [b, 1, t_y, t_x]
159
+ """
160
+ device = duration.device
161
+
162
+ b, _, t_y, t_x = mask.shape
163
+ cum_duration = torch.cumsum(duration, -1)
164
+
165
+ cum_duration_flat = cum_duration.view(b * t_x)
166
+ path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
167
+ path = path.view(b, t_x, t_y)
168
+ path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
169
+ path = path.unsqueeze(1).transpose(2,3) * mask
170
+ return path
171
+
172
+
173
+ def clip_grad_value_(parameters, clip_value, norm_type=2):
174
+ if isinstance(parameters, torch.Tensor):
175
+ parameters = [parameters]
176
+ parameters = list(filter(lambda p: p.grad is not None, parameters))
177
+ norm_type = float(norm_type)
178
+ if clip_value is not None:
179
+ clip_value = float(clip_value)
180
+
181
+ total_norm = 0
182
+ for p in parameters:
183
+ param_norm = p.grad.data.norm(norm_type)
184
+ total_norm += param_norm.item() ** norm_type
185
+ if clip_value is not None:
186
+ p.grad.data.clamp_(min=-clip_value, max=clip_value)
187
+ total_norm = total_norm ** (1. / norm_type)
188
+ return total_norm
modules/crepe.py ADDED
@@ -0,0 +1,327 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Optional,Union
2
+ try:
3
+ from typing import Literal
4
+ except Exception as e:
5
+ from typing_extensions import Literal
6
+ import numpy as np
7
+ import torch
8
+ import torchcrepe
9
+ from torch import nn
10
+ from torch.nn import functional as F
11
+ import scipy
12
+
13
+ #from:https://github.com/fishaudio/fish-diffusion
14
+
15
+ def repeat_expand(
16
+ content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
17
+ ):
18
+ """Repeat content to target length.
19
+ This is a wrapper of torch.nn.functional.interpolate.
20
+
21
+ Args:
22
+ content (torch.Tensor): tensor
23
+ target_len (int): target length
24
+ mode (str, optional): interpolation mode. Defaults to "nearest".
25
+
26
+ Returns:
27
+ torch.Tensor: tensor
28
+ """
29
+
30
+ ndim = content.ndim
31
+
32
+ if content.ndim == 1:
33
+ content = content[None, None]
34
+ elif content.ndim == 2:
35
+ content = content[None]
36
+
37
+ assert content.ndim == 3
38
+
39
+ is_np = isinstance(content, np.ndarray)
40
+ if is_np:
41
+ content = torch.from_numpy(content)
42
+
43
+ results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
44
+
45
+ if is_np:
46
+ results = results.numpy()
47
+
48
+ if ndim == 1:
49
+ return results[0, 0]
50
+ elif ndim == 2:
51
+ return results[0]
52
+
53
+
54
+ class BasePitchExtractor:
55
+ def __init__(
56
+ self,
57
+ hop_length: int = 512,
58
+ f0_min: float = 50.0,
59
+ f0_max: float = 1100.0,
60
+ keep_zeros: bool = True,
61
+ ):
62
+ """Base pitch extractor.
63
+
64
+ Args:
65
+ hop_length (int, optional): Hop length. Defaults to 512.
66
+ f0_min (float, optional): Minimum f0. Defaults to 50.0.
67
+ f0_max (float, optional): Maximum f0. Defaults to 1100.0.
68
+ keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
69
+ """
70
+
71
+ self.hop_length = hop_length
72
+ self.f0_min = f0_min
73
+ self.f0_max = f0_max
74
+ self.keep_zeros = keep_zeros
75
+
76
+ def __call__(self, x, sampling_rate=44100, pad_to=None):
77
+ raise NotImplementedError("BasePitchExtractor is not callable.")
78
+
79
+ def post_process(self, x, sampling_rate, f0, pad_to):
80
+ if isinstance(f0, np.ndarray):
81
+ f0 = torch.from_numpy(f0).float().to(x.device)
82
+
83
+ if pad_to is None:
84
+ return f0
85
+
86
+ f0 = repeat_expand(f0, pad_to)
87
+
88
+ if self.keep_zeros:
89
+ return f0
90
+
91
+ vuv_vector = torch.zeros_like(f0)
92
+ vuv_vector[f0 > 0.0] = 1.0
93
+ vuv_vector[f0 <= 0.0] = 0.0
94
+
95
+ # 去掉0频率, 并线性插值
96
+ nzindex = torch.nonzero(f0).squeeze()
97
+ f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
98
+ time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
99
+ time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
100
+
101
+ if f0.shape[0] <= 0:
102
+ return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
103
+
104
+ if f0.shape[0] == 1:
105
+ return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
106
+
107
+ # 大概可以用 torch 重写?
108
+ f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
109
+ vuv_vector = vuv_vector.cpu().numpy()
110
+ vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
111
+
112
+ return f0,vuv_vector
113
+
114
+
115
+ class MaskedAvgPool1d(nn.Module):
116
+ def __init__(
117
+ self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
118
+ ):
119
+ """An implementation of mean pooling that supports masked values.
120
+
121
+ Args:
122
+ kernel_size (int): The size of the median pooling window.
123
+ stride (int, optional): The stride of the median pooling window. Defaults to None.
124
+ padding (int, optional): The padding of the median pooling window. Defaults to 0.
125
+ """
126
+
127
+ super(MaskedAvgPool1d, self).__init__()
128
+ self.kernel_size = kernel_size
129
+ self.stride = stride or kernel_size
130
+ self.padding = padding
131
+
132
+ def forward(self, x, mask=None):
133
+ ndim = x.dim()
134
+ if ndim == 2:
135
+ x = x.unsqueeze(1)
136
+
137
+ assert (
138
+ x.dim() == 3
139
+ ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
140
+
141
+ # Apply the mask by setting masked elements to zero, or make NaNs zero
142
+ if mask is None:
143
+ mask = ~torch.isnan(x)
144
+
145
+ # Ensure mask has the same shape as the input tensor
146
+ assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
147
+
148
+ masked_x = torch.where(mask, x, torch.zeros_like(x))
149
+ # Create a ones kernel with the same number of channels as the input tensor
150
+ ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
151
+
152
+ # Perform sum pooling
153
+ sum_pooled = nn.functional.conv1d(
154
+ masked_x,
155
+ ones_kernel,
156
+ stride=self.stride,
157
+ padding=self.padding,
158
+ groups=x.size(1),
159
+ )
160
+
161
+ # Count the non-masked (valid) elements in each pooling window
162
+ valid_count = nn.functional.conv1d(
163
+ mask.float(),
164
+ ones_kernel,
165
+ stride=self.stride,
166
+ padding=self.padding,
167
+ groups=x.size(1),
168
+ )
169
+ valid_count = valid_count.clamp(min=1) # Avoid division by zero
170
+
171
+ # Perform masked average pooling
172
+ avg_pooled = sum_pooled / valid_count
173
+
174
+ # Fill zero values with NaNs
175
+ avg_pooled[avg_pooled == 0] = float("nan")
176
+
177
+ if ndim == 2:
178
+ return avg_pooled.squeeze(1)
179
+
180
+ return avg_pooled
181
+
182
+
183
+ class MaskedMedianPool1d(nn.Module):
184
+ def __init__(
185
+ self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
186
+ ):
187
+ """An implementation of median pooling that supports masked values.
188
+
189
+ This implementation is inspired by the median pooling implementation in
190
+ https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
191
+
192
+ Args:
193
+ kernel_size (int): The size of the median pooling window.
194
+ stride (int, optional): The stride of the median pooling window. Defaults to None.
195
+ padding (int, optional): The padding of the median pooling window. Defaults to 0.
196
+ """
197
+
198
+ super(MaskedMedianPool1d, self).__init__()
199
+ self.kernel_size = kernel_size
200
+ self.stride = stride or kernel_size
201
+ self.padding = padding
202
+
203
+ def forward(self, x, mask=None):
204
+ ndim = x.dim()
205
+ if ndim == 2:
206
+ x = x.unsqueeze(1)
207
+
208
+ assert (
209
+ x.dim() == 3
210
+ ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
211
+
212
+ if mask is None:
213
+ mask = ~torch.isnan(x)
214
+
215
+ assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
216
+
217
+ masked_x = torch.where(mask, x, torch.zeros_like(x))
218
+
219
+ x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
220
+ mask = F.pad(
221
+ mask.float(), (self.padding, self.padding), mode="constant", value=0
222
+ )
223
+
224
+ x = x.unfold(2, self.kernel_size, self.stride)
225
+ mask = mask.unfold(2, self.kernel_size, self.stride)
226
+
227
+ x = x.contiguous().view(x.size()[:3] + (-1,))
228
+ mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
229
+
230
+ # Combine the mask with the input tensor
231
+ #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
232
+ x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
233
+
234
+ # Sort the masked tensor along the last dimension
235
+ x_sorted, _ = torch.sort(x_masked, dim=-1)
236
+
237
+ # Compute the count of non-masked (valid) values
238
+ valid_count = mask.sum(dim=-1)
239
+
240
+ # Calculate the index of the median value for each pooling window
241
+ median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
242
+
243
+ # Gather the median values using the calculated indices
244
+ median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
245
+
246
+ # Fill infinite values with NaNs
247
+ median_pooled[torch.isinf(median_pooled)] = float("nan")
248
+
249
+ if ndim == 2:
250
+ return median_pooled.squeeze(1)
251
+
252
+ return median_pooled
253
+
254
+
255
+ class CrepePitchExtractor(BasePitchExtractor):
256
+ def __init__(
257
+ self,
258
+ hop_length: int = 512,
259
+ f0_min: float = 50.0,
260
+ f0_max: float = 1100.0,
261
+ threshold: float = 0.05,
262
+ keep_zeros: bool = False,
263
+ device = None,
264
+ model: Literal["full", "tiny"] = "full",
265
+ use_fast_filters: bool = True,
266
+ ):
267
+ super().__init__(hop_length, f0_min, f0_max, keep_zeros)
268
+
269
+ self.threshold = threshold
270
+ self.model = model
271
+ self.use_fast_filters = use_fast_filters
272
+ self.hop_length = hop_length
273
+ if device is None:
274
+ self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
275
+ else:
276
+ self.dev = torch.device(device)
277
+ if self.use_fast_filters:
278
+ self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
279
+ self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
280
+
281
+ def __call__(self, x, sampling_rate=44100, pad_to=None):
282
+ """Extract pitch using crepe.
283
+
284
+
285
+ Args:
286
+ x (torch.Tensor): Audio signal, shape (1, T).
287
+ sampling_rate (int, optional): Sampling rate. Defaults to 44100.
288
+ pad_to (int, optional): Pad to length. Defaults to None.
289
+
290
+ Returns:
291
+ torch.Tensor: Pitch, shape (T // hop_length,).
