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Browse files- README.md +14 -0
- app.py +264 -0
- attentions.py +300 -0
- commons.py +172 -0
- config.json +948 -0
- gitattributes.txt +38 -0
- gitignore.txt +382 -0
- mel_processing.py +101 -0
- models.py +533 -0
- modules.py +388 -0
- requirements.txt +17 -0
- transforms.py +193 -0
- utils.py +225 -0
README.md
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---
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title: Vits Models
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emoji: 🏃
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colorFrom: pink
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.17.0
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app_file: app.py
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pinned: false
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license: apache-2.0
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duplicated_from: sayashi/vits-models
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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# coding=utf-8
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import os
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import re
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import argparse
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import utils
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import commons
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import json
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import torch
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import gradio as gr
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from models import SynthesizerTrn
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from text import text_to_sequence, _clean_text
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from torch import no_grad, LongTensor
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import gradio.processing_utils as gr_processing_utils
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import logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
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hps_ms = utils.get_hparams_from_file(r'config/config.json')
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audio_postprocess_ori = gr.Audio.postprocess
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def audio_postprocess(self, y):
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data = audio_postprocess_ori(self, y)
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if data is None:
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return None
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return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
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gr.Audio.postprocess = audio_postprocess
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def get_text(text, hps, is_symbol):
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text_norm, clean_text = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = LongTensor(text_norm)
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return text_norm, clean_text
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def create_tts_fn(net_g_ms, speaker_id):
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def tts_fn(text, language, noise_scale, noise_scale_w, length_scale, is_symbol):
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text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
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if limitation:
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text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
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max_len = 100
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if is_symbol:
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max_len *= 3
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if text_len > max_len:
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return "Error: Text is too long", None
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if not is_symbol:
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if language == 0:
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text = f"[ZH]{text}[ZH]"
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elif language == 1:
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text = f"[JA]{text}[JA]"
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else:
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text = f"{text}"
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stn_tst, clean_text = get_text(text, hps_ms, is_symbol)
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with no_grad():
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x_tst = stn_tst.unsqueeze(0).to(device)
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x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
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sid = LongTensor([speaker_id]).to(device)
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audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
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length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
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return "Success", (22050, audio)
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return tts_fn
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def create_to_symbol_fn(hps):
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def to_symbol_fn(is_symbol_input, input_text, temp_lang):
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if temp_lang == 0:
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clean_text = f'[ZH]{input_text}[ZH]'
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elif temp_lang == 1:
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clean_text = f'[JA]{input_text}[JA]'
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else:
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clean_text = input_text
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return _clean_text(clean_text, hps.data.text_cleaners) if is_symbol_input else ''
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return to_symbol_fn
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def change_lang(language):
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if language == 0:
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return 0.6, 0.668, 1.2
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elif language == 1:
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return 0.6, 0.668, 1
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else:
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return 0.6, 0.668, 1
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download_audio_js = """
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() =>{{
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let root = document.querySelector("body > gradio-app");
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if (root.shadowRoot != null)
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root = root.shadowRoot;
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let audio = root.querySelector("#tts-audio-{audio_id}").querySelector("audio");
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let text = root.querySelector("#input-text-{audio_id}").querySelector("textarea");
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if (audio == undefined)
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return;
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text = text.value;
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if (text == undefined)
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text = Math.floor(Math.random()*100000000);
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audio = audio.src;
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let oA = document.createElement("a");
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oA.download = text.substr(0, 20)+'.wav';
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oA.href = audio;
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document.body.appendChild(oA);
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oA.click();
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oA.remove();
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}}
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--device', type=str, default='cpu')
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parser.add_argument('--api', action="store_true", default=False)
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parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
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args = parser.parse_args()
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device = torch.device(args.device)
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models = []
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with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
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models_info = json.load(f)
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for i, info in models_info.items():
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if not info['enable']:
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continue
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sid = info['sid']
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name_en = info['name_en']
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name_zh = info['name_zh']
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title = info['title']
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cover = f"pretrained_models/{i}/{info['cover']}"
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example = info['example']
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language = info['language']
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net_g_ms = SynthesizerTrn(
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len(hps_ms.symbols),
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hps_ms.data.filter_length // 2 + 1,
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hps_ms.train.segment_size // hps_ms.data.hop_length,
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n_speakers=hps_ms.data.n_speakers if info['type'] == "multi" else 0,
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**hps_ms.model)
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utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None)
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_ = net_g_ms.eval().to(device)
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models.append((sid, name_en, name_zh, title, cover, example, language, net_g_ms, create_tts_fn(net_g_ms, sid), create_to_symbol_fn(hps_ms)))
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with gr.Blocks() as app:
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gr.Markdown(
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"# <center> vits-models\n"
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"## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n"
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"## <center> ·请不要生成会对个人以及组织造成侵害的内容\n"
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"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.vits-models)\n\n"
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"[Open In Colab]"
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"(https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)"
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" without queue and length limitation.(无需等待队列,并且没有长度限制)\n\n"
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"[Finetune your own model](https://github.com/SayaSS/vits-finetuning)"
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)
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with gr.Tabs():
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with gr.TabItem("EN"):
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for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models:
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with gr.TabItem(name_en):
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with gr.Row():
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gr.Markdown(
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'<div align="center">'
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f'<a><strong>{title}</strong></a>'
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f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
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'</div>'
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)
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(label="Text (100 words limitation)" if limitation else "Text", lines=5, value=example, elem_id=f"input-text-en-{name_en.replace(' ','')}")
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lang = gr.Dropdown(label="Language", choices=["Chinese", "Japanese", "Mix(wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA])"],
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type="index", value=language)
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with gr.Accordion(label="Advanced Options", open=False):
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symbol_input = gr.Checkbox(value=False, label="Symbol input")
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symbol_list = gr.Dataset(label="Symbol list", components=[input_text],
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samples=[[x] for x in hps_ms.symbols])
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symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
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btn = gr.Button(value="Generate", variant="primary")
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with gr.Row():
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ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
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nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
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ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
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with gr.Column():
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o1 = gr.Textbox(label="Output Message")
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o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio-en-{name_en.replace(' ','')}")
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download = gr.Button("Download Audio")
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btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls, symbol_input], outputs=[o1, o2], api_name=f"tts-{name_en}")
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download.click(None, [], [], _js=download_audio_js.format(audio_id=f"en-{name_en.replace(' ', '')}"))
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lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
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symbol_input.change(
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to_symbol_fn,
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[symbol_input, input_text, lang],
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[input_text]
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)
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symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
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_js=f"""
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(i,symbols) => {{
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let root = document.querySelector("body > gradio-app");
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if (root.shadowRoot != null)
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root = root.shadowRoot;
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let text_input = root.querySelector("#input-text-en-{name_en.replace(' ', '')}").querySelector("textarea");
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let startPos = text_input.selectionStart;
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let endPos = text_input.selectionEnd;
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let oldTxt = text_input.value;
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let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
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text_input.value = result;
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let x = window.scrollX, y = window.scrollY;
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text_input.focus();
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text_input.selectionStart = startPos + symbols[i].length;
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text_input.selectionEnd = startPos + symbols[i].length;
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text_input.blur();
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window.scrollTo(x, y);
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return text_input.value;
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}}""")
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with gr.TabItem("中文"):
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for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models:
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with gr.TabItem(name_zh):
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with gr.Row():
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gr.Markdown(
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'<div align="center">'
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f'<a><strong>{title}</strong></a>'
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f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
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'</div>'
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)
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(label="文本 (100字上限)" if limitation else "文本", lines=5, value=example, elem_id=f"input-text-zh-{name_zh}")
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lang = gr.Dropdown(label="语言", choices=["中文", "日语", "中日混合(中文用[ZH][ZH]包裹起来,日文用[JA][JA]包裹起来)"],
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type="index", value="中文"if language == "Chinese" else "日语")
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with gr.Accordion(label="高级选项", open=False):
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symbol_input = gr.Checkbox(value=False, label="符号输入")
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symbol_list = gr.Dataset(label="符号列表", components=[input_text],
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samples=[[x] for x in hps_ms.symbols])
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symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
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btn = gr.Button(value="生成", variant="primary")
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with gr.Row():
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ns = gr.Slider(label="控制感情变化程度", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
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nsw = gr.Slider(label="控制音素发音长度", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
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ls = gr.Slider(label="控制整体语速", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
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with gr.Column():
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o1 = gr.Textbox(label="输出信息")
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o2 = gr.Audio(label="输出音频", elem_id=f"tts-audio-zh-{name_zh}")
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download = gr.Button("下载音频")
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+
btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls, symbol_input], outputs=[o1, o2])
|
237 |
+
download.click(None, [], [], _js=download_audio_js.format(audio_id=f"zh-{name_zh}"))
|
238 |
+
lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
|
239 |
+
symbol_input.change(
|
240 |
+
to_symbol_fn,
|
241 |
+
[symbol_input, input_text, lang],
|
242 |
+
[input_text]
|
243 |
+
)
|
244 |
+
symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
|
245 |
+
_js=f"""
|
246 |
+
(i,symbols) => {{
|
247 |
+
let root = document.querySelector("body > gradio-app");
|
248 |
+
if (root.shadowRoot != null)
|
249 |
+
root = root.shadowRoot;
|
250 |
+
let text_input = root.querySelector("#input-text-zh-{name_zh}").querySelector("textarea");
|
251 |
+
let startPos = text_input.selectionStart;
|
252 |
+
let endPos = text_input.selectionEnd;
|
253 |
+
let oldTxt = text_input.value;
|
254 |
+
let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
|
255 |
+
text_input.value = result;
|
256 |
+
let x = window.scrollX, y = window.scrollY;
|
257 |
+
text_input.focus();
|
258 |
+
text_input.selectionStart = startPos + symbols[i].length;
|
259 |
+
text_input.selectionEnd = startPos + symbols[i].length;
|
260 |
+
text_input.blur();
|
261 |
+
window.scrollTo(x, y);
|
262 |
+
return text_input.value;
|
263 |
+
}}""")
|
264 |
+
app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)
|
attentions.py
ADDED
@@ -0,0 +1,300 @@
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|
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|
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|
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|
|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import commons
|
7 |
+
from modules import LayerNorm
|
8 |
+
|
9 |
+
|
10 |
+
class Encoder(nn.Module):
|
11 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
|
12 |
+
super().__init__()
|
13 |
+
self.hidden_channels = hidden_channels
|
14 |
+
self.filter_channels = filter_channels
|
15 |
+
self.n_heads = n_heads
|
16 |
+
self.n_layers = n_layers
|
17 |
+
self.kernel_size = kernel_size
|
18 |
+
self.p_dropout = p_dropout
|
19 |
+
self.window_size = window_size
|
20 |
+
|
21 |
+
self.drop = nn.Dropout(p_dropout)
|
22 |
+
self.attn_layers = nn.ModuleList()
|
23 |
+
self.norm_layers_1 = nn.ModuleList()
|
24 |
+
self.ffn_layers = nn.ModuleList()
|
25 |
+
self.norm_layers_2 = nn.ModuleList()
|
26 |
+
for i in range(self.n_layers):
|
27 |
+
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
|
28 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
29 |
+
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
|
30 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
31 |
+
|
32 |
+
def forward(self, x, x_mask):
|
33 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
34 |
+
x = x * x_mask
|
35 |
+
for i in range(self.n_layers):
|
36 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
37 |
+
y = self.drop(y)
|
38 |
+
x = self.norm_layers_1[i](x + y)
|
39 |
+
|
40 |
+
y = self.ffn_layers[i](x, x_mask)
|
41 |
+
y = self.drop(y)
|
42 |
+
x = self.norm_layers_2[i](x + y)
|
43 |
+
x = x * x_mask
|
44 |
+
return x
|
45 |
+
|
46 |
+
|
47 |
+
class Decoder(nn.Module):
|
48 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
|
49 |
+
super().__init__()
|
50 |
+
self.hidden_channels = hidden_channels
|
51 |
+
self.filter_channels = filter_channels
|
52 |
+
self.n_heads = n_heads
|
53 |
+
self.n_layers = n_layers
|
54 |
+
self.kernel_size = kernel_size
|
55 |
+
self.p_dropout = p_dropout
|
56 |
+
self.proximal_bias = proximal_bias
|
57 |
+
self.proximal_init = proximal_init
|
58 |
+
|
59 |
+
self.drop = nn.Dropout(p_dropout)
|
60 |
+
self.self_attn_layers = nn.ModuleList()
|
61 |
+
self.norm_layers_0 = nn.ModuleList()
|
62 |
+
self.encdec_attn_layers = nn.ModuleList()
|
63 |
+
self.norm_layers_1 = nn.ModuleList()
|
64 |
+
self.ffn_layers = nn.ModuleList()
|
65 |
+
self.norm_layers_2 = nn.ModuleList()
|
66 |
+
for i in range(self.n_layers):
|
67 |
+
self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
|
68 |
+
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
69 |
+
self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
70 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
71 |
+
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
72 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
73 |
+
|
74 |
+
def forward(self, x, x_mask, h, h_mask):
|
75 |
+
"""
|
76 |
+
x: decoder input
|
77 |
+
h: encoder output
|
78 |
+
"""
|
79 |
+
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
80 |
+
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
81 |
+
x = x * x_mask
|
82 |
+
for i in range(self.n_layers):
|
83 |
+
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
84 |
+
y = self.drop(y)
|
85 |
+
x = self.norm_layers_0[i](x + y)
|
86 |
+
|
87 |
+
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
88 |
+
y = self.drop(y)
|
89 |
+
x = self.norm_layers_1[i](x + y)
|
90 |
+
|
91 |
+
y = self.ffn_layers[i](x, x_mask)
|
92 |
+
y = self.drop(y)
|
93 |
+
x = self.norm_layers_2[i](x + y)
|
94 |
+
x = x * x_mask
|
95 |
+
return x
|
96 |
+
|
97 |
+
|
98 |
+
class MultiHeadAttention(nn.Module):
|
99 |
+
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):
|
100 |
+
super().__init__()
|
101 |
+
assert channels % n_heads == 0
|
102 |
+
|
103 |
+
self.channels = channels
|
104 |
+
self.out_channels = out_channels
|
105 |
+
self.n_heads = n_heads
|
106 |
+
self.p_dropout = p_dropout
|
107 |
+
self.window_size = window_size
|
108 |
+
self.heads_share = heads_share
|
109 |
+
self.block_length = block_length
|
110 |
+
self.proximal_bias = proximal_bias
|
111 |
+
self.proximal_init = proximal_init
|
112 |
+
self.attn = None
|
113 |
+
|
114 |
+
self.k_channels = channels // n_heads
|
115 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
116 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
117 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
118 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
119 |
+
self.drop = nn.Dropout(p_dropout)
|
120 |
+
|
121 |
+
if window_size is not None:
|
122 |
+
n_heads_rel = 1 if heads_share else n_heads
|
123 |
+
rel_stddev = self.k_channels**-0.5
|
124 |
+
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
125 |
+
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
126 |
+
|
127 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
128 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
129 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
130 |
+
if proximal_init:
|
131 |
+
with torch.no_grad():
|
132 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
133 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
134 |
+
|
135 |
+
def forward(self, x, c, attn_mask=None):
|
136 |
+
q = self.conv_q(x)
|
137 |
+
k = self.conv_k(c)
|
138 |
+
v = self.conv_v(c)
|
139 |
+
|
140 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
141 |
+
|
142 |
+
x = self.conv_o(x)
|
143 |
+
return x
|
144 |
+
|
145 |
+
def attention(self, query, key, value, mask=None):
|
146 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
147 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
148 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
149 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
150 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
151 |
+
|
152 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
153 |
+
if self.window_size is not None:
|
154 |
+
assert t_s == t_t, "Relative attention is only available for self-attention."
|
155 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
156 |
+
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
157 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
158 |
+
scores = scores + scores_local
|
159 |
+
if self.proximal_bias:
|
160 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
161 |
+
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
162 |
+
if mask is not None:
|
163 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
164 |
+
if self.block_length is not None:
|
165 |
+
assert t_s == t_t, "Local attention is only available for self-attention."
