diff --git a/.dockerignore b/.dockerignore index a90abd2db5603c591affd806232966447397c56f..bf1b3a0378eee07e7e2d9a8480820a14bd5093da 100644 --- a/.dockerignore +++ b/.dockerignore @@ -3,23 +3,16 @@ * +!/style_bert_vits2/ + !/bert/deberta-v2-large-japanese-char-wwm/ -!/common/ !/configs/ !/dict_data/default.csv !/model_assets/ -!/monotonic_align/ -!/text/ +!/static/ -!/attentions.py -!/commons.py !/config.py !/default_config.yml -!/infer.py -!/models.py -!/models_jp_extra.py -!/modules.py +!/initialize.py !/requirements.txt !/server_editor.py -!/transforms.py -!/utils.py diff --git a/.gitignore b/.gitignore index 526ad917c67916f19bea871b584894e016344078..56e45a48a9347abee03acc8e604df779a1f991bc 100644 --- a/.gitignore +++ b/.gitignore @@ -28,7 +28,6 @@ venv/ safetensors.ipynb *.wav -/static/ # pyopenjtalk's dictionary *.dic diff --git a/App.bat b/App.bat deleted file mode 100644 index 2f2b6534bdb03d42eac717940cc35f431207db3c..0000000000000000000000000000000000000000 --- a/App.bat +++ /dev/null @@ -1,11 +0,0 @@ -chcp 65001 > NUL -@echo off - -pushd %~dp0 -echo Running app.py... -venv\Scripts\python app.py - -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -popd -pause \ No newline at end of file diff --git a/Data/.gitignore b/Data/.gitignore deleted file mode 100644 index c96a04f008ee21e260b28f7701595ed59e2839e3..0000000000000000000000000000000000000000 --- a/Data/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -* -!.gitignore \ No newline at end of file diff --git a/Dataset.bat b/Dataset.bat deleted file mode 100644 index 03d3850a0db1cb60a4b9aadd56d3993fe9a77092..0000000000000000000000000000000000000000 --- a/Dataset.bat +++ /dev/null @@ -1,11 +0,0 @@ -chcp 65001 > NUL -@echo off - -pushd %~dp0 -echo Running webui_dataset.py... -venv\Scripts\python webui_dataset.py - -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -popd -pause \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index b3259c8b59ee57195b77b81ccdccb9bf54738a34..287bd38084e5b968cc78448703848d0db1af7105 100644 --- a/Dockerfile +++ b/Dockerfile @@ -20,4 +20,4 @@ COPY --chown=user . $HOME/app RUN pip install --no-cache-dir -r $HOME/app/requirements.txt # 必要に応じて制限を変更してください -CMD ["python", "server_editor.py", "--line_length", "50", "--line_count", "3","--port", "7860"] +CMD ["python", "server_editor.py", "--line_length", "50", "--line_count", "3", "--skip_static_files", "--skip_default_models"] diff --git a/Dockerfile.train b/Dockerfile.train deleted file mode 100644 index 333fd4634229d47348abf13d00c713fbae17f4d9..0000000000000000000000000000000000000000 --- a/Dockerfile.train +++ /dev/null @@ -1,109 +0,0 @@ -# PaperspaceのGradient環境での学習環境構築用Dockerfileです。 -# 環境のみ構築するため、イメージには学習用のコードは含まれていません。 -# 以下を参照しました。 -# https://github.com/gradient-ai/base-container/tree/main/pt211-tf215-cudatk120-py311 - -# 主なバージョン等 -# Ubuntu 22.04 -# Python 3.10 -# PyTorch 2.1.2 (CUDA 11.8) -# CUDA Toolkit 12.0, CUDNN 8.9.7 - - -# ================================================================== -# Initial setup -# ------------------------------------------------------------------ - -# Ubuntu 22.04 as base image -FROM ubuntu:22.04 -# RUN yes| unminimize - -# Set ENV variables -ENV LANG C.UTF-8 -ENV SHELL=/bin/bash -ENV DEBIAN_FRONTEND=noninteractive - -ENV APT_INSTALL="apt-get install -y --no-install-recommends" -ENV PIP_INSTALL="python3 -m pip --no-cache-dir install --upgrade" -ENV GIT_CLONE="git clone --depth 10" - -# ================================================================== -# Tools -# ------------------------------------------------------------------ - -RUN apt-get update && \ - $APT_INSTALL \ - sudo \ - build-essential \ - ca-certificates \ - wget \ - curl \ - git \ - zip \ - unzip \ - nano \ - ffmpeg \ - software-properties-common \ - gnupg \ - python3 \ - python3-pip \ - python3-dev - -# ================================================================== -# Git-lfs -# ------------------------------------------------------------------ - -RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash && \ - $APT_INSTALL git-lfs - - -# Add symlink so python and python3 commands use same python3.9 executable -RUN ln -s /usr/bin/python3 /usr/local/bin/python - -# ================================================================== -# Installing CUDA packages (CUDA Toolkit 12.0 and CUDNN 8.9.7) -# ------------------------------------------------------------------ -RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin && \ - mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \ - wget https://developer.download.nvidia.com/compute/cuda/12.0.0/local_installers/cuda-repo-ubuntu2204-12-0-local_12.0.0-525.60.13-1_amd64.deb && \ - dpkg -i cuda-repo-ubuntu2204-12-0-local_12.0.0-525.60.13-1_amd64.deb && \ - cp /var/cuda-repo-ubuntu2204-12-0-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \ - apt-get update && \ - $APT_INSTALL cuda && \ - rm cuda-repo-ubuntu2204-12-0-local_12.0.0-525.60.13-1_amd64.deb - -# Installing CUDNN -RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub && \ - add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /" && \ - apt-get update && \ - $APT_INSTALL libcudnn8=8.9.7.29-1+cuda12.2 \ - libcudnn8-dev=8.9.7.29-1+cuda12.2 - - -ENV PATH=$PATH:/usr/local/cuda/bin -ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH - - -# ================================================================== -# PyTorch -# ------------------------------------------------------------------ - -# Based on https://pytorch.org/get-started/locally/ - -RUN $PIP_INSTALL torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 - - -RUN $PIP_INSTALL jupyterlab - -# Install requirements.txt from the project -COPY requirements.txt /tmp/requirements.txt -RUN $PIP_INSTALL -r /tmp/requirements.txt -RUN rm /tmp/requirements.txt - -# ================================================================== -# Startup -# ------------------------------------------------------------------ - -EXPOSE 8888 6006 - -CMD jupyter lab --allow-root --ip=0.0.0.0 --no-browser --ServerApp.trust_xheaders=True --ServerApp.disable_check_xsrf=False --ServerApp.allow_remote_access=True --ServerApp.allow_origin='*' --ServerApp.allow_credentials=True \ No newline at end of file diff --git a/Editor.bat b/Editor.bat deleted file mode 100644 index 7b0836c4fa8377cdbc8057581b29c35287c51fb1..0000000000000000000000000000000000000000 --- a/Editor.bat +++ /dev/null @@ -1,11 +0,0 @@ -chcp 65001 > NUL -@echo off - -pushd %~dp0 -echo Running server_editor.py --inbroser -venv\Scripts\python server_editor.py --inbrowser - -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -popd -pause \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 0ad25db4bd1d86c452db3f9602ccdbe172438f52..0000000000000000000000000000000000000000 --- a/LICENSE +++ /dev/null @@ -1,661 +0,0 @@ - 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If not, see . - -Also add information on how to contact you by electronic and paper mail. - - If your software can interact with users remotely through a computer -network, you should also make sure that it provides a way for users to -get its source. For example, if your program is a web application, its -interface could display a "Source" link that leads users to an archive -of the code. There are many ways you could offer source, and different -solutions will be better for different programs; see section 13 for the -specific requirements. - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU AGPL, see -. diff --git a/Merge.bat b/Merge.bat deleted file mode 100644 index 8e1dedba6dc343f9f69084b6b1e700b9b06032ce..0000000000000000000000000000000000000000 --- a/Merge.bat +++ /dev/null @@ -1,13 +0,0 @@ -chcp 65001 > NUL - -@echo off - -pushd %~dp0 - -echo Running webui_merge.py... -venv\Scripts\python webui_merge.py - -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -popd -pause \ No newline at end of file diff --git a/Style.bat b/Style.bat deleted file mode 100644 index 409cf9105b376d4526bc62c2c36d970f7b116533..0000000000000000000000000000000000000000 --- a/Style.bat +++ /dev/null @@ -1,12 +0,0 @@ -chcp 65001 > NUL - -@echo off - -pushd %~dp0 -echo Running webui_style_vectors.py... -venv\Scripts\python webui_style_vectors.py - -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -popd -pause \ No newline at end of file diff --git a/Train.bat b/Train.bat deleted file mode 100644 index 5a93d026fc2995233c193fc0b81e812d2c4f7601..0000000000000000000000000000000000000000 --- a/Train.bat +++ /dev/null @@ -1,13 +0,0 @@ -chcp 65001 > NUL - -@echo off - -pushd %~dp0 - -echo Running webui_train.py... -venv\Scripts\python webui_train.py - -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -popd -pause \ No newline at end of file diff --git a/app.py b/app.py deleted file mode 100644 index e17a0a56aecea31bb7c342ff019df143c50dcf29..0000000000000000000000000000000000000000 --- a/app.py +++ /dev/null @@ -1,500 +0,0 @@ -import argparse -import datetime -import json -import os -import sys -from pathlib import Path -from typing import Optional - -import gradio as gr -import torch -import yaml - -from common.constants import ( - DEFAULT_ASSIST_TEXT_WEIGHT, - DEFAULT_LENGTH, - DEFAULT_LINE_SPLIT, - DEFAULT_NOISE, - DEFAULT_NOISEW, - DEFAULT_SDP_RATIO, - DEFAULT_SPLIT_INTERVAL, - DEFAULT_STYLE, - DEFAULT_STYLE_WEIGHT, - GRADIO_THEME, - LATEST_VERSION, - Languages, -) -from common.log import logger -from common.tts_model import ModelHolder -from infer import InvalidToneError -from text.japanese import g2kata_tone, kata_tone2phone_tone, text_normalize - -# Get path settings -with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f: - path_config: dict[str, str] = yaml.safe_load(f.read()) - # dataset_root = path_config["dataset_root"] - assets_root = path_config["assets_root"] - -languages = [l.value for l in Languages] - - -def tts_fn( - model_name, - model_path, - text, - language, - reference_audio_path, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - line_split, - split_interval, - assist_text, - assist_text_weight, - use_assist_text, - style, - style_weight, - kata_tone_json_str, - use_tone, - speaker, - pitch_scale, - intonation_scale, -): - model_holder.load_model_gr(model_name, model_path) - - wrong_tone_message = "" - kata_tone: Optional[list[tuple[str, int]]] = None - if use_tone and kata_tone_json_str != "": - if language != "JP": - logger.warning("Only Japanese is supported for tone generation.") - wrong_tone_message = "アクセント指定は現在日本語のみ対応しています。" - if line_split: - logger.warning("Tone generation is not supported for line split.") - wrong_tone_message = ( - "アクセント指定は改行で分けて生成を使わない場合のみ対応しています。" - ) - try: - kata_tone = [] - json_data = json.loads(kata_tone_json_str) - # tupleを使うように変換 - for kana, tone in json_data: - assert isinstance(kana, str) and tone in (0, 1), f"{kana}, {tone}" - kata_tone.append((kana, tone)) - except Exception as e: - logger.warning(f"Error occurred when parsing kana_tone_json: {e}") - wrong_tone_message = f"アクセント指定が不正です: {e}" - kata_tone = None - - # toneは実際に音声合成に代入される際のみnot Noneになる - tone: Optional[list[int]] = None - if kata_tone is not None: - phone_tone = kata_tone2phone_tone(kata_tone) - tone = [t for _, t in phone_tone] - - speaker_id = model_holder.current_model.spk2id[speaker] - - start_time = datetime.datetime.now() - - try: - sr, audio = model_holder.current_model.infer( - text=text, - language=language, - reference_audio_path=reference_audio_path, - sdp_ratio=sdp_ratio, - noise=noise_scale, - noisew=noise_scale_w, - length=length_scale, - line_split=line_split, - split_interval=split_interval, - assist_text=assist_text, - assist_text_weight=assist_text_weight, - use_assist_text=use_assist_text, - style=style, - style_weight=style_weight, - given_tone=tone, - sid=speaker_id, - pitch_scale=pitch_scale, - intonation_scale=intonation_scale, - ) - except InvalidToneError as e: - logger.error(f"Tone error: {e}") - return f"Error: アクセント指定が不正です:\n{e}", None, kata_tone_json_str - except ValueError as e: - logger.error(f"Value error: {e}") - return f"Error: {e}", None, kata_tone_json_str - - end_time = datetime.datetime.now() - duration = (end_time - start_time).total_seconds() - - if tone is None and language == "JP": - # アクセント指定に使えるようにアクセント情報を返す - norm_text = text_normalize(text) - kata_tone = g2kata_tone(norm_text) - kata_tone_json_str = json.dumps(kata_tone, ensure_ascii=False) - elif tone is None: - kata_tone_json_str = "" - message = f"Success, time: {duration} seconds." - if wrong_tone_message != "": - message = wrong_tone_message + "\n" + message - return message, (sr, audio), kata_tone_json_str - - -initial_text = "こんにちは、初めまして。あなたの名前はなんていうの？" - -examples = [ - [initial_text, "JP"], - [ - """あなたがそんなこと言うなんて、私はとっても嬉しい。 -あなたがそんなこと言うなんて、私はとっても怒ってる。 -あなたがそんなこと言うなんて、私はとっても驚いてる。 -あなたがそんなこと言うなんて、私はとっても辛い。""", - "JP", - ], - [ # ChatGPTに考えてもらった告白セリフ - """私、ずっと前からあなたのことを見てきました。あなたの笑顔、優しさ、強さに、心惹かれていたんです。 -友達として過ごす中で、あなたのことがだんだんと特別な存在になっていくのがわかりました。 -えっと、私、あなたのことが好きです！もしよければ、私と付き合ってくれませんか？""", - "JP", - ], - [ # 夏目漱石『吾輩は猫である』 - """吾輩は猫である。名前はまだ無い。 -どこで生れたかとんと見当がつかぬ。なんでも薄暗いじめじめした所でニャーニャー泣いていた事だけは記憶している。 -吾輩はここで初めて人間というものを見た。しかもあとで聞くと、それは書生という、人間中で一番獰悪な種族であったそうだ。 -この書生というのは時々我々を捕まえて煮て食うという話である。""", - "JP", - ], - [ # 梶井基次郎『桜の樹の下には』 - """桜の樹の下には屍体が埋まっている！これは信じていいことなんだよ。 -何故って、桜の花があんなにも見事に咲くなんて信じられないことじゃないか。俺はあの美しさが信じられないので、このにさんにち不安だった。 -しかしいま、やっとわかるときが来た。桜の樹の下には屍体が埋まっている。これは信じていいことだ。""", - "JP", - ], - [ # ChatGPTと考えた、感情を表すセリフ - """やったー！テストで満点取れた！私とっても嬉しいな！ -どうして私の意見を無視するの？許せない！ムカつく！あんたなんか死ねばいいのに。 -あはははっ！この漫画めっちゃ笑える、見てよこれ、ふふふ、あはは。 -あなたがいなくなって、私は一人になっちゃって、泣いちゃいそうなほど悲しい。""", - "JP", - ], - [ # 上の丁寧語バージョン - """やりました！テストで満点取れましたよ！私とっても嬉しいです！ -どうして私の意見を無視するんですか？許せません！ムカつきます！あんたなんか死んでください。 -あはははっ！この漫画めっちゃ笑えます、見てくださいこれ、ふふふ、あはは。 -あなたがいなくなって、私は一人になっちゃって、泣いちゃいそうなほど悲しいです。""", - "JP", - ], - [ # ChatGPTに考えてもらった音声合成の説明文章 - """音声合成は、機械学習を活用して、テキストから人の声を再現する技術です。この技術は、言語の構造を解析し、それに基づいて音声を生成します。 -この分野の最新の研究成果を使うと、より自然で表現豊かな音声の生成が可能である。深層学習の応用により、感情やアクセントを含む声質の微妙な変化も再現することが出来る。""", - "JP", - ], - [ - "Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products.", - "EN", - ], - [ - "语音合成是人工制造人类语音。用于此目的的计算机系统称为语音合成器，可以通过软件或硬件产品实现。", - "ZH", - ], -] - -initial_md = f""" -# Style-Bert-VITS2 ver {LATEST_VERSION} 音声合成 - -- Ver 2.3で追加されたエディターのほうが実際に読み上げさせるには使いやすいかもしれません。`Editor.bat`か`python server_editor.py`で起動できます。 - -- 初期からある[jvnvのモデル](https://huggingface.co/litagin/style_bert_vits2_jvnv)は、[JVNVコーパス（言語音声と非言語音声を持つ日本語感情音声コーパス）](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus)で学習されたモデルです。ライセンスは[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ja)です。 -""" - -how_to_md = """ -下のように`model_assets`ディレクトリの中にモデルファイルたちを置いてください。 -``` -model_assets -├── your_model -│ ├── config.json -│ ├── your_model_file1.safetensors -│ ├── your_model_file2.safetensors -│ ├── ... -│ └── style_vectors.npy -└── another_model - ├── ... -``` -各モデルにはファイルたちが必要です： -- `config.json`：学習時の設定ファイル -- `*.safetensors`：学習済みモデルファイル（1つ以上が必要、複数可） -- `style_vectors.npy`：スタイルベクトルファイル - -上2つは`Train.bat`による学習で自動的に正しい位置に保存されます。`style_vectors.npy`は`Style.bat`を実行して指示に従って生成してください。 -""" - -style_md = f""" -- プリセットまたは音声ファイルから読み上げの声音・感情・スタイルのようなものを制御できます。 -- デフォルトの{DEFAULT_STYLE}でも、十分に読み上げる文に応じた感情で感情豊かに読み上げられます。このスタイル制御は、それを重み付きで上書きするような感じです。 -- 強さを大きくしすぎると発音が変になったり声にならなかったりと崩壊することがあります。 -- どのくらいに強さがいいかはモデルやスタイルによって異なるようです。 -- 音声ファイルを入力する場合は、学習データと似た声音の話者（特に同じ性別）でないとよい効果が出ないかもしれません。 -""" - - -def make_interactive(): - return gr.update(interactive=True, value="音声合成") - - -def make_non_interactive(): - return gr.update(interactive=False, value="音声合成（モデルをロードしてください）") - - -def gr_util(item): - if item == "プリセットから選ぶ": - return (gr.update(visible=True), gr.Audio(visible=False, value=None)) - else: - return (gr.update(visible=False), gr.update(visible=True)) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cpu", action="store_true", help="Use CPU instead of GPU") - parser.add_argument( - "--dir", "-d", type=str, help="Model directory", default=assets_root - ) - parser.add_argument( - "--share", action="store_true", help="Share this app publicly", default=False - ) - parser.add_argument( - "--server-name", - type=str, - default=None, - help="Server name for Gradio app", - ) - parser.add_argument( - "--no-autolaunch", - action="store_true", - default=False, - help="Do not launch app automatically", - ) - args = parser.parse_args() - model_dir = Path(args.dir) - - if args.cpu: - device = "cpu" - else: - device = "cuda" if torch.cuda.is_available() else "cpu" - - model_holder = ModelHolder(model_dir, device) - - model_names = model_holder.model_names - if len(model_names) == 0: - logger.error( - f"モデルが見つかりませんでした。{model_dir}にモデルを置いてください。" - ) - sys.exit(1) - initial_id = 0 - initial_pth_files = model_holder.model_files_dict[model_names[initial_id]] - - with gr.Blocks(theme=GRADIO_THEME) as app: - gr.Markdown(initial_md) - with gr.Accordion(label="使い方", open=False): - gr.Markdown(how_to_md) - with gr.Row(): - with gr.Column(): - with gr.Row(): - with gr.Column(scale=3): - model_name = gr.Dropdown( - label="モデル一覧", - choices=model_names, - value=model_names[initial_id], - ) - model_path = gr.Dropdown( - label="モデルファイル", - choices=initial_pth_files, - value=initial_pth_files[0], - ) - refresh_button = gr.Button("更新", scale=1, visible=True) - load_button = gr.Button("ロード", scale=1, variant="primary") - text_input = gr.TextArea(label="テキスト", value=initial_text) - pitch_scale = gr.Slider( - minimum=0.8, - maximum=1.5, - value=1, - step=0.05, - label="音程(1以外では音質劣化)", - visible=False, # pyworldが必要 - ) - intonation_scale = gr.Slider( - minimum=0, - maximum=2, - value=1, - step=0.1, - label="抑揚(1以外では音質劣化)", - visible=False, # pyworldが必要 - ) - - line_split = gr.Checkbox( - label="改行で分けて生成（分けたほうが感情が乗ります）", - value=DEFAULT_LINE_SPLIT, - ) - split_interval = gr.Slider( - minimum=0.0, - maximum=2, - value=DEFAULT_SPLIT_INTERVAL, - step=0.1, - label="改行ごとに挟む無音の長さ（秒）", - ) - line_split.change( - lambda x: (gr.Slider(visible=x)), - inputs=[line_split], - outputs=[split_interval], - ) - tone = gr.Textbox( - label="アクセント調整（数値は 0=低 か1=高 のみ）", - info="改行で分けない場合のみ使えます。万能ではありません。", - ) - use_tone = gr.Checkbox(label="アクセント調整を使う", value=False) - use_tone.change( - lambda x: (gr.Checkbox(value=False) if x else gr.Checkbox()), - inputs=[use_tone], - outputs=[line_split], - ) - language = gr.Dropdown(choices=languages, value="JP", label="Language") - speaker = gr.Dropdown(label="話者") - with gr.Accordion(label="詳細設定", open=False): - sdp_ratio = gr.Slider( - minimum=0, - maximum=1, - value=DEFAULT_SDP_RATIO, - step=0.1, - label="SDP Ratio", - ) - noise_scale = gr.Slider( - minimum=0.1, - maximum=2, - value=DEFAULT_NOISE, - step=0.1, - label="Noise", - ) - noise_scale_w = gr.Slider( - minimum=0.1, - maximum=2, - value=DEFAULT_NOISEW, - step=0.1, - label="Noise_W", - ) - length_scale = gr.Slider( - minimum=0.1, - maximum=2, - value=DEFAULT_LENGTH, - step=0.1, - label="Length", - ) - use_assist_text = gr.Checkbox( - label="Assist textを使う", value=False - ) - assist_text = gr.Textbox( - label="Assist text", - placeholder="どうして私の意見を無視するの？許せない、ムカつく！死ねばいいのに。", - info="このテキストの読み上げと似た声音・感情になりやすくなります。ただ抑揚やテンポ等が犠牲になる傾向があります。", - visible=False, - ) - assist_text_weight = gr.Slider( - minimum=0, - maximum=1, - value=DEFAULT_ASSIST_TEXT_WEIGHT, - step=0.1, - label="Assist textの強さ", - visible=False, - ) - use_assist_text.change( - lambda x: (gr.Textbox(visible=x), gr.Slider(visible=x)), - inputs=[use_assist_text], - outputs=[assist_text, assist_text_weight], - ) - with gr.Column(): - with gr.Accordion("スタイルについて詳細", open=False): - gr.Markdown(style_md) - style_mode = gr.Radio( - ["プリセットから選ぶ", "音声ファイルを入力"], - label="スタイルの指定方法", - value="プリセットから選ぶ", - ) - style = gr.Dropdown( - label=f"スタイル（{DEFAULT_STYLE}が平均スタイル）", - choices=["モデルをロードしてください"], - value="モデルをロードしてください", - ) - style_weight = gr.Slider( - minimum=0, - maximum=50, - value=DEFAULT_STYLE_WEIGHT, - step=0.1, - label="スタイルの強さ", - ) - ref_audio_path = gr.Audio( - label="参照音声", type="filepath", visible=False - ) - tts_button = gr.Button( - "音声合成（モデルをロードしてください）", - variant="primary", - interactive=False, - ) - text_output = gr.Textbox(label="情報") - audio_output = gr.Audio(label="結果") - with gr.Accordion("テキスト例", open=False): - gr.Examples(examples, inputs=[text_input, language]) - - tts_button.click( - tts_fn, - inputs=[ - model_name, - model_path, - text_input, - language, - ref_audio_path, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - line_split, - split_interval, - assist_text, - assist_text_weight, - use_assist_text, - style, - style_weight, - tone, - use_tone, - speaker, - pitch_scale, - intonation_scale, - ], - outputs=[text_output, audio_output, tone], - ) - - model_name.change( - model_holder.update_model_files_gr, - inputs=[model_name], - outputs=[model_path], - ) - - model_path.change(make_non_interactive, outputs=[tts_button]) - - refresh_button.click( - model_holder.update_model_names_gr, - outputs=[model_name, model_path, tts_button], - ) - - load_button.click( - model_holder.load_model_gr, - inputs=[model_name, model_path], - outputs=[style, tts_button, speaker], - ) - - style_mode.change( - gr_util, - inputs=[style_mode], - outputs=[style, ref_audio_path], - ) - - app.launch( - inbrowser=not args.no_autolaunch, share=args.share, server_name=args.server_name - ) diff --git a/bert/bert_models.json b/bert/bert_models.json deleted file mode 100644 index cd5906d116d51629aaf94a85cf3d2df7e8032941..0000000000000000000000000000000000000000 --- a/bert/bert_models.