292
+ """
293
+
294
+ assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
295
+ assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
296
+
297
+ x = x.to(self.dev)
298
+ f0, pd = torchcrepe.predict(
299
+ x,
300
+ sampling_rate,
301
+ self.hop_length,
302
+ self.f0_min,
303
+ self.f0_max,
304
+ pad=True,
305
+ model=self.model,
306
+ batch_size=1024,
307
+ device=x.device,
308
+ return_periodicity=True,
309
+ )
310
+
311
+ # Filter, remove silence, set uv threshold, refer to the original warehouse readme
312
+ if self.use_fast_filters:
313
+ pd = self.median_filter(pd)
314
+ else:
315
+ pd = torchcrepe.filter.median(pd, 3)
316
+
317
+ pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
318
+ f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
319
+
320
+ if self.use_fast_filters:
321
+ f0 = self.mean_filter(f0)
322
+ else:
323
+ f0 = torchcrepe.filter.mean(f0, 3)
324
+
325
+ f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
326
+
327
+ return self.post_process(x, sampling_rate, f0, pad_to)
modules/ddsp.py ADDED
@@ -0,0 +1,190 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.nn as nn
3
+ from torch.nn import functional as F
4
+ import torch.fft as fft
5
+ import numpy as np
6
+ import librosa as li
7
+ import math
8
+ from scipy.signal import get_window
9
+
10
+
11
+ def safe_log(x):
12
+ return torch.log(x + 1e-7)
13
+
14
+
15
+ @torch.no_grad()
16
+ def mean_std_loudness(dataset):
17
+ mean = 0
18
+ std = 0
19
+ n = 0
20
+ for _, _, l in dataset:
21
+ n += 1
22
+ mean += (l.mean().item() - mean) / n
23
+ std += (l.std().item() - std) / n
24
+ return mean, std
25
+
26
+
27
+ def multiscale_fft(signal, scales, overlap):
28
+ stfts = []
29
+ for s in scales:
30
+ S = torch.stft(
31
+ signal,
32
+ s,
33
+ int(s * (1 - overlap)),
34
+ s,
35
+ torch.hann_window(s).to(signal),
36
+ True,
37
+ normalized=True,
38
+ return_complex=True,
39
+ ).abs()
40
+ stfts.append(S)
41
+ return stfts
42
+
43
+
44
+ def resample(x, factor: int):
45
+ batch, frame, channel = x.shape
46
+ x = x.permute(0, 2, 1).reshape(batch * channel, 1, frame)
47
+
48
+ window = torch.hann_window(
49
+ factor * 2,
50
+ dtype=x.dtype,
51
+ device=x.device,
52
+ ).reshape(1, 1, -1)
53
+ y = torch.zeros(x.shape[0], x.shape[1], factor * x.shape[2]).to(x)
54
+ y[..., ::factor] = x
55
+ y[..., -1:] = x[..., -1:]
56
+ y = torch.nn.functional.pad(y, [factor, factor])
57
+ y = torch.nn.functional.conv1d(y, window)[..., :-1]
58
+
59
+ y = y.reshape(batch, channel, factor * frame).permute(0, 2, 1)
60
+
61
+ return y
62
+
63
+
64
+ def upsample(signal, factor):
65
+ signal = signal.permute(0, 2, 1)
66
+ signal = nn.functional.interpolate(signal, size=signal.shape[-1] * factor)
67
+ return signal.permute(0, 2, 1)
68
+
69
+
70
+ def remove_above_nyquist(amplitudes, pitch, sampling_rate):
71
+ n_harm = amplitudes.shape[-1]
72
+ pitches = pitch * torch.arange(1, n_harm + 1).to(pitch)
73
+ aa = (pitches < sampling_rate / 2).float() + 1e-4
74
+ return amplitudes * aa
75
+
76
+
77
+ def scale_function(x):
78
+ return 2 * torch.sigmoid(x) ** (math.log(10)) + 1e-7
79
+
80
+
81
+ def extract_loudness(signal, sampling_rate, block_size, n_fft=2048):
82
+ S = li.stft(
83
+ signal,
84
+ n_fft=n_fft,
85
+ hop_length=block_size,
86
+ win_length=n_fft,
87
+ center=True,
88
+ )
89
+ S = np.log(abs(S) + 1e-7)
90
+ f = li.fft_frequencies(sampling_rate, n_fft)
91
+ a_weight = li.A_weighting(f)
92
+
93
+ S = S + a_weight.reshape(-1, 1)
94
+
95
+ S = np.mean(S, 0)[..., :-1]
96
+
97
+ return S
98
+
99
+
100
+ def extract_pitch(signal, sampling_rate, block_size):
101
+ length = signal.shape[-1] // block_size
102
+ f0 = crepe.predict(
103
+ signal,
104
+ sampling_rate,
105
+ step_size=int(1000 * block_size / sampling_rate),
106
+ verbose=1,
107
+ center=True,
108
+ viterbi=True,
109
+ )
110
+ f0 = f0[1].reshape(-1)[:-1]
111
+
112
+ if f0.shape[-1] != length:
113
+ f0 = np.interp(
114
+ np.linspace(0, 1, length, endpoint=False),
115
+ np.linspace(0, 1, f0.shape[-1], endpoint=False),
116
+ f0,
117
+ )
118
+
119
+ return f0
120
+
121
+
122
+ def mlp(in_size, hidden_size, n_layers):
123
+ channels = [in_size] + (n_layers) * [hidden_size]
124
+ net = []
125
+ for i in range(n_layers):
126
+ net.append(nn.Linear(channels[i], channels[i + 1]))
127
+ net.append(nn.LayerNorm(channels[i + 1]))
128
+ net.append(nn.LeakyReLU())
129
+ return nn.Sequential(*net)
130
+
131
+
132
+ def gru(n_input, hidden_size):
133
+ return nn.GRU(n_input * hidden_size, hidden_size, batch_first=True)
134
+
135
+
136
+ def harmonic_synth(pitch, amplitudes, sampling_rate):
137
+ n_harmonic = amplitudes.shape[-1]
138
+ omega = torch.cumsum(2 * math.pi * pitch / sampling_rate, 1)
139
+ omegas = omega * torch.arange(1, n_harmonic + 1).to(omega)
140
+ signal = (torch.sin(omegas) * amplitudes).sum(-1, keepdim=True)
141
+ return signal
142
+
143
+
144
+ def amp_to_impulse_response(amp, target_size):
145
+ amp = torch.stack([amp, torch.zeros_like(amp)], -1)
146
+ amp = torch.view_as_complex(amp)
147
+ amp = fft.irfft(amp)
148
+
149
+ filter_size = amp.shape[-1]
150
+
151
+ amp = torch.roll(amp, filter_size // 2, -1)
152
+ win = torch.hann_window(filter_size, dtype=amp.dtype, device=amp.device)
153
+
154
+ amp = amp * win
155
+
156
+ amp = nn.functional.pad(amp, (0, int(target_size) - int(filter_size)))
157
+ amp = torch.roll(amp, -filter_size // 2, -1)
158
+
159
+ return amp
160
+
161
+
162
+ def fft_convolve(signal, kernel):
163
+ signal = nn.functional.pad(signal, (0, signal.shape[-1]))
164
+ kernel = nn.functional.pad(kernel, (kernel.shape[-1], 0))
165
+
166
+ output = fft.irfft(fft.rfft(signal) * fft.rfft(kernel))
167
+ output = output[..., output.shape[-1] // 2:]
168
+
169
+ return output
170
+
171
+
172
+ def init_kernels(win_len, win_inc, fft_len, win_type=None, invers=False):
173
+ if win_type == 'None' or win_type is None:
174
+ window = np.ones(win_len)
175
+ else:
176
+ window = get_window(win_type, win_len, fftbins=True) # **0.5
177
+
178
+ N = fft_len
179
+ fourier_basis = np.fft.rfft(np.eye(N))[:win_len]
180
+ real_kernel = np.real(fourier_basis)
181
+ imag_kernel = np.imag(fourier_basis)
182
+ kernel = np.concatenate([real_kernel, imag_kernel], 1).T
183
+
184
+ if invers:
185
+ kernel = np.linalg.pinv(kernel).T
186
+
187
+ kernel = kernel * window
188
+ kernel = kernel[:, None, :]
189
+ return torch.from_numpy(kernel.astype(np.float32)), torch.from_numpy(window[None, :, None].astype(np.float32))
190
+
modules/losses.py ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch.nn import functional as F
3
+
4
+ import modules.commons as commons
5
+
6
+
7
+ def feature_loss(fmap_r, fmap_g):
8
+ loss = 0
9
+ for dr, dg in zip(fmap_r, fmap_g):
10
+ for rl, gl in zip(dr, dg):
11
+ rl = rl.float().detach()
12
+ gl = gl.float()
13
+ loss += torch.mean(torch.abs(rl - gl))
14
+
15
+ return loss * 2
16
+
17
+
18
+ def discriminator_loss(disc_real_outputs, disc_generated_outputs):
19
+ loss = 0
20
+ r_losses = []
21
+ g_losses = []
22
+ for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
23
+ dr = dr.float()
24
+ dg = dg.float()
25
+ r_loss = torch.mean((1-dr)**2)
26
+ g_loss = torch.mean(dg**2)
27
+ loss += (r_loss + g_loss)
28
+ r_losses.append(r_loss.item())
29
+ g_losses.append(g_loss.item())
30
+
31
+ return loss, r_losses, g_losses
32
+
33
+
34
+ def generator_loss(disc_outputs):
35
+ loss = 0
36
+ gen_losses = []
37
+ for dg in disc_outputs:
38
+ dg = dg.float()
39
+ l = torch.mean((1-dg)**2)
40
+ gen_losses.append(l)
41
+ loss += l
42
+
43
+ return loss, gen_losses
44
+
45
+
46
+ def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
47
+ """
48
+ z_p, logs_q: [b, h, t_t]
49
+ m_p, logs_p: [b, h, t_t]
50
+ """
51
+ z_p = z_p.float()
52
+ logs_q = logs_q.float()
53
+ m_p = m_p.float()
54
+ logs_p = logs_p.float()
55
+ z_mask = z_mask.float()
56
+ #print(logs_p)
57
+ kl = logs_p - logs_q - 0.5
58
+ kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
59
+ kl = torch.sum(kl * z_mask)
60
+ l = kl / torch.sum(z_mask)
61
+ return l
modules/mel_processing.py ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import os
3
+ import random
4
+ import torch
5
+ from torch import nn
6
+ import torch.nn.functional as F
7
+ import torch.utils.data
8
+ import numpy as np
9
+ import librosa
10
+ import librosa.util as librosa_util
11
+ from librosa.util import normalize, pad_center, tiny
12
+ from scipy.signal import get_window
13
+ from scipy.io.wavfile import read
14
+ from librosa.filters import mel as librosa_mel_fn
15
+
16
+ MAX_WAV_VALUE = 32768.0
17
+
18
+
19
+ def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
20
+ """
21
+ PARAMS
22
+ ------
23
+ C: compression factor
24
+ """
25
+ return torch.log(torch.clamp(x, min=clip_val) * C)
26
+
27
+
28
+ def dynamic_range_decompression_torch(x, C=1):
29
+ """
30
+ PARAMS
31
+ ------
32
+ C: compression factor used to compress
33
+ """
34
+ return torch.exp(x) / C
35
+
36
+
37
+ def spectral_normalize_torch(magnitudes):
38
+ output = dynamic_range_compression_torch(magnitudes)
39
+ return output
40
+
41
+
42
+ def spectral_de_normalize_torch(magnitudes):
43
+ output = dynamic_range_decompression_torch(magnitudes)
44
+ return output
45
+
46
+
47
+ mel_basis = {}
48
+ hann_window = {}
49
+
50
+
51
+ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
52
+ if torch.min(y) < -1.:
53
+ print('min value is ', torch.min(y))
54
+ if torch.max(y) > 1.:
55
+ print('max value is ', torch.max(y))
56
+
57
+ global hann_window
58
+ dtype_device = str(y.dtype) + '_' + str(y.device)
59
+ wnsize_dtype_device = str(win_size) + '_' + dtype_device
60
+ if wnsize_dtype_device not in hann_window:
61
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
62
+
63
+ y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
64
+ y = y.squeeze(1)
65
+
66
+ spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
67
+ center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
68
+
69
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
70
+ return spec
71
+
72
+
73
+ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
74
+ global mel_basis
75
+ dtype_device = str(spec.dtype) + '_' + str(spec.device)
76
+ fmax_dtype_device = str(fmax) + '_' + dtype_device
77
+ if fmax_dtype_device not in mel_basis:
78
+ mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
79
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
80
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
81
+ spec = spectral_normalize_torch(spec)
82
+ return spec
83
+
84
+
85
+ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
86
+ if torch.min(y) < -1.:
87
+ print('min value is ', torch.min(y))
88
+ if torch.max(y) > 1.:
89
+ print('max value is ', torch.max(y))
90
+
91
+ global mel_basis, hann_window
92
+ dtype_device = str(y.dtype) + '_' + str(y.device)
93
+ fmax_dtype_device = str(fmax) + '_' + dtype_device
94
+ wnsize_dtype_device = str(win_size) + '_' + dtype_device
95
+ if fmax_dtype_device not in mel_basis:
96
+ mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
97
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
98
+ if wnsize_dtype_device not in hann_window:
99
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
100
+
101
+ y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
102
+ y = y.squeeze(1)
103
+
104
+ spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
105
+ center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
106
+
107
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
108
+
109
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
110
+ spec = spectral_normalize_torch(spec)
111
+
112
+ return spec
modules/modules.py ADDED
@@ -0,0 +1,342 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import numpy as np
4
+ import scipy
5
+ import torch
6
+ from torch import nn
7
+ from torch.nn import functional as F
8
+
9
+ from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
10
+ from torch.nn.utils import weight_norm, remove_weight_norm
11
+
12
+ import modules.commons as commons
13
+ from modules.commons import init_weights, get_padding
14
+
15
+
16
+ LRELU_SLOPE = 0.1
17
+
18
+
19
+ class LayerNorm(nn.Module):
20
+ def __init__(self, channels, eps=1e-5):
21
+ super().__init__()
22
+ self.channels = channels
23
+ self.eps = eps
24
+
25
+ self.gamma = nn.Parameter(torch.ones(channels))
26
+ self.beta = nn.Parameter(torch.zeros(channels))
27
+
28
+ def forward(self, x):
29
+ x = x.transpose(1, -1)
30
+ x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
31
+ return x.transpose(1, -1)
32
+
33
+
34
+ class ConvReluNorm(nn.Module):
35
+ def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
36
+ super().__init__()
37
+ self.in_channels = in_channels
38
+ self.hidden_channels = hidden_channels
39
+ self.out_channels = out_channels
40
+ self.kernel_size = kernel_size
41
+ self.n_layers = n_layers
42
+ self.p_dropout = p_dropout
43
+ assert n_layers > 1, "Number of layers should be larger than 0."