|
166 |
+
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
167 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
168 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
169 |
+
p_attn = self.drop(p_attn)
|
170 |
+
output = torch.matmul(p_attn, value)
|
171 |
+
if self.window_size is not None:
|
172 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
173 |
+
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
174 |
+
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
175 |
+
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
176 |
+
return output, p_attn
|
177 |
+
|
178 |
+
def _matmul_with_relative_values(self, x, y):
|
179 |
+
"""
|
180 |
+
x: [b, h, l, m]
|
181 |
+
y: [h or 1, m, d]
|
182 |
+
ret: [b, h, l, d]
|
183 |
+
"""
|
184 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
185 |
+
return ret
|
186 |
+
|
187 |
+
def _matmul_with_relative_keys(self, x, y):
|
188 |
+
"""
|
189 |
+
x: [b, h, l, d]
|
190 |
+
y: [h or 1, m, d]
|
191 |
+
ret: [b, h, l, m]
|
192 |
+
"""
|
193 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
194 |
+
return ret
|
195 |
+
|
196 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
197 |
+
max_relative_position = 2 * self.window_size + 1
|
198 |
+
# Pad first before slice to avoid using cond ops.
|
199 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
200 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
201 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
202 |
+
if pad_length > 0:
|
203 |
+
padded_relative_embeddings = F.pad(
|
204 |
+
relative_embeddings,
|
205 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
206 |
+
else:
|
207 |
+
padded_relative_embeddings = relative_embeddings
|
208 |
+
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
209 |
+
return used_relative_embeddings
|
210 |
+
|
211 |
+
def _relative_position_to_absolute_position(self, x):
|
212 |
+
"""
|
213 |
+
x: [b, h, l, 2*l-1]
|
214 |
+
ret: [b, h, l, l]
|
215 |
+
"""
|
216 |
+
batch, heads, length, _ = x.size()
|
217 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
218 |
+
x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
219 |
+
|
220 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
221 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
222 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
223 |
+
|
224 |
+
# Reshape and slice out the padded elements.
|
225 |
+
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
226 |
+
return x_final
|
227 |
+
|
228 |
+
def _absolute_position_to_relative_position(self, x):
|
229 |
+
"""
|
230 |
+
x: [b, h, l, l]
|
231 |
+
ret: [b, h, l, 2*l-1]
|
232 |
+
"""
|
233 |
+
batch, heads, length, _ = x.size()
|
234 |
+
# padd along column
|
235 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
236 |
+
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
237 |
+
# add 0's in the beginning that will skew the elements after reshape
|
238 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
239 |
+
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
240 |
+
return x_final
|
241 |
+
|
242 |
+
def _attention_bias_proximal(self, length):
|
243 |
+
"""Bias for self-attention to encourage attention to close positions.
|
244 |
+
Args:
|
245 |
+
length: an integer scalar.
|
246 |
+
Returns:
|
247 |
+
a Tensor with shape [1, 1, length, length]
|
248 |
+
"""
|
249 |
+
r = torch.arange(length, dtype=torch.float32)
|
250 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
251 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
252 |
+
|
253 |
+
|
254 |
+
class FFN(nn.Module):
|
255 |
+
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
256 |
+
super().__init__()
|
257 |
+
self.in_channels = in_channels
|
258 |
+
self.out_channels = out_channels
|
259 |
+
self.filter_channels = filter_channels
|
260 |
+
self.kernel_size = kernel_size
|
261 |
+
self.p_dropout = p_dropout
|
262 |
+
self.activation = activation
|
263 |
+
self.causal = causal
|
264 |
+
|
265 |
+
if causal:
|
266 |
+
self.padding = self._causal_padding
|
267 |
+
else:
|
268 |
+
self.padding = self._same_padding
|
269 |
+
|
270 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
271 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
272 |
+
self.drop = nn.Dropout(p_dropout)
|
273 |
+
|
274 |
+
def forward(self, x, x_mask):
|
275 |
+
x = self.conv_1(self.padding(x * x_mask))
|
276 |
+
if self.activation == "gelu":
|
277 |
+
x = x * torch.sigmoid(1.702 * x)
|
278 |
+
else:
|
279 |
+
x = torch.relu(x)
|
280 |
+
x = self.drop(x)
|
281 |
+
x = self.conv_2(self.padding(x * x_mask))
|
282 |
+
return x * x_mask
|
283 |
+
|
284 |
+
def _causal_padding(self, x):
|
285 |
+
if self.kernel_size == 1:
|
286 |
+
return x
|
287 |
+
pad_l = self.kernel_size - 1
|
288 |
+
pad_r = 0
|
289 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
290 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
291 |
+
return x
|
292 |
+
|
293 |
+
def _same_padding(self, x):
|
294 |
+
if self.kernel_size == 1:
|
295 |
+
return x
|
296 |
+
pad_l = (self.kernel_size - 1) // 2
|
297 |
+
pad_r = self.kernel_size // 2
|
298 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
299 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
300 |
+
return x
|
commons.py
ADDED
@@ -0,0 +1,172 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch.nn import functional as F
|
4 |
+
import torch.jit
|
5 |
+
|
6 |
+
|
7 |
+
def script_method(fn, _rcb=None):
|
8 |
+
return fn
|
9 |
+
|
10 |
+
|
11 |
+
def script(obj, optimize=True, _frames_up=0, _rcb=None):
|
12 |
+
return obj
|
13 |
+
|
14 |
+
|
15 |
+
torch.jit.script_method = script_method
|
16 |
+
torch.jit.script = script
|
17 |
+
|
18 |
+
|
19 |
+
def init_weights(m, mean=0.0, std=0.01):
|
20 |
+
classname = m.__class__.__name__
|
21 |
+
if classname.find("Conv") != -1:
|
22 |
+
m.weight.data.normal_(mean, std)
|
23 |
+
|
24 |
+
|
25 |
+
def get_padding(kernel_size, dilation=1):
|
26 |
+
return int((kernel_size*dilation - dilation)/2)
|
27 |
+
|
28 |
+
|
29 |
+
def convert_pad_shape(pad_shape):
|
30 |
+
l = pad_shape[::-1]
|
31 |
+
pad_shape = [item for sublist in l for item in sublist]
|
32 |
+
return pad_shape
|
33 |
+
|
34 |
+
|
35 |
+
def intersperse(lst, item):
|
36 |
+
result = [item] * (len(lst) * 2 + 1)
|
37 |
+
result[1::2] = lst
|
38 |
+
return result
|
39 |
+
|
40 |
+
|
41 |
+
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
42 |
+
"""KL(P||Q)"""
|
43 |
+
kl = (logs_q - logs_p) - 0.5
|
44 |
+
kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
|
45 |
+
return kl
|
46 |
+
|
47 |
+
|
48 |
+
def rand_gumbel(shape):
|
49 |
+
"""Sample from the Gumbel distribution, protect from overflows."""
|
50 |
+
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
51 |
+
return -torch.log(-torch.log(uniform_samples))
|
52 |
+
|
53 |
+
|
54 |
+
def rand_gumbel_like(x):
|
55 |
+
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
56 |
+
return g
|
57 |
+
|
58 |
+
|
59 |
+
def slice_segments(x, ids_str, segment_size=4):
|
60 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
61 |
+
for i in range(x.size(0)):
|
62 |
+
idx_str = ids_str[i]
|
63 |
+
idx_end = idx_str + segment_size
|
64 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
65 |
+
return ret
|
66 |
+
|
67 |
+
|
68 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
69 |
+
b, d, t = x.size()
|
70 |
+
if x_lengths is None:
|
71 |
+
x_lengths = t
|
72 |
+
ids_str_max = x_lengths - segment_size + 1
|
73 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
74 |
+
ret = slice_segments(x, ids_str, segment_size)
|
75 |
+
return ret, ids_str
|
76 |
+
|
77 |
+
|
78 |
+
def get_timing_signal_1d(
|
79 |
+
length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
80 |
+
position = torch.arange(length, dtype=torch.float)
|
81 |
+
num_timescales = channels // 2
|
82 |
+
log_timescale_increment = (
|
83 |
+
math.log(float(max_timescale) / float(min_timescale)) /
|
84 |
+
(num_timescales - 1))
|
85 |
+
inv_timescales = min_timescale * torch.exp(
|
86 |
+
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
|
87 |
+
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
88 |
+
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
89 |
+
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
90 |
+
signal = signal.view(1, channels, length)
|
91 |
+
return signal
|
92 |
+
|
93 |
+
|
94 |
+
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
95 |
+
b, channels, length = x.size()
|
96 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
97 |
+
return x + signal.to(dtype=x.dtype, device=x.device)
|
98 |
+
|
99 |
+
|
100 |
+
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
101 |
+
b, channels, length = x.size()
|
102 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
103 |
+
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
104 |
+
|
105 |
+
|
106 |
+
def subsequent_mask(length):
|
107 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
108 |
+
return mask
|
109 |
+
|
110 |
+
|
111 |
+
@torch.jit.script
|
112 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
113 |
+
n_channels_int = n_channels[0]
|
114 |
+
in_act = input_a + input_b
|
115 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
116 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
117 |
+
acts = t_act * s_act
|
118 |
+
return acts
|
119 |
+
|
120 |
+
|
121 |
+
def convert_pad_shape(pad_shape):
|
122 |
+
l = pad_shape[::-1]
|
123 |
+
pad_shape = [item for sublist in l for item in sublist]
|
124 |
+
return pad_shape
|
125 |
+
|
126 |
+
|
127 |
+
def shift_1d(x):
|
128 |
+
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
129 |
+
return x
|
130 |
+
|
131 |
+
|
132 |
+
def sequence_mask(length, max_length=None):
|
133 |
+
if max_length is None:
|
134 |
+
max_length = length.max()
|
135 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
136 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
137 |
+
|
138 |
+
|
139 |
+
def generate_path(duration, mask):
|
140 |
+
"""
|
141 |
+
duration: [b, 1, t_x]
|
142 |
+
mask: [b, 1, t_y, t_x]
|
143 |
+
"""
|
144 |
+
device = duration.device
|
145 |
+
|
146 |
+
b, _, t_y, t_x = mask.shape
|
147 |
+
cum_duration = torch.cumsum(duration, -1)
|
148 |
+
|
149 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
150 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
151 |
+
path = path.view(b, t_x, t_y)
|
152 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
153 |
+
path = path.unsqueeze(1).transpose(2,3) * mask
|
154 |
+
return path
|
155 |
+
|
156 |
+
|
157 |
+
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
158 |
+
if isinstance(parameters, torch.Tensor):
|
159 |
+
parameters = [parameters]
|
160 |
+
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
161 |
+
norm_type = float(norm_type)
|
162 |
+
if clip_value is not None:
|
163 |
+
clip_value = float(clip_value)
|
164 |
+
|
165 |
+
total_norm = 0
|
166 |
+
for p in parameters:
|
167 |
+
param_norm = p.grad.data.norm(norm_type)
|
168 |
+
total_norm += param_norm.item() ** norm_type
|
169 |
+
if clip_value is not None:
|
170 |
+
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
171 |
+
total_norm = total_norm ** (1. / norm_type)
|
172 |
+
return total_norm
|
config.json
ADDED
@@ -0,0 +1,948 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
|
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1 |
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{
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2 |
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166 |
+
"\u5317\u6e2f\u706b\u5c71",
|
167 |
+
"\u5947\u9510\u9a8f",
|
168 |
+
"\u82e6\u6da9\u7cd6\u971c",
|
169 |
+
"\u5c0f\u5c0f\u8695\u8327",
|
170 |
+
"\u9a8f\u5ddd\u624b\u7eb2\uff08\u7eff\u5e3d\u6076\u9b54\uff09",
|
171 |
+
"\u79cb\u5ddd\u5f25\u751f\uff08\u5c0f\u5c0f\u7406\u4e8b\u957f\uff09",
|
172 |
+
"\u4e59\u540d\u53f2\u60a6\u5b50\uff08\u4e59\u540d\u8bb0\u8005\uff09",
|
173 |
+
"\u6850\u751f\u9662\u8475",
|
174 |
+
"\u5b89\u5fc3\u6cfd\u523a\u523a\u7f8e",
|
175 |
+
"\u6a2b\u672c\u7406\u5b50",
|
176 |
+
"\u795e\u91cc\u7eeb\u534e\uff08\u9f9f\u9f9f\uff09",
|
177 |
+
"\u7434",
|
178 |
+
"\u7a7a\uff08\u7a7a\u54e5\uff09",
|
179 |
+
"\u4e3d\u838e",
|
180 |
+
"\u8367\uff08\u8367\u59b9\uff09",
|
181 |
+
"\u82ad\u82ad\u62c9",
|
182 |
+
"\u51ef\u4e9a",
|
183 |
+
"\u8fea\u5362\u514b",
|
184 |
+
"\u96f7\u6cfd",
|
185 |
+
"\u5b89\u67cf",
|
186 |
+
"\u6e29\u8fea",
|
187 |
+
"\u9999\u83f1",
|
188 |
+
"\u5317\u6597",
|
189 |
+
"\u884c\u79cb",
|
190 |
+
"\u9b48",
|
191 |
+
"\u51dd\u5149",
|
192 |
+
"\u53ef\u8389",
|
193 |
+
"\u949f\u79bb",
|
194 |
+
"\u83f2\u8c22\u5c14\uff08\u7687\u5973\uff09",
|
195 |
+
"\u73ed\u5c3c\u7279",
|
196 |
+
"\u8fbe\u8fbe\u5229\u4e9a\uff08\u516c\u5b50\uff09",
|
197 |
+
"\u8bfa\u827e\u5c14\uff08\u5973\u4ec6\uff09",
|
198 |
+
"\u4e03\u4e03",
|
199 |
+
"\u91cd\u4e91",
|
200 |
+
"\u7518\u96e8\uff08\u6930\u7f8a\uff09",
|
201 |
+
"\u963f\u8d1d\u591a",
|
202 |
+
"\u8fea\u5965\u5a1c\uff08\u732b\u732b\uff09",
|
203 |
+
"\u83ab\u5a1c",
|
204 |
+
"\u523b\u6674",
|
205 |
+
"\u7802\u7cd6",
|
206 |
+
"\u8f9b\u7131",
|
207 |
+
"\u7f57\u838e\u8389\u4e9a",
|
208 |
+
"\u80e1\u6843",
|
209 |
+
"\u67ab\u539f\u4e07\u53f6\uff08\u4e07\u53f6\uff09",
|
210 |
+
"\u70df\u7eef",
|
211 |
+
"\u5bb5\u5bab",
|
212 |
+
"\u6258\u9a6c",
|
213 |
+
"\u4f18\u83c8",
|
214 |
+
"\u96f7\u7535\u5c06\u519b\uff08\u96f7\u795e\uff09",
|
215 |
+
"\u65e9\u67da",
|
216 |
+
"\u73ca\u745a\u5bab\u5fc3\u6d77\uff08\u5fc3\u6d77\uff0c\u6263\u6263\u7c73\uff09",
|
217 |
+
"\u4e94\u90ce",
|
218 |
+
"\u4e5d\u6761\u88df\u7f57",
|
219 |
+
"\u8352\u6cf7\u4e00\u6597\uff08\u4e00\u6597\uff09",
|
220 |
+
"\u57c3\u6d1b\u4f0a",
|
221 |
+
"\u7533\u9e64",
|
222 |
+
"\u516b\u91cd\u795e\u5b50\uff08\u795e\u5b50\uff09",
|
223 |
+
"\u795e\u91cc\u7eeb\u4eba\uff08\u7eeb\u4eba\uff09",
|
224 |
+
"\u591c\u5170",
|
225 |
+
"\u4e45\u5c90\u5fcd",
|
226 |
+
"\u9e7f\u91ce\u82d1\u5e73\u85cf",
|
227 |
+
"\u63d0\u7eb3\u91cc",
|
228 |
+
"\u67ef\u83b1",
|
229 |
+
"\u591a\u8389",
|
230 |
+
"\u4e91\u5807",
|
231 |
+
"\u7eb3\u897f\u59b2\uff08\u8349\u795e\uff09",
|
232 |
+
"\u6df1\u6e0a\u4f7f\u5f92",
|
233 |
+
"\u59ae\u9732",
|
234 |
+
"\u8d5b\u8bfa",
|
235 |
+
"\u503a\u52a1\u5904\u7406\u4eba",
|
236 |
+
"\u574e\u8482\u4e1d",
|
237 |
+
"\u771f\u5f13\u5feb\u8f66",
|
238 |
+
"\u79cb\u4eba",
|
239 |
+
"\u671b\u65cf",
|
240 |
+
"\u827e\u5c14\u83f2",
|
241 |
+
"\u827e\u8389\u4e1d",
|
242 |
+
"\u827e\u4f26",
|
243 |
+
"\u963f\u6d1b\u74e6",
|
244 |
+
"\u5929\u91ce",
|
245 |
+
"\u5929\u76ee\u5341\u4e94",
|
246 |
+
"\u611a\u4eba\u4f17-\u5b89\u5fb7\u70c8",
|
247 |
+
"\u5b89\u987a",
|
248 |
+
"\u5b89\u897f",
|
249 |
+
"\u8475",
|
250 |
+
"\u9752\u6728",
|
251 |
+
"\u8352\u5ddd\u5e78\u6b21",
|
252 |
+
"\u8352\u8c37",
|
253 |
+
"\u6709\u6cfd",
|
254 |
+
"\u6d45\u5ddd",
|
255 |
+
"\u9ebb\u7f8e",
|
256 |
+
"\u51dd\u5149\u52a9\u624b",
|
257 |
+
"\u963f\u6258",
|
258 |
+
"\u7afa\u5b50",
|
259 |
+
"\u767e\u8bc6",
|
260 |
+
"\u767e\u95fb",
|
261 |
+
"\u767e\u6653",
|
262 |
+
"\u767d\u672f",
|
263 |
+
"\u8d1d\u96c5\u7279\u4e3d\u5947",
|
264 |
+
"\u4e3d\u5854",
|
265 |
+
"\u5931\u843d\u8ff7\u8fed",
|
266 |
+
"\u7f2d\u4e71\u661f\u68d8",
|
267 |
+
"\u4f0a\u7538",
|
268 |
+
"\u4f0f\u7279\u52a0\u5973\u5b69",
|
269 |
+
"\u72c2\u70ed\u84dd\u8c03",
|
270 |
+
"\u8389\u8389\u5a05",
|
271 |
+
"\u841d\u838e\u8389\u5a05",
|
272 |
+
"\u516b\u91cd\u6a31",
|
273 |
+
"\u516b\u91cd\u971e",
|
274 |
+
"\u5361\u83b2",
|
275 |
+
"\u7b2c\u516d\u591c\u60f3\u66f2",
|
276 |
+
"\u5361\u841d\u5c14",
|
277 |
+
"\u59ec\u5b50",
|
278 |
+
"\u6781\u5730\u6218\u5203",
|
279 |
+
"\u5e03\u6d1b\u59ae\u5a05",
|
280 |
+
"\u6b21\u751f\u94f6\u7ffc",
|
281 |
+
"\u7406\u4e4b\u5f8b\u8005%26\u5e0c\u513f",
|
282 |
+
"\u7406\u4e4b\u5f8b\u8005",
|
283 |
+
"\u8ff7\u57ce\u9a87\u5154",
|
284 |
+
"\u5e0c\u513f",
|
285 |
+
"\u9b47\u591c\u661f\u6e0a",
|
286 |
+
"\u9ed1\u5e0c\u513f",
|
287 |
+
"\u5e15\u6735\u83f2\u8389\u4e1d",
|
288 |
+
"\u4e0d\u706d\u661f\u951a",
|
289 |
+
"\u5929\u5143\u9a91\u82f1",
|
290 |
+
"\u5e7d\u5170\u9edb\u5c14",
|
291 |
+
"\u6d3e\u8499bh3",
|
292 |
+
"\u7231\u9171",
|
293 |
+
"\u7eef\u7389\u4e38",
|
294 |
+
"\u5fb7\u4e3d\u838e",
|
295 |
+
"\u6708\u4e0b\u521d\u62e5",
|
296 |
+
"\u6714\u591c\u89c2\u661f",
|
297 |
+
"\u66ae\u5149\u9a91\u58eb",
|
298 |
+
"\u683c\u857e\u4fee",
|
299 |
+
"\u7559\u4e91\u501f\u98ce\u771f\u541b",
|
300 |
+
"\u6885\u6bd4\u4e4c\u65af",
|
301 |
+
"\u4eff\u72b9\u5927",
|
302 |
+
"\u514b\u83b1\u56e0",
|
303 |
+
"\u5723\u5251\u5e7d\u5170\u9edb\u5c14",
|
304 |
+
"\u5996\u7cbe\u7231\u8389",
|
305 |
+
"\u7279\u65af\u62c9zero",
|
306 |
+
"\u82cd\u7384",
|
307 |
+
"\u82e5\u6c34",
|
308 |
+
"\u897f\u7433",
|
309 |
+
"\u6234\u56e0\u65af\u96f7\u5e03",
|
310 |
+
"\u8d1d\u62c9",
|
311 |
+
"\u8d64\u9e22",
|
312 |
+
"\u9547\u9b42\u6b4c",
|
313 |
+
"\u6e21\u9e26",
|
314 |
+
"\u4eba\u4e4b\u5f8b\u8005",
|
315 |
+
"\u7231\u8389\u5e0c\u96c5",
|
316 |
+
"\u5929\u7a79\u6e38\u4fa0",
|
317 |
+
"\u742a\u4e9a\u5a1c",
|
318 |
+
"\u7a7a\u4e4b\u5f8b\u8005",
|
319 |
+
"\u85aa\u708e\u4e4b\u5f8b\u8005",
|
320 |
+
"\u4e91\u58a8\u4e39\u5fc3",
|
321 |
+
"\u7b26\u534e",
|
322 |
+
"\u8bc6\u4e4b\u5f8b\u8005",
|
323 |
+
"\u7279\u74e6\u6797",
|
324 |
+
"\u7ef4\u5c14\u8587",
|
325 |
+
"\u82bd\u8863",
|
326 |
+
"\u96f7\u4e4b\u5f8b\u8005",
|
327 |
+
"\u65ad\u7f6a\u5f71\u821e",
|
328 |
+
"\u963f\u6ce2\u5c3c\u4e9a",
|
329 |
+
"\u698e\u672c",
|
330 |
+
"\u5384\u5c3c\u65af\u7279",
|
331 |
+
"\u6076\u9f99",
|
332 |
+
"\u8303\u4e8c\u7237",
|
333 |
+
"\u6cd5\u62c9",
|
334 |
+
"\u611a\u4eba\u4f17\u58eb\u5175",
|
335 |
+
"\u611a\u4eba\u4f17\u58eb\u5175a",
|
336 |
+
"\u611a\u4eba\u4f17\u58eb\u5175b",
|
337 |
+
"\u611a\u4eba\u4f17\u58eb\u5175c",
|
338 |
+
"\u611a\u4eba\u4f17a",
|
339 |
+
"\u611a\u4eba\u4f17b",
|
340 |
+
"\u98de\u98de",
|
341 |
+
"\u83f2\u5229\u514b\u65af",
|
342 |
+
"\u5973\u6027\u8ddf\u968f\u8005",
|
343 |
+
"\u9022\u5ca9",
|
344 |
+
"\u6446\u6e21\u4eba",
|
345 |
+
"\u72c2\u8e81\u7684\u7537\u4eba",
|
346 |
+
"\u5965\u5179",
|
347 |
+
"\u8299\u841d\u62c9",
|
348 |
+
"\u8ddf\u968f\u8005",
|
349 |
+
"\u871c\u6c41\u751f\u7269",
|
350 |
+
"\u9ec4\u9ebb\u5b50",
|
351 |
+
"\u6e0a\u4e0a",
|
352 |
+
"\u85e4\u6728",
|
353 |
+
"\u6df1\u89c1",
|
354 |
+
"\u798f\u672c",
|
355 |
+
"\u8299\u84c9",
|
356 |
+
"\u53e4\u6cfd",
|
357 |
+
"\u53e4\u7530",
|
358 |
+
"\u53e4\u5c71",
|
359 |
+
"\u53e4\u8c37\u6607",
|
360 |
+
"\u5085\u4e09\u513f",
|
361 |
+
"\u9ad8\u8001\u516d",
|
362 |
+
"\u77ff\u5de5\u5192",
|
363 |
+
"\u5143\u592a",
|
364 |
+
"\u5fb7\u5b89\u516c",
|
365 |
+
"\u8302\u624d\u516c",
|
366 |
+
"\u6770\u62c9\u5fb7",
|
367 |
+
"\u845b\u7f57\u4e3d",
|
368 |
+
"\u91d1\u5ffd\u5f8b",
|
369 |
+
"\u516c\u4fca",
|
370 |
+
"\u9505\u5df4",
|
371 |
+
"\u6b4c\u5fb7",
|
372 |
+
"\u963f\u8c6a",
|
373 |
+
"\u72d7\u4e09\u513f",
|
374 |
+
"\u845b\u745e\u4e1d",
|
375 |
+
"\u82e5\u5fc3",
|
376 |
+
"\u963f\u5c71\u5a46",
|
377 |
+
"\u602a\u9e1f",
|
378 |
+
"\u5e7f\u7af9",
|
379 |
+
"\u89c2\u6d77",
|
380 |
+
"\u5173\u5b8f",
|
381 |
+
"\u871c\u6c41\u536b\u5175",
|
382 |
+
"\u5b88\u536b1",
|
383 |
+
"\u50b2\u6162\u7684\u5b88\u536b",
|
384 |
+
"\u5bb3\u6015\u7684\u5b88\u536b",
|
385 |
+
"\u8d35\u5b89",
|
386 |
+
"\u76d6\u4f0a",
|
387 |
+
"\u963f\u521b",
|
388 |
+
"\u54c8\u592b\u4e39",
|
389 |
+
"\u65e5\u8bed\u963f\u8d1d\u591a\uff08\u91ce\u5c9b\u5065\u513f\uff09",
|
390 |
+
"\u65e5\u8bed\u57c3\u6d1b\u4f0a\uff08\u9ad8\u57a3\u5f69\u9633\uff09",
|
391 |
+
"\u65e5\u8bed\u5b89\u67cf\uff08\u77f3\u89c1\u821e\u83dc\u9999\uff09",
|
392 |
+
"\u65e5\u8bed\u795e\u91cc\u7eeb\u534e\uff08\u65e9\u89c1\u6c99\u7ec7\uff09",
|
393 |
+
"\u65e5\u8bed\u795e\u91cc\u7eeb\u4eba\uff08\u77f3\u7530\u5f70\uff09",
|
394 |
+
"\u65e5\u8bed\u767d\u672f\uff08\u6e38\u4f50\u6d69\u4e8c\uff09",
|
395 |
+
"\u65e5\u8bed\u82ad\u82ad\u62c9\uff08\u9b3c\u5934\u660e\u91cc\uff09",
|
396 |
+
"\u65e5\u8bed\u5317\u6597\uff08\u5c0f\u6e05\u6c34\u4e9a\u7f8e\uff09",
|
397 |
+
"\u65e5\u8bed\u73ed\u5c3c\u7279\uff08\u9022\u5742\u826f\u592a\uff09",
|
398 |
+
"\u65e5\u8bed\u574e\u8482\u4e1d\uff08\u67da\u6728\u51c9\u9999\uff09",
|
399 |
+
"\u65e5\u8bed\u91cd\u4e91\uff08\u9f50\u85e4\u58ee\u9a6c\uff09",
|
400 |
+
"\u65e5\u8bed\u67ef\u83b1\uff08\u524d\u5ddd\u51c9\u5b50\uff09",
|
401 |
+
"\u65e5\u8bed\u8d5b\u8bfa\uff08\u5165\u91ce\u81ea\u7531\uff09",
|
402 |
+
"\u65e5\u8bed\u6234\u56e0\u65af\u96f7\u5e03\uff08\u6d25\u7530\u5065\u6b21\u90ce\uff09",
|
403 |
+
"\u65e5\u8bed\u8fea\u5362\u514b\uff08\u5c0f\u91ce\u8d24\u7ae0\uff09",
|
404 |
+
"\u65e5\u8bed\u8fea\u5965\u5a1c\uff08\u4e95\u6cfd\u8bd7\u7ec7\uff09",
|
405 |
+
"\u65e5\u8bed\u591a\u8389\uff08\u91d1\u7530\u670b\u5b50\uff09",
|
406 |
+
"\u65e5\u8bed\u4f18\u83c8\uff08\u4f50\u85e4\u5229\u5948\uff09",
|
407 |
+
"\u65e5\u8bed\u83f2\u8c22\u5c14\uff08\u5185\u7530\u771f\u793c\uff09",
|
408 |
+
"\u65e5\u8bed\u7518\u96e8\uff08\u4e0a\u7530\u4e3d\u5948\uff09",
|
409 |
+
"\u65e5\u8bed\uff08\u7560\u4e2d\u7950\uff09",
|
410 |
+
"\u65e5\u8bed\u9e7f\u91ce\u9662\u5e73\u85cf\uff08\u4e95\u53e3\u7950\u4e00\uff09",
|
411 |
+
"\u65e5\u8bed\u7a7a\uff08\u5800\u6c5f\u77ac\uff09",
|
412 |
+
"\u65e5\u8bed\u8367\uff08\u60a0\u6728\u78a7\uff09",
|
413 |
+
"\u65e5\u8bed\u80e1\u6843\uff08\u9ad8\u6865\u674e\u4f9d\uff09",
|
414 |
+
"\u65e5\u8bed\u4e00\u6597\uff08\u897f\u5ddd\u8d35\u6559\uff09",
|
415 |
+
"\u65e5\u8bed\u51ef\u4e9a\uff08\u9e1f\u6d77\u6d69\u8f85\uff09",
|
416 |
+
"\u65e5\u8bed\u4e07\u53f6\uff08\u5c9b\u5d0e\u4fe1\u957f\uff09",
|
417 |
+
"\u65e5\u8bed\u523b\u6674\uff08\u559c\u591a\u6751\u82f1\u68a8\uff09",
|
418 |
+
"\u65e5\u8bed\u53ef\u8389\uff08\u4e45\u91ce\u7f8e\u54b2\uff09",
|
419 |
+
"\u65e5\u8bed\u5fc3\u6d77\uff08\u4e09\u68ee\u94c3\u5b50\uff09",
|
420 |
+
"\u65e5\u8bed\u4e5d\u6761\u88df\u7f57\uff08\u6fd1\u6237\u9ebb\u6c99\u7f8e\uff09",
|
421 |
+
"\u65e5\u8bed\u4e3d\u838e\uff08\u7530\u4e2d\u7406\u60e0\uff09",