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "deberta-v2-large-japanese-char-wwm": { - "repo_id": "ku-nlp/deberta-v2-large-japanese-char-wwm", - "files": ["pytorch_model.bin"] - }, - "chinese-roberta-wwm-ext-large": { - "repo_id": "hfl/chinese-roberta-wwm-ext-large", - "files": ["pytorch_model.bin"] - }, - "deberta-v3-large": { - "repo_id": "microsoft/deberta-v3-large", - "files": ["spm.model", "pytorch_model.bin"] - } -} diff --git a/bert_gen.py b/bert_gen.py deleted file mode 100644 index a5f7c258f3aa2fa5973baa58d73100a038146bad..0000000000000000000000000000000000000000 --- a/bert_gen.py +++ /dev/null @@ -1,85 +0,0 @@ -import argparse -from concurrent.futures import ThreadPoolExecutor - -import torch -import torch.multiprocessing as mp -from tqdm import tqdm - -import commons -import utils -from common.log import logger -from common.stdout_wrapper import SAFE_STDOUT -from config import config -from text import cleaned_text_to_sequence, get_bert - - -def process_line(x): - line, add_blank = x - device = config.bert_gen_config.device - if config.bert_gen_config.use_multi_device: - rank = mp.current_process()._identity - rank = rank[0] if len(rank) > 0 else 0 - if torch.cuda.is_available(): - gpu_id = rank % torch.cuda.device_count() - device = torch.device(f"cuda:{gpu_id}") - else: - device = torch.device("cpu") - wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|") - phone = phones.split(" ") - tone = [int(i) for i in tone.split(" ")] - word2ph = [int(i) for i in word2ph.split(" ")] - word2ph = [i for i in word2ph] - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - - bert_path = wav_path.replace(".WAV", ".wav").replace(".wav", ".bert.pt") - - try: - bert = torch.load(bert_path) - assert bert.shape[-1] == len(phone) - except Exception: - bert = get_bert(text, word2ph, language_str, device) - assert bert.shape[-1] == len(phone) - torch.save(bert, bert_path) - - -preprocess_text_config = config.preprocess_text_config - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "-c", "--config", type=str, default=config.bert_gen_config.config_path - ) - parser.add_argument( - "--num_processes", type=int, default=config.bert_gen_config.num_processes - ) - args, _ = parser.parse_known_args() - config_path = args.config - hps = utils.get_hparams_from_file(config_path) - lines = [] - with open(hps.data.training_files, encoding="utf-8") as f: - lines.extend(f.readlines()) - - with open(hps.data.validation_files, encoding="utf-8") as f: - lines.extend(f.readlines()) - add_blank = [hps.data.add_blank] * len(lines) - - if len(lines) != 0: - num_processes = args.num_processes - with ThreadPoolExecutor(max_workers=num_processes) as executor: - _ = list( - tqdm( - executor.map(process_line, zip(lines, add_blank)), - total=len(lines), - file=SAFE_STDOUT, - ) - ) - - logger.info(f"bert.pt is generated! total: {len(lines)} bert.pt files.") diff --git a/clustering.ipynb b/clustering.ipynb deleted file mode 100644 index 12e728ef6b35c8fb62f7860eed62fe0d3080bdd0..0000000000000000000000000000000000000000 --- a/clustering.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# スタイルベクトルをもっと詳しく作りたい人向け\n", - "\n", - "JVNVコーパスの例を使いながらクラスタリングをいろいろいじったり、また正解ラベルからベクトルを作りたい人向けです。\n", - "もっといろいろ足したりいろんなスタイルベクトルを作って遊べると思います。\n", - "ある程度慣れている人向けです。\n", - "\n", - "JVNVコーパスのように**スタイルが既にファイル名等で分かれている場合は、最後の方のセルを使えばそれを利用してスタイルを作ることができます。**\n", - "\n", - "例では[JVNVコーパス](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus)のjvnv-M1を使います。\n", - "\n", - "## そもそもスタイルベクトルとは\n", - "[この話者識別モデル](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM)を使って生成された、1つの音声ファイルにつき256次元のベクトルです。話者識別用のものですが、感情や声音の特徴も含まれているので、スタイルベクトルとして使えます。\n", - "\n", - "このStyle-Bert-VITS2では、この256次元のベクトルをエンコーダに注入して学習しているので、推論時にそのベクトルを入れてあげる必要があります。ある感情を強く表していると思われるベクトルを入れると、その感情を強く表現した音声が生成される、という仕組みです。\n", - "\n", - "もともとが話者識別用なので、「この感情はこのベクトル」のような普遍的なスタイルベクトルは使えません。なのでこのように、いちいちデータセットごとにベクトルを作る必要があります。\n", - "\n", - "## モデルを使うために必要なもの\n", - "- `model_assets/{model_name}/model_name.safetensors`: 学習の結果出力されるモデルファイル。これは自動的にこの場所に置かれ、スタイルベクトルとは全く独立。\n", - "- `model_assets/{model_name}/style_vectors.npy`: スタイルベクトルのnumpyファイル。ベクトルをいくつかいれる。\n", - "- `model_assets/{model_name}/config.json`: モデルの設定ファイル（学習前準備で自動的に生成されるはず）。これの以下の項目を設定。\n", - "```json\n", - "{\n", - " \"data\": {\n", - " \"num_styles\": 4, // スタイルベクトルの数\n", - " \"style2id\": { // スタイルベクトルの名前とidの対応\n", - " \"Neutral\": 0,\n", - " \"Angry\": 1,\n", - " \"Happy\": 2,\n", - " \"Sad\": 3\n", - " }\n", - " }\n", - "}\n", - "```\n", - "ここでidは0から始まる整数で、スタイルベクトルのnumpyファイルの何番目のベクトルかを指定します。最初のNeutralは含めたほうがよさそうで、WebUIや下では全スタイルベクトルの平均を入れています。" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "\n", - "wav_dir = \"Data/jvnv-M1/wavs\"\n", - "\n", - "embs = []\n", - "names= []\n", - "for file in os.listdir(wav_dir):\n", - " if file.endswith(\".npy\"):\n", - " xvec = np.load(os.path.join(wav_dir, file))\n", - " embs.append(np.expand_dims(xvec, axis=0))\n", - " names.append(file)\n", - "\n", - "x = np.concatenate(embs, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TSNEで可視化\n", - "from sklearn.manifold import TSNE\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "# 特徴量の関係か、コサイン距離が良さげ\n", - "tsne = TSNE(n_components=2, random_state=42, metric=\"cosine\")\n", - "\n", - "x_tsne = tsne.fit_transform(x)\n", - "\n", - "plt.figure(figsize=(7, 7))\n", - "plt.scatter(x_tsne[:, 0], x_tsne[:, 1])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.cluster import KMeans, AgglomerativeClustering\n", - "\n", - "method = \"k\"\n", - "n_clusters = 5\n", - "\n", - "if method == \"k\":\n", - " model = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)\n", - "elif method == \"a\":\n", - " model = AgglomerativeClustering(n_clusters=n_clusters)\n", - "\n", - "y_predict = model.fit_predict(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上は直接生のベクトルでクラスタリングしてますが、次のようにt-SNEで図のように2次元に削減したものをクラスタリングしたほうが、識別がきれいに分かれる、ことが多いような気がします、が詳しくは分かりません:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "y_predict = model.fit_predict(x_tsne)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "centroids = []\n", - "for i in range(n_clusters):\n", - " centroids.append(x[y_predict == i].mean(axis=0))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TSNEで可視化\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.figure(figsize=(7, 7))\n", - "plt.scatter(x_tsne[:, 0], x_tsne[:, 1], c=y_predict)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.spatial.distance import cdist\n", - "from IPython.display import Audio, display\n", - "\n", - "# 各クラスターのセントロイドに最も近い点に対応するファイル名を取得\n", - "closest_files = []\n", - "for center_idx in range(len(centroids)):\n", - " closest_idx = np.argmin(\n", - " cdist(centroids[center_idx : center_idx + 1], x, metric=\"cosine\")\n", - " )\n", - " closest_files.append(names[closest_idx])\n", - "\n", - "# 対応する音声ファイルをJupyterノートブック上で再生\n", - "for file_name in closest_files:\n", - " wav_path = os.path.join(wav_dir, file_name.replace(\".npy\", \"\"))\n", - " if os.path.exists(wav_path):\n", - " print(wav_path)\n", - " display(Audio(wav_path))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# meanとcentroidを保存\n", - "mean = x.mean(axis=0)\n", - "save_vectors = np.vstack([mean, centroids])\n", - "os.makedirs(\"model_assets/jvnv-M1\", exist_ok=True)\n", - "np.save(\"model_assets/jvnv-M1/style_vectors.npy\", save_vectors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 正解ラベルがファイル名から分かる場合の作り方\n", - "JVNVコーパス等でファイル名によってスタイルラベルが分かる場合、以下のようにしてスタイルベクトルを作ることができます（デフォルトのJVNVモデルのスタイルはこれで作成しています）。" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# JVNVコーパスの場合は正解はファイル名の最初の方から分かる。それを使って正解ラベルを作成\n", - "label_dict = {\"ang\": 0, \"dis\": 1, \"fea\": 2, \"hap\": 3, \"sad\": 4, \"sur\": 5}\n", - "\n", - "y_true = [label_dict[name[3:6]] for name in names]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# 正解のセントロイドを計算\n", - "y_true = np.array(y_true)\n", - "true_centroids = []\n", - "for i in range(6):\n", - " true_centroids.append(np.mean(x[y_true == i], axis=0))\n", - "\n", - "true_centroids = np.array(true_centroids)\n", - "\n", - "# すべてのベクトルの平均を計算\n", - "mean = np.mean(x, axis=0)\n", - "\n", - "# 保存\n", - "os.makedirs(\"model_assets/jvnv-M1\", exist_ok=True)\n", - "np.save(\"model_assets/jvnv-M1/style_vectors.npy\", np.vstack([mean, true_centroids]))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TSNEで正解ラベルを可視化\n", - "import matplotlib.pyplot as plt\n", - "\n", - "cmap = plt.get_cmap(\"tab10\")\n", - "\n", - "true_label_dict = {0: \"ang\", 1: \"dis\", 2: \"fea\", 3: \"hap\", 4: \"sad\", 5: \"sur\"}\n", - "\n", - "plt.figure(figsize=(7, 7))\n", - "for i in true_label_dict:\n", - " plt.scatter(\n", - " x_tsne[y_true == i, 0],\n", - " x_tsne[y_true == i, 1],\n", - " color=cmap(i),\n", - " label=f\"{true_label_dict[i]}\",\n", - " )\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "手動でconfig.jsonのstyle2idとnum_stylesを設定するのを忘れずに。" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.11" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/colab.ipynb b/colab.ipynb deleted file mode 100644 index f4e932b25b84f1aaa0b582231f17c81fbb970625..0000000000000000000000000000000000000000 --- a/colab.ipynb +++ /dev/null @@ -1,410 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Style-Bert-VITS2 (ver 2.3) のGoogle Colabでの学習\n", - "\n", - "Google Colab上でStyle-Bert-VITS2の学習を行うことができます。\n", - "\n", - "このnotebookでは、通常使用ではあなたのGoogle Driveにフォルダ`Style-Bert-VITS2`を作り、その内部での作業を行います。他のフォルダには触れません。\n", - "Google Driveを使わない場合は、初期設定のところで適切なパスを指定してください。\n", - "\n", - "## 流れ\n", - "\n", - "### 学習を最初からやりたいとき\n", - "上から順に実行していけばいいです。音声合成に必要なファイルはGoogle Driveの`Style-Bert-VITS2/model_assets/`に保存されます。また、途中経過も`Style-Bert-VITS2/Data/`に保存されるので、学習を中断したり、途中から再開することもできます。\n", - "\n", - "### 学習を途中から再開したいとき\n", - "0と1を行い、3の前処理は飛ばして、4から始めてください。スタイル分け5は、学習が終わったら必要なら行ってください。\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 0. 環境構築\n", - "\n", - "Style-Bert-VITS2の環境をcolab上に構築します。グラボモードが有効になっていることを確認し、以下のセルを順に実行してください。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#@title このセルを実行して環境構築してください。\n", - "#@markdown 最後に赤文字でエラーや警告が出ても何故かうまくいくみたいです。\n", - "\n", - "!git clone https://github.com/litagin02/Style-Bert-VITS2.git\n", - "%cd Style-Bert-VITS2/\n", - "!pip install -r requirements.txt\n", - "!apt install libcublas11\n", - "!python initialize.py --skip_jvnv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Google driveを使う方はこちらを実行してください。\n", - "\n", - "from google.colab import drive\n", - "drive.mount(\"/content/drive\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. 初期設定\n", - "\n", - "学習とその結果を保存するディレクトリ名を指定します。\n", - "Google driveの場合はそのまま実行、カスタマイズしたい方は変更して実行してください。" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# 学習に必要なファイルや途中経過が保存されるディレクトリ\n", - "dataset_root = \"/content/drive/MyDrive/Style-Bert-VITS2/Data\"\n", - "\n", - "# 学習結果（音声合成に必要なファイルたち）が保存されるディレクトリ\n", - "assets_root = \"/content/drive/MyDrive/Style-Bert-VITS2/model_assets\"\n", - "\n", - "import yaml\n", - "\n", - "\n", - "with open(\"configs/paths.yml\", \"w\", encoding=\"utf-8\") as f:\n", - " yaml.dump({\"dataset_root\": dataset_root, \"assets_root\": assets_root}, f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. 学習に使うデータ準備\n", - "\n", - "すでに音声ファイル（1ファイル2-12秒程度）とその書き起こしデータがある場合は2.2を、ない場合は2.1を実行してください。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 音声ファイルからのデータセットの作成（ある人はスキップ可）\n", - "\n", - "音声ファイル（1ファイル2-12秒程度）とその書き起こしのデータセットを持っていない方は、（日本語の）音声ファイルのみから以下の手順でデータセットを作成することができます。Google drive上の`Style-Bert-VITS2/inputs/`フォルダに音声ファイル（wavファイル形式、1ファイルでも複数ファイルでも可）を置いて、下を実行すると、データセットが作られ、自動的に正しい場所へ配置されます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 元となる音声ファイル（wav形式）を入れるディレクトリ\n", - "input_dir = \"/content/drive/MyDrive/Style-Bert-VITS2/inputs\"\n", - "# モデル名（話者名）を入力\n", - "model_name = \"your_model_name\"\n", - "\n", - "# こういうふうに書き起こして欲しいという例文（句読点の入れ方・笑い方や固有名詞等）\n", - "initial_prompt = \"こんにちは。元気、ですかー？ふふっ、私は……ちゃんと元気だよ！\"\n", - "\n", - "!python slice.py -i {input_dir} -o {dataset_root}/{model_name}/raw\n", - "!python transcribe.py -i {dataset_root}/{model_name}/raw -o {dataset_root}/{model_name}/esd.list --speaker_name {model_name} --compute_type float16 --initial_prompt {initial_prompt}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "成功したらそのまま3へ進んでください" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 音声ファイルと書き起こしデータがすでにある場合\n", - "\n", - "指示に従って適切にデータセットを配置してください。\n", - "\n", - "次のセルを実行して、学習データをいれるフォルダ（1で設定した`dataset_root`）を作成します。" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "esCNJl704h52" - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(dataset_root, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "次に、学習に必要なデータを、Google driveに作成された`Style-Bert-VITS2/Data`フォルダに配置します。\n", - "\n", - "まず音声データ（wavファイルで1ファイルが2-12秒程度の、長すぎず短すぎない発話のものをいくつか）と、書き起こしテキストを用意してください。wavファイル名やモデルの名前は空白を含まない半角で、wavファイルの拡張子は小文字`.wav`である必要があります。\n", - "\n", - "書き起こしテキストは、次の形式で記述してください。\n", - "```\n", - "****.wav|{話者名}|{言語ID、ZHかJPかEN}|{書き起こしテキスト}\n", - "```\n", - "\n", - "例：\n", - "```\n", - "wav_number1.wav|hanako|JP|こんにちは、聞こえて、いますか？\n", - "wav_next.wav|taro|JP|はい、聞こえています……。\n", - "english_teacher.wav|Mary|EN|How are you? I'm fine, thank you, and you?\n", - "...\n", - "```\n", - "日本語話者の単一話者データセットで構いません。\n", - "\n", - "### データセットの配置\n", - "\n", - "次にモデルの名前を適当に決めてください（空白を含まない半角英数字がよいです）。\n", - "そして、書き起こしファイルを`esd.list`という名前で保存し、またwavファイルも`raw`というフォルダを作成し、あなたのGoogle Driveの中の（上で自動的に作られるはずの）`Data`フォルダのなかに、次のように配置します。\n", - "```\n", - "├── Data\n", - "│ ├── {モデルの名前}\n", - "│ │ ├── esd.list\n", - "│ │ ├── raw\n", - "│ │ │ ├── ****.wav\n", - "│ │ │ ├── ****.wav\n", - "│ │ │ ├── ...\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5r85-W20ECcr" - }, - "source": [ - "## 3. 学習の前処理\n", - "\n", - "次に学習の前処理を行います。必要なパラメータをここで指定します。次のセルに設定等を入力して実行してください。「～～かどうか」は`True`もしくは`False`を指定してください。" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "CXR7kjuF5GlE" - }, - "outputs": [], - "source": [ - "# 上でつけたフォルダの名前`Data/{model_name}/`\n", - "model_name = \"your_model_name\"\n", - "\n", - "# JP-Extra （日本語特化版）を使うかどうか。日本語の能力が向上する代わりに英語と中国語は使えなくなります。\n", - "use_jp_extra = True\n", - "\n", - "# 学習のバッチサイズ。VRAMのはみ出具合に応じて調整してください。\n", - "batch_size = 4\n", - "\n", - "# 学習のエポック数（データセットを合計何周するか）。\n", - "# 100ぐらいで十分かもしれませんが、もっと多くやると質が上がるのかもしれません。\n", - "epochs = 100\n", - "\n", - "# 保存頻度。何ステップごとにモデルを保存するか。分からなければデフォルトのままで。\n", - "save_every_steps = 1000\n", - "\n", - "# 音声ファイルの音量を正規化するかどうか\n", - "normalize = False\n", - "\n", - "# 音声ファイルの開始・終了にある無音区間を削除するかどうか\n", - "trim = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "上のセルが実行されたら、次のセルを実行して学習の前処理を行います。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xMVaOIPLabV5", - "outputId": "15fac868-9132-45d9-9f5f-365b6aeb67b0" - }, - "outputs": [], - "source": [ - "from webui_train import preprocess_all\n", - "\n", - "preprocess_all(\n", - " model_name=model_name,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " save_every_steps=save_every_steps,\n", - " num_processes=2,\n", - " normalize=normalize,\n", - " trim=trim,\n", - " freeze_EN_bert=False,\n", - " freeze_JP_bert=False,\n", - " freeze_ZH_bert=False,\n", - " freeze_style=False,\n", - " freeze_decoder=False, # ここをTrueにするともしかしたら違う結果になるかもしれません。\n", - " use_jp_extra=use_jp_extra,\n", - " val_per_lang=0,\n", - " log_interval=200,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. 学習\n", - "\n", - "前処理が正常に終わったら、学習を行います。次のセルを実行すると学習が始まります。\n", - "\n", - "学習の結果は、上で指定した`save_every_steps`の間隔で、Google Driveの中の`Style-Bert-VITS2/Data/{モデルの名前}/model_assets/`フォルダに保存されます。\n", - "\n", - "このフォルダをダウンロードし、ローカルのStyle-Bert-VITS2の`model_assets`フォルダに上書きすれば、学習結果を使うことができます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "laieKrbEb6Ij", - "outputId": "72238c88-f294-4ed9-84f6-84c1c17999ca" - }, - "outputs": [], - "source": [ - "# 上でつけたモデル名を入力。学習を途中からする場合はきちんとモデルが保存されているフォルダ名を入力。\n", - "model_name = \"your_model_name\"\n", - "\n", - "\n", - "import yaml\n", - "from webui_train import get_path\n", - "\n", - "dataset_path, _, _, _, config_path = get_path(model_name)\n", - "\n", - "with open(\"default_config.yml\", \"r\", encoding=\"utf-8\") as f:\n", - " yml_data = yaml.safe_load(f)\n", - "yml_data[\"model_name\"] = model_name\n", - "with open(\"config.yml\", \"w\", encoding=\"utf-8\") as f:\n", - " yaml.dump(yml_data, f, allow_unicode=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 日本語特化版を「使う」場合\n", - "!python train_ms_jp_extra.py --config {config_path} --model {dataset_path} --assets_root {assets_root}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 日本語特化版を「使わない」場合\n", - "!python train_ms.py --config {config_path} --model {dataset_path} --assets_root {assets_root}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "c7g0hrdeP1Tl", - "outputId": "94f9a6f6-027f-4554-ce0c-60ac56251c22" - }, - "outputs": [], - "source": [ - "#@title 学習結果を試すならここから\n", - "!python app.py --share --dir {assets_root}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. スタイル分け" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!python webui_style_vectors.py --share" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. マージ" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!python webui_merge.py --share" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/common/constants.py b/common/constants.py deleted file mode 100644 index 751d5e123409506c52ace9ef764a1402b672b66a..0000000000000000000000000000000000000000 --- a/common/constants.py +++ /dev/null @@ -1,28 +0,0 @@ -import enum - -# Built-in theme: "default", "base", "monochrome", "soft", "glass" -# See https://huggingface.co/spaces/gradio/theme-gallery for more themes -GRADIO_THEME: str = "NoCrypt/miku" - -LATEST_VERSION: str = "2.3" - -USER_DICT_DIR = "dict_data" - -DEFAULT_STYLE: str = "Neutral" -DEFAULT_STYLE_WEIGHT: float = 5.0 - - -class Languages(str, enum.Enum): - JP = "JP" - EN = "EN" - ZH = "ZH" - - -DEFAULT_SDP_RATIO: float = 0.2 -DEFAULT_NOISE: float = 0.6 -DEFAULT_NOISEW: float = 0.8 -DEFAULT_LENGTH: float = 1.0 -DEFAULT_LINE_SPLIT: bool = True -DEFAULT_SPLIT_INTERVAL: float = 0.5 -DEFAULT_ASSIST_TEXT_WEIGHT: float = 0.7 -DEFAULT_ASSIST_TEXT_WEIGHT: float = 1.0 diff --git a/common/log.py b/common/log.py deleted file mode 100644 index 51dca5f3f39047ed9fb58f59d765ca0a332bc49f..0000000000000000000000000000000000000000 --- a/common/log.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -logger封装 -""" -from loguru import logger - -from .stdout_wrapper import SAFE_STDOUT - -# 移除所有默认的处理器 -logger.remove() - -# 自定义格式并添加到标准输出 -log_format = ( - "{time:MM-DD HH:mm:ss} |{level:^8}| {file}:{line} | {message}" -) - -logger.add(SAFE_STDOUT, format=log_format, backtrace=True, diagnose=True) diff --git a/common/stdout_wrapper.py b/common/stdout_wrapper.py deleted file mode 100644 index af5eaaedbe163824792474c68540cef8bccefd9d..0000000000000000000000000000000000000000 --- a/common/stdout_wrapper.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -`sys.stdout` wrapper for both Google Colab and local environment. -""" - -import sys -import tempfile - - -class StdoutWrapper: - def __init__(self): - self.temp_file = tempfile.NamedTemporaryFile(mode="w+", delete=False) - self.original_stdout = sys.stdout - - def write(self, message: str): - self.temp_file.write(message) - self.temp_file.flush() - print(message, end="", file=self.original_stdout) - - def flush(self): - self.temp_file.flush() - - def read(self): - self.temp_file.seek(0) - return self.temp_file.read() - - def close(self): - self.temp_file.close() - - def fileno(self): - return self.temp_file.fileno() - - -try: - import google.colab - - SAFE_STDOUT = StdoutWrapper() -except ImportError: - SAFE_STDOUT = sys.stdout diff --git a/common/subprocess_utils.py b/common/subprocess_utils.py deleted file mode 100644 index 9a0e3237b49ed3fe6f92584942575125c32451ea..0000000000000000000000000000000000000000 --- a/common/subprocess_utils.py +++ /dev/null @@ -1,32 +0,0 @@ -import subprocess -import sys - -from .log import logger -from .stdout_wrapper import SAFE_STDOUT - -python = sys.executable - - -def run_script_with_log(cmd: list[str], ignore_warning=False) -> tuple[bool, str]: - logger.info(f"Running: {' '.join(cmd)}") - result = subprocess.run( - [python] + cmd, - stdout=SAFE_STDOUT, # type: ignore - stderr=subprocess.PIPE, - text=True, - ) - if result.returncode != 0: - logger.error(f"Error: {' '.join(cmd)}\n{result.stderr}") - return False, result.stderr - elif result.stderr and not ignore_warning: - logger.warning(f"Warning: {' '.join(cmd)}\n{result.stderr}") - return True, result.stderr - logger.success(f"Success: {' '.join(cmd)}") - return True, "" - - -def second_elem_of(original_function): - def inner_function(*args, **kwargs): - return original_function(*args, **kwargs)[1] - - return inner_function diff --git a/common/tts_model.py b/common/tts_model.py deleted file mode 100644 index de4e830dfba3744a1c165d7befc2b239705c8443..0000000000000000000000000000000000000000 --- a/common/tts_model.py +++ /dev/null @@ -1,335 +0,0 @@ -import os -import warnings -from pathlib import Path -from typing import Optional, Union - -import gradio as gr -import numpy as np - -import torch -from gradio.processing_utils import convert_to_16_bit_wav - -import utils -from infer import get_net_g, infer -from models import SynthesizerTrn -from models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra - -from .constants import ( - DEFAULT_ASSIST_TEXT_WEIGHT, - DEFAULT_LENGTH, - DEFAULT_LINE_SPLIT, - DEFAULT_NOISE, - DEFAULT_NOISEW, - DEFAULT_SDP_RATIO, - DEFAULT_SPLIT_INTERVAL, - DEFAULT_STYLE, - DEFAULT_STYLE_WEIGHT, -) -from .log import logger - - -def adjust_voice(fs, wave, pitch_scale, intonation_scale): - if pitch_scale == 1.0 and intonation_scale == 1.0: - # 初期値の場合は、音質劣化を避けるためにそのまま返す - return fs, wave - - try: - import pyworld - except ImportError: - raise ImportError( - "pyworld is not installed. Please install it by `pip install pyworld`" - ) - - # pyworldでf0を加工して合成 - # pyworldよりもよいのがあるかもしれないが…… - - wave = wave.astype(np.double) - f0, t = pyworld.harvest(wave, fs) - # 質が高そうだしとりあえずharvestにしておく - - sp = pyworld.cheaptrick(wave, f0, t, fs) - ap = pyworld.d4c(wave, f0, t, fs) - - non_zero_f0 = [f for f in f0 if f != 0] - f0_mean = sum(non_zero_f0) / len(non_zero_f0) - - for i, f in enumerate(f0): - if f == 0: - continue - f0[i] = pitch_scale * f0_mean + intonation_scale * (f - f0_mean) - - wave = pyworld.synthesize(f0, sp, ap, fs) - return fs, wave - - -class Model: - def __init__( - self, model_path: Path, config_path: Path, style_vec_path: Path, device: str - ): - self.model_path: Path = model_path - self.config_path: Path = config_path - self.style_vec_path: Path = style_vec_path - self.device: str = device - self.hps: utils.HParams = utils.get_hparams_from_file(self.config_path) - self.spk2id: dict[str, int] = self.hps.data.spk2id - self.id2spk: dict[int, str] = {v: k for k, v in self.spk2id.items()} - - self.num_styles: int = self.hps.data.num_styles - if hasattr(self.hps.data, "style2id"): - self.style2id: dict[str, int] = self.hps.data.style2id - else: - self.style2id: dict[str, int] = {str(i): i for i in range(self.num_styles)} - if len(self.style2id) != self.num_styles: - raise ValueError( - f"Number of styles ({self.num_styles}) does not match the number of style2id ({len(self.style2id)})" - ) - - self.style_vectors: np.ndarray = np.load(self.style_vec_path) - if self.style_vectors.shape[0] != self.num_styles: - raise ValueError( - f"The number of styles ({self.num_styles}) does not match the number of style vectors ({self.style_vectors.shape[0]})" - ) - - self.net_g: Union[SynthesizerTrn, SynthesizerTrnJPExtra, None] = None - - def load_net_g(self): - self.net_g = get_net_g( - model_path=str(self.model_path), - version=self.hps.version, - device=self.device, - hps=self.hps, - ) - - def get_style_vector(self, style_id: int, weight: float = 1.0) -> np.ndarray: - mean = self.style_vectors[0] - style_vec = self.style_vectors[style_id] - style_vec = mean + (style_vec - mean) * weight - return style_vec - - def get_style_vector_from_audio( - self, audio_path: str, weight: float = 1.0 - ) -> np.ndarray: - from style_gen import get_style_vector - - xvec = get_style_vector(audio_path) - mean = self.style_vectors[0] - xvec = mean + (xvec - mean) * weight - return xvec - - def infer( - self, - text: str, - language: str = "JP", - sid: int = 0, - reference_audio_path: Optional[str] = None, - sdp_ratio: float = DEFAULT_SDP_RATIO, - noise: float = DEFAULT_NOISE, - noisew: float = DEFAULT_NOISEW, - length: float = DEFAULT_LENGTH, - line_split: bool = DEFAULT_LINE_SPLIT, - split_interval: float = DEFAULT_SPLIT_INTERVAL, - assist_text: Optional[str] = None, - assist_text_weight: float = DEFAULT_ASSIST_TEXT_WEIGHT, - use_assist_text: bool = False, - style: str = DEFAULT_STYLE, - style_weight: float = DEFAULT_STYLE_WEIGHT, - given_tone: Optional[list[int]] = None, - pitch_scale: float = 1.0, - intonation_scale: float = 1.0, - ignore_unknown: bool = False, - ) -> tuple[int, np.ndarray]: - logger.info(f"Start generating audio data from text:\n{text}") - if language != "JP" and self.hps.version.endswith("JP-Extra"): - raise ValueError( - "The model is trained with JP-Extra, but the language is not JP" - ) - if reference_audio_path == "": - reference_audio_path = None - if assist_text == "" or not use_assist_text: - assist_text = None - - if self.net_g is None: - self.load_net_g() - if reference_audio_path is None: - style_id = self.style2id[style] - style_vector = self.get_style_vector(style_id, style_weight) - else: - style_vector = self.get_style_vector_from_audio( - reference_audio_path, style_weight - ) - if not line_split: - with torch.no_grad(): - audio = infer( - text=text, - sdp_ratio=sdp_ratio, - noise_scale=noise, - noise_scale_w=noisew, - length_scale=length, - sid=sid, - language=language, - hps=self.hps, - net_g=self.net_g, - device=self.device, - assist_text=assist_text, - assist_text_weight=assist_text_weight, - style_vec=style_vector, - given_tone=given_tone, - ignore_unknown=ignore_unknown, - ) - else: - texts = text.split("\n") - texts = [t for t in texts if t != ""] - audios = [] - with torch.no_grad(): - for i, t in enumerate(texts): - audios.append( - infer( - text=t, - sdp_ratio=sdp_ratio, - noise_scale=noise, - noise_scale_w=noisew, - length_scale=length, - sid=sid, - language=language, - hps=self.hps, - net_g=self.net_g, - device=self.device, - assist_text=assist_text, - assist_text_weight=assist_text_weight, - style_vec=style_vector, - ignore_unknown=ignore_unknown, - ) - ) - if i != len(texts) - 1: - audios.append(np.zeros(int(44100 * split_interval))) - audio = np.concatenate(audios) - logger.info("Audio data generated successfully") - if not (pitch_scale == 1.0 and intonation_scale == 1.0): - _, audio = adjust_voice( - fs=self.hps.data.sampling_rate, - wave=audio, - pitch_scale=pitch_scale, - intonation_scale=intonation_scale, - ) - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - audio = convert_to_16_bit_wav(audio) - return (self.hps.data.sampling_rate, audio) - - -class ModelHolder: - def __init__(self, root_dir: Path, device: str): - self.root_dir: Path = root_dir - self.device: str = device - self.model_files_dict: dict[str, list[Path]] = {} - self.current_model: Optional[Model] = None - self.model_names: list[str] = [] - self.models: list[Model] = [] - self.refresh() - - def refresh(self): - self.model_files_dict = {} - self.model_names = [] - self.current_model = None - - model_dirs = [d for d in self.root_dir.iterdir() if d.is_dir()] - for model_dir in model_dirs: - model_files = [ - f - for f in model_dir.iterdir() - if f.suffix in [".pth", ".pt", ".safetensors"] - ] - if len(model_files) == 0: - logger.warning(f"No model files found in {model_dir}, so skip it") - continue - config_path = model_dir / "config.json" - if not config_path.exists(): - logger.warning( - f"Config file {config_path} not found, so skip {model_dir}" - ) - continue - self.model_files_dict[model_dir.name] = model_files - self.model_names.append(model_dir.name) - - def models_info(self): - if hasattr(self, "_models_info"): - return self._models_info - result = [] - for name, files in self.model_files_dict.items(): - # Get styles - config_path = self.root_dir / name / "config.json" - hps = utils.get_hparams_from_file(config_path) - style2id: dict[str, int] = hps.data.style2id - styles = list(style2id.keys()) - result.append( - { - "name": name, - "files": [str(f) for f in files], - "styles": styles, - } - ) - self._models_info = result - return result - - def load_model(self, model_name: str, model_path_str: str): - model_path = Path(model_path_str) - if model_name not in self.model_files_dict: - raise ValueError(f"Model `{model_name}` is not found") - if model_path not in self.model_files_dict[model_name]: - raise ValueError(f"Model file `{model_path}` is not found") - if self.current_model is None or self.current_model.model_path != model_path: - self.current_model = Model( - model_path=model_path, - config_path=self.root_dir / model_name / "config.json", - style_vec_path=self.root_dir / model_name / "style_vectors.npy", - device=self.device, - ) - return self.current_model - - def load_model_gr( - self, model_name: str, model_path_str: str - ) -> tuple[gr.Dropdown, gr.Button, gr.Dropdown]: - model_path = Path(model_path_str) - if model_name not in self.model_files_dict: - raise ValueError(f"Model `{model_name}` is not found") - if model_path not in self.model_files_dict[model_name]: - raise ValueError(f"Model file `{model_path}` is not found") - if ( - self.current_model is not None - and self.current_model.model_path == model_path - ): - # Already loaded - speakers = list(self.current_model.spk2id.keys()) - styles = list(self.current_model.style2id.keys()) - return ( - gr.Dropdown(choices=styles, value=styles[0]), - gr.Button(interactive=True, value="音声合成"), - gr.Dropdown(choices=speakers, value=speakers[0]), - ) - self.current_model = Model( - model_path=model_path, - config_path=self.root_dir / model_name / "config.json", - style_vec_path=self.root_dir / model_name / "style_vectors.npy", - device=self.device, - ) - speakers = list(self.current_model.spk2id.keys()) - styles = list(self.current_model.style2id.keys()) - return ( - gr.Dropdown(choices=styles, value=styles[0]), - gr.Button(interactive=True, value="音声合成"), - gr.Dropdown(choices=speakers, value=speakers[0]), - ) - - def update_model_files_gr(self, model_name: str) -> gr.Dropdown: - model_files = self.model_files_dict[model_name] - return gr.Dropdown(choices=model_files, value=model_files[0]) - - def update_model_names_gr(self) -> tuple[gr.Dropdown, gr.Dropdown, gr.Button]: - self.refresh() - initial_model_name = self.model_names[0] - initial_model_files = self.model_files_dict[initial_model_name] - return ( - gr.Dropdown(choices=self.model_names, value=initial_model_name), - gr.Dropdown(choices=initial_model_files, value=initial_model_files[0]), - gr.Button(interactive=False), # For tts_button - ) diff --git a/commons.py b/commons.py deleted file mode 100644 index 081b8a061286150f5a6f449601488f3e3f7b0207..0000000000000000000000000000000000000000 --- a/commons.py +++ /dev/null @@ -1,152 +0,0 @@ -import math -import torch -from torch.nn import functional as F - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -def convert_pad_shape(pad_shape): - layer = pad_shape[::-1] - pad_shape = [item for sublist in layer for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += ( - 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) - ) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - gather_indices = ids_str.view(x.size(0), 1, 1).repeat( - 1, x.size(1), 1 - ) + torch.arange(segment_size, device=x.device) - return torch.gather(x, 2, gather_indices) - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0) - ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( - num_timescales - 1 - ) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment - ) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2, 3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1.0 / norm_type) - return total_norm diff --git a/config.py b/config.py index bbd764e7b96650b2407c0670ab2cfd5f842f6f9a..a05cbd16e91bd209a09b17f46079c7507b63f7a6 100644 --- a/config.py +++ b/config.py @@ -2,14 +2,21 @@ @Desc: 全局配置文件读取 """ -import os import shutil -from typing import Dict, List +from pathlib import Path +from typing import Any import torch import yaml -from common.log import logger +from style_bert_vits2.logging import logger + + +class PathConfig: + def __init__(self, dataset_root: str, assets_root: str): + self.dataset_root = Path(dataset_root) + self.assets_root = Path(assets_root) + # If not cuda available, set possible devices to cpu cuda_available = torch.cuda.is_available() @@ -19,17 +26,17 @@ class Resample_config: """重采样配置""" def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100): - self.sampling_rate: int = sampling_rate # 目标采样率 - self.in_dir: str = in_dir # 待处理音频目录路径 - self.out_dir: str = out_dir # 重采样输出路径 + self.sampling_rate = sampling_rate # 目标采样率 + self.in_dir = Path(in_dir) # 待处理音频目录路径 + self.out_dir = Path(out_dir) # 重采样输出路径 @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): + def from_dict(cls, dataset_path: Path, data: dict[str, Any]): """从字典中生成实例""" # 不检查路径是否有效，此逻辑在resample.py中处理 - data["in_dir"] = os.path.join(dataset_path, data["in_dir"]) - data["out_dir"] = os.path.join(dataset_path, data["out_dir"]) + data["in_dir"] = dataset_path / data["in_dir"] + data["out_dir"] = dataset_path / data["out_dir"] return cls(**data) @@ -48,39 +55,32 @@ class Preprocess_text_config: max_val_total: int = 10000, clean: bool = True, ): - self.transcription_path: str = ( - transcription_path # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。 - ) - self.cleaned_path: str = ( - cleaned_path # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成 - ) - self.train_path: str = ( - train_path # 训练集路径，可以不填。不填则将在原始文本目录生成 - ) - self.val_path: str = ( - val_path # 验证集路径，可以不填。不填则将在原始文本目录生成 - ) - self.config_path: str = config_path # 配置文件路径 - self.val_per_lang: int = val_per_lang # 每个speaker的验证集条数 - self.max_val_total: int = ( - max_val_total # 验证集最大条数，多于的会被截断并放到训练集中 - ) - self.clean: bool = clean # 是否进行数据清洗 + self.transcription_path = Path(transcription_path) + self.train_path = Path(train_path) + if cleaned_path == "" or cleaned_path is None: + self.cleaned_path = self.transcription_path.with_name( + self.transcription_path.name + ".cleaned" + ) + else: + self.cleaned_path = Path(cleaned_path) + self.val_path = Path(val_path) + self.config_path = Path(config_path) + self.val_per_lang = val_per_lang + self.max_val_total = max_val_total + self.clean = clean @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): + def from_dict(cls, dataset_path: Path, data: dict[str, Any]): """从字典中生成实例""" - data["transcription_path"] = os.path.join( - dataset_path, data["transcription_path"] - ) + data["transcription_path"] = dataset_path / data["transcription_path"] if data["cleaned_path"] == "" or data["cleaned_path"] is None: - data["cleaned_path"] = None + data["cleaned_path"] = "" else: - data["cleaned_path"] = os.path.join(dataset_path, data["cleaned_path"]) - data["train_path"] = os.path.join(dataset_path, data["train_path"]) - data["val_path"] = os.path.join(dataset_path, data["val_path"]) - data["config_path"] = os.path.join(dataset_path, data["config_path"]) + data["cleaned_path"] = dataset_path / data["cleaned_path"] + data["train_path"] = dataset_path / data["train_path"] + data["val_path"] = dataset_path / data["val_path"] + data["config_path"] = dataset_path / data["config_path"] return cls(**data) @@ -91,11 +91,11 @@ class Bert_gen_config: def __init__( self, config_path: str, - num_processes: int = 2, + num_processes: int = 1, device: str = "cuda", use_multi_device: bool = False, ): - self.config_path = config_path + self.config_path = Path(config_path) self.num_processes = num_processes if not cuda_available: device = "cpu" @@ -103,8 +103,8 @@ class Bert_gen_config: self.use_multi_device = use_multi_device @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - data["config_path"] = os.path.join(dataset_path, data["config_path"]) + def from_dict(cls, dataset_path: Path, data: dict[str, Any]): + data["config_path"] = dataset_path / data["config_path"] return cls(**data) @@ -118,15 +118,15 @@ class Style_gen_config: num_processes: int = 4, device: str = "cuda", ): - self.config_path = config_path + self.config_path = Path(config_path) self.num_processes = num_processes if not cuda_available: device = "cpu" self.device = device @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - data["config_path"] = os.path.join(dataset_path, data["config_path"]) + def from_dict(cls, dataset_path: Path, data: dict[str, Any]): + data["config_path"] = dataset_path / data["config_path"] return cls(**data) @@ -137,7 +137,7 @@ class Train_ms_config: def __init__( self, config_path: str, - env: Dict[str, any], + env: dict[str, Any], # base: Dict[str, any], model_dir: str, num_workers: int, @@ -146,16 +146,18 @@ class Train_ms_config: ): self.env = env # 需要加载的环境变量 # self.base = base # 底模配置 - self.model_dir = model_dir # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录 - self.config_path = config_path # 配置文件路径 + self.model_dir = Path( + model_dir + ) # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录 + self.config_path = Path(config_path) # 配置文件路径 self.num_workers = num_workers # worker数量 self.spec_cache = spec_cache # 是否启用spec缓存 self.keep_ckpts = keep_ckpts # ckpt数量 @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): + def from_dict(cls, dataset_path: Path, data: dict[str, Any]): # data["model"] = os.path.join(dataset_path, data["model"]) - data["config_path"] = os.path.join(dataset_path, data["config_path"]) + data["config_path"] = dataset_path / data["config_path"] return cls(**data) @@ -175,20 +177,18 @@ class Webui_config: ): if not cuda_available: device = "cpu" - self.device: str = device - self.model: str = model # 端口号 - self.config_path: str = config_path # 是否公开部署，对外网开放 - self.port: int = port # 是否开启debug模式 - self.share: bool = share # 模型路径 - self.debug: bool = debug # 配置文件路径 - self.language_identification_library: str = ( - language_identification_library # 语种识别库 - ) + self.device = device + self.model = Path(model) + self.config_path = Path(config_path) + self.port: int = port + self.share: bool = share + self.debug: bool = debug + self.language_identification_library: str = language_identification_library @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - data["config_path"] = os.path.join(dataset_path, data["config_path"]) - data["model"] = os.path.join(dataset_path, data["model"]) + def from_dict(cls, dataset_path: Path, data: dict[str, Any]): + data["config_path"] = dataset_path / data["config_path"] + data["model"] = dataset_path / data["model"] return cls(**data) @@ -199,7 +199,7 @@ class Server_config: device: str = "cuda", limit: int = 100, language: str = "JP", - origins: List[str] = None, + origins: list[str] = ["*"], ): self.port: int = port if not cuda_available: @@ -207,10 +207,10 @@ class Server_config: self.device: str = device self.language: str = language self.limit: int = limit - self.origins: List[str] = origins + self.origins: list[str] = origins @classmethod - def from_dict(cls, data: Dict[str, any]): + def from_dict(cls, data: dict[str, Any]): return cls(**data) @@ -222,32 +222,33 @@ class Translate_config: self.secret_key = secret_key @classmethod - def from_dict(cls, data: Dict[str, any]): + def from_dict(cls, data: dict[str, Any]): return cls(**data) class Config: - def __init__(self, config_path: str, path_config: dict[str, str]): - if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"): + def __init__(self, config_path: str, path_config: PathConfig): + if not Path(config_path).exists(): shutil.copy(src="default_config.yml", dst=config_path) logger.info( f"A configuration file {config_path} has been generated based on the default configuration file default_config.yml." ) logger.info( - "If you have no special needs, please do not modify default_config.yml." + "Please do not modify default_config.yml. Instead, modify config.yml." ) # sys.exit(0) - with open(file=config_path, mode="r", encoding="utf-8") as file: - yaml_config: Dict[str, any] = yaml.safe_load(file.read()) + with open(config_path, encoding="utf-8") as file: + yaml_config: dict[str, Any] = yaml.safe_load(file.read()) model_name: str = yaml_config["model_name"] self.model_name: str = model_name if "dataset_path" in yaml_config: - dataset_path = yaml_config["dataset_path"] + dataset_path = Path(yaml_config["dataset_path"]) else: - dataset_path = os.path.join(path_config["dataset_root"], model_name) - self.dataset_path: str = dataset_path - self.assets_root: str = path_config["assets_root"] - self.out_dir = os.path.join(self.assets_root, model_name) + dataset_path = path_config.dataset_root / model_name + self.dataset_path = dataset_path + self.dataset_root = path_config.dataset_root + self.assets_root = path_config.assets_root + self.out_dir = self.assets_root / model_name self.resample_config: Resample_config = Resample_config.from_dict( dataset_path, yaml_config["resample"] ) @@ -276,16 +277,31 @@ class Config: # ) -with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f: - path_config: dict[str, str] = yaml.safe_load(f.read()) - # Should contain the following keys: - # - dataset_root: the root directory of the dataset, default to "Data" - # - assets_root: the root directory of the assets, default to "model_assets" +# Load and initialize the configuration + + +def get_path_config() -> PathConfig: + path_config_path = Path("configs/paths.yml") + if not path_config_path.exists(): + shutil.copy(src="configs/default_paths.yml", dst=path_config_path) + logger.info( + f"A configuration file {path_config_path} has been generated based on the default configuration file default_paths.yml." + ) + logger.info( + "Please do not modify configs/default_paths.yml. Instead, modify configs/paths.yml." + ) + with open(path_config_path, encoding="utf-8") as file: + path_config_dict: dict[str, str] = yaml.safe_load(file.read()) + return PathConfig(**path_config_dict) + +def get_config() -> Config: + path_config = get_path_config() + try: + config = Config("config.yml", path_config) + except (TypeError, KeyError): + logger.warning("Old config.yml found. Replace it with default_config.yml.") + shutil.copy(src="default_config.yml", dst="config.yml") + config = Config("config.yml", path_config) -try: - config = Config("config.yml", path_config) -except (TypeError, KeyError): - logger.warning("Old config.yml found. Replace it with default_config.yml.") - shutil.copy(src="default_config.yml", dst="config.yml") - config = Config("config.yml", path_config) + return config diff --git a/configs/config.json b/configs/config.json index 25e86db6b9d506591db50521a1ab534b6643db3c..75629e9ea99064dc237cc991508966b6057eb0bf 100644 --- a/configs/config.json +++ b/configs/config.json @@ -1,5 +1,5 @@ { - "model_name": "your_model_name", + "model_name": "Dummy", "train": { "log_interval": 200, "eval_interval": 1000, @@ -24,8 +24,9 @@ "freeze_encoder": false }, "data": { - "training_files": "Data/your_model_name/filelists/train.list", - "validation_files": "Data/your_model_name/filelists/val.list", + "use_jp_extra": false, + "training_files": "Data/Dummy/train.list", + "validation_files": "Data/Dummy/val.list", "max_wav_value": 32768.0, "sampling_rate": 44100, "filter_length": 2048, @@ -68,5 +69,5 @@ "use_spectral_norm": false, "gin_channels": 256 }, - "version": "2.3" + "version": "2.5.0" } diff --git a/configs/configs_jp_extra.json b/configs/config_jp_extra.json similarity index 92% rename from configs/configs_jp_extra.json rename to configs/config_jp_extra.json index 616d1d31d516108748a6b0a18e281ab17459cd29..d7e135ad0cd8507346dec5d4487b3d396eff8dc5 100644 --- a/configs/configs_jp_extra.json +++ b/configs/config_jp_extra.json @@ -1,4 +1,5 @@ { + "model_name": "Dummy", "train": { "log_interval": 200, "eval_interval": 1000, @@ -27,8 +28,8 @@ }, "data": { "use_jp_extra": true, - "training_files": "filelists/train.list", - "validation_files": "filelists/val.list", + "training_files": "Data/Dummy/train.list", + "validation_files": "Data/Dummy/val.list", "max_wav_value": 32768.0, "sampling_rate": 44100, "filter_length": 2048, @@ -75,5 +76,5 @@ "initial_channel": 64 } }, - "version": "2.3-JP-Extra" + "version": "2.5.0-JP-Extra" } diff --git a/configs/default_paths.yml b/configs/default_paths.yml new file mode 100644 index 0000000000000000000000000000000000000000..d743748f7f52d5c41f1487b53872143c2f010222 --- /dev/null +++ b/configs/default_paths.yml @@ -0,0 +1,8 @@ +# Root directory of the training dataset. +# The training dataset of {model_name} should be placed in {dataset_root}/{model_name}. +dataset_root: Data + +# Root directory of the model assets (for inference). +# In training, the model assets will be saved to {assets_root}/{model_name}, +# and in inference, we load all the models from {assets_root}. +assets_root: model_assets diff --git a/data_utils.py b/data_utils.py deleted file mode 100644 index ac038c22fb542d70500002ae120fa56605e9b03e..0000000000000000000000000000000000000000 --- a/data_utils.py +++ /dev/null @@ -1,456 +0,0 @@ -import os -import random -import sys - -import numpy as np -import torch -import torch.utils.data -from tqdm import tqdm - -import commons -from config import config -from mel_processing import mel_spectrogram_torch, spectrogram_torch -from