44
+
45
+ self.conv_layers = nn.ModuleList()
46
+ self.norm_layers = nn.ModuleList()
47
+ self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
48
+ self.norm_layers.append(LayerNorm(hidden_channels))
49
+ self.relu_drop = nn.Sequential(
50
+ nn.ReLU(),
51
+ nn.Dropout(p_dropout))
52
+ for _ in range(n_layers-1):
53
+ self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
54
+ self.norm_layers.append(LayerNorm(hidden_channels))
55
+ self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
56
+ self.proj.weight.data.zero_()
57
+ self.proj.bias.data.zero_()
58
+
59
+ def forward(self, x, x_mask):
60
+ x_org = x
61
+ for i in range(self.n_layers):
62
+ x = self.conv_layers[i](x * x_mask)
63
+ x = self.norm_layers[i](x)
64
+ x = self.relu_drop(x)
65
+ x = x_org + self.proj(x)
66
+ return x * x_mask
67
+
68
+
69
+ class DDSConv(nn.Module):
70
+ """
71
+ Dialted and Depth-Separable Convolution
72
+ """
73
+ def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
74
+ super().__init__()
75
+ self.channels = channels
76
+ self.kernel_size = kernel_size
77
+ self.n_layers = n_layers
78
+ self.p_dropout = p_dropout
79
+
80
+ self.drop = nn.Dropout(p_dropout)
81
+ self.convs_sep = nn.ModuleList()
82
+ self.convs_1x1 = nn.ModuleList()
83
+ self.norms_1 = nn.ModuleList()
84
+ self.norms_2 = nn.ModuleList()
85
+ for i in range(n_layers):
86
+ dilation = kernel_size ** i
87
+ padding = (kernel_size * dilation - dilation) // 2
88
+ self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
89
+ groups=channels, dilation=dilation, padding=padding
90
+ ))
91
+ self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
92
+ self.norms_1.append(LayerNorm(channels))
93
+ self.norms_2.append(LayerNorm(channels))
94
+
95
+ def forward(self, x, x_mask, g=None):
96
+ if g is not None:
97
+ x = x + g
98
+ for i in range(self.n_layers):
99
+ y = self.convs_sep[i](x * x_mask)
100
+ y = self.norms_1[i](y)
101
+ y = F.gelu(y)
102
+ y = self.convs_1x1[i](y)
103
+ y = self.norms_2[i](y)
104
+ y = F.gelu(y)
105
+ y = self.drop(y)
106
+ x = x + y
107
+ return x * x_mask
108
+
109
+
110
+ class WN(torch.nn.Module):
111
+ def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
112
+ super(WN, self).__init__()
113
+ assert(kernel_size % 2 == 1)
114
+ self.hidden_channels =hidden_channels
115
+ self.kernel_size = kernel_size,
116
+ self.dilation_rate = dilation_rate
117
+ self.n_layers = n_layers
118
+ self.gin_channels = gin_channels
119
+ self.p_dropout = p_dropout
120
+
121
+ self.in_layers = torch.nn.ModuleList()
122
+ self.res_skip_layers = torch.nn.ModuleList()
123
+ self.drop = nn.Dropout(p_dropout)
124
+
125
+ if gin_channels != 0:
126
+ cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
127
+ self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
128
+
129
+ for i in range(n_layers):
130
+ dilation = dilation_rate ** i
131
+ padding = int((kernel_size * dilation - dilation) / 2)
132
+ in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
133
+ dilation=dilation, padding=padding)
134
+ in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
135
+ self.in_layers.append(in_layer)
136
+
137
+ # last one is not necessary
138
+ if i < n_layers - 1:
139
+ res_skip_channels = 2 * hidden_channels
140
+ else:
141
+ res_skip_channels = hidden_channels
142
+
143
+ res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
144
+ res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
145
+ self.res_skip_layers.append(res_skip_layer)
146
+
147
+ def forward(self, x, x_mask, g=None, **kwargs):
148
+ output = torch.zeros_like(x)
149
+ n_channels_tensor = torch.IntTensor([self.hidden_channels])
150
+
151
+ if g is not None:
152
+ g = self.cond_layer(g)
153
+
154
+ for i in range(self.n_layers):
155
+ x_in = self.in_layers[i](x)
156
+ if g is not None:
157
+ cond_offset = i * 2 * self.hidden_channels
158
+ g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
159
+ else:
160
+ g_l = torch.zeros_like(x_in)
161
+
162
+ acts = commons.fused_add_tanh_sigmoid_multiply(
163
+ x_in,
164
+ g_l,
165
+ n_channels_tensor)
166
+ acts = self.drop(acts)
167
+
168
+ res_skip_acts = self.res_skip_layers[i](acts)
169
+ if i < self.n_layers - 1:
170
+ res_acts = res_skip_acts[:,:self.hidden_channels,:]
171
+ x = (x + res_acts) * x_mask
172
+ output = output + res_skip_acts[:,self.hidden_channels:,:]
173
+ else:
174
+ output = output + res_skip_acts
175
+ return output * x_mask
176
+
177
+ def remove_weight_norm(self):
178
+ if self.gin_channels != 0:
179
+ torch.nn.utils.remove_weight_norm(self.cond_layer)
180
+ for l in self.in_layers:
181
+ torch.nn.utils.remove_weight_norm(l)
182
+ for l in self.res_skip_layers:
183
+ torch.nn.utils.remove_weight_norm(l)
184
+
185
+
186
+ class ResBlock1(torch.nn.Module):
187
+ def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
188
+ super(ResBlock1, self).__init__()
189
+ self.convs1 = nn.ModuleList([
190
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
191
+ padding=get_padding(kernel_size, dilation[0]))),
192
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
193
+ padding=get_padding(kernel_size, dilation[1]))),
194
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
195
+ padding=get_padding(kernel_size, dilation[2])))
196
+ ])
197
+ self.convs1.apply(init_weights)
198
+
199
+ self.convs2 = nn.ModuleList([
200
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
201
+ padding=get_padding(kernel_size, 1))),
202
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
203
+ padding=get_padding(kernel_size, 1))),
204
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
205
+ padding=get_padding(kernel_size, 1)))
206
+ ])
207
+ self.convs2.apply(init_weights)
208
+
209
+ def forward(self, x, x_mask=None):
210
+ for c1, c2 in zip(self.convs1, self.convs2):
211
+ xt = F.leaky_relu(x, LRELU_SLOPE)
212
+ if x_mask is not None:
213
+ xt = xt * x_mask
214
+ xt = c1(xt)
215
+ xt = F.leaky_relu(xt, LRELU_SLOPE)
216
+ if x_mask is not None:
217
+ xt = xt * x_mask
218
+ xt = c2(xt)
219
+ x = xt + x
220
+ if x_mask is not None:
221
+ x = x * x_mask
222
+ return x
223
+
224
+ def remove_weight_norm(self):
225
+ for l in self.convs1:
226
+ remove_weight_norm(l)
227
+ for l in self.convs2:
228
+ remove_weight_norm(l)
229
+
230
+
231
+ class ResBlock2(torch.nn.Module):
232
+ def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
233
+ super(ResBlock2, self).__init__()
234
+ self.convs = nn.ModuleList([
235
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
236
+ padding=get_padding(kernel_size, dilation[0]))),
237
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
238
+ padding=get_padding(kernel_size, dilation[1])))
239
+ ])
240
+ self.convs.apply(init_weights)
241
+
242
+ def forward(self, x, x_mask=None):
243
+ for c in self.convs:
244
+ xt = F.leaky_relu(x, LRELU_SLOPE)
245
+ if x_mask is not None:
246
+ xt = xt * x_mask
247
+ xt = c(xt)
248
+ x = xt + x
249
+ if x_mask is not None:
250
+ x = x * x_mask
251
+ return x
252
+
253
+ def remove_weight_norm(self):
254
+ for l in self.convs:
255
+ remove_weight_norm(l)
256
+
257
+
258
+ class Log(nn.Module):
259
+ def forward(self, x, x_mask, reverse=False, **kwargs):
260
+ if not reverse:
261
+ y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
262
+ logdet = torch.sum(-y, [1, 2])
263
+ return y, logdet
264
+ else:
265
+ x = torch.exp(x) * x_mask
266
+ return x
267
+
268
+
269
+ class Flip(nn.Module):
270
+ def forward(self, x, *args, reverse=False, **kwargs):
271
+ x = torch.flip(x, [1])
272
+ if not reverse:
273
+ logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
274
+ return x, logdet
275
+ else:
276
+ return x
277
+
278
+
279
+ class ElementwiseAffine(nn.Module):
280
+ def __init__(self, channels):
281
+ super().__init__()
282
+ self.channels = channels
283
+ self.m = nn.Parameter(torch.zeros(channels,1))
284
+ self.logs = nn.Parameter(torch.zeros(channels,1))
285
+
286
+ def forward(self, x, x_mask, reverse=False, **kwargs):
287
+ if not reverse:
288
+ y = self.m + torch.exp(self.logs) * x
289
+ y = y * x_mask
290
+ logdet = torch.sum(self.logs * x_mask, [1,2])
291
+ return y, logdet
292
+ else:
293
+ x = (x - self.m) * torch.exp(-self.logs) * x_mask
294
+ return x
295
+
296
+
297
+ class ResidualCouplingLayer(nn.Module):
298
+ def __init__(self,
299
+ channels,
300
+ hidden_channels,
301
+ kernel_size,
302
+ dilation_rate,
303
+ n_layers,
304
+ p_dropout=0,
305
+ gin_channels=0,
306
+ mean_only=False):
307
+ assert channels % 2 == 0, "channels should be divisible by 2"
308
+ super().__init__()
309
+ self.channels = channels
310
+ self.hidden_channels = hidden_channels
311
+ self.kernel_size = kernel_size
312
+ self.dilation_rate = dilation_rate
313
+ self.n_layers = n_layers
314
+ self.half_channels = channels // 2
315
+ self.mean_only = mean_only
316
+
317
+ self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
318
+ self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
319
+ self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
320
+ self.post.weight.data.zero_()
321
+ self.post.bias.data.zero_()
322
+
323
+ def forward(self, x, x_mask, g=None, reverse=False):
324
+ x0, x1 = torch.split(x, [self.half_channels]*2, 1)
325
+ h = self.pre(x0) * x_mask
326
+ h = self.enc(h, x_mask, g=g)
327
+ stats = self.post(h) * x_mask
328
+ if not self.mean_only:
329
+ m, logs = torch.split(stats, [self.half_channels]*2, 1)
330
+ else:
331
+ m = stats
332
+ logs = torch.zeros_like(m)
333
+
334
+ if not reverse:
335
+ x1 = m + x1 * torch.exp(logs) * x_mask
336
+ x = torch.cat([x0, x1], 1)
337
+ logdet = torch.sum(logs, [1,2])
338
+ return x, logdet
339
+ else:
340
+ x1 = (x1 - m) * torch.exp(-logs) * x_mask
341
+ x = torch.cat([x0, x1], 1)
342
+ return x
onnx/model_onnx.py ADDED
@@ -0,0 +1,328 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import torch
4
+ from torch import nn
5
+ from torch.nn import functional as F
6
+
7
+ import modules.attentions as attentions
8
+ import modules.commons as commons
9
+ import modules.modules as modules
10
+
11
+ from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
12
+ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
13
+ from modules.commons import init_weights, get_padding
14
+ from vdecoder.hifigan.models import Generator
15
+ from utils import f0_to_coarse
16
+
17
+ class ResidualCouplingBlock(nn.Module):
18
+ def __init__(self,
19
+ channels,
20
+ hidden_channels,
21
+ kernel_size,
22
+ dilation_rate,
23
+ n_layers,
24
+ n_flows=4,
25
+ gin_channels=0):
26
+ super().__init__()
27
+ self.channels = channels
28
+ self.hidden_channels = hidden_channels
29
+ self.kernel_size = kernel_size
30
+ self.dilation_rate = dilation_rate
31
+ self.n_layers = n_layers
32
+ self.n_flows = n_flows
33
+ self.gin_channels = gin_channels
34
+
35
+ self.flows = nn.ModuleList()
36
+ for i in range(n_flows):
37
+ self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
38
+ self.flows.append(modules.Flip())
39
+
40
+ def forward(self, x, x_mask, g=None, reverse=False):
41
+ if not reverse:
42
+ for flow in self.flows:
43
+ x, _ = flow(x, x_mask, g=g, reverse=reverse)
44
+ else:
45
+ for flow in reversed(self.flows):
46
+ x = flow(x, x_mask, g=g, reverse=reverse)
47
+ return x
48
+
49
+
50
+ class Encoder(nn.Module):
51
+ def __init__(self,
52
+ in_channels,
53
+ out_channels,
54
+ hidden_channels,
55
+ kernel_size,
56
+ dilation_rate,
57
+ n_layers,
58
+ gin_channels=0):
59
+ super().__init__()
60
+ self.in_channels = in_channels
61
+ self.out_channels = out_channels
62
+ self.hidden_channels = hidden_channels
63
+ self.kernel_size = kernel_size
64
+ self.dilation_rate = dilation_rate
65
+ self.n_layers = n_layers
66
+ self.gin_channels = gin_channels
67
+
68
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
69
+ self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
70
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
71
+
72
+ def forward(self, x, x_lengths, g=None):
73
+ # print(x.shape,x_lengths.shape)
74
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
75
+ x = self.pre(x) * x_mask
76
+ x = self.enc(x, x_mask, g=g)
77
+ stats = self.proj(x) * x_mask
78
+ m, logs = torch.split(stats, self.out_channels, dim=1)
79
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
80
+ return z, m, logs, x_mask
81
+
82
+
83
+ class TextEncoder(nn.Module):
84
+ def __init__(self,
85
+ in_channels,
86
+ out_channels,
87
+ hidden_channels,
88
+ kernel_size,
89
+ dilation_rate,
90
+ n_layers,
91
+ gin_channels=0,
92
+ filter_channels=None,
93
+ n_heads=None,
94
+ p_dropout=None):
95
+ super().__init__()
96
+ self.in_channels = in_channels
97
+ self.out_channels = out_channels
98
+ self.hidden_channels = hidden_channels
99
+ self.kernel_size = kernel_size
100
+ self.dilation_rate = dilation_rate
101
+ self.n_layers = n_layers
102
+ self.gin_channels = gin_channels
103
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
104
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
105
+ self.f0_emb = nn.Embedding(256, hidden_channels)
106
+
107
+ self.enc_ = attentions.Encoder(
108
+ hidden_channels,
109
+ filter_channels,
110
+ n_heads,
111
+ n_layers,
112
+ kernel_size,
113
+ p_dropout)
114
+
115
+ def forward(self, x, x_lengths, f0=None):