|
422 |
+
"\u65e5\u8bed\u83ab\u5a1c\uff08\u5c0f\u539f\u597d\u7f8e\uff09",
|
423 |
+
"\u65e5\u8bed\u7eb3\u897f\u59b2\uff08\u7530\u6751\u7531\u52a0\u8389\uff09",
|
424 |
+
"\u65e5\u8bed\u59ae\u9732\uff08\u91d1\u5143\u5bff\u5b50\uff09",
|
425 |
+
"\u65e5\u8bed\u51dd\u5149\uff08\u5927\u539f\u6c99\u8036\u9999\uff09",
|
426 |
+
"\u65e5\u8bed\u8bfa\u827e\u5c14\uff08\u9ad8\u5c3e\u594f\u97f3\uff09",
|
427 |
+
"\u65e5\u8bed\u5965\u5179\uff08\u589e\u8c37\u5eb7\u7eaa\uff09",
|
428 |
+
"\u65e5\u8bed\u6d3e\u8499\uff08\u53e4\u8d3a\u8475\uff09",
|
429 |
+
"\u65e5\u8bed\u7434\uff08\u658b\u85e4\u5343\u548c\uff09",
|
430 |
+
"\u65e5\u8bed\u4e03\u4e03\uff08\u7530\u6751\u7531\u52a0\u8389\uff09",
|
431 |
+
"\u65e5\u8bed\u96f7\u7535\u5c06\u519b\uff08\u6cfd\u57ce\u7f8e\u96ea\uff09",
|
432 |
+
"\u65e5\u8bed\u96f7\u6cfd\uff08\u5185\u5c71\u6602\u8f89\uff09",
|
433 |
+
"\u65e5\u8bed\u7f57\u838e\u8389\u4e9a\uff08\u52a0\u9688\u4e9a\u8863\uff09",
|
434 |
+
"\u65e5\u8bed\u65e9\u67da\uff08\u6d32\u5d0e\u7eeb\uff09",
|
435 |
+
"\u65e5\u8bed\u6563\u5175\uff08\u67ff\u539f\u5f7b\u4e5f\uff09",
|
436 |
+
"\u65e5\u8bed\u7533\u9e64\uff08\u5ddd\u6f84\u7eeb\u5b50\uff09",
|
437 |
+
"\u65e5\u8bed\u4e45\u5c90\u5fcd\uff08\u6c34\u6865\u9999\u7ec7\uff09",
|
438 |
+
"\u65e5\u8bed\u5973\u58eb\uff08\u5e84\u5b50\u88d5\u8863\uff09",
|
439 |
+
"\u65e5\u8bed\u7802\u7cd6\uff08\u85e4\u7530\u831c\uff09",
|
440 |
+
"\u65e5\u8bed\u8fbe\u8fbe\u5229\u4e9a\uff08\u6728\u6751\u826f\u5e73\uff09",
|
441 |
+
"\u65e5\u8bed\u6258\u9a6c\uff08\u68ee\u7530\u6210\u4e00\uff09",
|
442 |
+
"\u65e5\u8bed\u63d0\u7eb3\u91cc\uff08\u5c0f\u6797\u6c99\u82d7\uff09",
|
443 |
+
"\u65e5\u8bed\u6e29\u8fea\uff08\u6751\u6fd1\u6b65\uff09",
|
444 |
+
"\u65e5\u8bed\u9999\u83f1\uff08\u5c0f\u6cfd\u4e9a\u674e\uff09",
|
445 |
+
"\u65e5\u8bed\u9b48\uff08\u677e\u5188\u796f\u4e1e\uff09",
|
446 |
+
"\u65e5\u8bed\u884c\u79cb\uff08\u7686\u5ddd\u7eaf\u5b50\uff09",
|
447 |
+
"\u65e5\u8bed\u8f9b\u7131\uff08\u9ad8\u6865\u667a\u79cb\uff09",
|
448 |
+
"\u65e5\u8bed\u516b\u91cd\u795e\u5b50\uff08\u4f50\u4ed3\u7eeb\u97f3\uff09",
|
449 |
+
"\u65e5\u8bed\u70df\u7eef\uff08\u82b1\u5b88\u7531\u7f8e\u91cc\uff09",
|
450 |
+
"\u65e5\u8bed\u591c\u5170\uff08\u8fdc\u85e4\u7eeb\uff09",
|
451 |
+
"\u65e5\u8bed\u5bb5\u5bab\uff08\u690d\u7530\u4f73\u5948\uff09",
|
452 |
+
"\u65e5\u8bed\u4e91\u5807\uff08\u5c0f\u5ca9\u4e95\u5c0f\u9e1f\uff09",
|
453 |
+
"\u65e5\u8bed\u949f\u79bb\uff08\u524d\u91ce\u667a\u662d\uff09",
|
454 |
+
"\u6770\u514b",
|
455 |
+
"\u963f\u5409",
|
456 |
+
"\u6c5f\u821f",
|
457 |
+
"\u9274\u79cb",
|
458 |
+
"\u5609\u4e49",
|
459 |
+
"\u7eaa\u82b3",
|
460 |
+
"\u666f\u6f84",
|
461 |
+
"\u7ecf\u7eb6",
|
462 |
+
"\u666f\u660e",
|
463 |
+
"\u664b\u4f18",
|
464 |
+
"\u963f\u9e20",
|
465 |
+
"\u9152\u5ba2",
|
466 |
+
"\u4e54\u5c14",
|
467 |
+
"\u4e54\u745f\u592b",
|
468 |
+
"\u7ea6\u987f",
|
469 |
+
"\u4e54\u4f0a\u65af",
|
470 |
+
"\u5c45\u5b89",
|
471 |
+
"\u541b\u541b",
|
472 |
+
"\u987a\u5409",
|
473 |
+
"\u7eaf\u4e5f",
|
474 |
+
"\u91cd\u4f50",
|
475 |
+
"\u5927\u5c9b\u7eaf\u5e73",
|
476 |
+
"\u84b2\u6cfd",
|
477 |
+
"\u52d8\u89e3\u7531\u5c0f\u8def\u5065\u4e09\u90ce",
|
478 |
+
"\u67ab",
|
479 |
+
"\u67ab\u539f\u4e49\u5e86",
|
480 |
+
"\u836b\u5c71",
|
481 |
+
"\u7532\u6590\u7530\u9f8d\u99ac",
|
482 |
+
"\u6d77\u6597",
|
483 |
+
"\u60df\u795e\u6674\u4e4b\u4ecb",
|
484 |
+
"\u9e7f\u91ce\u5948\u5948",
|
485 |
+
"\u5361\u7435\u8389\u4e9a",
|
486 |
+
"\u51ef\u745f\u7433",
|
487 |
+
"\u52a0\u85e4\u4fe1\u609f",
|
488 |
+
"\u52a0\u85e4\u6d0b\u5e73",
|
489 |
+
"\u80dc\u5bb6",
|
490 |
+
"\u8305\u847a\u4e00\u5e86",
|
491 |
+
"\u548c\u662d",
|
492 |
+
"\u4e00\u6b63",
|
493 |
+
"\u4e00\u9053",
|
494 |
+
"\u6842\u4e00",
|
495 |
+
"\u5e86\u6b21\u90ce",
|
496 |
+
"\u963f\u8d24",
|
497 |
+
"\u5065\u53f8",
|
498 |
+
"\u5065\u6b21\u90ce",
|
499 |
+
"\u5065\u4e09\u90ce",
|
500 |
+
"\u5929\u7406",
|
501 |
+
"\u6740\u624ba",
|
502 |
+
"\u6740\u624bb",
|
503 |
+
"\u6728\u5357\u674f\u5948",
|
504 |
+
"\u6728\u6751",
|
505 |
+
"\u56fd\u738b",
|
506 |
+
"\u6728\u4e0b",
|
507 |
+
"\u5317\u6751",
|
508 |
+
"\u6e05\u60e0",
|
509 |
+
"\u6e05\u4eba",
|
510 |
+
"\u514b\u5217\u95e8\u7279",
|
511 |
+
"\u9a91\u58eb",
|
512 |
+
"\u5c0f\u6797",
|
513 |
+
"\u5c0f\u6625",
|
514 |
+
"\u5eb7\u62c9\u5fb7",
|
515 |
+
"\u5927\u8089\u4e38",
|
516 |
+
"\u7434\u7f8e",
|
517 |
+
"\u5b8f\u4e00",
|
518 |
+
"\u5eb7\u4ecb",
|
519 |
+
"\u5e78\u5fb7",
|
520 |
+
"\u9ad8\u5584",
|
521 |
+
"\u68a2",
|
522 |
+
"\u514b\u7f57\u7d22",
|
523 |
+
"\u4e45\u4fdd",
|
524 |
+
"\u4e5d\u6761\u9570\u6cbb",
|
525 |
+
"\u4e45\u6728\u7530",
|
526 |
+
"\u6606\u94a7",
|
527 |
+
"\u83ca\u5730\u541b",
|
528 |
+
"\u4e45\u5229\u987b",
|
529 |
+
"\u9ed1\u7530",
|
530 |
+
"\u9ed1\u6cfd\u4eac\u4e4b\u4ecb",
|
531 |
+
"\u54cd\u592a",
|
532 |
+
"\u5c9a\u59d0",
|
533 |
+
"\u5170\u6eaa",
|
534 |
+
"\u6f9c\u9633",
|
535 |
+
"\u52b3\u4f26\u65af",
|
536 |
+
"\u4e50\u660e",
|
537 |
+
"\u83b1\u8bfa",
|
538 |
+
"\u83b2",
|
539 |
+
"\u826f\u5b50",
|
540 |
+
"\u674e\u5f53",
|
541 |
+
"\u674e\u4e01",
|
542 |
+
"\u5c0f\u4e50",
|
543 |
+
"\u7075",
|
544 |
+
"\u5c0f\u73b2",
|
545 |
+
"\u7433\u7405a",
|
546 |
+
"\u7433\u7405b",
|
547 |
+
"\u5c0f\u5f6c",
|
548 |
+
"\u5c0f\u5fb7",
|
549 |
+
"\u5c0f\u697d",
|
550 |
+
"\u5c0f\u9f99",
|
551 |
+
"\u5c0f\u5434",
|
552 |
+
"\u5c0f\u5434\u7684\u8bb0\u5fc6",
|
553 |
+
"\u7406\u6b63",
|
554 |
+
"\u963f\u9f99",
|
555 |
+
"\u5362\u5361",
|
556 |
+
"\u6d1b\u6210",
|
557 |
+
"\u7f57\u5de7",
|
558 |
+
"\u5317\u98ce\u72fc",
|
559 |
+
"\u5362\u6b63",
|
560 |
+
"\u840d\u59e5\u59e5",
|
561 |
+
"\u524d\u7530",
|
562 |
+
"\u771f\u663c",
|
563 |
+
"\u9ebb\u7eaa",
|
564 |
+
"\u771f",
|
565 |
+
"\u611a\u4eba\u4f17-\u9a6c\u514b\u897f\u59c6",
|
566 |
+
"\u5973\u6027a",
|
567 |
+
"\u5973\u6027b",
|
568 |
+
"\u5973\u6027a\u7684\u8ddf\u968f\u8005",
|
569 |
+
"\u963f\u5b88",
|
570 |
+
"\u739b\u683c\u4e3d\u7279",
|
571 |
+
"\u771f\u7406",
|
572 |
+
"\u739b\u4e54\u4e3d",
|
573 |
+
"\u739b\u6587",
|
574 |
+
"\u6b63\u80dc",
|
575 |
+
"\u660c\u4fe1",
|
576 |
+
"\u5c06\u53f8",
|
577 |
+
"\u6b63\u4eba",
|
578 |
+
"\u8def\u7237",
|
579 |
+
"\u8001\u7ae0",
|
580 |
+
"\u677e\u7530",
|
581 |
+
"\u677e\u672c",
|
582 |
+
"\u677e\u6d66",
|
583 |
+
"\u677e\u5742",
|
584 |
+
"\u8001\u5b5f",
|
585 |
+
"\u5b5f\u4e39",
|
586 |
+
"\u5546\u4eba\u968f\u4ece",
|
587 |
+
"\u4f20\u4ee4\u5175",
|
588 |
+
"\u7c73\u6b47\u5c14",
|
589 |
+
"\u5fa1\u8206\u6e90\u4e00\u90ce",
|
590 |
+
"\u5fa1\u8206\u6e90\u6b21\u90ce",
|
591 |
+
"\u5343\u5ca9\u519b\u6559\u5934",
|
592 |
+
"\u5343\u5ca9\u519b\u58eb\u5175",
|
593 |
+
"\u660e\u535a",
|
594 |
+
"\u660e\u4fca",
|
595 |
+
"\u7f8e\u94c3",
|
596 |
+
"\u7f8e\u548c",
|
597 |
+
"\u963f\u5e78",
|
598 |
+
"\u524a\u6708\u7b51\u9633\u771f\u541b",
|
599 |
+
"\u94b1\u773c\u513f",
|
600 |
+
"\u68ee\u5f66",
|
601 |
+
"\u5143\u52a9",
|
602 |
+
"\u7406\u6c34\u53e0\u5c71\u771f\u541b",
|
603 |
+
"\u7406\u6c34\u758a\u5c71\u771f\u541b",
|
604 |
+
"\u6731\u8001\u677f",
|
605 |
+
"\u6728\u6728",
|
606 |
+
"\u6751\u4e0a",
|
607 |
+
"\u6751\u7530",
|
608 |
+
"\u6c38\u91ce",
|
609 |
+
"\u957f\u91ce\u539f\u9f99\u4e4b\u4ecb",
|
610 |
+
"\u957f\u6fd1",
|
611 |
+
"\u4e2d\u91ce\u5fd7\u4e43",
|
612 |
+
"\u83dc\u83dc\u5b50",
|
613 |
+
"\u6960\u6960",
|
614 |
+
"\u6210\u6fd1",
|
615 |
+
"\u963f\u5185",
|
616 |
+
"\u5b81\u7984",
|
617 |
+
"\u725b\u5fd7",
|
618 |
+
"\u4fe1\u535a",
|
619 |
+
"\u4f38\u592b",
|
620 |
+
"\u91ce\u65b9",
|
621 |
+
"\u8bfa\u62c9",
|
622 |
+
"\u7eaa\u9999",
|
623 |
+
"\u8bfa\u66fc",
|
624 |
+
"\u4fee\u5973",
|
625 |
+
"\u7eaf\u6c34\u7cbe\u7075",
|
626 |
+
"\u5c0f\u5ddd",
|
627 |
+
"\u5c0f\u4ed3\u6faa",
|
628 |
+
"\u5188\u6797",
|
629 |
+
"\u5188\u5d0e\u7ed8\u91cc\u9999",
|
630 |
+
"\u5188\u5d0e\u9646\u6597",
|
631 |
+
"\u5965\u62c9\u592b",
|
632 |
+
"\u8001\u79d1",
|
633 |
+
"\u9b3c\u5a46\u5a46",
|
634 |
+
"\u5c0f\u91ce\u5bfa",
|
635 |
+
"\u5927\u6cb3\u539f\u4e94\u53f3\u536b\u95e8",
|
636 |
+
"\u5927\u4e45\u4fdd\u5927\u4ecb",
|
637 |
+
"\u5927\u68ee",
|
638 |
+
"\u5927\u52a9",
|
639 |
+
"\u5965\u7279",
|
640 |
+
"\u6d3e\u8499",
|
641 |
+
"\u6d3e\u84992",
|
642 |
+
"\u75c5\u4ebaa",
|
643 |
+
"\u75c5\u4ebab",
|
644 |
+
"\u5df4\u987f",
|
645 |
+
"\u6d3e\u6069",
|
646 |
+
"\u670b\u4e49",
|
647 |
+
"\u56f4\u89c2\u7fa4\u4f17",
|
648 |
+
"\u56f4\u89c2\u7fa4\u4f17a",
|
649 |
+
"\u56f4\u89c2\u7fa4\u4f17b",
|
650 |
+
"\u56f4\u89c2\u7fa4\u4f17c",
|
651 |
+
"\u56f4\u89c2\u7fa4\u4f17d",
|
652 |
+
"\u56f4\u89c2\u7fa4\u4f17e",
|
653 |
+
"\u94dc\u96c0",
|
654 |
+
"\u963f\u80a5",
|
655 |
+
"\u5174\u53d4",
|
656 |
+
"\u8001\u5468\u53d4",
|
657 |
+
"\u516c\u4e3b",
|
658 |
+
"\u5f7c\u5f97",
|
659 |
+
"\u4e7e\u5b50",
|
660 |
+
"\u828a\u828a",
|
661 |
+
"\u4e7e\u73ae",
|
662 |
+
"\u7eee\u547d",
|
663 |
+
"\u675e\u5e73",
|
664 |
+
"\u79cb\u6708",
|
665 |
+
"\u6606\u6069",
|
666 |
+
"\u96f7\u7535\u5f71",
|
667 |
+
"\u5170\u9053\u5c14",
|
668 |
+
"\u96f7\u8499\u5fb7",
|
669 |
+
"\u5192\u5931\u7684\u5e15\u62c9\u5fb7",
|
670 |
+
"\u4f36\u4e00",
|
671 |
+
"\u73b2\u82b1",
|
672 |
+
"\u963f\u4ec1",
|
673 |
+
"\u5bb6\u81e3\u4eec",
|
674 |
+
"\u68a8\u7ed8",
|
675 |
+
"\u8363\u6c5f",
|
676 |
+
"\u620e\u4e16",
|
677 |
+
"\u6d6a\u4eba",
|
678 |
+
"\u7f57\u4f0a\u65af",
|
679 |
+
"\u5982\u610f",
|
680 |
+
"\u51c9\u5b50",
|
681 |
+
"\u5f69\u9999",
|
682 |
+
"\u9152\u4e95",
|
683 |
+
"\u5742\u672c",
|
684 |
+
"\u6714\u6b21\u90ce",
|
685 |
+
"\u6b66\u58eba",
|
686 |
+
"\u6b66\u58ebb",
|
687 |
+
"\u6b66\u58ebc",
|
688 |
+
"\u6b66\u58ebd",
|
689 |
+
"\u73ca\u745a",
|
690 |
+
"\u4e09\u7530",
|
691 |
+
"\u838e\u62c9",
|
692 |
+
"\u7b39\u91ce",
|
693 |
+
"\u806a\u7f8e",
|
694 |
+
"\u806a",
|
695 |
+
"\u5c0f\u767e\u5408",
|
696 |
+
"\u6563\u5175",
|
697 |
+
"\u5bb3\u6015\u7684\u5c0f\u5218",
|
698 |
+
"\u8212\u4f2f\u7279",
|
699 |
+
"\u8212\u8328",
|
700 |
+
"\u6d77\u9f99",
|
701 |
+
"\u4e16\u5b50",
|
702 |
+
"\u8c22\u5c14\u76d6",
|
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]
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}
|
gitattributes.txt
ADDED
@@ -0,0 +1,38 @@
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|
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*.safetensors filter=lfs diff=lfs merge=lfs -text
|
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
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*.tar.* filter=lfs diff=lfs merge=lfs -text
|
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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doom/cover.png filter=lfs diff=lfs merge=lfs -text
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zenyatta/cover.png filter=lfs diff=lfs merge=lfs -text
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pretrained_models/doom/cover.png filter=lfs diff=lfs merge=lfs -text
|
38 |
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pretrained_models/zenyatta/cover.png filter=lfs diff=lfs merge=lfs -text