text import cleaned_text_to_sequence -from common.log import logger -from utils import load_filepaths_and_text, load_wav_to_torch - -"""Multi speaker version""" - - -class TextAudioSpeakerLoader(torch.utils.data.Dataset): - """ - 1) loads audio, speaker_id, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths_sid_text, hparams): - self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - self.spk_map = hparams.spk2id - self.hparams = hparams - self.use_jp_extra = getattr(hparams, "use_jp_extra", False) - - self.use_mel_spec_posterior = getattr( - hparams, "use_mel_posterior_encoder", False - ) - if self.use_mel_spec_posterior: - self.n_mel_channels = getattr(hparams, "n_mel_channels", 80) - - self.cleaned_text = getattr(hparams, "cleaned_text", False) - - self.add_blank = hparams.add_blank - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 384) - - random.seed(1234) - random.shuffle(self.audiopaths_sid_text) - self._filter() - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - - audiopaths_sid_text_new = [] - lengths = [] - skipped = 0 - logger.info("Init dataset...") - for _id, spk, language, text, phones, tone, word2ph in tqdm( - self.audiopaths_sid_text, file=sys.stdout - ): - audiopath = f"{_id}" - if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len: - phones = phones.split(" ") - tone = [int(i) for i in tone.split(" ")] - word2ph = [int(i) for i in word2ph.split(" ")] - audiopaths_sid_text_new.append( - [audiopath, spk, language, text, phones, tone, word2ph] - ) - lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) - else: - skipped += 1 - logger.info( - "skipped: " - + str(skipped) - + ", total: " - + str(len(self.audiopaths_sid_text)) - ) - self.audiopaths_sid_text = audiopaths_sid_text_new - self.lengths = lengths - - def get_audio_text_speaker_pair(self, audiopath_sid_text): - # separate filename, speaker_id and text - audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text - - bert, ja_bert, en_bert, phones, tone, language = self.get_text( - text, word2ph, phones, tone, language, audiopath - ) - - spec, wav = self.get_audio(audiopath) - sid = torch.LongTensor([int(self.spk_map[sid])]) - style_vec = torch.FloatTensor(np.load(f"{audiopath}.npy")) - if self.use_jp_extra: - return (phones, spec, wav, sid, tone, language, ja_bert, style_vec) - else: - return ( - phones, - spec, - wav, - sid, - tone, - language, - bert, - ja_bert, - en_bert, - style_vec, - ) - - def get_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError( - "{} {} SR doesn't match target {} SR".format( - filename, sampling_rate, self.sampling_rate - ) - ) - audio_norm = audio / self.max_wav_value - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if self.use_mel_spec_posterior: - spec_filename = spec_filename.replace(".spec.pt", ".mel.pt") - try: - spec = torch.load(spec_filename) - except: - if self.use_mel_spec_posterior: - spec = mel_spectrogram_torch( - audio_norm, - self.filter_length, - self.n_mel_channels, - self.sampling_rate, - self.hop_length, - self.win_length, - self.hparams.mel_fmin, - self.hparams.mel_fmax, - center=False, - ) - else: - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - if config.train_ms_config.spec_cache: - torch.save(spec, spec_filename) - return spec, audio_norm - - def get_text(self, text, word2ph, phone, tone, language_str, wav_path): - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - if self.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert_path = wav_path.replace(".wav", ".bert.pt") - try: - bert_ori = torch.load(bert_path) - assert bert_ori.shape[-1] == len(phone) - except Exception as e: - logger.warning("Bert load Failed") - logger.warning(e) - - if language_str == "ZH": - bert = bert_ori - ja_bert = torch.zeros(1024, len(phone)) - en_bert = torch.zeros(1024, len(phone)) - elif language_str == "JP": - bert = torch.zeros(1024, len(phone)) - ja_bert = bert_ori - en_bert = torch.zeros(1024, len(phone)) - elif language_str == "EN": - bert = torch.zeros(1024, len(phone)) - ja_bert = torch.zeros(1024, len(phone)) - en_bert = bert_ori - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - return bert, ja_bert, en_bert, phone, tone, language - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def __getitem__(self, index): - return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) - - def __len__(self): - return len(self.audiopaths_sid_text) - - -class TextAudioSpeakerCollate: - """Zero-pads model inputs and targets""" - - def __init__(self, return_ids=False, use_jp_extra=False): - self.return_ids = return_ids - self.use_jp_extra = use_jp_extra - - def __call__(self, batch): - """Collate's training batch from normalized text, audio and speaker identities - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized, sid] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True - ) - - max_text_len = max([len(x[0]) for x in batch]) - max_spec_len = max([x[1].size(1) for x in batch]) - max_wav_len = max([x[2].size(1) for x in batch]) - - text_lengths = torch.LongTensor(len(batch)) - spec_lengths = torch.LongTensor(len(batch)) - wav_lengths = torch.LongTensor(len(batch)) - sid = torch.LongTensor(len(batch)) - - text_padded = torch.LongTensor(len(batch), max_text_len) - tone_padded = torch.LongTensor(len(batch), max_text_len) - language_padded = torch.LongTensor(len(batch), max_text_len) - # This is ZH bert if not use_jp_extra, JA bert if use_jp_extra - bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) - if not self.use_jp_extra: - ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) - en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) - style_vec = torch.FloatTensor(len(batch), 256) - - spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) - wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) - text_padded.zero_() - tone_padded.zero_() - language_padded.zero_() - spec_padded.zero_() - wav_padded.zero_() - bert_padded.zero_() - if not self.use_jp_extra: - ja_bert_padded.zero_() - en_bert_padded.zero_() - style_vec.zero_() - - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - text = row[0] - text_padded[i, : text.size(0)] = text - text_lengths[i] = text.size(0) - - spec = row[1] - spec_padded[i, :, : spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wav = row[2] - wav_padded[i, :, : wav.size(1)] = wav - wav_lengths[i] = wav.size(1) - - sid[i] = row[3] - - tone = row[4] - tone_padded[i, : tone.size(0)] = tone - - language = row[5] - language_padded[i, : language.size(0)] = language - - bert = row[6] - bert_padded[i, :, : bert.size(1)] = bert - - if self.use_jp_extra: - style_vec[i, :] = row[7] - else: - ja_bert = row[7] - ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert - - en_bert = row[8] - en_bert_padded[i, :, : en_bert.size(1)] = en_bert - style_vec[i, :] = row[9] - - if self.use_jp_extra: - return ( - text_padded, - text_lengths, - spec_padded, - spec_lengths, - wav_padded, - wav_lengths, - sid, - tone_padded, - language_padded, - bert_padded, - style_vec, - ) - else: - return ( - text_padded, - text_lengths, - spec_padded, - spec_lengths, - wav_padded, - wav_lengths, - sid, - tone_padded, - language_padded, - bert_padded, - ja_bert_padded, - en_bert_padded, - style_vec, - ) - - -class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): - """ - Maintain similar input lengths in a batch. - Length groups are specified by boundaries. - Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. - - It removes samples which are not included in the boundaries. - Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. - """ - - def __init__( - self, - dataset, - batch_size, - boundaries, - num_replicas=None, - rank=None, - shuffle=True, - ): - super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) - self.lengths = dataset.lengths - self.batch_size = batch_size - self.boundaries = boundaries - - self.buckets, self.num_samples_per_bucket = self._create_buckets() - logger.info(f"Bucket info: {self.num_samples_per_bucket}") - # logger.info( - # f"Unused samples: {len(self.lengths) - sum(self.num_samples_per_bucket)}" - # ) - # ↑マイナスになることあるし、別にこれは使われないサンプル数ではないようだ…… - # バケットの仕組みはよく分からない - - self.total_size = sum(self.num_samples_per_bucket) - self.num_samples = self.total_size // self.num_replicas - - def _create_buckets(self): - buckets = [[] for _ in range(len(self.boundaries) - 1)] - for i in range(len(self.lengths)): - length = self.lengths[i] - idx_bucket = self._bisect(length) - if idx_bucket != -1: - buckets[idx_bucket].append(i) - - try: - for i in range(len(buckets) - 1, 0, -1): - if len(buckets[i]) == 0: - buckets.pop(i) - self.boundaries.pop(i + 1) - assert all(len(bucket) > 0 for bucket in buckets) - # When one bucket is not traversed - except Exception as e: - logger.info("Bucket warning ", e) - for i in range(len(buckets) - 1, -1, -1): - if len(buckets[i]) == 0: - buckets.pop(i) - self.boundaries.pop(i + 1) - - num_samples_per_bucket = [] - for i in range(len(buckets)): - len_bucket = len(buckets[i]) - total_batch_size = self.num_replicas * self.batch_size - rem = ( - total_batch_size - (len_bucket % total_batch_size) - ) % total_batch_size - num_samples_per_bucket.append(len_bucket + rem) - return buckets, num_samples_per_bucket - - def __iter__(self): - # deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.epoch) - - indices = [] - if self.shuffle: - for bucket in self.buckets: - indices.append(torch.randperm(len(bucket), generator=g).tolist()) - else: - for bucket in self.buckets: - indices.append(list(range(len(bucket)))) - - batches = [] - for i in range(len(self.buckets)): - bucket = self.buckets[i] - len_bucket = len(bucket) - if len_bucket == 0: - continue - ids_bucket = indices[i] - num_samples_bucket = self.num_samples_per_bucket[i] - - # add extra samples to make it evenly divisible - rem = num_samples_bucket - len_bucket - ids_bucket = ( - ids_bucket - + ids_bucket * (rem // len_bucket) - + ids_bucket[: (rem % len_bucket)] - ) - - # subsample - ids_bucket = ids_bucket[self.rank :: self.num_replicas] - - # batching - for j in range(len(ids_bucket) // self.batch_size): - batch = [ - bucket[idx] - for idx in ids_bucket[ - j * self.batch_size : (j + 1) * self.batch_size - ] - ] - batches.append(batch) - - if self.shuffle: - batch_ids = torch.randperm(len(batches), generator=g).tolist() - batches = [batches[i] for i in batch_ids] - self.batches = batches - - assert len(self.batches) * self.batch_size == self.num_samples - return iter(self.batches) - - def _bisect(self, x, lo=0, hi=None): - if hi is None: - hi = len(self.boundaries) - 1 - - if hi > lo: - mid = (hi + lo) // 2 - if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: - return mid - elif x <= self.boundaries[mid]: - return self._bisect(x, lo, mid) - else: - return self._bisect(x, mid + 1, hi) - else: - return -1 - - def __len__(self): - return self.num_samples // self.batch_size diff --git a/default_config.yml b/default_config.yml index c7cc19ea8a81049c01168ffa119af143907061b8..8ee8bb89623c960794cad8863a70ba71c0dbed28 100644 --- a/default_config.yml +++ b/default_config.yml @@ -22,7 +22,7 @@ preprocess_text: bert_gen: config_path: "config.json" - num_processes: 2 + num_processes: 1 device: "cuda" use_multi_device: false diff --git a/default_style.py b/default_style.py deleted file mode 100644 index 9198ca8b2f69a3386ec384b96170df4753110b99..0000000000000000000000000000000000000000 --- a/default_style.py +++ /dev/null @@ -1,30 +0,0 @@ -import os -from common.log import logger -from common.constants import DEFAULT_STYLE - -import numpy as np -import json - - -def set_style_config(json_path, output_path): - with open(json_path, "r", encoding="utf-8") as f: - json_dict = json.load(f) - json_dict["data"]["num_styles"] = 1 - json_dict["data"]["style2id"] = {DEFAULT_STYLE: 0} - with open(output_path, "w", encoding="utf-8") as f: - json.dump(json_dict, f, indent=2, ensure_ascii=False) - logger.info(f"Save style config (only {DEFAULT_STYLE}) to {output_path}") - - -def save_mean_vector(wav_dir, output_path): - embs = [] - for file in os.listdir(wav_dir): - if file.endswith(".npy"): - xvec = np.load(os.path.join(wav_dir, file)) - embs.append(np.expand_dims(xvec, axis=0)) - - x = np.concatenate(embs, axis=0) # (N, 256) - mean = np.mean(x, axis=0) # (256,) - only_mean = np.stack([mean]) # (1, 256) - np.save(output_path, only_mean) - logger.info(f"Saved mean style vector to {output_path}") diff --git a/dict_data/.gitignore b/dict_data/.gitignore deleted file mode 100644 index 0b60cb63595da629299b2a9de95e2f8b58f81da7..0000000000000000000000000000000000000000 --- a/dict_data/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -!.gitignore -!default.csv diff --git a/dict_data/default.csv b/dict_data/default.csv index 8869f97445544f4462016192497983eef94ffead..84085cde232b8c9fcc834ae3eb8e5dcc4f3a0653 100644 --- a/dict_data/default.csv +++ b/dict_data/default.csv @@ -1,3 +1,5 @@ Ｂｅｒｔ,,,8609,名詞,固有名詞,一般,*,*,*,Ｂｅｒｔ,バアト,バアト,0/3,* ＶＩＴＳ,,,8609,名詞,固有名詞,一般,*,*,*,ＶＩＴＳ,ビッツ,ビッツ,0/3,* -ＶＩＴＳ２,,,8609,名詞,固有名詞,一般,*,*,*,ＶＩＴＳ２,ビッツツウ,ビッツツウ,4/5,* +ＶＩＴＳ二,,,8609,名詞,固有名詞,一般,*,*,*,ＶＩＴＳ二,ビッツツー,ビッツツー,4/5,* +ＢｅｒｔＶＩＴＳ,,,8609,名詞,固有名詞,一般,*,*,*,ＢｅｒｔＶＩＴＳ,バアトビッツ,バアトビッツ,4/6,* +担々麺,,,8609,名詞,固有名詞,一般,*,*,*,担々麺,タンタンメン,タンタンメン,3/6,* diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md deleted file mode 100644 index 77cdca51f08f756648810d9534615a60be4e648c..0000000000000000000000000000000000000000 --- a/docs/CHANGELOG.md +++ /dev/null @@ -1,129 +0,0 @@ -# Changelog - -## v2.3 (2024-02-25) - -### 大きな変更 - -#### ユーザー辞書機能 -あらかじめ辞書に固有名詞を追加することができ、それが学習時・音声合成時の読み取得部分に適応されます。辞書の追加・編集は次のエディタ経由で行ってください。 - -辞書部分の[実装](/text/user_dict/) は、中のREADMEにある通り、[VOICEVOX Editor](https://github.com/VOICEVOX/voicevox) のものを使っており、この部分のコードライセンスはLGPL-3. - -#### 音声合成専用エディタ - -音声合成専用エディタを追加。今までのWebUIでできた機能のほか、次のような機能が使えます（つまり既存の日本語音声合成ソフトウェアのエディタを真似ました）： -- セリフ単位でキャラや設定を変更しながら原稿を作り、それを一括で生成したり、原稿を保存等したり読み込んだり -- GUIよる分かりやすいアクセント調整 -- ユーザー辞書への単語追加や編集 - -`Editor.bat`をダブルクリックか`python server_editor.py --inbrowser`で起動します。エディター部分は[こちらの別リポジトリ](https://github.com/litagin02/Style-Bert-VITS2-Editor)になります。フロントエンド初心者なのでプルリクや改善案等をお待ちしています。 - -### 改善 -- 学習時にデコーダー部分を凍結するオプションの追加。品質がもしかしたら上がるかもしれません。 -- - -## v2.2 (2024-02-09) - -### 変更・機能追加 -- bfloat16オプションはデメリットしか無さそうなので、常にオフで学習するよう変更 -- バッチサイズのデフォルトを4から2に変更。学習が遅い場合はバッチサイズを下げて試してみて、VRAMに余裕があれば上げてください。JP-Extra使用時でのバッチサイズごとのVRAM使用量目安は、1: 6GB, 2: 8GB, 3: 10GB, 4: 12GB くらいのようです。 -- 学習の際の検証データ数をデフォルトで0に変更し、また検証データ数を学習用WebUIで指定できるようにした -- Tensorboardのログ間隔を学習用WebUIで指定できるようにした -- UIのテーマを`common/constants.py`の`GRADIO_THEME`で指定できるようにした - -### バグ修正 -- JP-Extra使用時にバッチサイズが1だと学習中にエラーが発生するバグを修正 -- 「こんにちは!?!?!?!?」等、感嘆符等の記号が連続すると学習・音声合成でエラーになるバグを修正 -- `—` (em dash, U+2014) や `―` (quotation dash, U+2015) 等のダッシュやハイフンの各種変種が、種類によって`-`（通常の半角ハイフン）に正規化されたりされていなかったりする処理を、全て正規化するように修正 - -## v2.1 (2024-02-07) - -### 変更 -- 学習の際、デフォルトではbfloat16オプションを使わないよう変更（学習が発散したり質が下がることがある模様） -- 学習の際のメモリ使用量を削減しようと頑張った - -### バグ修正や改善 -- 学習WebUIからTensorboardのログを見れるように -- 音声合成（やそのAPI）において、同時に別の話者が選択され音声合成がリクエストされた場合に発生するエラーを修正 -- モデルマージ時に、そのレシピを`recipe.json`ファイルへ保存するように変更 -- 「改行で分けて生成」がより感情が乗る旨の明記等、軽微な説明文の改善 -- 「`ーーそれは面白い`」や「`なるほど。ーーーそういうことか。`」等、長音記号の前が母音でない場合、長音記号`ー`でなくダッシュ`―`の勘違いだと思われるので、ダッシュ記号として処理するように変更 - -## v2.0.1 (2024-02-05) - -軽微なバグ修正や改善 -- スタイルベクトルに`NaN`が含まれていた場合（主に音声ファイルが極端に短い場合に発生）、それを学習リストから除外するように修正 -- colabにマージの追加 -- 学習時のプログレスバーの表示がおかしかったのを修正 -- デフォルトのjvnvモデルをJP-Extra版にアップデート。新しいモデルを使いたい方は手動で[こちら](https://huggingface.co/litagin/style_bert_vits2_jvnv/tree/main)からダウンロードするか、`python initialize.py`をするか、[このbatファイル](https://github.com/litagin02/Style-Bert-VITS2/releases/download/2.0.1/Update-to-JP-Extra.bat)を`Style-Bert-VITS2`フォルダがある場所（インストールbatファイルとかがあったところ）においてダブルクリックしてください。 - -## v2.0 (2024-02-03) - -### 大きい変更 -モデル構造に [Bert-VITS2の日本語特化モデル JP-Extra](https://github.com/fishaudio/Bert-VITS2/releases/tag/JP-Exta) を取り込んだものを使えるように変更、[事前学習モデル](https://huggingface.co/litagin/Style-Bert-VITS2-2.0-base-JP-Extra)も[Bert-VITS2 JP-Extra](https://huggingface.co/Stardust-minus/Bert-VITS2-Japanese-Extra)のものを改造してStyle-Bert-VITS2で使えるようにしました (モデル構造を見直して日本語での学習をしていただいた [@Stardust-minus](https://github.com/Stardust-minus) 様に感謝します) -- これにより、日本語の発音やアクセントや抑揚や自然性が向上する傾向があります -- スタイルベクトルを使ったスタイルの操作は変わらず使えます -- ただしJP-Extraでは英語と中国語の音声合成は（現状は）できません -- 旧モデルも引き続き使うことができ、また旧モデルで学習することもできます -- デフォルトのJVNVモデルは現在は旧verのままです - -### 改善 -- `Merge.bat`で、声音マージを、より細かく「声質」と「声の高さ」の点でマージできるように。 - -### バグ修正 -- PyTorchのバージョンに由来するバグを修正（torchのバージョンを2.1.2に固定） -- `―`（ダッシュ、長音記号ではない）が2連続すると学習・音声合成でエラーになるバグを修正 -- 「三円」等「ん＋母音」のアクセントの仮名表記が「サネン」等になり、また偶にエラーが発生する問題を修正（「ん」の音素表記を内部的には「N」で統一） - -## v1.3 (2024-01-09) - -### 大きい変更 -- 元々のBert-VITS2に存在した、日本語の発音・アクセント処理部分のバグを修正・リファクタリング - - `車両`が`シャリヨオ`、`思う`が`オモオ`、`見つける`が`ミッケル`等に発音・学習されており、その単語以降のアクセント情報が全て死んでいた - - `私はそれを見る`のアクセントが`ワ➚タシ➘ワ　ソ➚レ➘オ　ミ➘ル`だったのを`ワ➚タシワ　ソ➚レオ　ミ➘ル`に修正 - - 学習・音声合成で無視されていたアルファベット・ギリシャ文字を無視しないように変更（基本はアルファベット読みだけど簡単な単語は読めるらしい、学習の際は念のためカタカナ等にしたほうがよいです） - - 修正の影響で、前処理時に（今まで無視されていた）読めない漢字等で引っかかるようになりました。その場合は書き起こしを確認して修正するようにしてください。 -- アクセントを調整して音声合成できるように（完全に制御できるわけではないが改善される場合がある）。 - -これまでのモデルもこれまで通り使え、アクセントや発音等が改善される可能性があります。新しいバージョンで学習し直すとより良くなる可能性もあります。が劇的に良くなるかは分かりません。 - -### 改善 -- `Dataset.bat`の音声スライスと書き起こしをよりカスタマイズできるように（スライスの秒数設定や書き起こしのWhisperモデル指定や言語指定等） -- `Style.bat`のスタイル分けで、スタイルごとのサンプル音声を指定した数だけ複数再生できるように。また新しい次元削減方法（UMAP）と新しいスタイル分けの方法（DBSCAN）を追加（UMAPのほうがよくスタイルが分かれるかもしれません） -- `App.bat`での音声合成時に複数話者モデルの場合に話者を指定できるように -- colabの[ノートブック](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)で、音声ファイルのみからデータセットを作成するオプション部分を追加 -- クラウド実行等の際にパスの指定をこちらでできるように、パスの設定を`configs/paths.yml`にまとめた（colabの[ノートブック](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)もそれに伴って更新）。デフォルトは`dataset_root: Data`と`assets_root: model_assets`なので、クラウド等でやる方はここを変更してください。 -- どのステップ数の出力がよいかの「一つの」指標として [SpeechMOS](https://github.com/tarepan/SpeechMOS) を使うスクリプトを追加： -```bash -python speech_mos.py -m -``` -ステップごとの自然性評価が表示され、`mos_results`フォルダの`mos_{model_name}.csv`と`mos_{model_name}.png`に結果が保存される。読み上げさせたい文章を変えたかったら中のファイルを弄って各自調整してください。あくまでアクセントや感情表現や抑揚を全く考えない基準での評価で、目安のひとつなので、実際に読み上げさせて選別するのが一番だと思います。 -- 学習時のウォームアップオプションを機能するように（ [@kale4eat](https://github.com/kale4eat) 様によるPRです、ありがとうございます！）。前処理時に生成される`config.json`の`train`の`warmup_epochs`を変更することで、ウォームアップのエポック数を変更できます。デフォルトは`0`で今までと同じ学習率の挙動です。 - -### その他 -- `Dataset.bat`の音声スライスでノーマライズ機能を削除（学習前処理で行えるため） -- `Train.bat`の音量ノーマライズと無音切り詰めをデフォルトでオフに変更 -- 学習時の進捗を全体エポック数で表示し、学習全体の進捗を見やすいように( [@RedRayz](https://github.com/RedRayz) 様によるPRです、ありがとうございます！) -- その他バグ修正等（ [@tinjyuu](https://github.com/@tinjyuu) 様、 [@darai0512](https://github.com/darai0512) 様ありがとうございます！） -- `config.json`にスタイル埋め込み部分を学習しない`freeze_style`オプションを追加（デフォルトは`false`） - -### TIPS -- 日本語学習の場合、`config.json`の`freeze_bert`と`freeze_en_bert`を`true`にしておくと、英語と中国語の発話能力が学習の過程で落ちないかもしれませんが、あまり比較していなので分かりません。 - -## v1.2 (2023-12-31) - -- グラボがないユーザーでの音声合成をサポート、`Install-Style-Bert-VITS2-CPU.bat`でインストール。 -- Google Colabでの学習をサポート、[ノートブック](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb)を追加 -- 音声合成のAPIサーバーを追加、`python server_fastapi.py`で起動します。API仕様は起動後に`/docs`にて確認ください。（ [@darai0512](https://github.com/darai0512) 様によるPRです、ありがとうございます！） -- 学習時に自動的にデフォルトスタイル Neutral を生成するように。特にスタイル指定が必要のない方は、学習したらそのまま音声合成を試せます。これまで通りスタイルを自分で作ることもできます。 -- マージ機能の新規追加: `Merge.bat`, `webui_merge.py` -- 前処理のリサンプリング時に音声ファイルの開始・終了部分の無音を削除するオプションを追加（デフォルトでオン） -- `スタイルテキスト (style text)`がスタイル指定と紛らわしかったので、`アシストテキスト (assist text)`に変更 -- その他コードのリファクタリング - -## v1.1 (2023-12-29) -- TrainとDatasetのWebUIの改良・調整（一括事前処理ボタン等） -- 前処理のリサンプリング時に音量を正規化するオプションを追加（デフォルトでオン） - -## v1.0 (2023-12-27) -- 初版 diff --git a/docs/CLI.md b/docs/CLI.md deleted file mode 100644 index 08e2fd03e5863936a77f3b10ba9b3682f1109c96..0000000000000000000000000000000000000000 --- a/docs/CLI.md +++ /dev/null @@ -1,96 +0,0 @@ -# CLI - -## 0. Install and global paths settings - -```bash -git clone https://github.com/litagin02/Style-Bert-VITS2.git -cd Style-Bert-VITS2 -python -m venv venv -venv\Scripts\activate -pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 -pip install -r requirements.txt -``` - -Then download the necessary models and the default TTS model, and set the global paths. -```bash -python initialize.py [--skip_jvnv] [--dataset_root ] [--assets_root ] -``` - -Optional: -- `--skip_jvnv`: Skip downloading the default JVNV voice models (use this if you only have to train your own models). -- `--dataset_root`: Default: `Data`. Root directory of the training dataset. The training dataset of `{model_name}` should be placed in `{dataset_root}/{model_name}`. -- `--assets_root`: Default: `model_assets`. Root directory of the model assets (for inference). In training, the model assets will be saved to `{assets_root}/{model_name}`, and in inference, we load all the models from `{assets_root}`. - - -## 1. Dataset preparation - -### 1.1. Slice wavs -```bash -python slice.py --model_name [-i ] [-m ] [-M ] -``` - -Required: -- `model_name`: Name of the speaker (to be used as the name of the trained model). - -Optional: -- `input_dir`: Path to the directory containing the audio files to slice (default: `inputs`) -- `min_sec`: Minimum duration of the sliced audio files in seconds (default: 2). -- `max_sec`: Maximum duration of the sliced audio files in seconds (default: 12). - -### 1.2. Transcribe wavs - -```bash -python transcribe.py --model_name -``` -Required: -- `model_name`: Name of the speaker (to be used as the name of the trained model). - -Optional -- `--initial_prompt`: Initial prompt to use for the transcription (default value is specific to Japanese). -- `--device`: `cuda` or `cpu` (default: `cuda`). -- `--language`: `jp`, `en`, or `en` (default: `jp`). -- `--model`: Whisper model, default: `large-v3` -- `--compute_type`: default: `bfloat16` - -## 2. Preprocess - -```bash -python preprocess_all.py -m [--use_jp_extra] [-b ] [-e ] [-s ] [--num_processes ] [--normalize] [--trim] [--val_per_lang ] [--log_interval ] [--freeze_EN_bert] [--freeze_JP_bert] [--freeze_ZH_bert] [--freeze_style] [--freeze_decoder] -``` - -Required: -- `model_name`: Name of the speaker (to be used as the name of the trained model). - -Optional: -- `--batch_size`, `-b`: Batch size (default: 2). -- `--epochs`, `-e`: Number of epochs (default: 100). -- `--save_every_steps`, `-s`: Save every steps (default: 1000). -- `--num_processes`: Number of processes (default: half of the number of CPU cores). -- `--normalize`: Loudness normalize audio. -- `--trim`: Trim silence. -- `--freeze_EN_bert`: Freeze English BERT. -- `--freeze_JP_bert`: Freeze Japanese BERT. -- `--freeze_ZH_bert`: Freeze Chinese BERT. -- `--freeze_style`: Freeze style vector. -- `--freeze_decoder`: Freeze decoder. -- `--use_jp_extra`: Use JP-Extra model. -- `--val_per_lang`: Validation data per language (default: 0). -- `--log_interval`: Log interval (default: 200). - -## 3. Train - -Training settings are automatically loaded from the above process. - -If NOT using JP-Extra model: -```bash -python train_ms.py [--repo_id /] -``` - -If using JP-Extra model: -```bash -python train_ms_jp_extra.py [--repo_id /] [--skip_default_style] -``` - -Optional: -- `--repo_id`: Hugging Face repository ID to upload the trained model to. You should have logged in using `huggingface-cli login` before running this command. -- `--skip_default_style`: Skip making the default style vector. Use this if you want to resume training (since the default style vector is already made). diff --git a/docs/README_en.md b/docs/README_en.md deleted file mode 100644 index 7dd53ae0d3b9331a3612280816c491dc1f3913b0..0000000000000000000000000000000000000000 --- a/docs/README_en.md +++ /dev/null @@ -1,125 +0,0 @@ -# This English README is for 1.x versions. WIP for 2.x versions. - -# Style-Bert-VITS2 - -Bert-VITS2 with more controllable voice styles. - -https://github.com/litagin02/Style-Bert-VITS2/assets/139731664/b907c1b8-43aa-46e6-b03f-f6362f5a5a1e - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb) - -Online demo: https://huggingface.co/spaces/litagin/Style-Bert-VITS2-JVNV - -This repository is based on [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2) v2.1, so many thanks to the original author! - -- [Update History](docs/CHANGELOG.md) - -**Overview** - -- Based on Bert-VITS2 v2.1, which generates emotionally rich voices from entered text, this version allows free control of emotions and speaking styles, including intensity. -- Easy to install and train for people without Git or Python (for Windows users), much is borrowed from [EasyBertVits2](https://github.com/Zuntan03/EasyBertVits2/). Training on Google Colab is also supported: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/litagin02/Style-Bert-VITS2/blob/master/colab.ipynb) -- If used only for voice synthesis, it can operate on CPU without a graphics card. -- Also includes an API server for integration with others (PR by [@darai0512](https://github.com/darai0512), thank you). -- Originally, Bert-VITS2's strength was to read "happy text happily, sad text sadly", so even without using the added style specification in this fork, you can generate emotionally rich voices. - - -## How to Use - - - -### Operating Environment - -We have confirmed the operation in Windows Command Prompt, WSL2, and Linux (Ubuntu Desktop) for each UI and API Server (please be creative with path specifications in WSL). - -### Installation - -#### For Those Unfamiliar with Git or Python - -Assuming Windows: - -1. Download and unzip [this zip file](https://github.com/litagin02/Style-Bert-VITS2/releases/download/1.3/Style-Bert-VITS2.zip). - - If you have a graphics card, double-click `Install-Style-Bert-VITS2.bat`. - - If you don't have a graphics card, double-click `Install-Style-Bert-VITS2-CPU.bat`. -2. Wait for the necessary environment to install automatically. -3. After that, if the WebUI for voice synthesis launches automatically, the installation is successful. The default model will be downloaded, so you can play with it immediately. - -For updates, please double-click `Update-Style-Bert-VITS2.bat`. - -#### For Those Familiar with Git and Python - -```bash -git clone https://github.com/litagin02/Style-Bert-VITS2.git -cd Style-Bert-VITS2 -python -m venv venv -venv\Scripts\activate -pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 -pip install -r requirements.txt -python initialize.py # Download necessary models and default TTS model -``` -Don't forget the last step. - -### Voice Synthesis -Double-click `App.bat` or run `python app.py` to launch the WebUI. The default model is downloaded during installation, so you can use it even if you haven't trained it. - -The structure of the model files required for voice synthesis is as follows (you don't need to place them manually): - -``` -model_assets -├── your_model -│ ├── config.json -│ ├── your_model_file1.safetensors -│ ├── your_model_file2.safetensors -│ ├── ... -│ └── style_vectors.npy -└── another_model - ├── ... -``` - -For inference, `config.json`, `*.safetensors`, and `style_vectors.npy` are necessary. If you want to share a model, please share these three files. - -Among them, `style_vectors.npy` is a file necessary to control the style. By default, the average style "Neutral" is generated during training. -If you want to use multiple styles for more detailed control, please refer to "Generating Styles" below (even with only the average style, if the training data is emotionally rich, sufficiently emotionally rich voices can be generated). - -### Training - -Double-click Train.bat or run `python webui_train.py` to launch the WebUI. - -### Generating Styles -For those who want to use styles other than the default "Neutral". - -- Double-click `Style.bat` or run `python webui_style_vectors.py` to launch the WebUI. -- It is independent of training, so you can do it even during training, and you can redo it any number of times after training is complete (preprocessing must be finished). -- For more details on the specifications of the style, please refer to [clustering.ipynb](../clustering.ipynb). - -### Dataset Creation - -- Double-click `Dataset.bat` or run `python webui_dataset.py` to launch the WebUI for creating datasets from audio files. You can use this tool to learn from audio files only. - -Note: If you want to manually correct the dataset, remove noise, etc., you may find [Aivis](https://github.com/tsukumijima/Aivis) or its Windows-compatible dataset part [Aivis Dataset](https://github.com/litagin02/Aivis-Dataset) useful. However, if there are many files, etc., it may be sufficient to simply cut out and create a dataset with this tool. - -Please experiment to see what kind of dataset is best. - -### API Server -Run `python server_fastapi.py` in the constructed environment to launch the API server. -Please check the API specification after launching at `/docs`. - -By default, CORS settings are allowed for all domains. -As much as possible, change the value of server.origins in `config.yml` and limit it to trusted domains (if you delete the key, you can disable the CORS settings). - -### Merging -You can create a new model by mixing two models in terms of "voice", "emotional expression", and "tempo". -Double-click `Merge.bat` or run `python webui_merge.py` to launch the WebUI. - -## Relation to Bert-VITS2 v2.1 -Basically, it's just a slight modification of the Bert-VITS2 v2.1 model structure. The [pre-trained model](https://huggingface.co/litagin/Style-Bert-VITS2-1.0-base) is also essentially the same as Bert-VITS2 v2.1 (unnecessary weights have been removed and converted to safetensors). - -The differences are as follows: - -- Like [EasyBertVits2](https://github.com/Zuntan03/EasyBertVits2), it is easy to use even for people who do not know Python or Git. -- Changed the model for emotional embedding (from 1024-dimensional [wav2vec2-large-robust-12-ft-emotion-msp-dim](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim) to 256-dimensional [wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM), which is more for speaker identification than emotional embedding) -- Removed vector quantization from embeddings and replaced it with just a fully connected layer. -- By creating a style vector file `style_vectors.npy`, you can generate voices using that style and continuously specify the strength of the effect. -- Various WebUIs created -- Support for bf16 training -- Support for safetensors format, defaulting to using safetensors -- Other minor bug fixes and refactoring \ No newline at end of file diff --git a/docs/paperspace.md b/docs/paperspace.md deleted file mode 100644 index 2ae9895e476e47f2afcb29c010e0e90d569ec9d8..0000000000000000000000000000000000000000 --- a/docs/paperspace.md +++ /dev/null @@ -1,86 +0,0 @@ -# Paperspace gradient で学習する - -詳しいコマンドの叩き方は[こちら](CLI.md)を参照してください。 - -## 事前準備 -- Paperspace のアカウントを作成し必要なら課金する -- Projectを作る -- NotebookはStart from Scratchを選択して空いてるGPUマシンを選ぶ - -## 使い方 - -以下では次のような方針でやっています。 - -- `/storage/`は永続ストレージなので、事前学習モデルとかを含めてリポジトリをクローンするとよい。 -- `/notebooks/`はノートブックごとに変わるストレージなので（同一ノートブック違うランタイムだと共有されるらしい）、データセットやその結果を保存する。ただ容量が多い場合はあふれる可能性があるので`/tmp/`に保存するとよいかもしれない。 -- hugging faceアカウントを作り、（プライベートな）リポジトリを作って、学習元データを置いたり、学習結果を随時アップロードする。 - -### 1. 環境を作る - -以下はデフォルトの`Start from Scratch`で作成した環境の場合。[Dockerfile.train](../Dockerfile.train)を使ったカスタムイメージをするとPythonの環境構築の手間がちょっと省けるので、それを使いたい人は`Advanced Options / Container / Name`に[`litagin/mygradient:latest`](https://hub.docker.com/r/litagin/mygradient/tags)を指定すると使えます（pipの箇所が不要になる等）。 - -まずは永続ストレージにgit clone -```bash -mkdir -p /storage/sbv2 -cd /storage/sbv2 -git clone https://github.com/litagin02/Style-Bert-VITS2.git -``` -環境構築（デフォルトはPyTorch 1.x系、Python 3.9の模様） -```bash -cd /storage/sbv2/Style-Bert-VITS2 -pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 && pip install -r requirements.txt -``` -事前学習済みモデル等のダウンロード、またパスを`/notebooks/`以下のものに設定 -```bash -python initialize.py --skip_jvnv --dataset_root /notebooks/Data --assets_root /notebooks/model_assets -``` - -### 2. データセットの準備 -以下では`username/voices`というデータセットリポジトリにある`Foo.zip`というデータセットを使うことを想定しています。 -```bash -cd /notebooks -huggingface-cli login # 事前にトークンが必要 -huggingface-cli download username/voices Foo.zip --repo-type dataset --local-dir . -``` - -- zipファイル中身が既に`raw`と`esd.list`があるデータ（スライス・書き起こし済み）の場合 -```bash -mkdir -p Data/Foo -unzip Foo.zip -d Data/Foo -rm Foo.zip -cd /storage/sbv2/Style-Bert-VITS2 -``` - -- zipファイルが音声ファイルのみの場合 -```bash -mkdir inputs -unzip Foo.zip -d inputs -cd /storage/sbv2/Style-Bert-VITS2 -python slice.py --model_name Foo -i /notebooks/inputs -python transcribe.py --model_name Foo -``` - -それが終わったら、以下のコマンドで一括前処理を行う（パラメータは各自お好み、バッチサイズ5か6でVRAM 16GBギリくらい）。 -```bash -python preprocess_all.py --model_name Foo -b 5 -e 300 --use_jp_extra -``` - -### 3. 学習 - -Hugging faceの`username/sbv2-private`というモデルリポジトリに学習済みモデルをアップロードすることを想定しています。事前に`huggingface-cli login`でログインしておくこと。 -```bash -python train_ms_jp_extra.py --repo_id username/sbv2-private -``` -(JP-Extraでない場合は`train_ms.py`を使う) - -### 4. 学習再開 - -Notebooksの時間制限が切れてから別Notebooksで同じモデルを学習を再開する場合（環境構築は必要）。 -```bash -huggingface-cli login -cd /notebooks -huggingface-cli download username/sbv2-private --include "Data/Foo/*" --local-dir . -cd /storage/sbv2/Style-Bert-VITS2 -python train_ms_jp_extra.py --repo_id username/sbv2-private --skip_default_style -``` -前回の設定が残っているので特に前処理等は不要。 \ No newline at end of file diff --git a/gen_yaml.py b/gen_yaml.py deleted file mode 100644 index 91301accbba6765cb687fd40c67cdd508dc7a3b9..0000000000000000000000000000000000000000 --- a/gen_yaml.py +++ /dev/null @@ -1,32 +0,0 @@ -import os -import shutil -import yaml -import argparse - -parser = argparse.ArgumentParser( - description="config.ymlの生成。あらかじめ前準備をしたデータをバッチファイルなどで連続で学習する時にtrain_ms.pyより前に使用する。" -) -# そうしないと最後の前準備したデータで学習してしまう -parser.add_argument("--model_name", type=str, help="Model name", required=True) -parser.add_argument( - "--dataset_path", - type=str, - help="Dataset path(example: Data\\your_model_name)", - required=True, -) -args = parser.parse_args() - - -def gen_yaml(model_name, dataset_path): - if not os.path.exists("config.yml"): - shutil.copy(src="default_config.yml", dst="config.yml") - with open("config.yml", "r", encoding="utf-8") as f: - yml_data = yaml.safe_load(f) - yml_data["model_name"] = model_name - yml_data["dataset_path"] = dataset_path - with open("config.yml", "w", encoding="utf-8") as f: - yaml.dump(yml_data, f, allow_unicode=True) - - -if __name__ == "__main__": - gen_yaml(args.model_name, args.dataset_path) diff --git a/infer.py b/infer.py deleted file mode 100644 index 505d447893feb205671fea1db34828c2d2d08961..0000000000000000000000000000000000000000 --- a/infer.py +++ /dev/null @@ -1,317 +0,0 @@ -import torch - -import commons -import utils -from models import SynthesizerTrn -from models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra -from text import cleaned_text_to_sequence, get_bert -from text.cleaner import clean_text -from text.symbols import symbols -from common.log import logger - - -class InvalidToneError(ValueError): - pass - - -def get_net_g(model_path: str, version: str, device: str, hps): - if version.endswith("JP-Extra"): - logger.info("Using JP-Extra model") - net_g = SynthesizerTrnJPExtra( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model, - ).to(device) - else: - logger.info("Using normal model") - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model, - ).to(device) - net_g.state_dict() - _ = net_g.eval() - if model_path.endswith(".pth") or model_path.endswith(".pt"): - _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True) - elif model_path.endswith(".safetensors"): - _ = utils.load_safetensors(model_path, net_g, True) - else: - raise ValueError(f"Unknown model format: {model_path}") - return net_g - - -def get_text( - text, - language_str, - hps, - device, - assist_text=None, - assist_text_weight=0.7, - given_tone=None, - ignore_unknown=False, -): - use_jp_extra = hps.version.endswith("JP-Extra") - norm_text, phone, tone, word2ph = clean_text( - text, language_str, use_jp_extra, ignore_unknown=ignore_unknown - ) - if given_tone is not None: - if len(given_tone) != len(phone): - raise InvalidToneError( - f"Length of given_tone ({len(given_tone)}) != length of phone ({len(phone)})" - ) - tone = given_tone - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if hps.data.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert_ori = get_bert( - norm_text, - word2ph, - language_str, - device, - assist_text, - assist_text_weight, - ignore_unknown, - ) - del word2ph - assert bert_ori.shape[-1] == len(phone), phone - - if language_str == "ZH": - bert = bert_ori - ja_bert = torch.zeros(1024, len(phone)) - en_bert = torch.zeros(1024, len(phone)) - elif language_str == "JP": - bert = torch.zeros(1024, len(phone)) - ja_bert = bert_ori - en_bert = torch.zeros(1024, len(phone)) - elif language_str == "EN": - bert = torch.zeros(1024, len(phone)) - ja_bert = torch.zeros(1024, len(phone)) - en_bert = bert_ori - else: - raise ValueError("language_str should be ZH, JP or EN") - - assert bert.shape[-1] == len( - phone - ), f"Bert seq len {bert.shape[-1]} != {len(phone)}" - - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - return bert, ja_bert, en_bert, phone, tone, language - - -def infer( - text, - style_vec, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - sid: int, # In the original Bert-VITS2, its speaker_name: str, but here it's id - language, - hps, - net_g, - device, - skip_start=False, - skip_end=False, - assist_text=None, - assist_text_weight=0.7, - given_tone=None, - ignore_unknown=False, -): - is_jp_extra = hps.version.endswith("JP-Extra") - bert, ja_bert, en_bert, phones, tones, lang_ids = get_text( - text, - language, - hps, - device, - assist_text=assist_text, - assist_text_weight=assist_text_weight, - given_tone=given_tone, - ignore_unknown=ignore_unknown, - ) - if skip_start: - phones = phones[3:] - tones = tones[3:] - lang_ids = lang_ids[3:] - bert = bert[:, 3:] - ja_bert = ja_bert[:, 3:] - en_bert = en_bert[:, 3:] - if skip_end: - phones = phones[:-2] - tones = tones[:-2] - lang_ids = lang_ids[:-2] - bert = bert[:, :-2] - ja_bert = ja_bert[:, :-2] - en_bert = en_bert[:, :-2] - with torch.no_grad(): - x_tst = phones.to(device).unsqueeze(0) - tones = tones.to(device).unsqueeze(0) - lang_ids = lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - ja_bert = ja_bert.to(device).unsqueeze(0) - en_bert = en_bert.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - style_vec = torch.from_numpy(style_vec).to(device).unsqueeze(0) - del phones - sid_tensor = torch.LongTensor([sid]).to(device) - if is_jp_extra: - output = net_g.infer( - x_tst, - x_tst_lengths, - sid_tensor, - tones, - lang_ids, - ja_bert, - style_vec=style_vec, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - ) - else: - output = net_g.infer( - x_tst, - x_tst_lengths, - sid_tensor, - tones, - lang_ids, - bert, - ja_bert, - en_bert, - style_vec=style_vec, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - ) - audio = output[0][0, 0].data.cpu().float().numpy() - del ( - x_tst, - tones, - lang_ids, - bert, - x_tst_lengths, - sid_tensor, - ja_bert, - en_bert, - style_vec, - ) # , emo - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return audio - - -def infer_multilang( - text, - style_vec, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - sid, - language, - hps, - net_g, - device, - skip_start=False, - skip_end=False, -): - bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], [] - # emo = get_emo_(reference_audio, emotion, sid) - # if isinstance(reference_audio, np.ndarray): - # emo = get_clap_audio_feature(reference_audio, device) - # else: - # emo = get_clap_text_feature(emotion, device) - # emo = torch.squeeze(emo, dim=1) - for idx, (txt, lang) in enumerate(zip(text, language)): - _skip_start = (idx != 0) or (skip_start and idx == 0) - _skip_end = (idx != len(language) - 1) or skip_end - ( - temp_bert, - temp_ja_bert, - temp_en_bert, - temp_phones, - temp_tones, - temp_lang_ids, - ) = get_text(txt, lang, hps, device) - if _skip_start: - temp_bert = temp_bert[:, 3:] - temp_ja_bert = temp_ja_bert[:, 3:] - temp_en_bert = temp_en_bert[:, 3:] - temp_phones = temp_phones[3:] - temp_tones = temp_tones[3:] - temp_lang_ids = temp_lang_ids[3:] - if _skip_end: - temp_bert = temp_bert[:, :-2] - temp_ja_bert = temp_ja_bert[:, :-2] - temp_en_bert = temp_en_bert[:, :-2] - temp_phones = temp_phones[:-2] - temp_tones = temp_tones[:-2] - temp_lang_ids = temp_lang_ids[:-2] - bert.append(temp_bert) - ja_bert.append(temp_ja_bert) - en_bert.append(temp_en_bert) - phones.append(temp_phones) - tones.append(temp_tones) - lang_ids.append(temp_lang_ids) - bert = torch.concatenate(bert, dim=1) - ja_bert = torch.concatenate(ja_bert, dim=1) - en_bert = torch.concatenate(en_bert, dim=1) - phones = torch.concatenate(phones, dim=0) - tones = torch.concatenate(tones, dim=0) - lang_ids = torch.concatenate(lang_ids, dim=0) - with torch.no_grad(): - x_tst = phones.to(device).unsqueeze(0) - tones = tones.to(device).unsqueeze(0) - lang_ids = lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - ja_bert = ja_bert.to(device).unsqueeze(0) - en_bert = en_bert.to(device).unsqueeze(0) - # emo = emo.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - del phones - speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) - audio = ( - net_g.infer( - x_tst, - x_tst_lengths, - speakers, - tones, - lang_ids, - bert, - ja_bert, - en_bert, - style_vec=style_vec, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - )[0][0, 0] - .data.cpu() - .float() - .numpy() - ) - del ( - x_tst, - tones, - lang_ids, - bert, - x_tst_lengths, - speakers, - ja_bert, - en_bert, - ) # , emo - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return audio diff --git a/initialize.py b/initialize.py index 5e35061f285c172b0740951a4c192bfbd6656d48..267465e38d35f1da4ab4838ea7626aeef175fa1c 100644 --- a/initialize.py +++ b/initialize.py @@ -1,27 +1,23 @@ import argparse import json +import shutil from pathlib import Path import yaml from huggingface_hub import hf_hub_download -from common.log import logger +from style_bert_vits2.logging import logger def download_bert_models(): - with open("bert/bert_models.json", "r") as fp: + with open("bert/bert_models.json", encoding="utf-8") as fp: models = json.load(fp) for k, v in models.items(): local_path = Path("bert").joinpath(k) for file in v["files"]: if not Path(local_path).joinpath(file).exists(): logger.info(f"Downloading {k} {file}") - hf_hub_download( - v["repo_id"], - file, - local_dir=local_path, - local_dir_use_symlinks=False, - ) + hf_hub_download(v["repo_id"], file, local_dir=local_path) def download_slm_model(): @@ -29,12 +25,7 @@ def download_slm_model(): file = "pytorch_model.bin" if not Path(local_path).joinpath(file).exists(): logger.info(f"Downloading wavlm-base-plus {file}") - hf_hub_download( - "microsoft/wavlm-base-plus", - file, - local_dir=local_path, - local_dir_use_symlinks=False, - ) + hf_hub_download("microsoft/wavlm-base-plus", file, local_dir=local_path) def download_pretrained_models(): @@ -44,10 +35,7 @@ def download_pretrained_models(): if not Path(local_path).joinpath(file).exists(): logger.info(f"Downloading pretrained {file}") hf_hub_download( - "litagin/Style-Bert-VITS2-1.0-base", - file, - local_dir=local_path, - local_dir_use_symlinks=False, + "litagin/Style-Bert-VITS2-1.0-base", file, local_dir=local_path ) @@ -58,14 +46,11 @@ def download_jp_extra_pretrained_models(): if not Path(local_path).joinpath(file).exists(): logger.info(f"Downloading JP-Extra pretrained {file}") hf_hub_download( - "litagin/Style-Bert-VITS2-2.0-base-JP-Extra", - file, - local_dir=local_path, - local_dir_use_symlinks=False, + "litagin/Style-Bert-VITS2-2.0-base-JP-Extra", file, local_dir=local_path ) -def download_jvnv_models(): +def download_default_models(): files = [ "jvnv-F1-jp/config.json", "jvnv-F1-jp/jvnv-F1-jp_e160_s14000.safetensors", @@ -87,13 +72,34 @@ def download_jvnv_models(): "litagin/style_bert_vits2_jvnv", file, local_dir="model_assets", - local_dir_use_symlinks=False, ) + additional_files = { + "litagin/sbv2_koharune_ami": [ + "koharune-ami/config.json", + "koharune-ami/style_vectors.npy", + "koharune-ami/koharune-ami.safetensors", + ], + "litagin/sbv2_amitaro": [ + "amitaro/config.json", + "amitaro/style_vectors.npy", + "amitaro/amitaro.safetensors", + ], + } + for repo_id, files in additional_files.items(): + for file in files: + if not Path(f"model_assets/{file}").exists(): + logger.info(f"Downloading {file}") + hf_hub_download( + repo_id, + file, + local_dir="model_assets", + ) def main(): parser = argparse.ArgumentParser() - parser.add_argument("--skip_jvnv", action="store_true") + parser.add_argument("--skip_default_models", action="store_true") + parser.add_argument("--only_infer", action="store_true") parser.add_argument( "--dataset_root", type=str, @@ -110,21 +116,24 @@ def main(): download_bert_models() - download_slm_model() - - download_pretrained_models() + if not args.skip_default_models: + download_default_models() + if not args.only_infer: + download_slm_model() + download_pretrained_models() + download_jp_extra_pretrained_models() - download_jp_extra_pretrained_models() - - if not args.skip_jvnv: - download_jvnv_models() + # If configs/paths.yml not exists, create it + default_paths_yml = Path("configs/default_paths.yml") + paths_yml = Path("configs/paths.yml") + if not paths_yml.exists(): + shutil.copy(default_paths_yml, paths_yml) if args.dataset_root is None and args.assets_root is None: return # Change default paths if necessary - paths_yml = Path("configs/paths.yml") - with open(paths_yml, "r", encoding="utf-8") as f: + with open(paths_yml, encoding="utf-8") as f: yml_data = yaml.safe_load(f) if args.assets_root is not None: yml_data["assets_root"] = args.assets_root diff --git a/inputs/.gitignore b/inputs/.gitignore deleted file mode 100644 index d6b7ef32c8478a48c3994dcadc86837f4371184d..0000000000000000000000000000000000000000 --- a/inputs/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -* -!.gitignore diff --git a/losses.py b/losses.py deleted file mode 100644 index 763cc028511570a69924eb434bb648536171ebfc..0000000000000000000000000000000000000000 --- a/losses.py +++ /dev/null @@ -1,155 +0,0 @@ -import torch -import torchaudio -from transformers import AutoModel - -from common.log import logger - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - rl = rl.float().detach() - gl = gl.float() - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - dr = dr.float() - dg = dg.float() - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg**2) - loss += r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - dg = dg.float() - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - -def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): - """ - z_p, logs_q: [b, h, t_t] - m_p, logs_p: [b, h, t_t] - """ - z_p = z_p.float() - logs_q = logs_q.float() - m_p = m_p.float() - logs_p = logs_p.float() - z_mask = z_mask.float() - - kl = logs_p - logs_q - 0.5 - kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p) - kl = torch.sum(kl * z_mask) - l = kl / torch.sum(z_mask) - return l - - -class WavLMLoss(torch.nn.Module): - def __init__(self, model, wd, model_sr, slm_sr=16000): - super(WavLMLoss, self).