116
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
117
+ x = self.pre(x) * x_mask
118
+ x = x + self.f0_emb(f0.long()).transpose(1,2)
119
+ x = self.enc_(x * x_mask, x_mask)
120
+ stats = self.proj(x) * x_mask
121
+ m, logs = torch.split(stats, self.out_channels, dim=1)
122
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
123
+
124
+ return z, m, logs, x_mask
125
+
126
+
127
+
128
+ class DiscriminatorP(torch.nn.Module):
129
+ def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
130
+ super(DiscriminatorP, self).__init__()
131
+ self.period = period
132
+ self.use_spectral_norm = use_spectral_norm
133
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
134
+ self.convs = nn.ModuleList([
135
+ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
136
+ norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
137
+ norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
138
+ norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
139
+ norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
140
+ ])
141
+ self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
142
+
143
+ def forward(self, x):
144
+ fmap = []
145
+
146
+ # 1d to 2d
147
+ b, c, t = x.shape
148
+ if t % self.period != 0: # pad first
149
+ n_pad = self.period - (t % self.period)
150
+ x = F.pad(x, (0, n_pad), "reflect")
151
+ t = t + n_pad
152
+ x = x.view(b, c, t // self.period, self.period)
153
+
154
+ for l in self.convs:
155
+ x = l(x)
156
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
157
+ fmap.append(x)
158
+ x = self.conv_post(x)
159
+ fmap.append(x)
160
+ x = torch.flatten(x, 1, -1)
161
+
162
+ return x, fmap
163
+
164
+
165
+ class DiscriminatorS(torch.nn.Module):
166
+ def __init__(self, use_spectral_norm=False):
167
+ super(DiscriminatorS, self).__init__()
168
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
169
+ self.convs = nn.ModuleList([
170
+ norm_f(Conv1d(1, 16, 15, 1, padding=7)),
171
+ norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
172
+ norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
173
+ norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
174
+ norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
175
+ norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
176
+ ])
177
+ self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
178
+
179
+ def forward(self, x):
180
+ fmap = []
181
+
182
+ for l in self.convs:
183
+ x = l(x)
184
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
185
+ fmap.append(x)
186
+ x = self.conv_post(x)
187
+ fmap.append(x)
188
+ x = torch.flatten(x, 1, -1)
189
+
190
+ return x, fmap
191
+
192
+
193
+ class MultiPeriodDiscriminator(torch.nn.Module):
194
+ def __init__(self, use_spectral_norm=False):
195
+ super(MultiPeriodDiscriminator, self).__init__()
196
+ periods = [2,3,5,7,11]
197
+
198
+ discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
199
+ discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
200
+ self.discriminators = nn.ModuleList(discs)
201
+
202
+ def forward(self, y, y_hat):
203
+ y_d_rs = []
204
+ y_d_gs = []
205
+ fmap_rs = []
206
+ fmap_gs = []
207
+ for i, d in enumerate(self.discriminators):
208
+ y_d_r, fmap_r = d(y)
209
+ y_d_g, fmap_g = d(y_hat)
210
+ y_d_rs.append(y_d_r)
211
+ y_d_gs.append(y_d_g)
212
+ fmap_rs.append(fmap_r)
213
+ fmap_gs.append(fmap_g)
214
+
215
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
216
+
217
+
218
+ class SpeakerEncoder(torch.nn.Module):
219
+ def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
220
+ super(SpeakerEncoder, self).__init__()
221
+ self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
222
+ self.linear = nn.Linear(model_hidden_size, model_embedding_size)
223
+ self.relu = nn.ReLU()
224
+
225
+ def forward(self, mels):
226
+ self.lstm.flatten_parameters()
227
+ _, (hidden, _) = self.lstm(mels)
228
+ embeds_raw = self.relu(self.linear(hidden[-1]))
229
+ return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
230
+
231
+ def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
232
+ mel_slices = []
233
+ for i in range(0, total_frames-partial_frames, partial_hop):
234
+ mel_range = torch.arange(i, i+partial_frames)
235
+ mel_slices.append(mel_range)
236
+
237
+ return mel_slices
238
+
239
+ def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
240
+ mel_len = mel.size(1)
241
+ last_mel = mel[:,-partial_frames:]
242
+
243
+ if mel_len > partial_frames:
244
+ mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
245
+ mels = list(mel[:,s] for s in mel_slices)
246
+ mels.append(last_mel)
247
+ mels = torch.stack(tuple(mels), 0).squeeze(1)
248
+
249
+ with torch.no_grad():
250
+ partial_embeds = self(mels)
251
+ embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
252
+ #embed = embed / torch.linalg.norm(embed, 2)
253
+ else:
254
+ with torch.no_grad():
255
+ embed = self(last_mel)
256
+
257
+ return embed
258
+
259
+
260
+ class SynthesizerTrn(nn.Module):
261
+ """
262
+ Synthesizer for Training
263
+ """
264
+
265
+ def __init__(self,
266
+ spec_channels,
267
+ segment_size,
268
+ inter_channels,
269
+ hidden_channels,
270
+ filter_channels,
271
+ n_heads,
272
+ n_layers,
273
+ kernel_size,
274
+ p_dropout,
275
+ resblock,
276
+ resblock_kernel_sizes,
277
+ resblock_dilation_sizes,
278
+ upsample_rates,
279
+ upsample_initial_channel,
280
+ upsample_kernel_sizes,
281
+ gin_channels,
282
+ ssl_dim,
283
+ n_speakers,
284
+ **kwargs):
285
+
286
+ super().__init__()
287
+ self.spec_channels = spec_channels
288
+ self.inter_channels = inter_channels
289
+ self.hidden_channels = hidden_channels
290
+ self.filter_channels = filter_channels
291
+ self.n_heads = n_heads
292
+ self.n_layers = n_layers
293
+ self.kernel_size = kernel_size
294
+ self.p_dropout = p_dropout
295
+ self.resblock = resblock
296
+ self.resblock_kernel_sizes = resblock_kernel_sizes
297
+ self.resblock_dilation_sizes = resblock_dilation_sizes
298
+ self.upsample_rates = upsample_rates
299
+ self.upsample_initial_channel = upsample_initial_channel
300
+ self.upsample_kernel_sizes = upsample_kernel_sizes
301
+ self.segment_size = segment_size
302
+ self.gin_channels = gin_channels
303
+ self.ssl_dim = ssl_dim
304
+ self.emb_g = nn.Embedding(n_speakers, gin_channels)
305
+
306
+ self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
307
+ hps = {
308
+ "sampling_rate": 32000,
309
+ "inter_channels": 192,
310
+ "resblock": "1",
311
+ "resblock_kernel_sizes": [3, 7, 11],
312
+ "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
313
+ "upsample_rates": [10, 8, 2, 2],
314
+ "upsample_initial_channel": 512,
315
+ "upsample_kernel_sizes": [16, 16, 4, 4],
316
+ "gin_channels": 256,
317
+ }
318
+ self.dec = Generator(h=hps)
319
+ self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
320
+ self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
321
+
322
+ def forward(self, c, c_lengths, f0, g=None):
323
+ g = self.emb_g(g.unsqueeze(0)).transpose(1,2)
324
+ z_p, m_p, logs_p, c_mask = self.enc_p_(c.transpose(1,2), c_lengths, f0=f0_to_coarse(f0))
325
+ z = self.flow(z_p, c_mask, g=g, reverse=True)
326
+ o = self.dec(z * c_mask, g=g, f0=f0.float())
327
+ return o
328
+
onnx/model_onnx_48k.py ADDED
@@ -0,0 +1,328 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import torch
4
+ from torch import nn
5
+ from torch.nn import functional as F
6
+
7
+ import modules.attentions as attentions
8
+ import modules.commons as commons
9
+ import modules.modules as modules
10
+
11
+ from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
12
+ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
13
+ from modules.commons import init_weights, get_padding
14
+ from vdecoder.hifigan.models import Generator
15
+ from utils import f0_to_coarse
16
+
17
+ class ResidualCouplingBlock(nn.Module):
18
+ def __init__(self,
19
+ channels,
20
+ hidden_channels,
21
+ kernel_size,
22
+ dilation_rate,
23
+ n_layers,
24
+ n_flows=4,
25
+ gin_channels=0):
26
+ super().__init__()
27
+ self.channels = channels
28
+ self.hidden_channels = hidden_channels
29
+ self.kernel_size = kernel_size
30
+ self.dilation_rate = dilation_rate
31
+ self.n_layers = n_layers
32
+ self.n_flows = n_flows
33
+ self.gin_channels = gin_channels
34
+
35
+ self.flows = nn.ModuleList()
36
+ for i in range(n_flows):
37
+ self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
38
+ self.flows.append(modules.Flip())
39
+
40
+ def forward(self, x, x_mask, g=None, reverse=False):
41
+ if not reverse:
42
+ for flow in self.flows:
43
+ x, _ = flow(x, x_mask, g=g, reverse=reverse)
44
+ else:
45
+ for flow in reversed(self.flows):
46
+ x = flow(x, x_mask, g=g, reverse=reverse)
47
+ return x
48
+
49
+
50
+ class Encoder(nn.Module):
51
+ def __init__(self,
52
+ in_channels,
53
+ out_channels,
54
+ hidden_channels,
55
+ kernel_size,
56
+ dilation_rate,
57
+ n_layers,
58
+ gin_channels=0):
59
+ super().__init__()
60
+ self.in_channels = in_channels
61
+ self.out_channels = out_channels
62
+ self.hidden_channels = hidden_channels
63
+ self.kernel_size = kernel_size
64
+ self.dilation_rate = dilation_rate
65
+ self.n_layers = n_layers
66
+ self.gin_channels = gin_channels
67
+
68
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
69
+ self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
70
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
71
+
72
+ def forward(self, x, x_lengths, g=None):
73
+ # print(x.shape,x_lengths.shape)
74
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
75
+ x = self.pre(x) * x_mask
76
+ x = self.enc(x, x_mask, g=g)
77
+ stats = self.proj(x) * x_mask
78
+ m, logs = torch.split(stats, self.out_channels, dim=1)
79
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
80
+ return z, m, logs, x_mask
81
+
82
+
83
+ class TextEncoder(nn.Module):
84
+ def __init__(self,
85
+ in_channels,
86
+ out_channels,
87
+ hidden_channels,
88
+ kernel_size,
89
+ dilation_rate,
90
+ n_layers,
91
+ gin_channels=0,
92
+ filter_channels=None,
93
+ n_heads=None,
94
+ p_dropout=None):
95
+ super().__init__()
96
+ self.in_channels = in_channels
97
+ self.out_channels = out_channels
98
+ self.hidden_channels = hidden_channels
99
+ self.kernel_size = kernel_size
100
+ self.dilation_rate = dilation_rate
101
+ self.n_layers = n_layers
102
+ self.gin_channels = gin_channels
103
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
104
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
105
+ self.f0_emb = nn.Embedding(256, hidden_channels)
106
+
107
+ self.enc_ = attentions.Encoder(
108
+ hidden_channels,
109
+ filter_channels,
110
+ n_heads,
111
+ n_layers,
112
+ kernel_size,
113
+ p_dropout)
114
+
115
+ def forward(self, x, x_lengths, f0=None):
116
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
117
+ x = self.pre(x) * x_mask
118
+ x = x + self.f0_emb(f0.long()).transpose(1,2)
119
+ x = self.enc_(x * x_mask, x_mask)
120
+ stats = self.proj(x) * x_mask
121
+ m, logs = torch.split(stats, self.out_channels, dim=1)
122
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
123
+
124
+ return z, m, logs, x_mask
125
+
126
+
127
+
128
+ class DiscriminatorP(torch.nn.Module):
129
+ def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
130
+ super(DiscriminatorP, self).__init__()
131
+ self.period = period
132
+ self.use_spectral_norm = use_spectral_norm
133
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
134
+ self.convs = nn.ModuleList([
135
+ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
136
+ norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
137
+ norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
138
+ norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
139
+ norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
140
+ ])
141
+ self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
142
+
143
+ def forward(self, x):
144
+ fmap = []
145
+
146
+ # 1d to 2d
147
+ b, c, t = x.shape
148
+ if t % self.period != 0: # pad first
149
+ n_pad = self.period - (t % self.period)
150
+ x = F.pad(x, (0, n_pad), "reflect")
151
+ t = t + n_pad
152
+ x = x.view(b, c, t // self.period, self.period)
153
+
154
+ for l in self.convs:
155
+ x = l(x)
156
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
157
+ fmap.append(x)
158
+ x = self.conv_post(x)
159
+ fmap.append(x)
160
+ x = torch.flatten(x, 1, -1)
161
+
162
+ return x, fmap
163
+
164
+
165
+ class DiscriminatorS(torch.nn.Module):
166
+ def __init__(self, use_spectral_norm=False):
167
+ super(DiscriminatorS, self).__init__()
168
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
169
+ self.convs = nn.ModuleList([
170
+ norm_f(Conv1d(1, 16, 15, 1, padding=7)),
171
+ norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
172
+ norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
173
+ norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
174
+ norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
175
+ norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
176
+ ])
177
+ self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
178
+
179
+ def forward(self, x):
180
+ fmap = []
181
+
182
+ for l in self.convs:
183
+ x = l(x)
184
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
185
+ fmap.append(x)
186
+ x = self.conv_post(x)