|
gitignore.txt
ADDED
@@ -0,0 +1,382 @@
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|
|
1 |
+
## Ignore Visual Studio temporary files, build results, and
|
2 |
+
## files generated by popular Visual Studio add-ons.
|
3 |
+
##
|
4 |
+
## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore
|
5 |
+
|
6 |
+
# User-specific files
|
7 |
+
*.rsuser
|
8 |
+
*.suo
|
9 |
+
*.user
|
10 |
+
*.userosscache
|
11 |
+
*.sln.docstates
|
12 |
+
|
13 |
+
# User-specific files (MonoDevelop/Xamarin Studio)
|
14 |
+
*.userprefs
|
15 |
+
|
16 |
+
# Mono auto generated files
|
17 |
+
mono_crash.*
|
18 |
+
|
19 |
+
# Build results
|
20 |
+
[Dd]ebug/
|
21 |
+
[Dd]ebugPublic/
|
22 |
+
[Rr]elease/
|
23 |
+
[Rr]eleases/
|
24 |
+
x64/
|
25 |
+
x86/
|
26 |
+
[Ww][Ii][Nn]32/
|
27 |
+
[Aa][Rr][Mm]/
|
28 |
+
[Aa][Rr][Mm]64/
|
29 |
+
bld/
|
30 |
+
[Bb]in/
|
31 |
+
[Oo]bj/
|
32 |
+
[Oo]ut/
|
33 |
+
[Ll]og/
|
34 |
+
[Ll]ogs/
|
35 |
+
|
36 |
+
# Visual Studio 2015/2017 cache/options directory
|
37 |
+
.vs/
|
38 |
+
# Uncomment if you have tasks that create the project's static files in wwwroot
|
39 |
+
#wwwroot/
|
40 |
+
|
41 |
+
# Visual Studio 2017 auto generated files
|
42 |
+
Generated\ Files/
|
43 |
+
|
44 |
+
# MSTest test Results
|
45 |
+
[Tt]est[Rr]esult*/
|
46 |
+
[Bb]uild[Ll]og.*
|
47 |
+
|
48 |
+
# NUnit
|
49 |
+
*.VisualState.xml
|
50 |
+
TestResult.xml
|
51 |
+
nunit-*.xml
|
52 |
+
|
53 |
+
# Build Results of an ATL Project
|
54 |
+
[Dd]ebugPS/
|
55 |
+
[Rr]eleasePS/
|
56 |
+
dlldata.c
|
57 |
+
|
58 |
+
# Benchmark Results
|
59 |
+
BenchmarkDotNet.Artifacts/
|
60 |
+
|
61 |
+
# .NET Core
|
62 |
+
project.lock.json
|
63 |
+
project.fragment.lock.json
|
64 |
+
artifacts/
|
65 |
+
|
66 |
+
# ASP.NET Scaffolding
|
67 |
+
ScaffoldingReadMe.txt
|
68 |
+
|
69 |
+
# StyleCop
|
70 |
+
StyleCopReport.xml
|
71 |
+
|
72 |
+
# Files built by Visual Studio
|
73 |
+
*_i.c
|
74 |
+
*_p.c
|
75 |
+
*_h.h
|
76 |
+
*.ilk
|
77 |
+
*.meta
|
78 |
+
*.obj
|
79 |
+
*.iobj
|
80 |
+
*.pch
|
81 |
+
*.pdb
|
82 |
+
*.ipdb
|
83 |
+
*.pgc
|
84 |
+
*.pgd
|
85 |
+
*.rsp
|
86 |
+
*.sbr
|
87 |
+
*.tlb
|
88 |
+
*.tli
|
89 |
+
*.tlh
|
90 |
+
*.tmp
|
91 |
+
*.tmp_proj
|
92 |
+
*_wpftmp.csproj
|
93 |
+
*.log
|
94 |
+
*.vspscc
|
95 |
+
*.vssscc
|
96 |
+
.builds
|
97 |
+
*.pidb
|
98 |
+
*.svclog
|
99 |
+
*.scc
|
100 |
+
|
101 |
+
# Chutzpah Test files
|
102 |
+
_Chutzpah*
|
103 |
+
|
104 |
+
# Visual C++ cache files
|
105 |
+
ipch/
|
106 |
+
*.aps
|
107 |
+
*.ncb
|
108 |
+
*.opendb
|
109 |
+
*.opensdf
|
110 |
+
*.sdf
|
111 |
+
*.cachefile
|
112 |
+
*.VC.db
|
113 |
+
*.VC.VC.opendb
|
114 |
+
|
115 |
+
# Visual Studio profiler
|
116 |
+
*.psess
|
117 |
+
*.vsp
|
118 |
+
*.vspx
|
119 |
+
*.sap
|
120 |
+
|
121 |
+
# Visual Studio Trace Files
|
122 |
+
*.e2e
|
123 |
+
|
124 |
+
# TFS 2012 Local Workspace
|
125 |
+
$tf/
|
126 |
+
|
127 |
+
# Guidance Automation Toolkit
|
128 |
+
*.gpState
|
129 |
+
|
130 |
+
# ReSharper is a .NET coding add-in
|
131 |
+
_ReSharper*/
|
132 |
+
*.[Rr]e[Ss]harper
|
133 |
+
*.DotSettings.user
|
134 |
+
|
135 |
+
# TeamCity is a build add-in
|
136 |
+
_TeamCity*
|
137 |
+
|
138 |
+
# DotCover is a Code Coverage Tool
|
139 |
+
*.dotCover
|
140 |
+
|
141 |
+
# AxoCover is a Code Coverage Tool
|
142 |
+
.axoCover/*
|
143 |
+
!.axoCover/settings.json
|
144 |
+
|
145 |
+
# Coverlet is a free, cross platform Code Coverage Tool
|
146 |
+
coverage*.json
|
147 |
+
coverage*.xml
|
148 |
+
coverage*.info
|
149 |
+
|
150 |
+
# Visual Studio code coverage results
|
151 |
+
*.coverage
|
152 |
+
*.coveragexml
|
153 |
+
|
154 |
+
# NCrunch
|
155 |
+
_NCrunch_*
|
156 |
+
.*crunch*.local.xml
|
157 |
+
nCrunchTemp_*
|
158 |
+
|
159 |
+
# MightyMoose
|
160 |
+
*.mm.*
|
161 |
+
AutoTest.Net/
|
162 |
+
|
163 |
+
# Web workbench (sass)
|
164 |
+
.sass-cache/
|
165 |
+
|
166 |
+
# Installshield output folder
|
167 |
+
[Ee]xpress/
|
168 |
+
|
169 |
+
# DocProject is a documentation generator add-in
|
170 |
+
DocProject/buildhelp/
|
171 |
+
DocProject/Help/*.HxT
|
172 |
+
DocProject/Help/*.HxC
|
173 |
+
DocProject/Help/*.hhc
|
174 |
+
DocProject/Help/*.hhk
|
175 |
+
DocProject/Help/*.hhp
|
176 |
+
DocProject/Help/Html2
|
177 |
+
DocProject/Help/html
|
178 |
+
|
179 |
+
# Click-Once directory
|
180 |
+
publish/
|
181 |
+
|
182 |
+
# Publish Web Output
|
183 |
+
*.[Pp]ublish.xml
|
184 |
+
*.azurePubxml
|
185 |
+
# Note: Comment the next line if you want to checkin your web deploy settings,
|
186 |
+
# but database connection strings (with potential passwords) will be unencrypted
|
187 |
+
*.pubxml
|
188 |
+
*.publishproj
|
189 |
+
|
190 |
+
# Microsoft Azure Web App publish settings. Comment the next line if you want to
|
191 |
+
# checkin your Azure Web App publish settings, but sensitive information contained
|
192 |
+
# in these scripts will be unencrypted
|
193 |
+
PublishScripts/
|
194 |
+
|
195 |
+
# NuGet Packages
|
196 |
+
*.nupkg
|
197 |
+
# NuGet Symbol Packages
|
198 |
+
*.snupkg
|
199 |
+
# The packages folder can be ignored because of Package Restore
|
200 |
+
**/[Pp]ackages/*
|
201 |
+
# except build/, which is used as an MSBuild target.
|
202 |
+
!**/[Pp]ackages/build/
|
203 |
+
# Uncomment if necessary however generally it will be regenerated when needed
|
204 |
+
#!**/[Pp]ackages/repositories.config
|
205 |
+
# NuGet v3's project.json files produces more ignorable files
|
206 |
+
*.nuget.props
|
207 |
+
*.nuget.targets
|
208 |
+
|
209 |
+
# Microsoft Azure Build Output
|
210 |
+
csx/
|
211 |
+
*.build.csdef
|
212 |
+
|
213 |
+
# Microsoft Azure Emulator
|
214 |
+
ecf/
|
215 |
+
rcf/
|
216 |
+
|
217 |
+
# Windows Store app package directories and files
|
218 |
+
AppPackages/
|
219 |
+
BundleArtifacts/
|
220 |
+
Package.StoreAssociation.xml
|
221 |
+
_pkginfo.txt
|
222 |
+
*.appx
|
223 |
+
*.appxbundle
|
224 |
+
*.appxupload
|
225 |
+
|
226 |
+
# Visual Studio cache files
|
227 |
+
# files ending in .cache can be ignored
|
228 |
+
*.[Cc]ache
|
229 |
+
# but keep track of directories ending in .cache
|
230 |
+
!?*.[Cc]ache/
|
231 |
+
|
232 |
+
# Others
|
233 |
+
ClientBin/
|
234 |
+
~$*
|
235 |
+
*~
|
236 |
+
*.dbmdl
|
237 |
+
*.dbproj.schemaview
|
238 |
+
*.jfm
|
239 |
+
*.pfx
|
240 |
+
*.publishsettings
|
241 |
+
orleans.codegen.cs
|
242 |
+
|
243 |
+
# Including strong name files can present a security risk
|
244 |
+
# (https://github.com/github/gitignore/pull/2483#issue-259490424)
|
245 |
+
#*.snk
|
246 |
+
|
247 |
+
# Since there are multiple workflows, uncomment next line to ignore bower_components
|
248 |
+
# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)
|
249 |
+
#bower_components/
|
250 |
+
|
251 |
+
# RIA/Silverlight projects
|
252 |
+
Generated_Code/
|
253 |
+
|
254 |
+
# Backup & report files from converting an old project file
|
255 |
+
# to a newer Visual Studio version. Backup files are not needed,
|
256 |
+
# because we have git ;-)
|
257 |
+
_UpgradeReport_Files/
|
258 |
+
Backup*/
|
259 |
+
UpgradeLog*.XML
|
260 |
+
UpgradeLog*.htm
|
261 |
+
ServiceFabricBackup/
|
262 |
+
*.rptproj.bak
|
263 |
+
|
264 |
+
# SQL Server files
|
265 |
+
*.mdf
|
266 |
+
*.ldf
|
267 |
+
*.ndf
|
268 |
+
|
269 |
+
# Business Intelligence projects
|
270 |
+
*.rdl.data
|
271 |
+
*.bim.layout
|
272 |
+
*.bim_*.settings
|
273 |
+
*.rptproj.rsuser
|
274 |
+
*- [Bb]ackup.rdl
|
275 |
+
*- [Bb]ackup ([0-9]).rdl
|
276 |
+
*- [Bb]ackup ([0-9][0-9]).rdl
|
277 |
+
|
278 |
+
# Microsoft Fakes
|
279 |
+
FakesAssemblies/
|
280 |
+
|
281 |
+
# GhostDoc plugin setting file
|
282 |
+
*.GhostDoc.xml
|
283 |
+
|
284 |
+
# Node.js Tools for Visual Studio
|
285 |
+
.ntvs_analysis.dat
|
286 |
+
node_modules/
|
287 |
+
|
288 |
+
# Visual Studio 6 build log
|
289 |
+
*.plg
|
290 |
+
|
291 |
+
# Visual Studio 6 workspace options file
|
292 |
+
*.opt
|
293 |
+
|
294 |
+
# Visual Studio 6 auto-generated workspace file (contains which files were open etc.)
|
295 |
+
*.vbw
|
296 |
+
|
297 |
+
# Visual Studio LightSwitch build output
|
298 |
+
**/*.HTMLClient/GeneratedArtifacts
|
299 |
+
**/*.DesktopClient/GeneratedArtifacts
|
300 |
+
**/*.DesktopClient/ModelManifest.xml
|
301 |
+
**/*.Server/GeneratedArtifacts
|
302 |
+
**/*.Server/ModelManifest.xml
|
303 |
+
_Pvt_Extensions
|
304 |
+
|
305 |
+
# Paket dependency manager
|
306 |
+
.paket/paket.exe
|
307 |
+
paket-files/
|
308 |
+
|
309 |
+
# FAKE - F# Make
|
310 |
+
.fake/
|
311 |
+
|
312 |
+
# CodeRush personal settings
|
313 |
+
.cr/personal
|
314 |
+
|
315 |
+
# Python Tools for Visual Studio (PTVS)
|
316 |
+
__pycache__/
|
317 |
+
|
318 |
+
|
319 |
+
# Cake - Uncomment if you are using it
|
320 |
+
# tools/**
|
321 |
+
# !tools/packages.config
|
322 |
+
|
323 |
+
# Tabs Studio
|
324 |
+
*.tss
|
325 |
+
|
326 |
+
# Telerik's JustMock configuration file
|
327 |
+
*.jmconfig
|
328 |
+
|
329 |
+
# BizTalk build output
|
330 |
+
*.btp.cs
|
331 |
+
*.btm.cs
|
332 |
+
*.odx.cs
|
333 |
+
*.xsd.cs
|
334 |
+
|
335 |
+
# OpenCover UI analysis results
|
336 |
+
OpenCover/
|
337 |
+
|
338 |
+
# Azure Stream Analytics local run output
|
339 |
+
ASALocalRun/
|
340 |
+
|
341 |
+
# MSBuild Binary and Structured Log
|
342 |
+
*.binlog
|
343 |
+
|
344 |
+
# NVidia Nsight GPU debugger configuration file
|
345 |
+
*.nvuser
|
346 |
+
|
347 |
+
# MFractors (Xamarin productivity tool) working folder
|
348 |
+
.mfractor/
|
349 |
+
|
350 |
+
# Local History for Visual Studio
|
351 |
+
.localhistory/
|
352 |
+
|
353 |
+
# BeatPulse healthcheck temp database
|
354 |
+
healthchecksdb
|
355 |
+
|
356 |
+
# Backup folder for Package Reference Convert tool in Visual Studio 2017
|
357 |
+
MigrationBackup/
|
358 |
+
|
359 |
+
# Ionide (cross platform F# VS Code tools) working folder
|
360 |
+
.ionide/
|
361 |
+
|
362 |
+
# Fody - auto-generated XML schema
|
363 |
+
FodyWeavers.xsd
|
364 |
+
|
365 |
+
# build
|
366 |
+
build
|
367 |
+
monotonic_align/core.c
|
368 |
+
*.o
|
369 |
+
*.so
|
370 |
+
*.dll
|
371 |
+
|
372 |
+
# data
|
373 |
+
/config.json
|
374 |
+
/*.pth
|
375 |
+
*.wav
|
376 |
+
/monotonic_align/monotonic_align
|
377 |
+
/resources
|
378 |
+
/MoeGoe.spec
|
379 |
+
/dist/MoeGoe
|
380 |
+
/dist
|
381 |
+
|
382 |
+
.idea
|
mel_processing.py
ADDED
@@ -0,0 +1,101 @@
|
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|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.utils.data
|
3 |
+
from librosa.filters import mel as librosa_mel_fn
|
4 |
+
|
5 |
+
MAX_WAV_VALUE = 32768.0
|
6 |
+
|
7 |
+
|
8 |
+
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
9 |
+
"""
|
10 |
+
PARAMS
|
11 |
+
------
|
12 |
+
C: compression factor
|
13 |
+
"""
|
14 |
+
return torch.log(torch.clamp(x, min=clip_val) * C)
|
15 |
+
|
16 |
+
|
17 |
+
def dynamic_range_decompression_torch(x, C=1):
|
18 |
+
"""
|
19 |
+
PARAMS
|
20 |
+
------
|
21 |
+
C: compression factor used to compress
|
22 |
+
"""
|
23 |
+
return torch.exp(x) / C
|
24 |
+
|
25 |
+
|
26 |
+
def spectral_normalize_torch(magnitudes):
|
27 |
+
output = dynamic_range_compression_torch(magnitudes)
|
28 |
+
return output
|
29 |
+
|
30 |
+
|
31 |
+
def spectral_de_normalize_torch(magnitudes):
|
32 |
+
output = dynamic_range_decompression_torch(magnitudes)
|
33 |
+
return output
|
34 |
+
|
35 |
+
|
36 |
+
mel_basis = {}
|
37 |
+
hann_window = {}
|
38 |
+
|
39 |
+
|
40 |
+
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
41 |
+
if torch.min(y) < -1.:
|
42 |
+
print('min value is ', torch.min(y))
|
43 |
+
if torch.max(y) > 1.:
|
44 |
+
print('max value is ', torch.max(y))
|
45 |
+
|
46 |
+
global hann_window
|
47 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
48 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
49 |
+
if wnsize_dtype_device not in hann_window:
|
50 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
51 |
+
|
52 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
53 |
+
y = y.squeeze(1)
|
54 |
+
|
55 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
56 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
57 |
+
|
58 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
59 |
+
return spec
|
60 |
+
|
61 |
+
|
62 |
+
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
63 |
+
global mel_basis
|
64 |
+
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
65 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
66 |
+
if fmax_dtype_device not in mel_basis:
|
67 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
68 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
69 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
70 |
+
spec = spectral_normalize_torch(spec)
|
71 |
+
return spec
|
72 |
+
|
73 |
+
|
74 |
+
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
75 |
+
if torch.min(y) < -1.:
|
76 |
+
print('min value is ', torch.min(y))
|
77 |
+
if torch.max(y) > 1.:
|
78 |
+
print('max value is ', torch.max(y))
|
79 |
+
|
80 |
+
global mel_basis, hann_window
|
81 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
82 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
83 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
84 |
+
if fmax_dtype_device not in mel_basis:
|
85 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
86 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
87 |
+
if wnsize_dtype_device not in hann_window:
|
88 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
89 |
+
|
90 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
91 |
+
y = y.squeeze(1)
|
92 |
+
|
93 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
94 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
95 |
+
|
96 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
97 |
+
|
98 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
99 |
+
spec = spectral_normalize_torch(spec)
|
100 |
+
|
101 |
+
return spec
|
models.py
ADDED
@@ -0,0 +1,533 @@
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|
|
|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import commons
|
7 |
+
import modules
|
8 |
+
import attentions
|
9 |
+
import monotonic_align
|
10 |
+
|
11 |
+
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
|
12 |
+
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
13 |
+
from commons import init_weights, get_padding
|
14 |
+
|
15 |
+
|
16 |
+
class StochasticDurationPredictor(nn.Module):
|
17 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
18 |
+
super().__init__()
|
19 |
+
filter_channels = in_channels # it needs to be removed from future version.