__init__() - self.wavlm = AutoModel.from_pretrained(model) - self.wd = wd - self.resample = torchaudio.transforms.Resample(model_sr, slm_sr) - self.wavlm.eval() - for param in self.wavlm.parameters(): - param.requires_grad = False - - def forward(self, wav, y_rec): - with torch.no_grad(): - wav_16 = self.resample(wav) - wav_embeddings = self.wavlm( - input_values=wav_16, output_hidden_states=True - ).hidden_states - y_rec_16 = self.resample(y_rec) - y_rec_embeddings = self.wavlm( - input_values=y_rec_16, output_hidden_states=True - ).hidden_states - - floss = 0 - for er, eg in zip(wav_embeddings, y_rec_embeddings): - floss += torch.mean(torch.abs(er - eg)) - - return floss.mean() - - def generator(self, y_rec): - y_rec_16 = self.resample(y_rec) - y_rec_embeddings = self.wavlm( - input_values=y_rec_16, output_hidden_states=True - ).hidden_states - y_rec_embeddings = ( - torch.stack(y_rec_embeddings, dim=1) - .transpose(-1, -2) - .flatten(start_dim=1, end_dim=2) - ) - y_df_hat_g = self.wd(y_rec_embeddings) - loss_gen = torch.mean((1 - y_df_hat_g) ** 2) - - return loss_gen - - def discriminator(self, wav, y_rec): - with torch.no_grad(): - wav_16 = self.resample(wav) - wav_embeddings = self.wavlm( - input_values=wav_16, output_hidden_states=True - ).hidden_states - y_rec_16 = self.resample(y_rec) - y_rec_embeddings = self.wavlm( - input_values=y_rec_16, output_hidden_states=True - ).hidden_states - - y_embeddings = ( - torch.stack(wav_embeddings, dim=1) - .transpose(-1, -2) - .flatten(start_dim=1, end_dim=2) - ) - y_rec_embeddings = ( - torch.stack(y_rec_embeddings, dim=1) - .transpose(-1, -2) - .flatten(start_dim=1, end_dim=2) - ) - - y_d_rs = self.wd(y_embeddings) - y_d_gs = self.wd(y_rec_embeddings) - - y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs - - r_loss = torch.mean((1 - y_df_hat_r) ** 2) - g_loss = torch.mean((y_df_hat_g) ** 2) - - loss_disc_f = r_loss + g_loss - - return loss_disc_f.mean() - - def discriminator_forward(self, wav): - with torch.no_grad(): - wav_16 = self.resample(wav) - wav_embeddings = self.wavlm( - input_values=wav_16, output_hidden_states=True - ).hidden_states - y_embeddings = ( - torch.stack(wav_embeddings, dim=1) - .transpose(-1, -2) - .flatten(start_dim=1, end_dim=2) - ) - - y_d_rs = self.wd(y_embeddings) - - return y_d_rs diff --git a/mel_processing.py b/mel_processing.py deleted file mode 100644 index e9e7ec3622a0ee940e238f29c3f0d7509abfe59b..0000000000000000000000000000000000000000 --- a/mel_processing.py +++ /dev/null @@ -1,146 +0,0 @@ -import torch -import torch.utils.data -from librosa.filters import mel as librosa_mel_fn -import warnings - -# warnings.simplefilter(action='ignore', category=FutureWarning) -warnings.filterwarnings(action="ignore") -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + "_" + str(y.device) - wnsize_dtype_device = str(win_size) + "_" + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( - dtype=y.dtype, device=y.device - ) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - spec = torch.stft( - y, - n_fft, - hop_length=hop_size, - win_length=win_size, - window=hann_window[wnsize_dtype_device], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=False, - ) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + "_" + str(spec.device) - fmax_dtype_device = str(fmax) + "_" + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn( - sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax - ) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( - dtype=spec.dtype, device=spec.device - ) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch( - y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False -): - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + "_" + str(y.device) - fmax_dtype_device = str(fmax) + "_" + dtype_device - wnsize_dtype_device = str(win_size) + "_" + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn( - sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax - ) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( - dtype=y.dtype, device=y.device - ) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( - dtype=y.dtype, device=y.device - ) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - spec = torch.stft( - y, - n_fft, - hop_length=hop_size, - win_length=win_size, - window=hann_window[wnsize_dtype_device], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=False, - ) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/model_assets/amitaro/amitaro.safetensors b/model_assets/amitaro/amitaro.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..4ddd6c5e905ef8618ad0f38b8693e5c30cf86999 --- /dev/null +++ b/model_assets/amitaro/amitaro.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19725d9bfd9d4b4fb17072ed4d40e01f6cf89c22cd83c15e1cccbf3ddf6b81de +size 251150980 diff --git a/model_assets/amitaro/config.json b/model_assets/amitaro/config.json new file mode 100644 index 0000000000000000000000000000000000000000..c9cafe0d214da8c89d616fdba0554f16b1df1b58 --- /dev/null +++ b/model_assets/amitaro/config.json @@ -0,0 +1,91 @@ +{ + "model_name": "amitaro", + "train": { + "log_interval": 200, + "eval_interval": 1000, + "seed": 42, + "epochs": 100, + "learning_rate": 0.0001, + "betas": [0.8, 0.99], + "eps": 1e-9, + "batch_size": 4, + "bf16_run": false, + "fp16_run": false, + "lr_decay": 0.99996, + "segment_size": 16384, + "init_lr_ratio": 1, + "warmup_epochs": 0, + "c_mel": 45, + "c_kl": 1.0, + "c_commit": 100, + "skip_optimizer": false, + "freeze_ZH_bert": false, + "freeze_JP_bert": false, + "freeze_EN_bert": false, + "freeze_emo": false, + "freeze_style": false, + "freeze_decoder": false + }, + "data": { + "use_jp_extra": true, + "training_files": "Data\\amitaro\\train.list", + "validation_files": "Data\\amitaro\\val.list", + "max_wav_value": 32768.0, + "sampling_rate": 44100, + "filter_length": 2048, + "hop_length": 512, + "win_length": 2048, + "n_mel_channels": 128, + "mel_fmin": 0.0, + "mel_fmax": null, + "add_blank": true, + "n_speakers": 1, + "cleaned_text": true, + "spk2id": { + "あみたろ": 0 + }, + "num_styles": 5, + "style2id": { + "Neutral": 0, + "01": 1, + "02": 2, + "03": 3, + "04": 4 + } + }, + "model": { + "use_spk_conditioned_encoder": true, + "use_noise_scaled_mas": true, + "use_mel_posterior_encoder": false, + "use_duration_discriminator": false, + "use_wavlm_discriminator": true, + "inter_channels": 192, + "hidden_channels": 192, + "filter_channels": 768, + "n_heads": 2, + "n_layers": 6, + "kernel_size": 3, + "p_dropout": 0.1, + "resblock": "1", + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [ + [1, 3, 5], + [1, 3, 5], + [1, 3, 5] + ], + "upsample_rates": [8, 8, 2, 2, 2], + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [16, 16, 8, 2, 2], + "n_layers_q": 3, + "use_spectral_norm": false, + "gin_channels": 512, + "slm": { + "model": "./slm/wavlm-base-plus", + "sr": 16000, + "hidden": 768, + "nlayers": 13, + "initial_channel": 64 + } + }, + "version": "2.5.0-JP-Extra" +} diff --git a/model_assets/amitaro/style_vectors.npy b/model_assets/amitaro/style_vectors.npy new file mode 100644 index 0000000000000000000000000000000000000000..7c2408b55f878f6abd3a954b2a0069ce2b4943b1 --- /dev/null +++ b/model_assets/amitaro/style_vectors.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f04b3d679982f11fd29da6ea27f93af86e5876b93aa4fbf910215ce145513cc9 +size 5248 diff --git a/model_assets/koharune-ami/config.json b/model_assets/koharune-ami/config.json new file mode 100644 index 0000000000000000000000000000000000000000..9c87c72a2a40826c68bbe009351d075aff3de374 --- /dev/null +++ b/model_assets/koharune-ami/config.json @@ -0,0 +1,92 @@ +{ + "model_name": "小春音アミ", + "train": { + "log_interval": 200, + "eval_interval": 1000, + "seed": 42, + "epochs": 300, + "learning_rate": 0.0001, + "betas": [0.8, 0.99], + "eps": 1e-9, + "batch_size": 4, + "bf16_run": false, + "fp16_run": false, + "lr_decay": 0.99996, + "segment_size": 16384, + "init_lr_ratio": 1, + "warmup_epochs": 0, + "c_mel": 45, + "c_kl": 1.0, + "c_commit": 100, + "skip_optimizer": false, + "freeze_ZH_bert": false, + "freeze_JP_bert": false, + "freeze_EN_bert": false, + "freeze_emo": false, + "freeze_style": false, + "freeze_decoder": false + }, + "data": { + "use_jp_extra": true, + "training_files": "Data\\amitaro_combined\\train.list", + "validation_files": "Data\\amitaro_combined\\val.list", + "max_wav_value": 32768.0, + "sampling_rate": 44100, + "filter_length": 2048, + "hop_length": 512, + "win_length": 2048, + "n_mel_channels": 128, + "mel_fmin": 0.0, + "mel_fmax": null, + "add_blank": true, + "n_speakers": 1, + "cleaned_text": true, + "spk2id": { + "小春音アミ": 0 + }, + "num_styles": 6, + "style2id": { + "Neutral": 0, + "るんるん": 1, + "ささやきA（無声）": 2, + "ささやきB（有声）": 3, + "ノーマル": 4, + "よふかし": 5 + } + }, + "model": { + "use_spk_conditioned_encoder": true, + "use_noise_scaled_mas": true, + "use_mel_posterior_encoder": false, + "use_duration_discriminator": false, + "use_wavlm_discriminator": true, + "inter_channels": 192, + "hidden_channels": 192, + "filter_channels": 768, + "n_heads": 2, + "n_layers": 6, + "kernel_size": 3, + "p_dropout": 0.1, + "resblock": "1", + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [ + [1, 3, 5], + [1, 3, 5], + [1, 3, 5] + ], + "upsample_rates": [8, 8, 2, 2, 2], + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [16, 16, 8, 2, 2], + "n_layers_q": 3, + "use_spectral_norm": false, + "gin_channels": 512, + "slm": { + "model": "./slm/wavlm-base-plus", + "sr": 16000, + "hidden": 768, + "nlayers": 13, + "initial_channel": 64 + } + }, + "version": "2.5.0-JP-Extra" +} diff --git a/model_assets/koharune-ami/koharune-ami.safetensors b/model_assets/koharune-ami/koharune-ami.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..3f26bbd0eba2c0ec0cfd088f1999f93537f45126 --- /dev/null +++ b/model_assets/koharune-ami/koharune-ami.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:980119e6660fc241b1c297016933f43240b3fc6203a9f511dd2f2dac74042991 +size 251150980 diff --git a/model_assets/koharune-ami/style_vectors.npy b/model_assets/koharune-ami/style_vectors.npy new file mode 100644 index 0000000000000000000000000000000000000000..7bc1a07576171fe6dd2a42fd2948ca6f7e393f63 --- /dev/null +++ b/model_assets/koharune-ami/style_vectors.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:867527952ff45b6621fe18b8b51728c29abe3aeb80b74e06f235df71aa82ae5d +size 6272 diff --git a/monotonic_align/__init__.py b/monotonic_align/__init__.py deleted file mode 100644 index aed94600a6b01f4322b371b0c57d5b05713c4dac..0000000000000000000000000000000000000000 --- a/monotonic_align/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -from numpy import zeros, int32, float32 -from torch import from_numpy - -from .core import maximum_path_jit - - -def maximum_path(neg_cent, mask): - device = neg_cent.device - dtype = neg_cent.dtype - neg_cent = neg_cent.data.cpu().numpy().astype(float32) - path = zeros(neg_cent.shape, dtype=int32) - - t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32) - t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32) - maximum_path_jit(path, neg_cent, t_t_max, t_s_max) - return from_numpy(path).to(device=device, dtype=dtype) diff --git a/monotonic_align/core.py b/monotonic_align/core.py deleted file mode 100644 index 7c962adea65543ef426034c4d53c4f0e615e8181..0000000000000000000000000000000000000000 --- a/monotonic_align/core.py +++ /dev/null @@ -1,46 +0,0 @@ -import numba - - -@numba.jit( - numba.void( - numba.int32[:, :, ::1], - numba.float32[:, :, ::1], - numba.int32[::1], - numba.int32[::1], - ), - nopython=True, - nogil=True, -) -def maximum_path_jit(paths, values, t_ys, t_xs): - b = paths.shape[0] - max_neg_val = -1e9 - for i in range(int(b)): - path = paths[i] - value = values[i] - t_y = t_ys[i] - t_x = t_xs[i] - - v_prev = v_cur = 0.0 - index = t_x - 1 - - for y in range(t_y): - for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): - if x == y: - v_cur = max_neg_val - else: - v_cur = value[y - 1, x] - if x == 0: - if y == 0: - v_prev = 0.0 - else: - v_prev = max_neg_val - else: - v_prev = value[y - 1, x - 1] - value[y, x] += max(v_prev, v_cur) - - for y in range(t_y - 1, -1, -1): - path[y, index] = 1 - if index != 0 and ( - index == y or value[y - 1, index] < value[y - 1, index - 1] - ): - index = index - 1 diff --git a/preprocess_all.py b/preprocess_all.py deleted file mode 100644 index 82a3c202cfc58ad9650e6b533c1b07dff15f11ad..0000000000000000000000000000000000000000 --- a/preprocess_all.py +++ /dev/null @@ -1,96 +0,0 @@ -import argparse -from webui_train import preprocess_all -from multiprocessing import cpu_count - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--model_name", "-m", type=str, help="Model name", required=True - ) - parser.add_argument("--batch_size", "-b", type=int, help="Batch size", default=2) - parser.add_argument("--epochs", "-e", type=int, help="Epochs", default=100) - parser.add_argument( - "--save_every_steps", - "-s", - type=int, - help="Save every steps", - default=1000, - ) - parser.add_argument( - "--num_processes", - type=int, - help="Number of processes", - default=cpu_count() // 2, - ) - parser.add_argument( - "--normalize", - action="store_true", - help="Loudness normalize audio", - ) - parser.add_argument( - "--trim", - action="store_true", - help="Trim silence", - ) - parser.add_argument( - "--freeze_EN_bert", - action="store_true", - help="Freeze English BERT", - ) - parser.add_argument( - "--freeze_JP_bert", - action="store_true", - help="Freeze Japanese BERT", - ) - parser.add_argument( - "--freeze_ZH_bert", - action="store_true", - help="Freeze Chinese BERT", - ) - parser.add_argument( - "--freeze_style", - action="store_true", - help="Freeze style vector", - ) - parser.add_argument( - "--freeze_decoder", - action="store_true", - help="Freeze decoder", - ) - parser.add_argument( - "--use_jp_extra", - action="store_true", - help="Use JP-Extra model", - ) - parser.add_argument( - "--val_per_lang", - type=int, - help="Validation per language", - default=0, - ) - parser.add_argument( - "--log_interval", - type=int, - help="Log interval", - default=200, - ) - - args = parser.parse_args() - - preprocess_all( - model_name=args.model_name, - batch_size=args.batch_size, - epochs=args.epochs, - save_every_steps=args.save_every_steps, - num_processes=args.num_processes, - normalize=args.normalize, - trim=args.trim, - freeze_EN_bert=args.freeze_EN_bert, - freeze_JP_bert=args.freeze_JP_bert, - freeze_ZH_bert=args.freeze_ZH_bert, - freeze_style=args.freeze_style, - freeze_decoder=args.freeze_decoder, - use_jp_extra=args.use_jp_extra, - val_per_lang=args.val_per_lang, - log_interval=args.log_interval, - ) diff --git a/preprocess_text.py b/preprocess_text.py deleted file mode 100644 index 2c26941e7a11824ec14fa5518db728f9a3085e46..0000000000000000000000000000000000000000 --- a/preprocess_text.py +++ /dev/null @@ -1,167 +0,0 @@ -import json -import os -from collections import defaultdict -from random import shuffle -from typing import Optional - -import click -from tqdm import tqdm - -from config import config -from text.cleaner import clean_text -from common.stdout_wrapper import SAFE_STDOUT -from common.log import logger - -preprocess_text_config = config.preprocess_text_config - - -# Count lines for tqdm -def count_lines(file_path: str): - with open(file_path, "r", encoding="utf-8") as file: - return sum(1 for _ in file) - - -@click.command() -@click.option( - "--transcription-path", - default=preprocess_text_config.transcription_path, - type=click.Path(exists=True, file_okay=True, dir_okay=False), -) -@click.option("--cleaned-path", default=preprocess_text_config.cleaned_path) -@click.option("--train-path", default=preprocess_text_config.train_path) -@click.option("--val-path", default=preprocess_text_config.val_path) -@click.option( - "--config-path", - default=preprocess_text_config.config_path, - type=click.Path(exists=True, file_okay=True, dir_okay=False), -) -@click.option("--val-per-lang", default=preprocess_text_config.val_per_lang) -@click.option("--max-val-total", default=preprocess_text_config.max_val_total) -@click.option("--clean/--no-clean", default=preprocess_text_config.clean) -@click.option("-y", "--yml_config") -@click.option("--use_jp_extra", is_flag=True) -def preprocess( - transcription_path: str, - cleaned_path: Optional[str], - train_path: str, - val_path: str, - config_path: str, - val_per_lang: int, - max_val_total: int, - clean: bool, - yml_config: str, # 这个不要删 - use_jp_extra: bool, -): - if cleaned_path == "" or cleaned_path is None: - cleaned_path = transcription_path + ".cleaned" - - error_log_path = os.path.join(os.path.dirname(cleaned_path), "text_error.log") - error_count = 0 - - if clean: - total_lines = count_lines(transcription_path) - with open(cleaned_path, "w", encoding="utf-8") as out_file: - with open(transcription_path, "r", encoding="utf-8") as trans_file: - for line in tqdm(trans_file, file=SAFE_STDOUT, total=total_lines): - try: - utt, spk, language, text = line.strip().split("|") - norm_text, phones, tones, word2ph = clean_text( - text, language, use_jp_extra - ) - out_file.write( - "{}|{}|{}|{}|{}|{}|{}\n".format( - utt, - spk, - language, - norm_text, - " ".join(phones), - " ".join([str(i) for i in tones]), - " ".join([str(i) for i in word2ph]), - ) - ) - except Exception as e: - logger.error(f"An error occurred at line:\n{line.strip()}\n{e}") - with open(error_log_path, "a", encoding="utf-8") as error_log: - error_log.write(f"{line.strip()}\n{e}\n\n") - error_count += 1 - - transcription_path = cleaned_path - spk_utt_map = defaultdict(list) - spk_id_map = {} - current_sid = 0 - - with open(transcription_path, "r", encoding="utf-8") as f: - audioPaths = set() - countSame = 0 - countNotFound = 0 - for line in f.readlines(): - utt, spk, language, text, phones, tones, word2ph = line.strip().split("|") - if utt in audioPaths: - # 过滤数据集错误：相同的音频匹配多个文本，导致后续bert出问题 - logger.warning(f"Same audio matches multiple texts: {line}") - countSame += 1 - continue - if not os.path.isfile(utt): - # 过滤数据集错误：不存在对应音频 - logger.warning(f"Audio not found: {utt}") - countNotFound += 1 - continue - audioPaths.add(utt) - spk_utt_map[language].append(line) - if spk not in spk_id_map.keys(): - spk_id_map[spk] = current_sid - current_sid += 1 - if countSame > 0 or countNotFound > 0: - logger.warning( - f"Total repeated audios: {countSame}, Total number of audio not found: {countNotFound}" - ) - - train_list = [] - val_list = [] - - for spk, utts in spk_utt_map.items(): - shuffle(utts) - val_list += utts[:val_per_lang] - train_list += utts[val_per_lang:] - - shuffle(val_list) - if len(val_list) > max_val_total: - train_list += val_list[max_val_total:] - val_list = val_list[:max_val_total] - - with open(train_path, "w", encoding="utf-8") as f: - for line in train_list: - f.write(line) - - with open(val_path, "w", encoding="utf-8") as f: - for line in val_list: - f.write(line) - - json_config = json.load(open(config_path, encoding="utf-8")) - json_config["data"]["spk2id"] = spk_id_map - json_config["data"]["n_speakers"] = len(spk_id_map) - # 新增写入：写入训练版本、数据集路径 - # json_config["version"] = latest_version - json_config["data"]["training_files"] = os.path.normpath(train_path).replace( - "\\", "/" - ) - json_config["data"]["validation_files"] = os.path.normpath(val_path).replace( - "\\", "/" - ) - with open(config_path, "w", encoding="utf-8") as f: - json.dump(json_config, f, indent=2, ensure_ascii=False) - if error_count > 0: - logger.error( - f"An error occurred in {error_count} lines. Please check {error_log_path} for details. You can proceed with lines that do not have errors." - ) - raise Exception( - f"An error occurred in {error_count} lines. Please check {error_log_path} for details. You can proceed with lines that do not have errors." - ) - else: - logger.info( - "Training set and validation set generation from texts is complete!" - ) - - -if __name__ == "__main__": - preprocess() diff --git a/re_matching.py b/re_matching.py deleted file mode 100644 index dd464a5cca8fd00d2da35dfddf9a03295dd5b627..0000000000000000000000000000000000000000 --- a/re_matching.py +++ /dev/null @@ -1,81 +0,0 @@ -import re - - -def extract_language_and_text_updated(speaker, dialogue): - # 使用正则表达式匹配<语言>标签和其后的文本 - pattern_language_text = r"<(\S+?)