187
+ fmap.append(x)
188
+ x = torch.flatten(x, 1, -1)
189
+
190
+ return x, fmap
191
+
192
+
193
+ class MultiPeriodDiscriminator(torch.nn.Module):
194
+ def __init__(self, use_spectral_norm=False):
195
+ super(MultiPeriodDiscriminator, self).__init__()
196
+ periods = [2,3,5,7,11]
197
+
198
+ discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
199
+ discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
200
+ self.discriminators = nn.ModuleList(discs)
201
+
202
+ def forward(self, y, y_hat):
203
+ y_d_rs = []
204
+ y_d_gs = []
205
+ fmap_rs = []
206
+ fmap_gs = []
207
+ for i, d in enumerate(self.discriminators):
208
+ y_d_r, fmap_r = d(y)
209
+ y_d_g, fmap_g = d(y_hat)
210
+ y_d_rs.append(y_d_r)
211
+ y_d_gs.append(y_d_g)
212
+ fmap_rs.append(fmap_r)
213
+ fmap_gs.append(fmap_g)
214
+
215
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
216
+
217
+
218
+ class SpeakerEncoder(torch.nn.Module):
219
+ def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
220
+ super(SpeakerEncoder, self).__init__()
221
+ self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
222
+ self.linear = nn.Linear(model_hidden_size, model_embedding_size)
223
+ self.relu = nn.ReLU()
224
+
225
+ def forward(self, mels):
226
+ self.lstm.flatten_parameters()
227
+ _, (hidden, _) = self.lstm(mels)
228
+ embeds_raw = self.relu(self.linear(hidden[-1]))
229
+ return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
230
+
231
+ def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
232
+ mel_slices = []
233
+ for i in range(0, total_frames-partial_frames, partial_hop):
234
+ mel_range = torch.arange(i, i+partial_frames)
235
+ mel_slices.append(mel_range)
236
+
237
+ return mel_slices
238
+
239
+ def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
240
+ mel_len = mel.size(1)
241
+ last_mel = mel[:,-partial_frames:]
242
+
243
+ if mel_len > partial_frames:
244
+ mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
245
+ mels = list(mel[:,s] for s in mel_slices)
246
+ mels.append(last_mel)
247
+ mels = torch.stack(tuple(mels), 0).squeeze(1)
248
+
249
+ with torch.no_grad():
250
+ partial_embeds = self(mels)
251
+ embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
252
+ #embed = embed / torch.linalg.norm(embed, 2)
253
+ else:
254
+ with torch.no_grad():
255
+ embed = self(last_mel)
256
+
257
+ return embed
258
+
259
+
260
+ class SynthesizerTrn(nn.Module):
261
+ """
262
+ Synthesizer for Training
263
+ """
264
+
265
+ def __init__(self,
266
+ spec_channels,
267
+ segment_size,
268
+ inter_channels,
269
+ hidden_channels,
270
+ filter_channels,
271
+ n_heads,
272
+ n_layers,
273
+ kernel_size,
274
+ p_dropout,
275
+ resblock,
276
+ resblock_kernel_sizes,
277
+ resblock_dilation_sizes,
278
+ upsample_rates,
279
+ upsample_initial_channel,
280
+ upsample_kernel_sizes,
281
+ gin_channels,
282
+ ssl_dim,
283
+ n_speakers,
284
+ **kwargs):
285
+
286
+ super().__init__()
287
+ self.spec_channels = spec_channels
288
+ self.inter_channels = inter_channels
289
+ self.hidden_channels = hidden_channels
290
+ self.filter_channels = filter_channels
291
+ self.n_heads = n_heads
292
+ self.n_layers = n_layers
293
+ self.kernel_size = kernel_size
294
+ self.p_dropout = p_dropout
295
+ self.resblock = resblock
296
+ self.resblock_kernel_sizes = resblock_kernel_sizes
297
+ self.resblock_dilation_sizes = resblock_dilation_sizes
298
+ self.upsample_rates = upsample_rates
299
+ self.upsample_initial_channel = upsample_initial_channel
300
+ self.upsample_kernel_sizes = upsample_kernel_sizes
301
+ self.segment_size = segment_size
302
+ self.gin_channels = gin_channels
303
+ self.ssl_dim = ssl_dim
304
+ self.emb_g = nn.Embedding(n_speakers, gin_channels)
305
+
306
+ self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
307
+ hps = {
308
+ "sampling_rate": 48000,
309
+ "inter_channels": 192,
310
+ "resblock": "1",
311
+ "resblock_kernel_sizes": [3, 7, 11],
312
+ "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
313
+ "upsample_rates": [10, 8, 2, 2],
314
+ "upsample_initial_channel": 512,
315
+ "upsample_kernel_sizes": [16, 16, 4, 4],
316
+ "gin_channels": 256,
317
+ }
318
+ self.dec = Generator(h=hps)
319
+ self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
320
+ self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
321
+
322
+ def forward(self, c, c_lengths, f0, g=None):
323
+ g = self.emb_g(g.unsqueeze(0)).transpose(1,2)
324
+ z_p, m_p, logs_p, c_mask = self.enc_p_(c.transpose(1,2), c_lengths, f0=f0_to_coarse(f0))
325
+ z = self.flow(z_p, c_mask, g=g, reverse=True)
326
+ o = self.dec(z * c_mask, g=g, f0=f0.float())
327
+ return o
328
+
onnx/onnx_export.py ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import time
3
+ import numpy as np
4
+ import onnx
5
+ from onnxsim import simplify
6
+ import onnxruntime as ort
7
+ import onnxoptimizer
8
+ import torch
9
+ from model_onnx import SynthesizerTrn
10
+ import utils
11
+ from hubert import hubert_model_onnx
12
+
13
+ def main(HubertExport,NetExport):
14
+
15
+ path = "NyaruTaffy"
16
+
17
+ if(HubertExport):
18
+ device = torch.device("cuda")
19
+ hubert_soft = utils.get_hubert_model()
20
+ test_input = torch.rand(1, 1, 16000)
21
+ input_names = ["source"]
22
+ output_names = ["embed"]
23
+ torch.onnx.export(hubert_soft.to(device),
24
+ test_input.to(device),
25
+ "hubert3.0.onnx",
26
+ dynamic_axes={
27
+ "source": {
28
+ 2: "sample_length"
29
+ }
30
+ },
31
+ verbose=False,
32
+ opset_version=13,
33
+ input_names=input_names,
34
+ output_names=output_names)
35
+ if(NetExport):
36
+ device = torch.device("cuda")
37
+ hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
38
+ SVCVITS = SynthesizerTrn(
39
+ hps.data.filter_length // 2 + 1,
40
+ hps.train.segment_size // hps.data.hop_length,
41
+ **hps.model)
42
+ _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
43
+ _ = SVCVITS.eval().to(device)
44
+ for i in SVCVITS.parameters():
45
+ i.requires_grad = False
46
+ test_hidden_unit = torch.rand(1, 50, 256)
47
+ test_lengths = torch.LongTensor([50])
48
+ test_pitch = torch.rand(1, 50)
49
+ test_sid = torch.LongTensor([0])
50
+ input_names = ["hidden_unit", "lengths", "pitch", "sid"]
51
+ output_names = ["audio", ]
52
+ SVCVITS.eval()
53
+ torch.onnx.export(SVCVITS,
54
+ (
55
+ test_hidden_unit.to(device),
56
+ test_lengths.to(device),
57
+ test_pitch.to(device),
58
+ test_sid.to(device)
59
+ ),
60
+ f"checkpoints/{path}/model.onnx",
61
+ dynamic_axes={
62
+ "hidden_unit": [0, 1],
63
+ "pitch": [1]
64
+ },
65
+ do_constant_folding=False,
66
+ opset_version=16,
67
+ verbose=False,
68
+ input_names=input_names,
69
+ output_names=output_names)
70
+
71
+
72
+ if __name__ == '__main__':
73
+ main(False,True)
onnx/onnx_export_48k.py ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import time
3
+ import numpy as np
4
+ import onnx
5
+ from onnxsim import simplify
6
+ import onnxruntime as ort
7
+ import onnxoptimizer
8
+ import torch
9
+ from model_onnx_48k import SynthesizerTrn
10
+ import utils
11
+ from hubert import hubert_model_onnx
12
+
13
+ def main(HubertExport,NetExport):
14
+
15
+ path = "NyaruTaffy"
16
+
17
+ if(HubertExport):
18
+ device = torch.device("cuda")
19
+ hubert_soft = hubert_model_onnx.hubert_soft("hubert/model.pt")
20
+ test_input = torch.rand(1, 1, 16000)
21
+ input_names = ["source"]
22
+ output_names = ["embed"]
23
+ torch.onnx.export(hubert_soft.to(device),
24
+ test_input.to(device),
25
+ "hubert3.0.onnx",
26
+ dynamic_axes={
27
+ "source": {
28
+ 2: "sample_length"
29
+ }
30
+ },
31
+ verbose=False,
32
+ opset_version=13,
33
+ input_names=input_names,
34
+ output_names=output_names)
35
+ if(NetExport):
36
+ device = torch.device("cuda")
37
+ hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
38
+ SVCVITS = SynthesizerTrn(
39
+ hps.data.filter_length // 2 + 1,
40
+ hps.train.segment_size // hps.data.hop_length,
41
+ **hps.model)
42
+ _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
43
+ _ = SVCVITS.eval().to(device)
44
+ for i in SVCVITS.parameters():
45
+ i.requires_grad = False
46
+ test_hidden_unit = torch.rand(1, 50, 256)
47
+ test_lengths = torch.LongTensor([50])
48
+ test_pitch = torch.rand(1, 50)
49
+ test_sid = torch.LongTensor([0])
50
+ input_names = ["hidden_unit", "lengths", "pitch", "sid"]
51
+ output_names = ["audio", ]
52
+ SVCVITS.eval()
53
+ torch.onnx.export(SVCVITS,
54
+ (
55
+ test_hidden_unit.to(device),
56
+ test_lengths.to(device),
57
+ test_pitch.to(device),
58
+ test_sid.to(device)
59
+ ),
60
+ f"checkpoints/{path}/model.onnx",
61
+ dynamic_axes={
62
+ "hidden_unit": [0, 1],
63
+ "pitch": [1]
64
+ },
65
+ do_constant_folding=False,
66
+ opset_version=16,
67
+ verbose=False,
68
+ input_names=input_names,
69
+ output_names=output_names)
70
+
71
+
72
+ if __name__ == '__main__':
73
+ main(False,True)
preprocess_flist_config.py ADDED
@@ -0,0 +1,75 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import argparse
3
+ import re
4
+
5
+ from tqdm import tqdm
6
+ from random import shuffle
7
+ import json
8
+ import wave
9
+
10
+ config_template = json.load(open("configs_template/config_template.json"))
11
+
12
+ pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
13
+
14
+ def get_wav_duration(file_path):
15
+ with wave.open(file_path, 'rb') as wav_file:
16
+ # 获取音频帧数
17
+ n_frames = wav_file.getnframes()
18
+ # 获取采样率
19
+ framerate = wav_file.getframerate()
20
+ # 计算时长(秒)
21
+ duration = n_frames / float(framerate)
22
+ return duration
23
+
24
+ if __name__ == "__main__":
25
+ parser = argparse.ArgumentParser()
26
+ parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
27
+ parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
28
+ parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
29
+ args = parser.parse_args()
30
+
31
+ train = []
32
+ val = []
33
+ idx = 0
34
+ spk_dict = {}
35
+ spk_id = 0
36
+ for speaker in tqdm(os.listdir(args.source_dir)):
37
+ spk_dict[speaker] = spk_id
38
+ spk_id += 1
39
+ wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
40
+ new_wavs = []
41
+ for file in wavs:
42
+ if not file.endswith("wav"):
43
+ continue
44
+ #if not pattern.match(file):
45
+ # print(f"warning:文件名{file}中包含非字母数字下划线,可能会导致错误。(也可能不会)")
46
+ if get_wav_duration(file) < 0.3:
47
+ print("skip too short audio:", file)
48
+ continue
49
+ new_wavs.append(file)
50
+ wavs = new_wavs
51
+ shuffle(wavs)
52
+ train += wavs[2:]
53
+ val += wavs[:2]
54
+
55
+ shuffle(train)
56
+ shuffle(val)
57
+
58
+ print("Writing", args.train_list)
59
+ with open(args.train_list, "w") as f:
60
+ for fname in tqdm(train):
61
+ wavpath = fname
62
+ f.write(wavpath + "\n")
63
+
64
+ print("Writing", args.val_list)
65
+ with open(args.val_list, "w") as f:
66
+ for fname in tqdm(val):
67
+ wavpath = fname
68
+ f.write(wavpath + "\n")
69
+
70
+ config_template["spk"] = spk_dict
71
+ config_template["model"]["n_speakers"] = spk_id
72
+
73
+ print("Writing configs/config.json")
74
+ with open("configs/config.json", "w") as f:
75
+ json.dump(config_template, f, indent=2)
preprocess_hubert_f0.py ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import multiprocessing
3
+ import os
4
+ import argparse
5
+ from random import shuffle
6
+
7
+ import torch
8
+ from glob import glob
9
+ from tqdm import tqdm
10
+ from modules.mel_processing import spectrogram_torch
11
+
12
+ import utils
13
+ import logging
14
+
15
+ logging.getLogger("numba").setLevel(logging.WARNING)
16
+ import librosa
17
+ import numpy as np
18
+
19
+ hps = utils.get_hparams_from_file("configs/config.json")
20
+ sampling_rate = hps.data.sampling_rate
21
+ hop_length = hps.data.hop_length
22
+
23
+
24
+ def process_one(filename, hmodel):
25
+ # print(filename)
26
+ wav, sr = librosa.load(filename, sr=sampling_rate)
27
+ soft_path = filename + ".soft.pt"
28
+ if not os.path.exists(soft_path):
29
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
30
+ wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
31
+ wav16k = torch.from_numpy(wav16k).to(device)
32
+ c = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
33
+ torch.save(c.cpu(), soft_path)
34
+
35
+ f0_path = filename + ".f0.npy"
36
+ if not os.path.exists(f0_path):
37
+ f0 = utils.compute_f0_dio(
38
+ wav, sampling_rate=sampling_rate, hop_length=hop_length
39
+ )
40
+ np.save(f0_path, f0)
41
+
42
+ spec_path = filename.replace(".wav", ".spec.pt")
43
+ if not os.path.exists(spec_path):
44
+ # Process spectrogram
45
+ # The following code can't be replaced by torch.FloatTensor(wav)
46
+ # because load_wav_to_torch return a tensor that need to be normalized
47
+
48
+ audio, sr = utils.load_wav_to_torch(filename)
49
+ if sr != hps.data.sampling_rate:
50
+ raise ValueError(
51
+ "{} SR doesn't match target {} SR".format(
52
+ sr, hps.data.sampling_rate
53
+ )
54
+ )
55
+
56
+ audio_norm = audio / hps.data.max_wav_value
57
+ audio_norm = audio_norm.unsqueeze(0)
58
+
59
+ spec = spectrogram_torch(
60
+ audio_norm,
61
+ hps.data.filter_length,
62
+ hps.data.sampling_rate,
63
+ hps.data.hop_length,
64
+ hps.data.win_length,
65
+ center=False,
66
+ )
67
+ spec = torch.squeeze(spec, 0)
68
+ torch.save(spec, spec_path)
69
+
70
+
71
+ def process_batch(filenames):
72
+ print("Loading hubert for content...")