|
20 |
+
self.in_channels = in_channels
|
21 |
+
self.filter_channels = filter_channels
|
22 |
+
self.kernel_size = kernel_size
|
23 |
+
self.p_dropout = p_dropout
|
24 |
+
self.n_flows = n_flows
|
25 |
+
self.gin_channels = gin_channels
|
26 |
+
|
27 |
+
self.log_flow = modules.Log()
|
28 |
+
self.flows = nn.ModuleList()
|
29 |
+
self.flows.append(modules.ElementwiseAffine(2))
|
30 |
+
for i in range(n_flows):
|
31 |
+
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
32 |
+
self.flows.append(modules.Flip())
|
33 |
+
|
34 |
+
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
35 |
+
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
36 |
+
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
37 |
+
self.post_flows = nn.ModuleList()
|
38 |
+
self.post_flows.append(modules.ElementwiseAffine(2))
|
39 |
+
for i in range(4):
|
40 |
+
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
41 |
+
self.post_flows.append(modules.Flip())
|
42 |
+
|
43 |
+
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
44 |
+
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
45 |
+
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
46 |
+
if gin_channels != 0:
|
47 |
+
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
48 |
+
|
49 |
+
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
50 |
+
x = torch.detach(x)
|
51 |
+
x = self.pre(x)
|
52 |
+
if g is not None:
|
53 |
+
g = torch.detach(g)
|
54 |
+
x = x + self.cond(g)
|
55 |
+
x = self.convs(x, x_mask)
|
56 |
+
x = self.proj(x) * x_mask
|
57 |
+
|
58 |
+
if not reverse:
|
59 |
+
flows = self.flows
|
60 |
+
assert w is not None
|
61 |
+
|
62 |
+
logdet_tot_q = 0
|
63 |
+
h_w = self.post_pre(w)
|
64 |
+
h_w = self.post_convs(h_w, x_mask)
|
65 |
+
h_w = self.post_proj(h_w) * x_mask
|
66 |
+
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
67 |
+
z_q = e_q
|
68 |
+
for flow in self.post_flows:
|
69 |
+
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
70 |
+
logdet_tot_q += logdet_q
|
71 |
+
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
72 |
+
u = torch.sigmoid(z_u) * x_mask
|
73 |
+
z0 = (w - u) * x_mask
|
74 |
+
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
|
75 |
+
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
|
76 |
+
|
77 |
+
logdet_tot = 0
|
78 |
+
z0, logdet = self.log_flow(z0, x_mask)
|
79 |
+
logdet_tot += logdet
|
80 |
+
z = torch.cat([z0, z1], 1)
|
81 |
+
for flow in flows:
|
82 |
+
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
83 |
+
logdet_tot = logdet_tot + logdet
|
84 |
+
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
|
85 |
+
return nll + logq # [b]
|
86 |
+
else:
|
87 |
+
flows = list(reversed(self.flows))
|
88 |
+
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
89 |
+
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
90 |
+
for flow in flows:
|
91 |
+
z = flow(z, x_mask, g=x, reverse=reverse)
|
92 |
+
z0, z1 = torch.split(z, [1, 1], 1)
|
93 |
+
logw = z0
|
94 |
+
return logw
|
95 |
+
|
96 |
+
|
97 |
+
class DurationPredictor(nn.Module):
|
98 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
99 |
+
super().__init__()
|
100 |
+
|
101 |
+
self.in_channels = in_channels
|
102 |
+
self.filter_channels = filter_channels
|
103 |
+
self.kernel_size = kernel_size
|
104 |
+
self.p_dropout = p_dropout
|
105 |
+
self.gin_channels = gin_channels
|
106 |
+
|
107 |
+
self.drop = nn.Dropout(p_dropout)
|
108 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
109 |
+
self.norm_1 = modules.LayerNorm(filter_channels)
|
110 |
+
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
111 |
+
self.norm_2 = modules.LayerNorm(filter_channels)
|
112 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
113 |
+
|
114 |
+
if gin_channels != 0:
|
115 |
+
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
116 |
+
|
117 |
+
def forward(self, x, x_mask, g=None):
|
118 |
+
x = torch.detach(x)
|
119 |
+
if g is not None:
|
120 |
+
g = torch.detach(g)
|
121 |
+
x = x + self.cond(g)
|
122 |
+
x = self.conv_1(x * x_mask)
|
123 |
+
x = torch.relu(x)
|
124 |
+
x = self.norm_1(x)
|
125 |
+
x = self.drop(x)
|
126 |
+
x = self.conv_2(x * x_mask)
|
127 |
+
x = torch.relu(x)
|
128 |
+
x = self.norm_2(x)
|
129 |
+
x = self.drop(x)
|
130 |
+
x = self.proj(x * x_mask)
|
131 |
+
return x * x_mask
|
132 |
+
|
133 |
+
|
134 |
+
class TextEncoder(nn.Module):
|
135 |
+
def __init__(self,
|
136 |
+
n_vocab,
|
137 |
+
out_channels,
|
138 |
+
hidden_channels,
|
139 |
+
filter_channels,
|
140 |
+
n_heads,
|
141 |
+
n_layers,
|
142 |
+
kernel_size,
|
143 |
+
p_dropout):
|
144 |
+
super().__init__()
|
145 |
+
self.n_vocab = n_vocab
|
146 |
+
self.out_channels = out_channels
|
147 |
+
self.hidden_channels = hidden_channels
|
148 |
+
self.filter_channels = filter_channels
|
149 |
+
self.n_heads = n_heads
|
150 |
+
self.n_layers = n_layers
|
151 |
+
self.kernel_size = kernel_size
|
152 |
+
self.p_dropout = p_dropout
|
153 |
+
|
154 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
155 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
156 |
+
|
157 |
+
self.encoder = attentions.Encoder(
|
158 |
+
hidden_channels,
|
159 |
+
filter_channels,
|
160 |
+
n_heads,
|
161 |
+
n_layers,
|
162 |
+
kernel_size,
|
163 |
+
p_dropout)
|
164 |
+
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
165 |
+
|
166 |
+
def forward(self, x, x_lengths):
|
167 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
168 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
169 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
170 |
+
|
171 |
+
x = self.encoder(x * x_mask, x_mask)
|
172 |
+
stats = self.proj(x) * x_mask
|
173 |
+
|
174 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
175 |
+
return x, m, logs, x_mask
|
176 |
+
|
177 |
+
|
178 |
+
class ResidualCouplingBlock(nn.Module):
|
179 |
+
def __init__(self,
|
180 |
+
channels,
|
181 |
+
hidden_channels,
|
182 |
+
kernel_size,
|
183 |
+
dilation_rate,
|
184 |
+
n_layers,
|
185 |
+
n_flows=4,
|
186 |
+
gin_channels=0):
|
187 |
+
super().__init__()
|
188 |
+
self.channels = channels
|
189 |
+
self.hidden_channels = hidden_channels
|
190 |
+
self.kernel_size = kernel_size
|
191 |
+
self.dilation_rate = dilation_rate
|
192 |
+
self.n_layers = n_layers
|
193 |
+
self.n_flows = n_flows
|
194 |
+
self.gin_channels = gin_channels
|
195 |
+
|
196 |
+
self.flows = nn.ModuleList()
|
197 |
+
for i in range(n_flows):
|
198 |
+
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
199 |
+
self.flows.append(modules.Flip())
|
200 |
+
|
201 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
202 |
+
if not reverse:
|
203 |
+
for flow in self.flows:
|
204 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
205 |
+
else:
|
206 |
+
for flow in reversed(self.flows):
|
207 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
208 |
+
return x
|
209 |
+
|
210 |
+
|
211 |
+
class PosteriorEncoder(nn.Module):
|
212 |
+
def __init__(self,
|
213 |
+
in_channels,
|
214 |
+
out_channels,
|
215 |
+
hidden_channels,
|
216 |
+
kernel_size,
|
217 |
+
dilation_rate,
|
218 |
+
n_layers,
|
219 |
+
gin_channels=0):
|
220 |
+
super().__init__()
|
221 |
+
self.in_channels = in_channels
|
222 |
+
self.out_channels = out_channels
|
223 |
+
self.hidden_channels = hidden_channels
|
224 |
+
self.kernel_size = kernel_size
|
225 |
+
self.dilation_rate = dilation_rate
|
226 |
+
self.n_layers = n_layers
|
227 |
+
self.gin_channels = gin_channels
|
228 |
+
|
229 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
230 |
+
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
231 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
232 |
+
|
233 |
+
def forward(self, x, x_lengths, g=None):
|
234 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
235 |
+
x = self.pre(x) * x_mask
|
236 |
+
x = self.enc(x, x_mask, g=g)
|
237 |
+
stats = self.proj(x) * x_mask
|
238 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
239 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
240 |
+
return z, m, logs, x_mask
|
241 |
+
|
242 |
+
|
243 |
+
class Generator(torch.nn.Module):
|
244 |
+
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
245 |
+
super(Generator, self).__init__()
|
246 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
247 |
+
self.num_upsamples = len(upsample_rates)
|
248 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
249 |
+
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
250 |
+
|
251 |
+
self.ups = nn.ModuleList()
|
252 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
253 |
+
self.ups.append(weight_norm(
|
254 |
+
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
255 |
+
k, u, padding=(k-u)//2)))
|
256 |
+
|
257 |
+
self.resblocks = nn.ModuleList()
|
258 |
+
for i in range(len(self.ups)):
|
259 |
+
ch = upsample_initial_channel//(2**(i+1))
|
260 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
261 |
+
self.resblocks.append(resblock(ch, k, d))
|
262 |
+
|
263 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
264 |
+
self.ups.apply(init_weights)
|
265 |
+
|
266 |
+
if gin_channels != 0:
|
267 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
268 |
+
|
269 |
+
def forward(self, x, g=None):
|
270 |
+
x = self.conv_pre(x)
|
271 |
+
if g is not None:
|
272 |
+
x = x + self.cond(g)
|
273 |
+
|
274 |
+
for i in range(self.num_upsamples):
|
275 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
276 |
+
x = self.ups[i](x)
|
277 |
+
xs = None
|
278 |
+
for j in range(self.num_kernels):
|
279 |
+
if xs is None:
|
280 |
+
xs = self.resblocks[i*self.num_kernels+j](x)
|
281 |
+
else:
|
282 |
+
xs += self.resblocks[i*self.num_kernels+j](x)
|
283 |
+
x = xs / self.num_kernels
|
284 |
+
x = F.leaky_relu(x)
|
285 |
+
x = self.conv_post(x)
|
286 |
+
x = torch.tanh(x)
|
287 |
+
|
288 |
+
return x
|
289 |
+
|
290 |
+
def remove_weight_norm(self):
|
291 |
+
print('Removing weight norm...')
|
292 |
+
for l in self.ups:
|
293 |
+
remove_weight_norm(l)
|
294 |
+
for l in self.resblocks:
|
295 |
+
l.remove_weight_norm()
|
296 |
+
|
297 |
+
|
298 |
+
class DiscriminatorP(torch.nn.Module):
|
299 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
300 |
+
super(DiscriminatorP, self).__init__()
|
301 |
+
self.period = period
|
302 |
+
self.use_spectral_norm = use_spectral_norm
|
303 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
304 |
+
self.convs = nn.ModuleList([
|
305 |
+
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
306 |
+
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
307 |
+
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
308 |
+
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
309 |
+
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
310 |
+
])
|
311 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
312 |
+
|
313 |
+
def forward(self, x):
|
314 |
+
fmap = []
|
315 |
+
|
316 |
+
# 1d to 2d
|
317 |
+
b, c, t = x.shape
|
318 |
+
if t % self.period != 0: # pad first
|
319 |
+
n_pad = self.period - (t % self.period)
|
320 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
321 |
+
t = t + n_pad
|
322 |
+
x = x.view(b, c, t // self.period, self.period)
|
323 |
+
|
324 |
+
for l in self.convs:
|
325 |
+
x = l(x)
|
326 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
327 |
+
fmap.append(x)
|
328 |
+
x = self.conv_post(x)
|
329 |
+
fmap.append(x)
|
330 |
+
x = torch.flatten(x, 1, -1)
|
331 |
+
|
332 |
+
return x, fmap
|
333 |
+
|
334 |
+
|
335 |
+
class DiscriminatorS(torch.nn.Module):
|
336 |
+
def __init__(self, use_spectral_norm=False):
|
337 |
+
super(DiscriminatorS, self).__init__()
|
338 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
339 |
+
self.convs = nn.ModuleList([
|
340 |
+
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
341 |
+
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
342 |
+
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
343 |
+
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
344 |
+
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
345 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
346 |
+
])
|
347 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
348 |
+
|
349 |
+
def forward(self, x):
|
350 |
+
fmap = []
|
351 |
+
|
352 |
+
for l in self.convs:
|
353 |
+
x = l(x)
|
354 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
355 |
+
fmap.append(x)
|
356 |
+
x = self.conv_post(x)
|
357 |
+
fmap.append(x)
|
358 |
+
x = torch.flatten(x, 1, -1)
|
359 |
+
|
360 |
+
return x, fmap
|
361 |
+
|
362 |
+
|
363 |
+
class MultiPeriodDiscriminator(torch.nn.Module):
|
364 |
+
def __init__(self, use_spectral_norm=False):
|
365 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
366 |
+
periods = [2,3,5,7,11]
|
367 |
+
|
368 |
+
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
369 |
+
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
370 |
+
self.discriminators = nn.ModuleList(discs)
|
371 |
+
|
372 |
+
def forward(self, y, y_hat):
|
373 |
+
y_d_rs = []
|
374 |
+
y_d_gs = []
|
375 |
+
fmap_rs = []
|
376 |
+
fmap_gs = []
|
377 |
+
for i, d in enumerate(self.discriminators):
|
378 |
+
y_d_r, fmap_r = d(y)
|
379 |
+
y_d_g, fmap_g = d(y_hat)
|
380 |
+
y_d_rs.append(y_d_r)
|
381 |
+
y_d_gs.append(y_d_g)
|
382 |
+
fmap_rs.append(fmap_r)
|
383 |
+
fmap_gs.append(fmap_g)
|
384 |
+
|
385 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
386 |
+
|
387 |
+
|
388 |
+
|
389 |
+
class SynthesizerTrn(nn.Module):
|
390 |
+
"""
|
391 |
+
Synthesizer for Training
|
392 |
+
"""
|
393 |
+
|
394 |
+
def __init__(self,
|
395 |
+
n_vocab,
|
396 |
+
spec_channels,
|
397 |
+
segment_size,
|
398 |
+
inter_channels,
|
399 |
+
hidden_channels,
|
400 |
+
filter_channels,
|
401 |
+
n_heads,
|
402 |
+
n_layers,
|
403 |
+
kernel_size,
|
404 |
+
p_dropout,
|
405 |
+
resblock,
|
406 |
+
resblock_kernel_sizes,
|
407 |
+
resblock_dilation_sizes,
|
408 |
+
upsample_rates,
|
409 |
+
upsample_initial_channel,
|
410 |
+
upsample_kernel_sizes,
|
411 |
+
n_speakers=0,
|
412 |
+
gin_channels=0,
|
413 |
+
use_sdp=True,
|
414 |
+
**kwargs):
|
415 |
+
|
416 |
+
super().__init__()
|
417 |
+
self.n_vocab = n_vocab
|
418 |
+
self.spec_channels = spec_channels
|
419 |
+
self.inter_channels = inter_channels
|
420 |
+
self.hidden_channels = hidden_channels
|
421 |
+
self.filter_channels = filter_channels
|
422 |
+
self.n_heads = n_heads
|
423 |
+
self.n_layers = n_layers
|
424 |
+
self.kernel_size = kernel_size
|
425 |
+
self.p_dropout = p_dropout
|
426 |
+
self.resblock = resblock
|
427 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
428 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
429 |
+
self.upsample_rates = upsample_rates
|
430 |
+
self.upsample_initial_channel = upsample_initial_channel
|
431 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
432 |
+
self.segment_size = segment_size
|
433 |
+
self.n_speakers = n_speakers
|
434 |
+
self.gin_channels = gin_channels
|
435 |
+
|
436 |
+
self.use_sdp = use_sdp
|
437 |
+
|
438 |
+
self.enc_p = TextEncoder(n_vocab,
|
439 |
+
inter_channels,
|
440 |
+
hidden_channels,
|
441 |
+
filter_channels,
|
442 |
+
n_heads,
|
443 |
+
n_layers,
|
444 |
+
kernel_size,
|
445 |
+
p_dropout)
|
446 |
+
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
447 |
+
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
448 |
+
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
449 |
+
|
450 |
+
if use_sdp:
|
451 |
+
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
452 |
+
else:
|
453 |
+
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
|
454 |
+
|
455 |
+
if n_speakers > 1:
|
456 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
457 |
+
|
458 |
+
def forward(self, x, x_lengths, y, y_lengths, sid=None):
|
459 |
+
|
460 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
461 |
+
if self.n_speakers > 0:
|
462 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
463 |
+
else:
|
464 |
+
g = None
|
465 |
+
|
466 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
467 |
+
z_p = self.flow(z, y_mask, g=g)
|
468 |
+
|
469 |
+
with torch.no_grad():
|
470 |
+
# negative cross-entropy
|
471 |
+
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
|
472 |
+
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
|
473 |
+
neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
474 |
+
neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
475 |
+
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
|
476 |
+
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
|
477 |
+
|
478 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
479 |
+
attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
|
480 |
+
|
481 |
+
w = attn.sum(2)
|
482 |
+
if self.use_sdp:
|
483 |
+
l_length = self.dp(x, x_mask, w, g=g)
|
484 |
+
l_length = l_length / torch.sum(x_mask)
|
485 |
+
else:
|
486 |
+
logw_ = torch.log(w + 1e-6) * x_mask
|
487 |
+
logw = self.dp(x, x_mask, g=g)
|
488 |
+
l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
|
489 |
+
|
490 |
+
# expand prior
|
491 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
|
492 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
|
493 |
+
|
494 |
+
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
495 |
+
o = self.dec(z_slice, g=g)
|
496 |
+
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
497 |
+
|
498 |
+
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
|
499 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
500 |
+
if self.n_speakers > 0:
|
501 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
502 |
+
else:
|
503 |
+
g = None
|
504 |
+
|
505 |
+
if self.use_sdp:
|
506 |
+
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
|
507 |
+
else:
|
508 |
+
logw = self.dp(x, x_mask, g=g)
|
509 |
+
w = torch.exp(logw) * x_mask * length_scale
|
510 |
+
w_ceil = torch.ceil(w)
|
511 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
512 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
513 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
514 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
515 |
+
|
516 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
517 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
518 |
+
|
519 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
520 |
+
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
521 |
+
o = self.dec((z * y_mask)[:,:,:max_len], g=g)
|
522 |
+
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
523 |
+
|
524 |
+
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
|
525 |
+
assert self.n_speakers > 0, "n_speakers have to be larger than 0."