73
+ device = "cuda" if torch.cuda.is_available() else "cpu"
74
+ hmodel = utils.get_hubert_model().to(device)
75
+ print("Loaded hubert.")
76
+ for filename in tqdm(filenames):
77
+ process_one(filename, hmodel)
78
+
79
+
80
+ if __name__ == "__main__":
81
+ parser = argparse.ArgumentParser()
82
+ parser.add_argument(
83
+ "--in_dir", type=str, default="dataset/44k", help="path to input dir"
84
+ )
85
+
86
+ args = parser.parse_args()
87
+ filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True) # [:10]
88
+ shuffle(filenames)
89
+ multiprocessing.set_start_method("spawn", force=True)
90
+
91
+ num_processes = 1
92
+ chunk_size = int(math.ceil(len(filenames) / num_processes))
93
+ chunks = [
94
+ filenames[i : i + chunk_size] for i in range(0, len(filenames), chunk_size)
95
+ ]
96
+ print([len(c) for c in chunks])
97
+ processes = [
98
+ multiprocessing.Process(target=process_batch, args=(chunk,)) for chunk in chunks
99
+ ]
100
+ for p in processes:
101
+ p.start()
requirements.txt ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Flask
2
+ Flask_Cors
3
+ gradio>=3.7.0
4
+ numpy==1.23.0
5
+ pyworld==0.2.5
6
+ scipy==1.10.0
7
+ SoundFile==0.12.1
8
+ torch==1.13.1
9
+ torchaudio==0.13.1
10
+ torchcrepe
11
+ tqdm
12
+ scikit-maad
13
+ praat-parselmouth
14
+ onnx
15
+ onnxsim
16
+ onnxoptimizer
17
+ fairseq==0.12.2
18
+ librosa==0.9.1
19
+ tensorboard
20
+ tensorboardX
21
+ edge_tts
resample.py ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import argparse
3
+ import librosa
4
+ import numpy as np
5
+ from multiprocessing import Pool, cpu_count
6
+ from scipy.io import wavfile
7
+ from tqdm import tqdm
8
+
9
+
10
+ def process(item):
11
+ spkdir, wav_name, args = item
12
+ # speaker 's5', 'p280', 'p315' are excluded,
13
+ speaker = spkdir.replace("\\", "/").split("/")[-1]
14
+ wav_path = os.path.join(args.in_dir, speaker, wav_name)
15
+ if os.path.exists(wav_path) and '.wav' in wav_path:
16
+ os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
17
+ wav, sr = librosa.load(wav_path, sr=None)
18
+ wav, _ = librosa.effects.trim(wav, top_db=20)
19
+ peak = np.abs(wav).max()
20
+ if peak > 1.0:
21
+ wav = 0.98 * wav / peak
22
+ wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2)
23
+ wav2 /= max(wav2.max(), -wav2.min())
24
+ save_name = wav_name
25
+ save_path2 = os.path.join(args.out_dir2, speaker, save_name)
26
+ wavfile.write(
27
+ save_path2,
28
+ args.sr2,
29
+ (wav2 * np.iinfo(np.int16).max).astype(np.int16)
30
+ )
31
+
32
+
33
+
34
+ if __name__ == "__main__":
35
+ parser = argparse.ArgumentParser()
36
+ parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
37
+ parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
38
+ parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
39
+ args = parser.parse_args()
40
+ processs = cpu_count()-2 if cpu_count() >4 else 1
41
+ pool = Pool(processes=processs)
42
+
43
+ for speaker in os.listdir(args.in_dir):
44
+ spk_dir = os.path.join(args.in_dir, speaker)
45
+ if os.path.isdir(spk_dir):
46
+ print(spk_dir)
47
+ for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
48
+ pass
spec_gen.py ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from data_utils import TextAudioSpeakerLoader
2
+ import json
3
+ from tqdm import tqdm
4
+
5
+ from utils import HParams
6
+
7
+ config_path = 'configs/config.json'
8
+ with open(config_path, "r") as f:
9
+ data = f.read()
10
+ config = json.loads(data)
11
+ hps = HParams(**config)
12
+
13
+ train_dataset = TextAudioSpeakerLoader("filelists/train.txt", hps)
14
+ test_dataset = TextAudioSpeakerLoader("filelists/test.txt", hps)
15
+ eval_dataset = TextAudioSpeakerLoader("filelists/val.txt", hps)
16
+
17
+ for _ in tqdm(train_dataset):
18
+ pass
19
+ for _ in tqdm(eval_dataset):
20
+ pass
21
+ for _ in tqdm(test_dataset):
22
+ pass
train.py ADDED
@@ -0,0 +1,330 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ import multiprocessing
3
+ import time
4
+
5
+ logging.getLogger('matplotlib').setLevel(logging.WARNING)
6
+ logging.getLogger('numba').setLevel(logging.WARNING)
7
+
8
+ import os
9
+ import json
10
+ import argparse
11
+ import itertools
12
+ import math
13
+ import torch
14
+ from torch import nn, optim
15
+ from torch.nn import functional as F
16
+ from torch.utils.data import DataLoader
17
+ from torch.utils.tensorboard import SummaryWriter
18
+ import torch.multiprocessing as mp
19
+ import torch.distributed as dist
20
+ from torch.nn.parallel import DistributedDataParallel as DDP
21
+ from torch.cuda.amp import autocast, GradScaler
22
+
23
+ import modules.commons as commons
24
+ import utils
25
+ from data_utils import TextAudioSpeakerLoader, TextAudioCollate
26
+ from models import (
27
+ SynthesizerTrn,
28
+ MultiPeriodDiscriminator,
29
+ )
30
+ from modules.losses import (
31
+ kl_loss,
32
+ generator_loss, discriminator_loss, feature_loss
33
+ )
34
+
35
+ from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
36
+
37
+ torch.backends.cudnn.benchmark = True
38
+ global_step = 0
39
+ start_time = time.time()
40
+
41
+ # os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'
42
+
43
+
44
+ def main():
45
+ """Assume Single Node Multi GPUs Training Only"""
46
+ assert torch.cuda.is_available(), "CPU training is not allowed."
47
+ hps = utils.get_hparams()
48
+
49
+ n_gpus = torch.cuda.device_count()
50
+ os.environ['MASTER_ADDR'] = 'localhost'
51
+ os.environ['MASTER_PORT'] = hps.train.port
52
+
53
+ mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
54
+
55
+
56
+ def run(rank, n_gpus, hps):
57
+ global global_step
58
+ if rank == 0:
59
+ logger = utils.get_logger(hps.model_dir)
60
+ logger.info(hps)
61
+ utils.check_git_hash(hps.model_dir)
62
+ writer = SummaryWriter(log_dir=hps.model_dir)
63
+ writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
64
+
65
+ # for pytorch on win, backend use gloo
66
+ dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
67
+ torch.manual_seed(hps.train.seed)
68
+ torch.cuda.set_device(rank)
69
+ collate_fn = TextAudioCollate()
70
+ all_in_mem = hps.train.all_in_mem # If you have enough memory, turn on this option to avoid disk IO and speed up training.
71
+ train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem)
72
+ num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count()
73
+ if all_in_mem:
74
+ num_workers = 0
75
+ train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True,
76
+ batch_size=hps.train.batch_size, collate_fn=collate_fn)
77
+ if rank == 0:
78
+ eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem)
79
+ eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
80
+ batch_size=1, pin_memory=False,
81
+ drop_last=False, collate_fn=collate_fn)
82
+
83
+ net_g = SynthesizerTrn(
84
+ hps.data.filter_length // 2 + 1,
85
+ hps.train.segment_size // hps.data.hop_length,
86
+ **hps.model).cuda(rank)
87
+ net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
88
+ optim_g = torch.optim.AdamW(
89
+ net_g.parameters(),
90
+ hps.train.learning_rate,
91
+ betas=hps.train.betas,
92
+ eps=hps.train.eps)
93
+ optim_d = torch.optim.AdamW(
94
+ net_d.parameters(),
95
+ hps.train.learning_rate,
96
+ betas=hps.train.betas,
97
+ eps=hps.train.eps)
98
+ net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True)
99
+ net_d = DDP(net_d, device_ids=[rank])
100
+
101
+ skip_optimizer = False
102
+ try:
103
+ _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
104
+ optim_g, skip_optimizer)
105
+ _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
106
+ optim_d, skip_optimizer)
107
+ epoch_str = max(epoch_str, 1)
108
+ name=utils.latest_checkpoint_path(hps.model_dir, "D_*.pth")
109
+ global_step=int(name[name.rfind("_")+1:name.rfind(".")])+1
110
+ #global_step = (epoch_str - 1) * len(train_loader)
111
+ except:
112
+ print("load old checkpoint failed...")