|
526 |
+
g_src = self.emb_g(sid_src).unsqueeze(-1)
|
527 |
+
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
|
528 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
|
529 |
+
z_p = self.flow(z, y_mask, g=g_src)
|
530 |
+
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
531 |
+
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
532 |
+
return o_hat, y_mask, (z, z_p, z_hat)
|
533 |
+
|
modules.py
ADDED
@@ -0,0 +1,388 @@
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|
|
|
|
|
|
|
|
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 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
8 |
+
from torch.nn.utils import weight_norm, remove_weight_norm
|
9 |
+
|
10 |
+
import commons
|
11 |
+
from commons import init_weights, get_padding
|
12 |
+
from transforms import piecewise_rational_quadratic_transform
|
13 |
+
|
14 |
+
|
15 |
+
LRELU_SLOPE = 0.1
|
16 |
+
|
17 |
+
|
18 |
+
class LayerNorm(nn.Module):
|
19 |
+
def __init__(self, channels, eps=1e-5):
|
20 |
+
super().__init__()
|
21 |
+
self.channels = channels
|
22 |
+
self.eps = eps
|
23 |
+
|
24 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
25 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
26 |
+
|
27 |
+
def forward(self, x):
|
28 |
+
x = x.transpose(1, -1)
|
29 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
30 |
+
return x.transpose(1, -1)
|
31 |
+
|
32 |
+
|
33 |
+
class ConvReluNorm(nn.Module):
|
34 |
+
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
35 |
+
super().__init__()
|
36 |
+
self.in_channels = in_channels
|
37 |
+
self.hidden_channels = hidden_channels
|
38 |
+
self.out_channels = out_channels
|
39 |
+
self.kernel_size = kernel_size
|
40 |
+
self.n_layers = n_layers
|
41 |
+
self.p_dropout = p_dropout
|
42 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
43 |
+
|
44 |
+
self.conv_layers = nn.ModuleList()
|
45 |
+
self.norm_layers = nn.ModuleList()
|
46 |
+
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
47 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
48 |
+
self.relu_drop = nn.Sequential(
|
49 |
+
nn.ReLU(),
|
50 |
+
nn.Dropout(p_dropout))
|
51 |
+
for _ in range(n_layers-1):
|
52 |
+
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
53 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
54 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
55 |
+
self.proj.weight.data.zero_()
|
56 |
+
self.proj.bias.data.zero_()
|
57 |
+
|
58 |
+
def forward(self, x, x_mask):
|
59 |
+
x_org = x
|
60 |
+
for i in range(self.n_layers):
|
61 |
+
x = self.conv_layers[i](x * x_mask)
|
62 |
+
x = self.norm_layers[i](x)
|
63 |
+
x = self.relu_drop(x)
|
64 |
+
x = x_org + self.proj(x)
|
65 |
+
return x * x_mask
|
66 |
+
|
67 |
+
|
68 |
+
class DDSConv(nn.Module):
|
69 |
+
"""
|
70 |
+
Dialted and Depth-Separable Convolution
|
71 |
+
"""
|
72 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
73 |
+
super().__init__()
|
74 |
+
self.channels = channels
|
75 |
+
self.kernel_size = kernel_size
|
76 |
+
self.n_layers = n_layers
|
77 |
+
self.p_dropout = p_dropout
|
78 |
+
|
79 |
+
self.drop = nn.Dropout(p_dropout)
|
80 |
+
self.convs_sep = nn.ModuleList()
|
81 |
+
self.convs_1x1 = nn.ModuleList()
|
82 |
+
self.norms_1 = nn.ModuleList()
|
83 |
+
self.norms_2 = nn.ModuleList()
|
84 |
+
for i in range(n_layers):
|
85 |
+
dilation = kernel_size ** i
|
86 |
+
padding = (kernel_size * dilation - dilation) // 2
|
87 |
+
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
88 |
+
groups=channels, dilation=dilation, padding=padding
|
89 |
+
))
|
90 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
91 |
+
self.norms_1.append(LayerNorm(channels))
|
92 |
+
self.norms_2.append(LayerNorm(channels))
|
93 |
+
|
94 |
+
def forward(self, x, x_mask, g=None):
|
95 |
+
if g is not None:
|
96 |
+
x = x + g
|
97 |
+
for i in range(self.n_layers):
|
98 |
+
y = self.convs_sep[i](x * x_mask)
|
99 |
+
y = self.norms_1[i](y)
|
100 |
+
y = F.gelu(y)
|
101 |
+
y = self.convs_1x1[i](y)
|
102 |
+
y = self.norms_2[i](y)
|
103 |
+
y = F.gelu(y)
|
104 |
+
y = self.drop(y)
|
105 |
+
x = x + y
|
106 |
+
return x * x_mask
|
107 |
+
|
108 |
+
|
109 |
+
class WN(torch.nn.Module):
|
110 |
+
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
111 |
+
super(WN, self).__init__()
|
112 |
+
assert(kernel_size % 2 == 1)
|
113 |
+
self.hidden_channels =hidden_channels
|
114 |
+
self.kernel_size = kernel_size,
|
115 |
+
self.dilation_rate = dilation_rate
|
116 |
+
self.n_layers = n_layers
|
117 |
+
self.gin_channels = gin_channels
|
118 |
+
self.p_dropout = p_dropout
|
119 |
+
|
120 |
+
self.in_layers = torch.nn.ModuleList()
|
121 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
122 |
+
self.drop = nn.Dropout(p_dropout)
|
123 |
+
|
124 |
+
if gin_channels != 0:
|
125 |
+
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
126 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
127 |
+
|
128 |
+
for i in range(n_layers):
|
129 |
+
dilation = dilation_rate ** i
|
130 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
131 |
+
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
132 |
+
dilation=dilation, padding=padding)
|
133 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
134 |
+
self.in_layers.append(in_layer)
|
135 |
+
|
136 |
+
# last one is not necessary
|
137 |
+
if i < n_layers - 1:
|
138 |
+
res_skip_channels = 2 * hidden_channels
|
139 |
+
else:
|
140 |
+
res_skip_channels = hidden_channels
|
141 |
+
|
142 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
143 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
144 |
+
self.res_skip_layers.append(res_skip_layer)
|
145 |
+
|
146 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
147 |
+
output = torch.zeros_like(x)
|
148 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
149 |
+
|
150 |
+
if g is not None:
|
151 |
+
g = self.cond_layer(g)
|
152 |
+
|
153 |
+
for i in range(self.n_layers):
|
154 |
+
x_in = self.in_layers[i](x)
|
155 |
+
if g is not None:
|
156 |
+
cond_offset = i * 2 * self.hidden_channels
|
157 |
+
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
158 |
+
else:
|
159 |
+
g_l = torch.zeros_like(x_in)
|
160 |
+
|
161 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(
|
162 |
+
x_in,
|
163 |
+
g_l,
|
164 |
+
n_channels_tensor)
|
165 |
+
acts = self.drop(acts)
|
166 |
+
|
167 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
168 |
+
if i < self.n_layers - 1:
|
169 |
+
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
170 |
+
x = (x + res_acts) * x_mask
|
171 |
+
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
172 |
+
else:
|
173 |
+
output = output + res_skip_acts
|
174 |
+
return output * x_mask
|
175 |
+
|
176 |
+
def remove_weight_norm(self):
|
177 |
+
if self.gin_channels != 0:
|
178 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
179 |
+
for l in self.in_layers:
|
180 |
+
torch.nn.utils.remove_weight_norm(l)
|
181 |
+
for l in self.res_skip_layers:
|
182 |
+
torch.nn.utils.remove_weight_norm(l)
|
183 |
+
|
184 |
+
|
185 |
+
class ResBlock1(torch.nn.Module):
|
186 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
187 |
+
super(ResBlock1, self).__init__()
|
188 |
+
self.convs1 = nn.ModuleList([
|
189 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
190 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
191 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
192 |
+
padding=get_padding(kernel_size, dilation[1]))),
|
193 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
194 |
+
padding=get_padding(kernel_size, dilation[2])))
|
195 |
+
])
|
196 |
+
self.convs1.apply(init_weights)
|
197 |
+
|
198 |
+
self.convs2 = nn.ModuleList([
|
199 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
200 |
+
padding=get_padding(kernel_size, 1))),
|
201 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
202 |
+
padding=get_padding(kernel_size, 1))),
|
203 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
204 |
+
padding=get_padding(kernel_size, 1)))
|
205 |
+
])
|
206 |
+
self.convs2.apply(init_weights)
|
207 |
+
|
208 |
+
def forward(self, x, x_mask=None):
|
209 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
210 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
211 |
+
if x_mask is not None:
|
212 |
+
xt = xt * x_mask
|
213 |
+
xt = c1(xt)
|
214 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
215 |
+
if x_mask is not None:
|
216 |
+
xt = xt * x_mask
|
217 |
+
xt = c2(xt)
|
218 |
+
x = xt + x
|
219 |
+
if x_mask is not None:
|
220 |
+
x = x * x_mask
|
221 |
+
return x
|
222 |
+
|
223 |
+
def remove_weight_norm(self):
|
224 |
+
for l in self.convs1:
|
225 |
+
remove_weight_norm(l)
|
226 |
+
for l in self.convs2:
|
227 |
+
remove_weight_norm(l)
|
228 |
+
|
229 |
+
|
230 |
+
class ResBlock2(torch.nn.Module):
|
231 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
232 |
+
super(ResBlock2, self).__init__()
|
233 |
+
self.convs = nn.ModuleList([
|
234 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
235 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
236 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
237 |
+
padding=get_padding(kernel_size, dilation[1])))
|
238 |
+
])
|
239 |
+
self.convs.apply(init_weights)
|
240 |
+
|
241 |
+
def forward(self, x, x_mask=None):
|
242 |
+
for c in self.convs:
|
243 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
244 |
+
if x_mask is not None:
|
245 |
+
xt = xt * x_mask
|
246 |
+
xt = c(xt)
|
247 |
+
x = xt + x
|
248 |
+
if x_mask is not None:
|
249 |
+
x = x * x_mask
|
250 |
+
return x
|
251 |
+
|
252 |
+
def remove_weight_norm(self):
|
253 |
+
for l in self.convs:
|
254 |
+
remove_weight_norm(l)
|
255 |
+
|
256 |
+
|
257 |
+
class Log(nn.Module):
|
258 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
259 |
+
if not reverse:
|
260 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
261 |
+
logdet = torch.sum(-y, [1, 2])
|
262 |
+
return y, logdet
|
263 |
+
else:
|
264 |
+
x = torch.exp(x) * x_mask
|
265 |
+
return x
|
266 |
+
|
267 |
+
|
268 |
+
class Flip(nn.Module):
|
269 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
270 |
+
x = torch.flip(x, [1])
|
271 |
+
if not reverse:
|
272 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
273 |
+
return x, logdet
|
274 |
+
else:
|
275 |
+
return x
|
276 |
+
|
277 |
+
|
278 |
+
class ElementwiseAffine(nn.Module):
|
279 |
+
def __init__(self, channels):
|
280 |
+
super().__init__()
|
281 |
+
self.channels = channels
|
282 |
+
self.m = nn.Parameter(torch.zeros(channels,1))
|
283 |
+
self.logs = nn.Parameter(torch.zeros(channels,1))
|
284 |
+
|
285 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
286 |
+
if not reverse:
|
287 |
+
y = self.m + torch.exp(self.logs) * x
|
288 |
+
y = y * x_mask
|
289 |
+
logdet = torch.sum(self.logs * x_mask, [1,2])
|
290 |
+
return y, logdet
|
291 |
+
else:
|
292 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
293 |
+
return x
|
294 |
+
|
295 |
+
|
296 |
+
class ResidualCouplingLayer(nn.Module):
|
297 |
+
def __init__(self,
|
298 |
+
channels,
|
299 |
+
hidden_channels,
|
300 |
+
kernel_size,
|
301 |
+
dilation_rate,
|
302 |
+
n_layers,
|
303 |
+
p_dropout=0,
|
304 |
+
gin_channels=0,
|
305 |
+
mean_only=False):
|
306 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
307 |
+
super().__init__()
|
308 |
+
self.channels = channels
|
309 |
+
self.hidden_channels = hidden_channels
|
310 |
+
self.kernel_size = kernel_size
|
311 |
+
self.dilation_rate = dilation_rate
|
312 |
+
self.n_layers = n_layers
|
313 |
+
self.half_channels = channels // 2
|
314 |
+
self.mean_only = mean_only
|
315 |
+
|
316 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
317 |
+
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
318 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
319 |
+
self.post.weight.data.zero_()
|
320 |
+
self.post.bias.data.zero_()
|
321 |
+
|
322 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
323 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
324 |
+
h = self.pre(x0) * x_mask
|
325 |
+
h = self.enc(h, x_mask, g=g)
|
326 |
+
stats = self.post(h) * x_mask
|
327 |
+
if not self.mean_only:
|
328 |
+
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
329 |
+
else:
|
330 |
+
m = stats
|
331 |
+
logs = torch.zeros_like(m)
|
332 |
+
|
333 |
+
if not reverse:
|
334 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
335 |
+
x = torch.cat([x0, x1], 1)
|
336 |
+
logdet = torch.sum(logs, [1,2])
|
337 |
+
return x, logdet
|
338 |
+
else:
|
339 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
340 |
+
x = torch.cat([x0, x1], 1)
|
341 |
+
return x
|
342 |
+
|
343 |
+
|
344 |
+
class ConvFlow(nn.Module):
|
345 |
+
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
346 |
+
super().__init__()
|
347 |
+
self.in_channels = in_channels
|
348 |
+
self.filter_channels = filter_channels
|
349 |
+
self.kernel_size = kernel_size
|
350 |
+
self.n_layers = n_layers
|
351 |
+
self.num_bins = num_bins
|
352 |
+
self.tail_bound = tail_bound
|
353 |
+
self.half_channels = in_channels // 2
|
354 |
+
|
355 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
356 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
357 |
+
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
358 |
+
self.proj.weight.data.zero_()
|
359 |
+
self.proj.bias.data.zero_()
|
360 |
+
|
361 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
362 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
363 |
+
h = self.pre(x0)
|
364 |
+
h = self.convs(h, x_mask, g=g)
|
365 |
+
h = self.proj(h) * x_mask
|
366 |
+
|
367 |
+
b, c, t = x0.shape
|
368 |
+
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
369 |
+
|
370 |
+
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
371 |
+
unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
|
372 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
373 |
+
|
374 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
375 |
+
unnormalized_widths,
|
376 |
+
unnormalized_heights,
|
377 |
+
unnormalized_derivatives,
|
378 |
+
inverse=reverse,
|
379 |
+
tails='linear',
|
380 |
+
tail_bound=self.tail_bound
|
381 |
+
)
|
382 |
+
|
383 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
384 |
+
logdet = torch.sum(logabsdet * x_mask, [1,2])
|
385 |
+
if not reverse:
|
386 |
+
return x, logdet
|
387 |
+
else:
|
388 |
+
return x
|
requirements.txt
ADDED
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Cython
|
2 |
+
librosa
|
3 |
+
matplotlib
|
4 |
+
numpy
|
5 |
+
phonemizer
|
6 |
+
scipy
|
7 |
+
tensorboard
|
8 |
+
torch
|
9 |
+
torchvision
|
10 |
+
Unidecode
|
11 |
+
pyopenjtalk
|
12 |
+
ffmpeg
|
13 |
+
jamo
|
14 |
+
cn2an
|
15 |
+
gradio
|
16 |
+
pypinyin
|
17 |
+
jieba
|
transforms.py
ADDED
@@ -0,0 +1,193 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch.nn import functional as F
|
3 |
+
|
4 |
+
import numpy as np
|
5 |
+
|
6 |
+
|
7 |
+
DEFAULT_MIN_BIN_WIDTH = 1e-3
|
8 |
+
DEFAULT_MIN_BIN_HEIGHT = 1e-3
|
9 |
+
DEFAULT_MIN_DERIVATIVE = 1e-3
|
10 |
+
|
11 |
+
|
12 |
+
def piecewise_rational_quadratic_transform(inputs,
|
13 |
+
unnormalized_widths,
|
14 |
+
unnormalized_heights,
|
15 |
+
unnormalized_derivatives,
|
16 |
+
inverse=False,
|
17 |
+
tails=None,
|
18 |
+
tail_bound=1.,
|
19 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
20 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
21 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
22 |
+
|
23 |
+
if tails is None:
|
24 |
+
spline_fn = rational_quadratic_spline
|
25 |
+
spline_kwargs = {}
|
26 |
+
else:
|
27 |
+
spline_fn = unconstrained_rational_quadratic_spline
|
28 |
+
spline_kwargs = {
|
29 |
+
'tails': tails,
|
30 |
+
'tail_bound': tail_bound
|
31 |
+
}
|
32 |
+
|
33 |
+
outputs, logabsdet = spline_fn(
|
34 |
+
inputs=inputs,
|
35 |
+
unnormalized_widths=unnormalized_widths,
|
36 |
+
unnormalized_heights=unnormalized_heights,
|
37 |
+
unnormalized_derivatives=unnormalized_derivatives,
|
38 |
+
inverse=inverse,
|
39 |
+
min_bin_width=min_bin_width,
|
40 |
+
min_bin_height=min_bin_height,
|
41 |
+
min_derivative=min_derivative,
|
42 |
+
**spline_kwargs
|
43 |
+
)
|
44 |
+
return outputs, logabsdet
|
45 |
+
|
46 |
+
|
47 |
+
def searchsorted(bin_locations, inputs, eps=1e-6):
|
48 |
+
bin_locations[..., -1] += eps
|
49 |
+
return torch.sum(
|
50 |
+
inputs[..., None] >= bin_locations,
|
51 |
+
dim=-1
|
52 |
+
) - 1
|
53 |
+
|
54 |
+
|
55 |
+
def unconstrained_rational_quadratic_spline(inputs,
|
56 |
+
unnormalized_widths,
|
57 |
+
unnormalized_heights,
|
58 |
+
unnormalized_derivatives,
|
59 |
+
inverse=False,
|
60 |
+
tails='linear',
|
61 |
+
tail_bound=1.,
|
62 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
63 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
64 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
65 |
+
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
66 |
+
outside_interval_mask = ~inside_interval_mask
|
67 |
+
|
68 |
+
outputs = torch.zeros_like(inputs)
|
69 |
+
logabsdet = torch.zeros_like(inputs)
|
70 |
+
|
71 |
+
if tails == 'linear':
|
72 |
+
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
73 |
+
constant = np.log(np.exp(1 - min_derivative) - 1)
|
74 |
+
unnormalized_derivatives[..., 0] = constant
|
75 |
+
unnormalized_derivatives[..., -1] = constant
|
76 |
+
|
77 |
+
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
78 |
+
logabsdet[outside_interval_mask] = 0
|
79 |
+
else:
|
80 |
+
raise RuntimeError('{} tails are not implemented.'.format(tails))
|
81 |
+
|
82 |
+
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
|
83 |
+
inputs=inputs[inside_interval_mask],
|
84 |
+
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
85 |
+
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
86 |
+
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
87 |
+
inverse=inverse,
|
88 |
+
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
|
89 |
+
min_bin_width=min_bin_width,
|
90 |
+
min_bin_height=min_bin_height,
|
91 |
+
min_derivative=min_derivative
|
92 |
+
)
|
93 |
+
|
94 |
+
return outputs, logabsdet
|
95 |
+
|
96 |
+
def rational_quadratic_spline(inputs,
|
97 |
+
unnormalized_widths,
|
98 |
+
unnormalized_heights,
|
99 |
+
unnormalized_derivatives,
|
100 |
+
inverse=False,
|
101 |
+
left=0., right=1., bottom=0., top=1.,
|
102 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
103 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
104 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
105 |
+
if torch.min(inputs) < left or torch.max(inputs) > right:
|
106 |
+
raise ValueError('Input to a transform is not within its domain')
|
107 |
+
|
108 |
+
num_bins = unnormalized_widths.shape[-1]
|
109 |
+
|
110 |
+
if min_bin_width * num_bins > 1.0:
|
111 |
+
raise ValueError('Minimal bin width too large for the number of bins')
|
112 |
+
if min_bin_height * num_bins > 1.0:
|
113 |
+
raise ValueError('Minimal bin height too large for the number of bins')
|
114 |
+
|
115 |
+
widths = F.softmax(unnormalized_widths, dim=-1)
|
116 |
+
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
117 |
+
cumwidths = torch.cumsum(widths, dim=-1)
|
118 |
+
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
|
119 |
+
cumwidths = (right - left) * cumwidths + left
|
120 |
+
cumwidths[..., 0] = left
|
121 |
+
cumwidths[..., -1] = right
|
122 |
+
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
123 |
+
|
124 |
+
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
125 |
+
|
126 |
+
heights = F.softmax(unnormalized_heights, dim=-1)
|
127 |
+
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
128 |
+
cumheights = torch.cumsum(heights, dim=-1)
|
129 |
+
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
|
130 |
+
cumheights = (top - bottom) * cumheights + bottom
|
131 |
+
cumheights[..., 0] = bottom
|
132 |
+
cumheights[..., -1] = top
|
133 |
+
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
134 |
+
|
135 |
+
if inverse:
|
136 |
+
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
137 |
+
else:
|
138 |
+
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
139 |
+
|
140 |
+
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
141 |
+
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
142 |
+
|
143 |
+
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
144 |
+
delta = heights / widths
|
145 |
+
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
146 |
+
|
147 |
+
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
148 |
+
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
149 |
+
|
150 |
+
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
151 |
+
|
152 |
+
if inverse:
|
153 |
+
a = (((inputs - input_cumheights) * (input_derivatives
|
154 |
+
+ input_derivatives_plus_one
|
155 |
+
- 2 * input_delta)
|
156 |
+
+ input_heights * (input_delta - input_derivatives)))
|
157 |
+
b = (input_heights * input_derivatives
|
158 |
+
- (inputs - input_cumheights) * (input_derivatives
|
159 |
+
+ input_derivatives_plus_one
|
160 |
+
- 2 * input_delta))
|
161 |
+
c = - input_delta * (inputs - input_cumheights)
|
162 |
+
|
163 |
+
discriminant = b.pow(2) - 4 * a * c
|
164 |
+
assert (discriminant >= 0).all()
|
165 |
+
|
166 |
+
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
167 |
+
outputs = root * input_bin_widths + input_cumwidths
|
168 |
+
|
169 |
+
theta_one_minus_theta = root * (1 - root)
|
170 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
171 |
+
* theta_one_minus_theta)
|
172 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
|
173 |
+
+ 2 * input_delta * theta_one_minus_theta
|
174 |
+
+ input_derivatives * (1 - root).pow(2))
|
175 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
176 |
+
|
177 |
+
return outputs, -logabsdet
|
178 |
+
else:
|
179 |
+
theta = (inputs - input_cumwidths) / input_bin_widths
|
180 |
+
theta_one_minus_theta = theta * (1 - theta)
|
181 |
+
|
182 |
+
numerator = input_heights * (input_delta * theta.pow(2)
|
183 |
+
+ input_derivatives * theta_one_minus_theta)
|
184 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
185 |
+
* theta_one_minus_theta)
|
186 |
+
outputs = input_cumheights + numerator / denominator
|
187 |
+
|
188 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
|
189 |
+
+ 2 * input_delta * theta_one_minus_theta
|
190 |
+
+ input_derivatives * (1 - theta).pow(2))
|
191 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
192 |
+
|
193 |
+
return outputs, logabsdet
|
utils.py
ADDED
@@ -0,0 +1,225 @@
|
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|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import sys
|
3 |
+
import argparse
|
4 |
+
import logging
|
5 |
+
import json
|
6 |
+
import subprocess
|
7 |
+
import numpy as np
|
8 |
+
import librosa
|
9 |
+
import torch
|
10 |
+
|
11 |
+
MATPLOTLIB_FLAG = False
|
12 |
+
|
13 |
+
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
14 |
+
logger = logging
|
15 |
+
|
16 |
+
|
17 |
+
def load_checkpoint(checkpoint_path, model, optimizer=None):
|
18 |
+
assert os.path.isfile(checkpoint_path)
|
19 |
+
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
20 |
+
iteration = checkpoint_dict['iteration']
|
21 |
+
learning_rate = checkpoint_dict['learning_rate']
|
22 |
+
if optimizer is not None:
|
23 |
+
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
24 |
+
saved_state_dict = checkpoint_dict['model']
|
25 |
+
if hasattr(model, 'module'):
|
26 |
+
state_dict = model.module.state_dict()
|
27 |
+
else:
|
28 |
+
state_dict = model.state_dict()
|
29 |
+
new_state_dict= {}
|
30 |
+
for k, v in state_dict.items():
|
31 |
+
try:
|
32 |
+
new_state_dict[k] = saved_state_dict[k]
|
33 |
+
except:
|
34 |
+
logger.info("%s is not in the checkpoint" % k)
|
35 |
+
new_state_dict[k] = v
|
36 |
+
if hasattr(model, 'module'):
|
37 |
+
model.module.load_state_dict(new_state_dict)
|
38 |
+
else:
|
39 |
+
model.load_state_dict(new_state_dict)
|
40 |
+
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
41 |
+
checkpoint_path, iteration))
|
42 |
+
return model, optimizer, learning_rate, iteration
|
43 |
+
|
44 |
+
|
45 |
+
def plot_spectrogram_to_numpy(spectrogram):
|
46 |
+
global MATPLOTLIB_FLAG
|
47 |
+
if not MATPLOTLIB_FLAG:
|
48 |
+
import matplotlib
|
49 |
+
matplotlib.use("Agg")
|
50 |
+
MATPLOTLIB_FLAG = True
|
51 |
+
mpl_logger = logging.getLogger('matplotlib')
|
52 |
+
mpl_logger.setLevel(logging.WARNING)
|
53 |
+
import matplotlib.pylab as plt
|
54 |
+
import numpy as np
|
55 |
+
|
56 |
+
fig, ax = plt.subplots(figsize=(10,2))
|
57 |
+
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
58 |
+
interpolation='none')
|
59 |
+
plt.colorbar(im, ax=ax)
|
60 |
+
plt.xlabel("Frames")
|
61 |
+
plt.ylabel("Channels")
|
62 |
+
plt.tight_layout()
|
63 |
+
|
64 |
+
fig.canvas.draw()
|
65 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
66 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
67 |
+
plt.close()
|
68 |
+
return data
|
69 |
+
|
70 |
+
|
71 |
+
def plot_alignment_to_numpy(alignment, info=None):
|
72 |
+
global MATPLOTLIB_FLAG
|
73 |
+
if not MATPLOTLIB_FLAG:
|
74 |
+
import matplotlib
|
75 |
+
matplotlib.use("Agg")
|
76 |
+
MATPLOTLIB_FLAG = True
|
77 |
+
mpl_logger = logging.getLogger('matplotlib')
|
78 |
+
mpl_logger.setLevel(logging.WARNING)
|
79 |
+
import matplotlib.pylab as plt
|
80 |
+
import numpy as np
|
81 |
+
|
82 |
+
fig, ax = plt.subplots(figsize=(6, 4))
|
83 |
+
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
84 |
+
interpolation='none')
|
85 |
+
fig.colorbar(im, ax=ax)
|
86 |
+
xlabel = 'Decoder timestep'
|
87 |
+
if info is not None:
|
88 |
+
xlabel += '\n\n' + info
|
89 |
+
plt.xlabel(xlabel)
|
90 |
+
plt.ylabel('Encoder timestep')
|
91 |
+
plt.tight_layout()
|
92 |
+
|
93 |
+
fig.canvas.draw()
|
94 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
95 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
96 |
+
plt.close()
|
97 |
+
return data
|
98 |
+
|
99 |
+
|
100 |
+
def load_audio_to_torch(full_path, target_sampling_rate):
|
101 |
+
audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
|
102 |
+
return torch.FloatTensor(audio.astype(np.float32))
|
103 |
+
|
104 |
+
|
105 |
+
def load_filepaths_and_text(filename, split="|"):
|
106 |
+
with open(filename, encoding='utf-8') as f:
|
107 |
+
filepaths_and_text = [line.strip().split(split) for line in f]
|
108 |
+
return filepaths_and_text
|
109 |
+
|
110 |
+
|
111 |
+
def get_hparams(init=True):
|
112 |
+
parser = argparse.ArgumentParser()
|
113 |
+
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
|
114 |
+
help='JSON file for configuration')
|
115 |
+
parser.add_argument('-m', '--model', type=str, required=True,
|
116 |
+
help='Model name')
|
117 |
+
|
118 |
+
args = parser.parse_args()
|
119 |
+
model_dir = os.path.join("./logs", args.model)
|
120 |
+
|
121 |
+
if not os.path.exists(model_dir):
|
122 |
+
os.makedirs(model_dir)
|
123 |
+
|
124 |
+
config_path = args.config
|
125 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
126 |
+
if init:
|
127 |
+
with open(config_path, "r") as f:
|
128 |
+
data = f.read()
|
129 |
+
with open(config_save_path, "w") as f:
|
130 |
+
f.write(data)
|
131 |
+
else:
|
132 |
+
with open(config_save_path, "r") as f:
|
133 |
+
data = f.read()
|
134 |
+
config = json.loads(data)
|
135 |
+
|
136 |
+
hparams = HParams(**config)
|
137 |
+
hparams.model_dir = model_dir
|
138 |
+
return hparams
|
139 |
+
|
140 |
+
|
141 |
+
def get_hparams_from_dir(model_dir):
|
142 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
143 |
+
with open(config_save_path, "r") as f:
|
144 |
+
data = f.read()
|
145 |
+
config = json.loads(data)
|
146 |
+
|
147 |
+
hparams =HParams(**config)
|
148 |
+
hparams.model_dir = model_dir
|
149 |
+
return hparams
|
150 |
+
|
151 |
+
|
152 |
+
def get_hparams_from_file(config_path):
|
153 |
+
with open(config_path, "r") as f:
|
154 |
+
data = f.read()
|
155 |
+
config = json.loads(data)
|
156 |
+
|
157 |
+
hparams =HParams(**config)
|
158 |
+
return hparams
|
159 |
+
|
160 |
+
|
161 |
+
def check_git_hash(model_dir):
|
162 |
+
source_dir = os.path.dirname(os.path.realpath(__file__))
|
163 |
+
if not os.path.exists(os.path.join(source_dir, ".git")):
|
164 |
+
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
165 |
+
source_dir
|
166 |
+
))
|
167 |
+
return
|
168 |
+
|
169 |
+
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
170 |
+
|
171 |
+
path = os.path.join(model_dir, "githash")
|
172 |
+
if os.path.exists(path):
|
173 |
+
saved_hash = open(path).read()
|
174 |
+
if saved_hash != cur_hash:
|
175 |
+
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
176 |
+
saved_hash[:8], cur_hash[:8]))
|
177 |
+
else:
|
178 |
+
open(path, "w").write(cur_hash)
|
179 |
+
|
180 |
+
|
181 |
+
def get_logger(model_dir, filename="train.log"):
|
182 |
+
global logger
|
183 |
+
logger = logging.getLogger(os.path.basename(model_dir))
|
184 |
+
logger.setLevel(logging.DEBUG)
|
185 |
+
|
186 |
+
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
187 |
+
if not os.path.exists(model_dir):
|
188 |
+
os.makedirs(model_dir)
|
189 |
+
h = logging.FileHandler(os.path.join(model_dir, filename))
|
190 |
+
h.setLevel(logging.DEBUG)
|
191 |
+
h.setFormatter(formatter)
|
192 |
+
logger.addHandler(h)
|
193 |
+
return logger
|
194 |
+
|
195 |
+
|
196 |
+
class HParams():
|
197 |
+
def __init__(self, **kwargs):
|
198 |
+
for k, v in kwargs.items():
|
199 |
+
if type(v) == dict:
|
200 |
+
v = HParams(**v)
|
201 |
+
self[k] = v
|
202 |
+
|
203 |
+
def keys(self):
|
204 |
+
return self.__dict__.keys()
|
205 |
+
|
206 |
+
def items(self):
|
207 |
+
return self.__dict__.items()
|
208 |
+
|
209 |
+
def values(self):
|
210 |
+
return self.__dict__.values()
|
211 |
+
|
212 |
+
def __len__(self):
|
213 |
+
return len(self.__dict__)
|
214 |
+
|
215 |
+
def __getitem__(self, key):
|
216 |
+
return getattr(self, key)
|
217 |
+
|
218 |
+
def __setitem__(self, key, value):
|
219 |
+
return setattr(self, key, value)
|
220 |
+
|
221 |
+
def __contains__(self, key):
|
222 |
+
return key in self.__dict__
|
223 |
+
|
224 |
+
def __repr__(self):
|
225 |
+
return self.__dict__.__repr__()
|