113
+ epoch_str = 1
114
+ global_step = 0
115
+ if skip_optimizer:
116
+ epoch_str = 1
117
+ global_step = 0
118
+
119
+ warmup_epoch = hps.train.warmup_epochs
120
+ scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
121
+ scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
122
+
123
+ scaler = GradScaler(enabled=hps.train.fp16_run)
124
+
125
+ for epoch in range(epoch_str, hps.train.epochs + 1):
126
+ # update learning rate
127
+ if epoch > 1:
128
+ scheduler_g.step()
129
+ scheduler_d.step()
130
+ # set up warm-up learning rate
131
+ if epoch <= warmup_epoch:
132
+ for param_group in optim_g.param_groups:
133
+ param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
134
+ for param_group in optim_d.param_groups:
135
+ param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
136
+ # training
137
+ if rank == 0:
138
+ train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
139
+ [train_loader, eval_loader], logger, [writer, writer_eval])
140
+ else:
141
+ train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
142
+ [train_loader, None], None, None)
143
+
144
+
145
+ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
146
+ net_g, net_d = nets
147
+ optim_g, optim_d = optims
148
+ scheduler_g, scheduler_d = schedulers
149
+ train_loader, eval_loader = loaders
150
+ if writers is not None:
151
+ writer, writer_eval = writers
152
+
153
+ # train_loader.batch_sampler.set_epoch(epoch)
154
+ global global_step
155
+
156
+ net_g.train()
157
+ net_d.train()
158
+ for batch_idx, items in enumerate(train_loader):
159
+ c, f0, spec, y, spk, lengths, uv = items
160
+ g = spk.cuda(rank, non_blocking=True)
161
+ spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
162
+ c = c.cuda(rank, non_blocking=True)
163
+ f0 = f0.cuda(rank, non_blocking=True)
164
+ uv = uv.cuda(rank, non_blocking=True)
165
+ lengths = lengths.cuda(rank, non_blocking=True)
166
+ mel = spec_to_mel_torch(
167
+ spec,
168
+ hps.data.filter_length,
169
+ hps.data.n_mel_channels,
170
+ hps.data.sampling_rate,
171
+ hps.data.mel_fmin,
172
+ hps.data.mel_fmax)
173
+
174
+ with autocast(enabled=hps.train.fp16_run):
175
+ y_hat, ids_slice, z_mask, \
176
+ (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
177
+ spec_lengths=lengths)
178
+
179
+ y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
180
+ y_hat_mel = mel_spectrogram_torch(
181
+ y_hat.squeeze(1),
182
+ hps.data.filter_length,
183
+ hps.data.n_mel_channels,
184
+ hps.data.sampling_rate,
185
+ hps.data.hop_length,
186
+ hps.data.win_length,
187
+ hps.data.mel_fmin,
188
+ hps.data.mel_fmax
189
+ )
190
+ y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
191
+
192
+ # Discriminator
193
+ y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
194
+
195
+ with autocast(enabled=False):
196
+ loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
197
+ loss_disc_all = loss_disc
198
+
199
+ optim_d.zero_grad()
200
+ scaler.scale(loss_disc_all).backward()
201
+ scaler.unscale_(optim_d)
202
+ grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
203
+ scaler.step(optim_d)
204
+
205
+ with autocast(enabled=hps.train.fp16_run):
206
+ # Generator
207
+ y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
208
+ with autocast(enabled=False):
209
+ loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
210
+ loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
211
+ loss_fm = feature_loss(fmap_r, fmap_g)
212
+ loss_gen, losses_gen = generator_loss(y_d_hat_g)
213
+ loss_lf0 = F.mse_loss(pred_lf0, lf0)
214
+ loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
215
+ optim_g.zero_grad()
216
+ scaler.scale(loss_gen_all).backward()
217
+ scaler.unscale_(optim_g)
218
+ grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
219
+ scaler.step(optim_g)
220
+ scaler.update()
221
+
222
+ if rank == 0:
223
+ if global_step % hps.train.log_interval == 0:
224
+ lr = optim_g.param_groups[0]['lr']
225
+ losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
226
+ reference_loss=0
227
+ for i in losses:
228
+ reference_loss += i
229
+ logger.info('Train Epoch: {} [{:.0f}%]'.format(
230
+ epoch,
231
+ 100. * batch_idx / len(train_loader)))
232
+ logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}")
233
+
234
+ scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
235
+ "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
236
+ scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl,
237
+ "loss/g/lf0": loss_lf0})
238
+
239
+ # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
240
+ # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
241
+ # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
242
+ image_dict = {
243
+ "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
244
+ "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
245
+ "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
246
+ "all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
247
+ pred_lf0[0, 0, :].detach().cpu().numpy()),
248
+ "all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
249
+ norm_lf0[0, 0, :].detach().cpu().numpy())
250
+ }
251
+
252
+ utils.summarize(
253
+ writer=writer,
254
+ global_step=global_step,
255
+ images=image_dict,
256
+ scalars=scalar_dict
257
+ )
258
+
259
+ if global_step % hps.train.eval_interval == 0:
260
+ evaluate(hps, net_g, eval_loader, writer_eval)
261
+ utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
262
+ os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
263
+ utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
264
+ os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
265
+ keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
266
+ if keep_ckpts > 0:
267
+ utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
268
+
269
+ global_step += 1
270
+
271
+ if rank == 0:
272
+ global start_time
273
+ now = time.time()
274
+ durtaion = format(now - start_time, '.2f')
275
+ logger.info(f'====> Epoch: {epoch}, cost {durtaion} s')
276
+ start_time = now
277
+
278
+
279
+ def evaluate(hps, generator, eval_loader, writer_eval):
280
+ generator.eval()
281
+ image_dict = {}
282
+ audio_dict = {}
283
+ with torch.no_grad():
284
+ for batch_idx, items in enumerate(eval_loader):
285
+ c, f0, spec, y, spk, _, uv = items
286
+ g = spk[:1].cuda(0)
287
+ spec, y = spec[:1].cuda(0), y[:1].cuda(0)
288
+ c = c[:1].cuda(0)
289
+ f0 = f0[:1].cuda(0)
290
+ uv= uv[:1].cuda(0)
291
+ mel = spec_to_mel_torch(
292
+ spec,
293
+ hps.data.filter_length,
294
+ hps.data.n_mel_channels,
295
+ hps.data.sampling_rate,
296
+ hps.data.mel_fmin,
297
+ hps.data.mel_fmax)
298
+ y_hat = generator.module.infer(c, f0, uv, g=g)
299
+
300
+ y_hat_mel = mel_spectrogram_torch(
301
+ y_hat.squeeze(1).float(),
302
+ hps.data.filter_length,
303
+ hps.data.n_mel_channels,
304
+ hps.data.sampling_rate,
305
+ hps.data.hop_length,
306
+ hps.data.win_length,
307
+ hps.data.mel_fmin,
308
+ hps.data.mel_fmax
309
+ )
310
+
311
+ audio_dict.update({
312
+ f"gen/audio_{batch_idx}": y_hat[0],
313
+ f"gt/audio_{batch_idx}": y[0]
314
+ })
315
+ image_dict.update({
316
+ f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
317
+ "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
318
+ })
319
+ utils.summarize(
320
+ writer=writer_eval,
321
+ global_step=global_step,
322
+ images=image_dict,
323
+ audios=audio_dict,
324
+ audio_sampling_rate=hps.data.sampling_rate
325
+ )
326
+ generator.train()
327
+
328
+
329
+ if __name__ == "__main__":
330
+ main()
utils.py ADDED
@@ -0,0 +1,542 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import glob
3
+ import re
4
+ import sys
5
+ import argparse
6
+ import logging
7
+ import json
8
+ import subprocess
9
+ import warnings
10
+ import random
11
+ import functools
12
+
13
+ import librosa
14
+ import numpy as np
15
+ from scipy.io.wavfile import read
16
+ import torch
17
+ from torch.nn import functional as F
18
+ from modules.commons import sequence_mask
19
+ from hubert import hubert_model
20
+
21
+ MATPLOTLIB_FLAG = False
22
+
23
+ logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
24
+ logger = logging
25
+
26
+ f0_bin = 256
27
+ f0_max = 1100.0
28
+ f0_min = 50.0
29
+ f0_mel_min = 1127 * np.log(1 + f0_min / 700)
30
+ f0_mel_max = 1127 * np.log(1 + f0_max / 700)
31
+
32
+
33
+ # def normalize_f0(f0, random_scale=True):
34
+ # f0_norm = f0.clone() # create a copy of the input Tensor
35
+ # batch_size, _, frame_length = f0_norm.shape
36
+ # for i in range(batch_size):
37
+ # means = torch.mean(f0_norm[i, 0, :])
38
+ # if random_scale:
39
+ # factor = random.uniform(0.8, 1.2)
40
+ # else:
41
+ # factor = 1
42
+ # f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
43
+ # return f0_norm
44
+ # def normalize_f0(f0, random_scale=True):
45
+ # means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
46
+ # if random_scale:
47
+ # factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device)
48
+ # else:
49
+ # factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
50
+ # f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
51
+ # return f0_norm
52
+
53
+ def deprecated(func):
54
+ """This is a decorator which can be used to mark functions
55
+ as deprecated. It will result in a warning being emitted
56
+ when the function is used."""
57
+ @functools.wraps(func)
58
+ def new_func(*args, **kwargs):
59
+ warnings.simplefilter('always', DeprecationWarning) # turn off filter
60
+ warnings.warn("Call to deprecated function {}.".format(func.__name__),
61
+ category=DeprecationWarning,
62
+ stacklevel=2)
63
+ warnings.simplefilter('default', DeprecationWarning) # reset filter
64
+ return func(*args, **kwargs)
65
+ return new_func
66
+
67
+ def normalize_f0(f0, x_mask, uv, random_scale=True):
68
+ # calculate means based on x_mask
69
+ uv_sum = torch.sum(uv, dim=1, keepdim=True)
70
+ uv_sum[uv_sum == 0] = 9999
71
+ means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
72
+
73
+ if random_scale:
74
+ factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
75
+ else:
76
+ factor = torch.ones(f0.shape[0], 1).to(f0.device)
77
+ # normalize f0 based on means and factor
78
+ f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
79
+ if torch.isnan(f0_norm).any():
80
+ exit(0)
81
+ return f0_norm * x_mask
82
+
83
+ def compute_f0_uv_torchcrepe(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512,device=None,cr_threshold=0.05):
84
+ from modules.crepe import CrepePitchExtractor
85
+ x = wav_numpy
86
+ if p_len is None:
87
+ p_len = x.shape[0]//hop_length
88
+ else:
89
+ assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
90
+
91
+ f0_min = 50
92
+ f0_max = 1100
93
+ F0Creper = CrepePitchExtractor(hop_length=hop_length,f0_min=f0_min,f0_max=f0_max,device=device,threshold=cr_threshold)
94
+ f0,uv = F0Creper(x[None,:].float(),sampling_rate,pad_to=p_len)
95
+ return f0,uv
96
+
97
+ def plot_data_to_numpy(x, y):
98
+ global MATPLOTLIB_FLAG
99
+ if not MATPLOTLIB_FLAG:
100
+ import matplotlib
101
+ matplotlib.use("Agg")
102
+ MATPLOTLIB_FLAG = True
103
+ mpl_logger = logging.getLogger('matplotlib')
104
+ mpl_logger.setLevel(logging.WARNING)
105
+ import matplotlib.pylab as plt
106
+ import numpy as np
107
+
108
+ fig, ax = plt.subplots(figsize=(10, 2))
109
+ plt.plot(x)
110
+ plt.plot(y)
111
+ plt.tight_layout()
112
+
113
+ fig.canvas.draw()
114
+ data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
115
+ data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
116
+ plt.close()
117
+ return data
118
+
119
+
120
+
121
+ def interpolate_f0(f0):
122
+
123
+ data = np.reshape(f0, (f0.size, 1))
124
+
125
+ vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
126
+ vuv_vector[data > 0.0] = 1.0
127
+ vuv_vector[data <= 0.0] = 0.0
128
+
129
+ ip_data = data
130
+
131
+ frame_number = data.size
132
+ last_value = 0.0
133
+ for i in range(frame_number):
134
+ if data[i] <= 0.0:
135
+ j = i + 1
136
+ for j in range(i + 1, frame_number):
137
+ if data[j] > 0.0:
138
+ break
139
+ if j < frame_number - 1:
140
+ if last_value > 0.0:
141
+ step = (data[j] - data[i - 1]) / float(j - i)
142
+ for k in range(i, j):
143
+ ip_data[k] = data[i - 1] + step * (k - i + 1)
144
+ else:
145
+ for k in range(i, j):
146
+ ip_data[k] = data[j]
147
+ else:
148
+ for k in range(i, frame_number):
149
+ ip_data[k] = last_value
150
+ else:
151
+ ip_data[i] = data[i] # this may not be necessary
152
+ last_value = data[i]
153
+
154
+ return ip_data[:,0], vuv_vector[:,0]
155
+
156
+
157
+ def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
158
+ import parselmouth
159
+ x = wav_numpy
160
+ if p_len is None:
161
+ p_len = x.shape[0]//hop_length
162
+ else:
163
+ assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
164
+ time_step = hop_length / sampling_rate * 1000
165
+ f0_min = 50
166
+ f0_max = 1100
167
+ f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
168
+ time_step=time_step / 1000, voicing_threshold=0.6,
169
+ pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
170
+
171
+ pad_size=(p_len - len(f0) + 1) // 2
172
+ if(pad_size>0 or p_len - len(f0) - pad_size>0):
173
+ f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
174
+ return f0
175
+
176
+ def resize_f0(x, target_len):
177
+ source = np.array(x)
178
+ source[source<0.001] = np.nan
179
+ target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
180
+ res = np.nan_to_num(target)
181
+ return res
182
+
183
+ def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
184
+ import pyworld
185
+ if p_len is None:
186
+ p_len = wav_numpy.shape[0]//hop_length
187
+ f0, t = pyworld.dio(
188
+ wav_numpy.astype(np.double),
189
+ fs=sampling_rate,
190
+ f0_ceil=800,
191
+ frame_period=1000 * hop_length / sampling_rate,
192
+ )
193
+ f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
194
+ for index, pitch in enumerate(f0):
195
+ f0[index] = round(pitch, 1)
196
+ return resize_f0(f0, p_len)
197
+
198
+ def f0_to_coarse(f0):
199
+ is_torch = isinstance(f0, torch.Tensor)
200
+ f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
201
+ f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
202
+
203
+ f0_mel[f0_mel <= 1] = 1
204
+ f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
205
+ f0_coarse = (f0_mel + 0.5).int() if is_torch else np.rint(f0_mel).astype(np.int)
206
+ assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
207
+ return f0_coarse
208
+
209
+
210
+ def get_hubert_model():
211
+ vec_path = "hubert/checkpoint_best_legacy_500.pt"
212
+ print("load model(s) from {}".format(vec_path))
213
+ from fairseq import checkpoint_utils
214
+ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
215
+ [vec_path],
216
+ suffix="",
217
+ )
218
+ model = models[0]
219
+ model.eval()
220
+ return model
221
+
222
+ def get_hubert_content(hmodel, wav_16k_tensor):
223
+ feats = wav_16k_tensor
224
+ if feats.dim() == 2: # double channels
225
+ feats = feats.mean(-1)
226
+ assert feats.dim() == 1, feats.dim()
227
+ feats = feats.view(1, -1)
228
+ padding_mask = torch.BoolTensor(feats.shape).fill_(False)
229
+ inputs = {
230
+ "source": feats.to(wav_16k_tensor.device),
231
+ "padding_mask": padding_mask.to(wav_16k_tensor.device),
232
+ "output_layer": 9, # layer 9
233
+ }
234
+ with torch.no_grad():
235
+ logits = hmodel.extract_features(**inputs)
236
+ feats = hmodel.final_proj(logits[0])
237
+ return feats.transpose(1, 2)
238
+
239
+
240
+ def get_content(cmodel, y):
241
+ with torch.no_grad():
242
+ c = cmodel.extract_features(y.squeeze(1))[0]
243
+ c = c.transpose(1, 2)
244
+ return c
245
+
246
+
247
+
248
+ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
249
+ assert os.path.isfile(checkpoint_path)
250
+ checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
251
+ iteration = checkpoint_dict['iteration']
252
+ learning_rate = checkpoint_dict['learning_rate']
253
+ if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
254
+ optimizer.load_state_dict(checkpoint_dict['optimizer'])
255
+ saved_state_dict = checkpoint_dict['model']
256
+ if hasattr(model, 'module'):
257
+ state_dict = model.module.state_dict()
258
+ else:
259
+ state_dict = model.state_dict()
260
+ new_state_dict = {}
261
+ for k, v in state_dict.items():
262
+ try:
263
+ # assert "dec" in k or "disc" in k
264
+ # print("load", k)
265
+ new_state_dict[k] = saved_state_dict[k]
266
+ assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
267
+ except:
268
+ print("error, %s is not in the checkpoint" % k)
269
+ logger.info("%s is not in the checkpoint" % k)
270
+ new_state_dict[k] = v
271
+ if hasattr(model, 'module'):
272
+ model.module.load_state_dict(new_state_dict)
273
+ else:
274
+ model.load_state_dict(new_state_dict)
275
+ print("load ")
276
+ logger.info("Loaded checkpoint '{}' (iteration {})".format(
277
+ checkpoint_path, iteration))
278
+ return model, optimizer, learning_rate, iteration
279
+
280
+
281
+ def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
282
+ logger.info("Saving model and optimizer state at iteration {} to {}".format(
283
+ iteration, checkpoint_path))
284
+ if hasattr(model, 'module'):
285
+ state_dict = model.module.state_dict()
286
+ else:
287
+ state_dict = model.state_dict()
288
+ torch.save({'model': state_dict,
289
+ 'iteration': iteration,
290
+ 'optimizer': optimizer.state_dict(),
291
+ 'learning_rate': learning_rate}, checkpoint_path)
292
+
293
+ def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
294
+ """Freeing up space by deleting saved ckpts
295
+
296
+ Arguments:
297
+ path_to_models -- Path to the model directory
298
+ n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
299
+ sort_by_time -- True -> chronologically delete ckpts
300
+ False -> lexicographically delete ckpts
301
+ """
302
+ ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
303
+ name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
304
+ time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
305
+ sort_key = time_key if sort_by_time else name_key
306
+ x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
307
+ to_del = [os.path.join(path_to_models, fn) for fn in
308
+ (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
309
+ del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
310
+ del_routine = lambda x: [os.remove(x), del_info(x)]
311
+ rs = [del_routine(fn) for fn in to_del]
312
+
313
+ def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
314
+ for k, v in scalars.items():
315
+ writer.add_scalar(k, v, global_step)
316
+ for k, v in histograms.items():
317
+ writer.add_histogram(k, v, global_step)
318
+ for k, v in images.items():
319
+ writer.add_image(k, v, global_step, dataformats='HWC')
320
+ for k, v in audios.items():
321
+ writer.add_audio(k, v, global_step, audio_sampling_rate)
322
+
323
+
324
+ def latest_checkpoint_path(dir_path, regex="G_*.pth"):
325
+ f_list = glob.glob(os.path.join(dir_path, regex))
326
+ f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
327
+ x = f_list[-1]
328
+ print(x)
329
+ return x
330
+
331
+
332
+ def plot_spectrogram_to_numpy(spectrogram):
333
+ global MATPLOTLIB_FLAG
334
+ if not MATPLOTLIB_FLAG:
335
+ import matplotlib
336
+ matplotlib.use("Agg")
337
+ MATPLOTLIB_FLAG = True
338
+ mpl_logger = logging.getLogger('matplotlib')
339
+ mpl_logger.setLevel(logging.WARNING)
340
+ import matplotlib.pylab as plt
341
+ import numpy as np
342
+
343
+ fig, ax = plt.subplots(figsize=(10,2))
344
+ im = ax.imshow(spectrogram, aspect="auto", origin="lower",
345
+ interpolation='none')
346
+ plt.colorbar(im, ax=ax)
347
+ plt.xlabel("Frames")
348
+ plt.ylabel("Channels")
349
+ plt.tight_layout()
350
+
351
+ fig.canvas.draw()
352
+ data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
353
+ data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
354
+ plt.close()
355
+ return data
356
+
357
+
358
+ def plot_alignment_to_numpy(alignment, info=None):
359
+ global MATPLOTLIB_FLAG
360
+ if not MATPLOTLIB_FLAG:
361
+ import matplotlib
362
+ matplotlib.use("Agg")
363
+ MATPLOTLIB_FLAG = True
364
+ mpl_logger = logging.getLogger('matplotlib')
365
+ mpl_logger.setLevel(logging.WARNING)
366
+ import matplotlib.pylab as plt
367
+ import numpy as np
368
+
369
+ fig, ax = plt.subplots(figsize=(6, 4))
370
+ im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
371
+ interpolation='none')
372
+ fig.colorbar(im, ax=ax)
373
+ xlabel = 'Decoder timestep'
374
+ if info is not None:
375
+ xlabel += '\n\n' + info
376
+ plt.xlabel(xlabel)
377
+ plt.ylabel('Encoder timestep')
378
+ plt.tight_layout()
379
+
380
+ fig.canvas.draw()
381
+ data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
382
+ data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
383
+ plt.close()
384
+ return data
385
+
386
+
387
+ def load_wav_to_torch(full_path):
388
+ sampling_rate, data = read(full_path)
389
+ return torch.FloatTensor(data.astype(np.float32)), sampling_rate
390
+
391
+
392
+ def load_filepaths_and_text(filename, split="|"):
393
+ with open(filename, encoding='utf-8') as f:
394
+ filepaths_and_text = [line.strip().split(split) for line in f]
395
+ return filepaths_and_text
396
+
397
+
398
+ def get_hparams(init=True):
399
+ parser = argparse.ArgumentParser()
400
+ parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
401
+ help='JSON file for configuration')
402
+ parser.add_argument('-m', '--model', type=str, required=True,
403
+ help='Model name')
404
+
405
+ args = parser.parse_args()
406
+ model_dir = os.path.join("./logs", args.model)
407
+
408
+ if not os.path.exists(model_dir):
409
+ os.makedirs(model_dir)
410
+
411
+ config_path = args.config
412
+ config_save_path = os.path.join(model_dir, "config.json")
413
+ if init:
414
+ with open(config_path, "r") as f:
415
+ data = f.read()
416
+ with open(config_save_path, "w") as f:
417
+ f.write(data)
418
+ else:
419
+ with open(config_save_path, "r") as f:
420
+ data = f.read()
421
+ config = json.loads(data)
422
+
423
+ hparams = HParams(**config)
424
+ hparams.model_dir = model_dir
425
+ return hparams
426
+
427
+
428
+ def get_hparams_from_dir(model_dir):
429
+ config_save_path = os.path.join(model_dir, "config.json")
430
+ with open(config_save_path, "r") as f:
431
+ data = f.read()
432
+ config = json.loads(data)
433
+
434
+ hparams =HParams(**config)
435
+ hparams.model_dir = model_dir
436
+ return hparams
437
+
438
+
439
+ def get_hparams_from_file(config_path):
440
+ with open(config_path, "r") as f:
441
+ data = f.read()
442
+ config = json.loads(data)
443
+
444
+ hparams =HParams(**config)
445
+ return hparams
446
+
447
+
448
+ def check_git_hash(model_dir):
449
+ source_dir = os.path.dirname(os.path.realpath(__file__))
450
+ if not os.path.exists(os.path.join(source_dir, ".git")):
451
+ logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
452
+ source_dir
453
+ ))
454
+ return
455
+
456
+ cur_hash = subprocess.getoutput("git rev-parse HEAD")
457
+
458
+ path = os.path.join(model_dir, "githash")
459
+ if os.path.exists(path):
460
+ saved_hash = open(path).read()
461
+ if saved_hash != cur_hash:
462
+ logger.warn("git hash values are different. {}(saved) != {}(current)".format(
463
+ saved_hash[:8], cur_hash[:8]))
464
+ else:
465
+ open(path, "w").write(cur_hash)
466
+
467
+
468
+ def get_logger(model_dir, filename="train.log"):
469
+ global logger
470
+ logger = logging.getLogger(os.path.basename(model_dir))
471
+ logger.setLevel(logging.DEBUG)
472
+
473
+ formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
474
+ if not os.path.exists(model_dir):
475
+ os.makedirs(model_dir)
476
+ h = logging.FileHandler(os.path.join(model_dir, filename))
477
+ h.setLevel(logging.DEBUG)
478
+ h.setFormatter(formatter)
479
+ logger.addHandler(h)
480
+ return logger
481
+
482
+
483
+ def repeat_expand_2d(content, target_len):
484
+ # content : [h, t]
485
+
486
+ src_len = content.shape[-1]
487
+ target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
488
+ temp = torch.arange(src_len+1) * target_len / src_len
489
+ current_pos = 0
490
+ for i in range(target_len):
491
+ if i < temp[current_pos+1]:
492
+ target[:, i] = content[:, current_pos]
493
+ else:
494
+ current_pos += 1
495
+ target[:, i] = content[:, current_pos]
496
+
497
+ return target
498
+
499
+
500
+ def mix_model(model_paths,mix_rate,mode):
501
+ mix_rate = torch.FloatTensor(mix_rate)/100
502
+ model_tem = torch.load(model_paths[0])
503
+ models = [torch.load(path)["model"] for path in model_paths]
504
+ if mode == 0:
505
+ mix_rate = F.softmax(mix_rate,dim=0)
506
+ for k in model_tem["model"].keys():
507
+ model_tem["model"][k] = torch.zeros_like(model_tem["model"][k])
508
+ for i,model in enumerate(models):
509
+ model_tem["model"][k] += model[k]*mix_rate[i]
510
+ torch.save(model_tem,os.path.join(os.path.curdir,"output.pth"))
511
+ return os.path.join(os.path.curdir,"output.pth")
512
+
513
+ class HParams():
514
+ def __init__(self, **kwargs):
515
+ for k, v in kwargs.items():
516
+ if type(v) == dict:
517
+ v = HParams(**v)
518
+ self[k] = v
519
+
520
+ def keys(self):
521
+ return self.__dict__.keys()
522
+
523
+ def items(self):
524
+ return self.__dict__.items()
525
+
526
+ def values(self):
527
+ return self.__dict__.values()
528
+
529
+ def __len__(self):
530
+ return len(self.__dict__)
531
+
532
+ def __getitem__(self, key):
533
+ return getattr(self, key)
534
+
535
+ def __setitem__(self, key, value):
536
+ return setattr(self, key, value)
537
+
538
+ def __contains__(self, key):
539
+ return key in self.__dict__
540
+
541
+ def __repr__(self):
542
+ return self.__dict__.__repr__()