>([^<]+)" - matches = re.findall(pattern_language_text, dialogue, re.DOTALL) - speaker = speaker[1:-1] - # 清理文本：去除两边的空白字符 - matches_cleaned = [(lang.upper(), text.strip()) for lang, text in matches] - matches_cleaned.append(speaker) - return matches_cleaned - - -def validate_text(input_text): - # 验证说话人的正则表达式 - pattern_speaker = r"(\[\S+?\])((?:\s*<\S+?>[^<\[\]]+?)+)" - - # 使用re.DOTALL标志使.匹配包括换行符在内的所有字符 - matches = re.findall(pattern_speaker, input_text, re.DOTALL) - - # 对每个匹配到的说话人内容进行进一步验证 - for _, dialogue in matches: - language_text_matches = extract_language_and_text_updated(_, dialogue) - if not language_text_matches: - return ( - False, - "Error: Invalid format detected in dialogue content. Please check your input.", - ) - - # 如果输入的文本中没有找到任何匹配项 - if not matches: - return ( - False, - "Error: No valid speaker format detected. Please check your input.", - ) - - return True, "Input is valid." - - -def text_matching(text: str) -> list: - speaker_pattern = r"(\[\S+?\])(.+?)(?=\[\S+?\]|$)" - matches = re.findall(speaker_pattern, text, re.DOTALL) - result = [] - for speaker, dialogue in matches: - result.append(extract_language_and_text_updated(speaker, dialogue)) - return result - - -def cut_para(text): - splitted_para = re.split("[\n]", text) # 按段分 - splitted_para = [ - sentence.strip() for sentence in splitted_para if sentence.strip() - ] # 删除空字符串 - return splitted_para - - -def cut_sent(para): - para = re.sub("([。！;？\?])([^”’])", r"\1\n\2", para) # 单字符断句符 - para = re.sub("(\.{6})([^”’])", r"\1\n\2", para) # 英文省略号 - para = re.sub("(\…{2})([^”’])", r"\1\n\2", para) # 中文省略号 - para = re.sub("([。！？\?][”’])([^，。！？\?])", r"\1\n\2", para) - para = para.rstrip() # 段尾如果有多余的\n就去掉它 - return para.split("\n") - - -if __name__ == "__main__": - text = """ - [说话人1] - [说话人2]你好吗？元気ですか？こんにちは，世界。你好吗？ - [说话人3]谢谢。どういたしまして。 - """ - text_matching(text) - # 测试函数 - test_text = """ - [说话人1]你好，こんにちは！こんにちは，世界。 - [说话人2]你好吗？ - """ - text_matching(test_text) - res = validate_text(test_text) - print(res) diff --git a/requirements.txt b/requirements.txt index 21c199b9af0467a2fc577cf3b18678e82e2ccf81..8d8b249f7513d75a9d63322339099268223a38ae 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,24 @@ -gradio +# cmudict +# cn2an +# faster-whisper==0.10.1 +fastapi +# g2p_en +# GPUtil +# gradio +# jieba +# librosa==0.9.2 loguru -matplotlib num2words -numba -numpy +# protobuf==4.25 +# psutil +# punctuators +pyannote.audio>=3.1.0 +# pyloudnorm pyopenjtalk-dict -PyYAML -requests -safetensors -scipy -torch>=2.1,<2.2 +# pypinyin +pyworld-prebuilt +# stable_ts +# tensorboard +torch transformers +# umap-learn diff --git a/resample.py b/resample.py deleted file mode 100644 index 5aff5ef4907ede6a8ba3c27d0ec676884691206e..0000000000000000000000000000000000000000 --- a/resample.py +++ /dev/null @@ -1,131 +0,0 @@ -import argparse -import os -from concurrent.futures import ThreadPoolExecutor - -import librosa -import pyloudnorm as pyln -import soundfile -from tqdm import tqdm - -from common.log import logger -from common.stdout_wrapper import SAFE_STDOUT -from config import config - -DEFAULT_BLOCK_SIZE: float = 0.400 # seconds - - -class BlockSizeException(Exception): - pass - - -def normalize_audio(data, sr): - meter = pyln.Meter(sr, block_size=DEFAULT_BLOCK_SIZE) # create BS.1770 meter - try: - loudness = meter.integrated_loudness(data) - except ValueError as e: - raise BlockSizeException(e) - # logger.info(f"loudness: {loudness}") - data = pyln.normalize.loudness(data, loudness, -23.0) - return data - - -def process(item): - spkdir, wav_name, args = item - wav_path = os.path.join(args.in_dir, spkdir, wav_name) - if os.path.exists(wav_path) and wav_path.lower().endswith(".wav"): - wav, sr = librosa.load(wav_path, sr=args.sr) - if args.normalize: - try: - wav = normalize_audio(wav, sr) - except BlockSizeException: - logger.info( - f"Skip normalize due to less than {DEFAULT_BLOCK_SIZE} second audio: {wav_path}" - ) - if args.trim: - wav, _ = librosa.effects.trim(wav, top_db=30) - soundfile.write(os.path.join(args.out_dir, spkdir, wav_name), wav, sr) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--sr", - type=int, - default=config.resample_config.sampling_rate, - help="sampling rate", - ) - parser.add_argument( - "--in_dir", - "-i", - type=str, - default=config.resample_config.in_dir, - help="path to source dir", - ) - parser.add_argument( - "--out_dir", - "-o", - type=str, - default=config.resample_config.out_dir, - help="path to target dir", - ) - parser.add_argument( - "--num_processes", - type=int, - default=4, - help="cpu_processes", - ) - parser.add_argument( - "--normalize", - action="store_true", - default=False, - help="loudness normalize audio", - ) - parser.add_argument( - "--trim", - action="store_true", - default=False, - help="trim silence (start and end only)", - ) - args, _ = parser.parse_known_args() - # autodl 无卡模式会识别出46个cpu - if args.num_processes == 0: - processes = cpu_count() - 2 if cpu_count() > 4 else 1 - else: - processes = args.num_processes - - tasks = [] - - for dirpath, _, filenames in os.walk(args.in_dir): - # 子级目录 - spk_dir = os.path.relpath(dirpath, args.in_dir) - spk_dir_out = os.path.join(args.out_dir, spk_dir) - if not os.path.isdir(spk_dir_out): - os.makedirs(spk_dir_out, exist_ok=True) - for filename in filenames: - if filename.lower().endswith(".wav"): - twople = (spk_dir, filename, args) - tasks.append(twople) - - if len(tasks) == 0: - logger.error(f"No wav files found in {args.in_dir}") - raise ValueError(f"No wav files found in {args.in_dir}") - - # pool = Pool(processes=processes) - # for _ in tqdm( - # pool.imap_unordered(process, tasks), file=SAFE_STDOUT, total=len(tasks) - # ): - # pass - - # pool.close() - # pool.join() - - with ThreadPoolExecutor(max_workers=processes) as executor: - _ = list( - tqdm( - executor.map(process, tasks), - total=len(tasks), - file=SAFE_STDOUT, - ) - ) - - logger.info("Resampling Done!") diff --git a/scripts/Install-Style-Bert-VITS2-CPU.bat b/scripts/Install-Style-Bert-VITS2-CPU.bat deleted file mode 100644 index aaf3536adf501721e9f57d167b4fb078dee2144f..0000000000000000000000000000000000000000 --- a/scripts/Install-Style-Bert-VITS2-CPU.bat +++ /dev/null @@ -1,69 +0,0 @@ -chcp 65001 > NUL -@echo off - -@REM https://github.com/Zuntan03/EasyBertVits2 より引用・改変 - -pushd %~dp0 -set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass - -set CURL_CMD=C:\Windows\System32\curl.exe -if not exist %CURL_CMD% ( - echo [ERROR] %CURL_CMD% が見つかりません。 - pause & popd & exit /b 1 -) - -@REM lib フォルダがなければ作成 -if not exist lib\ ( mkdir lib ) - -@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード -%CURL_CMD% -Lo Style-Bert-VITS2.zip^ - https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる） -%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM 元のzipを削除 -del Style-Bert-VITS2.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動 -xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\ -rmdir /s /q Style-Bert-VITS2-master - -echo ---------------------------------------- -echo Setup Python and Virtual Environment -echo ---------------------------------------- - -@REM Pythonと仮想環境のセットアップを呼び出す（仮想環境が有効化されて戻ってくる） -call Style-Bert-VITS2\scripts\Setup-Python.bat ..\..\lib\python ..\venv -if %errorlevel% neq 0 ( popd & exit /b %errorlevel% ) - -@REM 依存関係インストール -pip install -r Style-Bert-VITS2\requirements.txt -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -echo ---------------------------------------- -echo Environment setup is complete. Start downloading the model. -echo ---------------------------------------- - -@REM Style-Bert-VITS2フォルダに移動 -pushd Style-Bert-VITS2 - -@REM 初期化（必要なモデルのダウンロード） -python initialize.py - -echo ---------------------------------------- -echo Model download is complete. Start the WebUI of the voice synthesis. -echo ---------------------------------------- - -@REM 音声合成WebUIの起動 -python app.py - -pause - -popd - -popd - diff --git a/scripts/Install-Style-Bert-VITS2.bat b/scripts/Install-Style-Bert-VITS2.bat deleted file mode 100644 index 6e99230c3c40a4af3890ebe2fd1e5f753d5e5f00..0000000000000000000000000000000000000000 --- a/scripts/Install-Style-Bert-VITS2.bat +++ /dev/null @@ -1,72 +0,0 @@ -chcp 65001 > NUL -@echo off - -@REM https://github.com/Zuntan03/EasyBertVits2 より引用・改変 - -pushd %~dp0 -set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass - -set CURL_CMD=C:\Windows\System32\curl.exe -if not exist %CURL_CMD% ( - echo [ERROR] %CURL_CMD% が見つかりません。 - pause & popd & exit /b 1 -) - -@REM lib フォルダがなければ作成 -if not exist lib\ ( mkdir lib ) - -@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード -%CURL_CMD% -Lo Style-Bert-VITS2.zip^ - https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる） -%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM 元のzipを削除 -del Style-Bert-VITS2.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動 -xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\ -rmdir /s /q Style-Bert-VITS2-master - -echo ---------------------------------------- -echo Setup Python and Virtual Environment -echo ---------------------------------------- - -@REM Pythonと仮想環境のセットアップを呼び出す（仮想環境が有効化されて戻ってくる） -call Style-Bert-VITS2\scripts\Setup-Python.bat ..\..\lib\python ..\venv -if %errorlevel% neq 0 ( popd & exit /b %errorlevel% ) - -@REM 依存関係インストール -pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -pip install -r Style-Bert-VITS2\requirements.txt -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -echo ---------------------------------------- -echo Environment setup is complete. Start downloading the model. -echo ---------------------------------------- - -@REM Style-Bert-VITS2フォルダに移動 -pushd Style-Bert-VITS2 - -@REM 初期化（必要なモデルのダウンロード） -python initialize.py - -echo ---------------------------------------- -echo Model download is complete. Start the WebUI of the voice synthesis. -echo ---------------------------------------- - -@REM 音声合成WebUIの起動 -python app.py - -pause - -popd - -popd - diff --git a/scripts/Setup-Python.bat b/scripts/Setup-Python.bat deleted file mode 100644 index 17fd23f237a6e040382322519b8c222d4ca8966f..0000000000000000000000000000000000000000 --- a/scripts/Setup-Python.bat +++ /dev/null @@ -1,76 +0,0 @@ -chcp 65001 > NUL - -@REM https://github.com/Zuntan03/EasyBertVits2 より引用・改変 - -@echo off -set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass -set CURL_CMD=C:\Windows\System32\curl.exe - -if not exist %CURL_CMD% ( - echo [ERROR] %CURL_CMD% が見つかりません。 - pause & exit /b 1 -) - -if "%1" neq "" ( - set PYTHON_DIR=%~dp0%~1 -) else ( - set PYTHON_DIR=%~dp0python -) -set PYTHON_CMD=%PYTHON_DIR%\python.exe - -if "%2" neq "" ( - set VENV_DIR=%~dp0%~2 -) else ( - set VENV_DIR=%~dp0venv -) - -echo PS_CMD: %PS_CMD% -echo CURL_CMD: %CURL_CMD% -echo PYTHON_CMD: %PYTHON_CMD% -echo VENV_DIR: %VENV_DIR% - -if not exist %PYTHON_DIR%\ ( - echo https://www.python.org/ - echo %CURL_CMD% -o python.zip https://www.python.org/ftp/python/3.10.11/python-3.10.11-embed-amd64.zip - %CURL_CMD% -o python.zip https://www.python.org/ftp/python/3.10.11/python-3.10.11-embed-amd64.zip - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) - - echo %PS_CMD% Expand-Archive -Path python.zip -DestinationPath %PYTHON_DIR% - %PS_CMD% Expand-Archive -Path python.zip -DestinationPath %PYTHON_DIR% - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) - - echo del python.zip - del python.zip - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) - - echo %PS_CMD% "&{(Get-Content '%PYTHON_DIR%/python310._pth') -creplace '#import site', 'import site' | Set-Content '%PYTHON_DIR%/python310._pth' }" - %PS_CMD% "&{(Get-Content '%PYTHON_DIR%/python310._pth') -creplace '#import site', 'import site' | Set-Content '%PYTHON_DIR%/python310._pth' }" - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) - - echo https://github.com/pypa/get-pip - echo %CURL_CMD% -o %PYTHON_DIR%\get-pip.py https://bootstrap.pypa.io/get-pip.py - %CURL_CMD% -o %PYTHON_DIR%\get-pip.py https://bootstrap.pypa.io/get-pip.py - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) - - echo %PYTHON_CMD% %PYTHON_DIR%\get-pip.py --no-warn-script-location - %PYTHON_CMD% %PYTHON_DIR%\get-pip.py --no-warn-script-location - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) - - echo %PYTHON_CMD% -m pip install virtualenv --no-warn-script-location - %PYTHON_CMD% -m pip install virtualenv --no-warn-script-location - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) -) - -if not exist %VENV_DIR%\ ( - echo %PYTHON_CMD% -m virtualenv --copies %VENV_DIR% - %PYTHON_CMD% -m virtualenv --copies %VENV_DIR% - if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) -) - -echo call %VENV_DIR%\Scripts\activate.bat -call %VENV_DIR%\Scripts\activate.bat -if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) - -echo python -m pip install --upgrade pip -python -m pip install --upgrade pip -if %errorlevel% neq 0 ( pause & exit /b %errorlevel% ) diff --git a/scripts/Update-Style-Bert-VITS2.bat b/scripts/Update-Style-Bert-VITS2.bat deleted file mode 100644 index 782f2efe9ca447c8f018a75b5a6332e8a521e973..0000000000000000000000000000000000000000 --- a/scripts/Update-Style-Bert-VITS2.bat +++ /dev/null @@ -1,44 +0,0 @@ -chcp 65001 > NUL -@echo off - -pushd %~dp0 -set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass - -set CURL_CMD=C:\Windows\System32\curl.exe -if not exist %CURL_CMD% ( - echo [ERROR] %CURL_CMD% が見つかりません。 - pause & popd & exit /b 1 -) - -@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード -%CURL_CMD% -Lo Style-Bert-VITS2.zip^ - https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる） -%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM 元のzipを削除 -del Style-Bert-VITS2.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動 -xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\ -rmdir /s /q Style-Bert-VITS2-master -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM 仮想環境のpip requirements.txtを更新 - -echo call .\Style-Bert-VITS2\scripts\activate.bat -call .\Style-Bert-VITS2\venv\Scripts\activate.bat -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -pip install -U -r Style-Bert-VITS2\requirements.txt -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -echo Style-Bert-VITS2のアップデートが完了しました。 - -pause - -popd \ No newline at end of file diff --git a/scripts/Update-to-JP-Extra.bat b/scripts/Update-to-JP-Extra.bat deleted file mode 100644 index 95738c698dc7f7a77441bed363db6d60097d689f..0000000000000000000000000000000000000000 --- a/scripts/Update-to-JP-Extra.bat +++ /dev/null @@ -1,51 +0,0 @@ -chcp 65001 > NUL -@echo off - -pushd %~dp0 -set PS_CMD=PowerShell -Version 5.1 -ExecutionPolicy Bypass - -set CURL_CMD=C:\Windows\System32\curl.exe -if not exist %CURL_CMD% ( - echo [ERROR] %CURL_CMD% が見つかりません。 - pause & popd & exit /b 1 -) - -@REM Style-Bert-VITS2.zip をGitHubのmasterの最新のものをダウンロード -%CURL_CMD% -Lo Style-Bert-VITS2.zip^ - https://github.com/litagin02/Style-Bert-VITS2/archive/refs/heads/master.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Style-Bert-VITS2.zip を解凍（フォルダ名前がBert-VITS2-masterになる） -%PS_CMD% Expand-Archive -Path Style-Bert-VITS2.zip -DestinationPath . -Force -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM 元のzipを削除 -del Style-Bert-VITS2.zip -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM Bert-VITS2-masterの中身をStyle-Bert-VITS2に上書き移動 -xcopy /QSY .\Style-Bert-VITS2-master\ .\Style-Bert-VITS2\ -rmdir /s /q Style-Bert-VITS2-master -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -@REM 仮想環境のpip requirements.txtを更新 - -echo call .\Style-Bert-VITS2\scripts\activate.bat -call .\Style-Bert-VITS2\venv\Scripts\activate.bat -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -pip install -U -r Style-Bert-VITS2\requirements.txt -if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% ) - -pushd Style-Bert-VITS2 - -@REM 初期化（必要なモデルのダウンロード） -python initialize.py - -echo Style-Bert-VITS2の2.xへのアップデートが完了しました。 - -pause - -popd - -popd \ No newline at end of file diff --git a/server_editor.py b/server_editor.py index f859315acadcbd5a90ab4dd39b4d1407b688ba14..8caea47e503f8aa0a4b6afac4a3fac70686bd53c 100644 --- a/server_editor.py +++ b/server_editor.py @@ -16,10 +16,9 @@ import zipfile from datetime import datetime from io import BytesIO from pathlib import Path -import yaml +from typing import Optional import numpy as np -import pyopenjtalk import requests import torch import uvicorn @@ -30,20 +29,31 @@ from fastapi.staticfiles import StaticFiles from pydantic import BaseModel from scipy.io import wavfile -from common.constants import ( +from config import get_path_config +from initialize import download_default_models +from style_bert_vits2.constants import ( DEFAULT_ASSIST_TEXT_WEIGHT, DEFAULT_NOISE, DEFAULT_NOISEW, DEFAULT_SDP_RATIO, DEFAULT_STYLE, DEFAULT_STYLE_WEIGHT, - LATEST_VERSION, + VERSION, Languages, ) -from common.log import logger -from common.tts_model import ModelHolder -from text.japanese import g2kata_tone, kata_tone2phone_tone -from text.user_dict import apply_word, update_dict, read_dict, rewrite_word, delete_word +from style_bert_vits2.logging import logger +from style_bert_vits2.nlp import bert_models +from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk +from style_bert_vits2.nlp.japanese.g2p_utils import g2kata_tone, kata_tone2phone_tone +from style_bert_vits2.nlp.japanese.normalizer import normalize_text +from style_bert_vits2.nlp.japanese.user_dict import ( + apply_word, + delete_word, + read_dict, + rewrite_word, + update_dict, +) +from style_bert_vits2.tts_model import TTSModelHolder, TTSModelInfo # ---フロントエンド部分に関する処理--- @@ -118,7 +128,7 @@ def download_and_extract(url, extract_to: Path): def new_release_available(latest_release): if LAST_DOWNLOAD_FILE.exists(): - with open(LAST_DOWNLOAD_FILE, "r") as file: + with open(LAST_DOWNLOAD_FILE) as file: last_download_str = file.read().strip() # 'Z'を除去して日時オブジェクトに変換 last_download_str = last_download_str.replace("Z", "+00:00") @@ -140,6 +150,19 @@ def save_last_download(latest_release): # ---フロントエンド部分に関する処理ここまで--- # 以降はAPIの設定 +# pyopenjtalk_worker を起動 +## pyopenjtalk_worker は TCP ソケットサーバーのため、ここで起動する +pyopenjtalk.initialize_worker() + +# pyopenjtalk の辞書を更新 +update_dict() + +# 事前に BERT モデル/トークナイザーをロードしておく +## ここでロードしなくても必要になった際に自動ロードされるが、時間がかかるため事前にロードしておいた方が体験が良い +## server_editor.py は日本語にしか対応していないため、日本語の BERT モデル/トークナイザーのみロードする +bert_models.load_model(Languages.JP) +bert_models.load_tokenizer(Languages.JP) + class AudioResponse(Response): media_type = "audio/wav" @@ -152,35 +175,32 @@ origins = [ "http://127.0.0.1:8000", ] -# Get path settings -with open(Path("configs/paths.yml"), "r", encoding="utf-8") as f: - path_config: dict[str, str] = yaml.safe_load(f.read()) - # dataset_root = path_config["dataset_root"] - assets_root = path_config["assets_root"] - +path_config = get_path_config() parser = argparse.ArgumentParser() -parser.add_argument("--model_dir", type=str, default="model_assets/") +parser.add_argument("--model_dir", type=str, default=path_config.assets_root) parser.add_argument("--device", type=str, default="cuda") parser.add_argument("--port", type=int, default=8000) parser.add_argument("--inbrowser", action="store_true") parser.add_argument("--line_length", type=int, default=None) parser.add_argument("--line_count", type=int, default=None) -parser.add_argument( - "--dir", "-d", type=str, help="Model directory", default=assets_root -) - +parser.add_argument("--skip_default_models", action="store_true") +parser.add_argument("--skip_static_files", action="store_true") args = parser.parse_args() device = args.device if device == "cuda" and not torch.cuda.is_available(): device = "cpu" model_dir = Path(args.model_dir) port = int(args.port) +if not args.skip_default_models: + download_default_models() +skip_static_files = bool(args.skip_static_files) -model_holder = ModelHolder(model_dir, device) +model_holder = TTSModelHolder(model_dir, device) if len(model_holder.model_names) == 0: logger.error(f"Models not found in {model_dir}.") sys.exit(1) + app = FastAPI() @@ -197,7 +217,7 @@ router = APIRouter() @router.get("/version") def version() -> str: - return LATEST_VERSION + return VERSION class MoraTone(BaseModel): @@ -205,14 +225,16 @@ class MoraTone(BaseModel): tone: int -class G2PRequest(BaseModel): +class TextRequest(BaseModel): text: str @router.post("/g2p") -async def read_item(item: G2PRequest): +async def read_item(item: TextRequest): try: - kata_tone_list = g2kata_tone(item.text, ignore_unknown=True) + # 最初に正規化しないと整合性がとれない + text = normalize_text(item.text) + kata_tone_list = g2kata_tone(text) except Exception as e: raise HTTPException( status_code=400, @@ -221,9 +243,14 @@ async def read_item(item: G2PRequest): return [MoraTone(mora=kata, tone=tone) for kata, tone in kata_tone_list] -@router.get("/models_info") +@router.post("/normalize") +async def normalize(item: TextRequest): + return normalize_text(item.text) + + +@router.get("/models_info", response_model=list[TTSModelInfo]) def models_info(): - return model_holder.models_info() + return model_holder.models_info class SynthesisRequest(BaseModel): @@ -243,6 +270,7 @@ class SynthesisRequest(BaseModel): silenceAfter: float = 0.5 pitchScale: float = 1.0 intonationScale: float = 1.0 + speaker: Optional[str] = None @router.post("/synthesis", response_class=AudioResponse) @@ -253,7 +281,7 @@ def synthesis(request: SynthesisRequest): detail=f"1行の文字数は{args.line_length}文字以下にしてください。", ) try: - model = model_holder.load_model( + model = model_holder.get_model( model_name=request.model, model_path_str=request.modelFile ) except Exception as e: @@ -268,12 +296,19 @@ def synthesis(request: SynthesisRequest): ] phone_tone = kata_tone2phone_tone(kata_tone_list) tone = [t for _, t in phone_tone] + try: + sid = 0 if request.speaker is None else model.spk2id[request.speaker] + except KeyError: + raise HTTPException( + status_code=400, + detail=f"Speaker {request.speaker} not found in {model.spk2id}", + ) sr, audio = model.infer( text=text, - language=request.language.value, + language=request.language, sdp_ratio=request.sdpRatio, noise=request.noise, - noisew=request.noisew, + noise_w=request.noisew, length=1 / request.speed, given_tone=tone, style=request.style, @@ -282,9 +317,9 @@ def synthesis(request: SynthesisRequest): assist_text_weight=request.assistTextWeight, use_assist_text=bool(request.assistText), line_split=False, - ignore_unknown=True, pitch_scale=request.pitchScale, intonation_scale=request.intonationScale, + speaker_id=sid, ) with BytesIO() as wavContent: @@ -305,6 +340,7 @@ def multi_synthesis(request: MultiSynthesisRequest): detail=f"行数は{args.line_count}行以下にしてください。", ) audios = [] + sr = None for i, req in enumerate(lines): if args.line_length is not None and len(req.text) > args.line_length: raise HTTPException( @@ -312,7 +348,7 @@ def multi_synthesis(request: MultiSynthesisRequest): detail=f"1行の文字数は{args.line_length}文字以下にしてください。", ) try: - model = model_holder.load_model( + model = model_holder.get_model( model_name=req.model, model_path_str=req.modelFile ) except Exception as e: @@ -329,10 +365,10 @@ def multi_synthesis(request: MultiSynthesisRequest): tone = [t for _, t in phone_tone] sr, audio = model.infer( text=text, - language=req.language.value, + language=req.language, sdp_ratio=req.sdpRatio, noise=req.noise, - noisew=req.noisew, + noise_w=req.noisew, length=1 / req.speed, given_tone=tone, style=req.style, @@ -341,7 +377,6 @@ def multi_synthesis(request: MultiSynthesisRequest): assist_text_weight=req.assistTextWeight, use_assist_text=bool(req.assistText), line_split=False, - ignore_unknown=True, pitch_scale=req.pitchScale, intonation_scale=req.intonationScale, ) @@ -407,7 +442,8 @@ def delete_user_dict_word(uuid: str): app.include_router(router, prefix="/api") if __name__ == "__main__": - download_static_files("litagin02", "Style-Bert-VITS2-Editor", "out.zip") + if not skip_static_files: + download_static_files("litagin02", "Style-Bert-VITS2-Editor", "out.zip") app.mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static") if args.inbrowser: webbrowser.open(f"http://localhost:{port}") diff --git a/server_fastapi.py b/server_fastapi.py deleted file mode 100644 index ce6ed0432f856f5fa21117a5e214904394319ba7..0000000000000000000000000000000000000000 --- a/server_fastapi.py +++ /dev/null @@ -1,277 +0,0 @@ -""" -API server for TTS -TODO: server_editor.pyと統合する? -""" - -import argparse -import os -import sys -from io import BytesIO -from pathlib import Path -from typing import Dict, Optional, Union -from urllib.parse import unquote - -import GPUtil -import psutil -import torch -import uvicorn -from fastapi import FastAPI, HTTPException, Query, Request, status -from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import FileResponse, Response -from scipy.io import wavfile - -from common.constants import ( - DEFAULT_ASSIST_TEXT_WEIGHT, - DEFAULT_LENGTH, - DEFAULT_LINE_SPLIT, - DEFAULT_NOISE, - DEFAULT_NOISEW, - DEFAULT_SDP_RATIO, - DEFAULT_SPLIT_INTERVAL, - DEFAULT_STYLE, - DEFAULT_STYLE_WEIGHT, - Languages, -) -from common.log import logger -from common.tts_model import Model, ModelHolder -from config import config - -ln = config.server_config.language - - -def raise_validation_error(msg: str, param: str): - logger.warning(f"Validation error: {msg}") - raise HTTPException( - status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, - detail=[dict(type="invalid_params", msg=msg, loc=["query", param])], - ) - - -class AudioResponse(Response): - media_type = "audio/wav" - - -def load_models(model_holder: ModelHolder): - model_holder.models = [] - for model_name, model_paths in model_holder.model_files_dict.items(): - model = Model( - model_path=model_paths[0], - config_path=model_holder.root_dir / model_name / "config.json", - style_vec_path=model_holder.root_dir / model_name / "style_vectors.npy", - device=model_holder.device, - ) - model.load_net_g() - model_holder.models.append(model) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cpu", action="store_true", help="Use CPU instead of GPU") - parser.add_argument( - "--dir", "-d", type=str, help="Model directory", default=config.assets_root - ) - args = parser.parse_args() - - if args.cpu: - device = "cpu" - else: - device = "cuda" if torch.cuda.is_available() else "cpu" - - model_dir = Path(args.dir) - model_holder = ModelHolder(model_dir, device) - if len(model_holder.model_names) == 0: - logger.error(f"Models not found in {model_dir}.") - sys.exit(1) - - logger.info("Loading models...") - load_models(model_holder) - limit = config.server_config.limit - app = FastAPI() - allow_origins = config.server_config.origins - if allow_origins: - logger.warning( - f"CORS allow_origins={config.server_config.origins}. If you don't want, modify config.yml" - ) - app.add_middleware( - CORSMiddleware, - allow_origins=config.server_config.origins, - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - app.logger = logger - - @app.get("/voice", response_class=AudioResponse) - async def voice( - request: Request, - text: str = Query(..., min_length=1, max_length=limit, description=f"セリフ"), - encoding: str = Query(None, description="textをURLデコードする(ex, `utf-8`)"), - model_id: int = Query( - 0, description="モデルID。`GET /models/info`のkeyの値を指定ください" - ), - speaker_name: str = Query( - None, - description="話者名(speaker_idより優先)。esd.listの2列目の文字列を指定", - ), - speaker_id: int = Query( - 0, description="話者ID。model_assets>[model]>config.json内のspk2idを確認" - ), - sdp_ratio: float = Query( - DEFAULT_SDP_RATIO, - description="SDP(Stochastic Duration Predictor)/DP混合比。比率が高くなるほどトーンのばらつきが大きくなる", - ), - noise: float = Query( - DEFAULT_NOISE, - description="サンプルノイズの割合。大きくするほどランダム性が高まる", - ), - noisew: float = Query( - DEFAULT_NOISEW, - description="SDPノイズ。大きくするほど発音の間隔にばらつきが出やすくなる", - ), - length: float = Query( - DEFAULT_LENGTH, - description="話速。基準は1で大きくするほど音声は長くなり読み上げが遅まる", - ), - language: Languages = Query(ln, description=f"textの言語"), - auto_split: bool = Query(DEFAULT_LINE_SPLIT, description="改行で分けて生成"), - split_interval: float = Query( - DEFAULT_SPLIT_INTERVAL, description="分けた場合に挟む無音の長さ（秒）" - ), - assist_text: Optional[str] = Query( - None, - description="このテキストの読み上げと似た声音・感情になりやすくなる。ただし抑揚やテンポ等が犠牲になる傾向がある", - ), - assist_text_weight: float = Query( - DEFAULT_ASSIST_TEXT_WEIGHT, description="assist_textの強さ" - ), - style: Optional[Union[int, str]] = Query(DEFAULT_STYLE, description="スタイル"), - style_weight: float = Query(DEFAULT_STYLE_WEIGHT, description="スタイルの強さ"), - reference_audio_path: Optional[str] = Query( - None, description="スタイルを音声ファイルで行う" - ), - ): - """Infer text to speech(テキストから感情付き音声を生成する)""" - logger.info( - f"{request.client.host}:{request.client.port}/voice { unquote(str(request.query_params) )}" - ) - if model_id >= len( - model_holder.models - ): # /models/refresh があるためQuery(le)で表現不可 - raise_validation_error(f"model_id={model_id} not found", "model_id") - - model = model_holder.models[model_id] - if speaker_name is None: - if speaker_id not in model.id2spk.keys(): - raise_validation_error( - f"speaker_id={speaker_id} not found", "speaker_id" - ) - else: - if speaker_name not in model.spk2id.keys(): - raise_validation_error( - f"speaker_name={speaker_name} not found", "speaker_name" - ) - speaker_id = model.spk2id[speaker_name] - if style not in model.style2id.keys(): - raise_validation_error(f"style={style} not found", "style") - if encoding is not None: - text = unquote(text, encoding=encoding) - sr, audio = model.infer( - text=text, - language=language, - sid=speaker_id, - reference_audio_path=reference_audio_path, - sdp_ratio=sdp_ratio, - noise=noise, - noisew=noisew, - length=length, - line_split=auto_split, - split_interval=split_interval, - assist_text=assist_text, - assist_text_weight=assist_text_weight, - use_assist_text=bool(assist_text), - style=style, - style_weight=style_weight, - ) - logger.success("Audio data generated and sent successfully") - with BytesIO() as wavContent: - wavfile.write(wavContent, sr, audio) - return Response(content=wavContent.getvalue(), media_type="audio/wav") - - @app.get("/models/info") - def get_loaded_models_info(): - """ロードされたモデル情報の取得""" - - result: Dict[str, Dict] = dict() - for model_id, model in enumerate(model_holder.models): - result[str(model_id)] = { - "config_path": model.config_path, - "model_path": model.model_path, - "device": model.device, - "spk2id": model.spk2id, - "id2spk": model.id2spk, - "style2id": model.style2id, - } - return result - - @app.post("/models/refresh") - def refresh(): - """モデルをパスに追加/削除した際などに読み込ませる""" - model_holder.refresh() - load_models(model_holder) - return get_loaded_models_info() - - @app.get("/status") - def get_status(): - """実行環境のステータスを取得""" - cpu_percent = psutil.cpu_percent(interval=1) - memory_info = psutil.virtual_memory() - memory_total = memory_info.total - memory_available = memory_info.available - memory_used = memory_info.used - memory_percent = memory_info.percent - gpuInfo = [] - devices = ["cpu"] - for i in range(torch.cuda.device_count()): - devices.append(f"cuda:{i}") - gpus = GPUtil.getGPUs() - for gpu in gpus: - gpuInfo.append( - { - "gpu_id": gpu.id, - "gpu_load": gpu.load, - "gpu_memory": { - "total": gpu.memoryTotal, - "used": gpu.memoryUsed, - "free": gpu.memoryFree, - }, - } - ) - return { - "devices": devices, - "cpu_percent": cpu_percent, - "memory_total": memory_total, - "memory_available": memory_available, - "memory_used": memory_used, - "memory_percent": memory_percent, - "gpu": gpuInfo, - } - - @app.get("/tools/get_audio", response_class=AudioResponse) - def get_audio( - request: Request, path: str = Query(..., description="local wav path") - ): - """wavデータを取得する""" - logger.info( - f"{request.client.host}:{request.client.port}/tools/get_audio { unquote(str(request.query_params) )}" - ) - if not os.path.isfile(path): - raise_validation_error(f"path={path} not found", "path") - if not path.lower().endswith(".wav"): - raise_validation_error(f"wav file not found in {path}", "path") - return FileResponse(path=path, media_type="audio/wav") - - logger.info(f"server listen: http://127.0.0.1:{config.server_config.port}") - logger.info(f"API docs: http://127.0.0.1:{config.server_config.port}/docs") - uvicorn.run( - app, port=config.server_config.port, host="0.0.0.0", log_level="warning" - ) diff --git a/slice.py b/slice.py deleted file mode 100644 index fc5ccbd24f3cc37848f1f7531e18bae8fcf8d2cf..0000000000000000000000000000000000000000 --- a/slice.py +++ /dev/null @@ -1,160 +0,0 @@ -import argparse -import os -import shutil -from pathlib import Path - -import soundfile as sf -import torch -import yaml -from tqdm import tqdm - -from common.log import logger -from common.stdout_wrapper import SAFE_STDOUT - -vad_model, utils = torch.hub.load( - repo_or_dir="snakers4/silero-vad", - model="silero_vad", - onnx=True, - trust_repo=True, -) - -(get_speech_timestamps, _, read_audio, *_) = utils - - -def get_stamps( - audio_file, min_silence_dur_ms: int = 700, min_sec: float = 2, max_sec: float = 12 -): - """ - min_silence_dur_ms: int (ミリ秒): - このミリ秒数以上を無音だと判断する。 - 逆に、この秒数以下の無音区間では区切られない。 - 小さくすると、音声がぶつ切りに小さくなりすぎ、 - 大きくすると音声一つ一つが長くなりすぎる。 - データセットによってたぶん要調整。 - min_sec: float (秒): - この秒数より小さい発話は無視する。 - max_sec: float (秒): - この秒数より大きい発話は無視する。 - """ - - sampling_rate = 16000 # 16kHzか8kHzのみ対応 - - min_ms = int(min_sec * 1000) - - wav = read_audio(audio_file, sampling_rate=sampling_rate) - speech_timestamps = get_speech_timestamps( - wav, - vad_model, - sampling_rate=sampling_rate, - min_silence_duration_ms=min_silence_dur_ms, - min_speech_duration_ms=min_ms, - max_speech_duration_s=max_sec, - ) - - return speech_timestamps - - -def split_wav( - audio_file, - target_dir="raw", - min_sec=2, - max_sec=12, - min_silence_dur_ms=700, -): - margin = 200 # ミリ秒単位で、音声の前後に余裕を持たせる - speech_timestamps = get_stamps( - audio_file, - min_silence_dur_ms=min_silence_dur_ms, - min_sec=min_sec, - max_sec=max_sec, - ) - - data, sr = sf.read(audio_file) - - total_ms = len(data) / sr * 1000 - - file_name = os.path.basename(audio_file).split(".")[0] - os.makedirs(target_dir, exist_ok=True) - - total_time_ms = 0 - count = 0 - - # タイムスタンプに従って分割し、ファイルに保存 - for i, ts in enumerate(speech_timestamps): - start_ms = max(ts["start"] / 16 - margin, 0) - end_ms = min(ts["end"] / 16 + margin, total_ms) - - start_sample = int(start_ms / 1000 * sr) - end_sample = int(end_ms / 1000 * sr) - segment = data[start_sample:end_sample] - - sf.write(os.path.join(target_dir, f"{file_name}-{i}.wav"), segment, sr) - total_time_ms += end_ms - start_ms - count += 1 - - return total_time_ms / 1000, count - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--min_sec", "-m", type=float, default=2, help="Minimum seconds of a slice" - ) - parser.add_argument( - "--max_sec", "-M", type=float, default=12, help="Maximum seconds of a slice" - ) - parser.add_argument( - "--input_dir", - "-i", - type=str, - default="inputs", - help="Directory of input wav files", - ) - parser.add_argument( - "--model_name", - type=str, - required=True, - help="The result will be in Data/{model_name}/raw/ (if Data is dataset_root in configs/paths.yml)", - ) - parser.add_argument( - "--min_silence_dur_ms", - "-s", - type=int, - default=700, - help="Silence above this duration (ms) is considered as a split point.", - ) - args = parser.parse_args() - - with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f: - path_config: dict[str, str] = yaml.safe_load(f.read()) - dataset_root = path_config["dataset_root"] - - input_dir = args.input_dir - output_dir = os.path.join(dataset_root, args.model_name, "raw") - min_sec = args.min_sec - max_sec = args.max_sec - min_silence_dur_ms = args.min_silence_dur_ms - - wav_files = Path(input_dir).glob("**/*.wav") - wav_files = list(wav_files) - logger.info(f"Found {len(wav_files)} wav files.") - if os.path.exists(output_dir): - logger.warning(f"Output directory {output_dir} already exists, deleting...") - shutil.rmtree(output_dir) - - total_sec = 0 - total_count = 0 - for wav_file in tqdm(wav_files, file=SAFE_STDOUT): - time_sec, count = split_wav( - audio_file=str(wav_file), - target_dir=output_dir, - min_sec=min_sec, - max_sec=max_sec, - min_silence_dur_ms=min_silence_dur_ms, - ) - total_sec += time_sec - total_count += count - - logger.info( - f"Slice done! Total time: {total_sec / 60:.2f} min, {total_count} files." - ) diff --git a/slm/wavlm-base-plus/.gitattributes b/slm/wavlm-base-plus/.gitattributes deleted file mode 100644 index 957b2579c6ef20995a09efd9a17f8fd90606f5ed..0000000000000000000000000000000000000000 --- a/slm/wavlm-base-plus/.gitattributes +++ /dev/null @@ -1,27 +0,0 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bin.* filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zstandard filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/slm/wavlm-base-plus/README.md b/slm/wavlm-base-plus/README.md deleted file mode 100644 index 541b2e0cc438f3bcb05aa363b09e62f722c87cab..0000000000000000000000000000000000000000 --- a/slm/wavlm-base-plus/README.md +++ /dev/null @@ -1,65 +0,0 @@ ---- -language: -- en -datasets: -tags: -- speech -inference: false ---- - -# WavLM-Base-Plus - -[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm) - -The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. - -**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. - -The model was pre-trained on: - -- 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875) -- 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909) -- 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390) - -[Paper: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) - -Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei - -**Abstract** -*Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.* - -The original model can be found under https://github.com/microsoft/unilm/tree/master/wavlm. - -# Usage - -This is an English pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be -used in inference. The model was pre-trained in English and should therefore perform well only in English. The model has been shown to work well on the [SUPERB benchmark](https://superbbenchmark.org/). - -**Note**: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence -of phonemes before fine-tuning. - -## Speech Recognition - -To fine-tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition). - -## Speech Classification - -To fine-tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio-classification). - -## Speaker Verification - -TODO - -## Speaker Diarization - -TODO - -# Contribution - -The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten). - -# License - -The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) - -![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wavlm.png) diff --git a/slm/wavlm-base-plus/config.json b/slm/wavlm-base-plus/config.json deleted file mode 100644 index b7b4e5f6c410f71283a59e26250e40855ca99310..0000000000000000000000000000000000000000 --- a/slm/wavlm-base-plus/config.json +++ /dev/null @@ -1,99 +0,0 @@ -{ - "_name_or_path": "wavlm-base-plus", - "activation_dropout": 0.0, - "adapter_kernel_size": 3, - "adapter_stride": 2, - "add_adapter": false, - "apply_spec_augment": true, - "architectures": [ - "WavLMModel" - ], - "attention_dropout": 0.1, - "bos_token_id": 1, - "classifier_proj_size": 256, - "codevector_dim": 256, - "contrastive_logits_temperature": 0.1, - "conv_bias": false, - "conv_dim": [ - 512, - 512, - 512, - 512, - 512, - 512, - 512 - ], - "conv_kernel": [ - 10, - 3, - 3, - 3, - 3, - 2, - 2 - ], - "conv_stride": [ - 5, - 2, - 2, - 2, - 2, - 2, - 2 - ], - "ctc_loss_reduction": "sum", - "ctc_zero_infinity": false, - "diversity_loss_weight": 0.1, - "do_stable_layer_norm": false, - "eos_token_id": 2, - "feat_extract_activation": "gelu", - "feat_extract_norm": "group", - "feat_proj_dropout": 0.1, - "feat_quantizer_dropout": 0.0, - "final_dropout": 0.0, - "freeze_feat_extract_train": true, - "hidden_act": "gelu", - "hidden_dropout": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "layerdrop": 0.05, - "mask_channel_length": 10, - "mask_channel_min_space": 1, - "mask_channel_other": 0.0, - "mask_channel_prob": 0.0, - "mask_channel_selection": "static", - "mask_feature_length": 10, - "mask_feature_min_masks": 0, - "mask_feature_prob": 0.0, - "mask_time_length": 10, - "mask_time_min_masks": 2, - "mask_time_min_space": 1, - "mask_time_other": 0.0, - "mask_time_prob": 0.05, - "mask_time_selection": "static", - "model_type": "wavlm", - "no_mask_channel_overlap": false, - "no_mask_time_overlap": false, - "num_adapter_layers": 3, - "num_attention_heads": 12, - "num_buckets": 320, - "num_codevector_groups": 2, - "num_codevectors_per_group": 320, - "num_conv_pos_embedding_groups": 16, - "num_conv_pos_embeddings": 128, - "num_ctc_classes": 80, - "num_feat_extract_layers": 7, - "num_hidden_layers": 12, - "num_negatives": 100, - "output_hidden_size": 768, - "pad_token_id": 0, - "proj_codevector_dim": 256, - "replace_prob": 0.5, - "torch_dtype": "float32", - "transformers_version": "4.13.0.dev0", - "use_weighted_layer_sum": false, - "vocab_size": 32, - "tokenizer_class": "Wav2Vec2CTCTokenizer" -} diff --git a/slm/wavlm-base-plus/preprocessor_config.json b/slm/wavlm-base-plus/preprocessor_config.json deleted file mode 100644 index 10f6def8c83d70a2b087a567dcf523b75152a80b..0000000000000000000000000000000000000000 --- a/slm/wavlm-base-plus/preprocessor_config.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "do_normalize": false, - "feature_extractor_type": "Wav2Vec2FeatureExtractor", - "feature_size": 1, - "padding_side": "right", - "padding_value": 0.0, - "return_attention_mask": true, - "sampling_rate": 16000 -} diff --git a/spec_gen.py b/spec_gen.py deleted file mode 100644 index b6715faff6fd9abdae8197fcc0e0aac1d50fde4a..0000000000000000000000000000000000000000 --- a/spec_gen.py +++ /dev/null @@ -1,87 +0,0 @@ -import torch -from tqdm import tqdm -from multiprocessing import Pool -from mel_processing import spectrogram_torch, mel_spectrogram_torch -from utils import load_wav_to_torch - - -class AudioProcessor: - def __init__( - self, - max_wav_value, - use_mel_spec_posterior, - filter_length, - n_mel_channels, - sampling_rate, - hop_length, - win_length, - mel_fmin, - mel_fmax, - ): - self.max_wav_value = max_wav_value - self.use_mel_spec_posterior = use_mel_spec_posterior - self.filter_length = filter_length - self.n_mel_channels = n_mel_channels - self.sampling_rate = sampling_rate - self.hop_length = hop_length - self.win_length = win_length - self.mel_fmin = mel_fmin - self.mel_fmax = mel_fmax - - def process_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - audio_norm = audio / self.max_wav_value - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if self.use_mel_spec_posterior: - spec_filename = spec_filename.replace(".spec.pt", ".mel.pt") - try: - spec = torch.load(spec_filename) - except: - if self.use_mel_spec_posterior: - spec = mel_spectrogram_torch( - audio_norm, - self.filter_length, - self.n_mel_channels, - self.sampling_rate, - self.hop_length, - self.win_length, - self.mel_fmin, - self.mel_fmax, - center=False, - ) - else: - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename) - return spec, audio_norm - - -# 使用示例 -processor = AudioProcessor( - max_wav_value=32768.0, - use_mel_spec_posterior=False, - filter_length=2048, - n_mel_channels=128, - sampling_rate=44100, - hop_length=512, - win_length=2048, - mel_fmin=0.0, - mel_fmax="null", -) - -with open("filelists/train.list", "r") as f: - filepaths = [line.split("|")[0] for line in f] # 取每一行的第一部分作为audiopath - -# 使用多进程处理 -with Pool(processes=32) as pool: # 使用4个进程 - with tqdm(total=len(filepaths)) as pbar: - for i, _ in enumerate(pool.imap_unordered(processor.process_audio, filepaths)): - pbar.update() diff --git a/speech_mos.py b/speech_mos.py deleted file mode 100644 index d69a23ab552e63f5af06768ef961eeeaed011cf6..0000000000000000000000000000000000000000 --- a/speech_mos.py +++ /dev/null @@ -1,158 +0,0 @@ -import argparse -import csv -import re -import warnings -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import torch -from tqdm import tqdm - -from common.log import logger -from common.tts_model import Model -from config import config - -warnings.filterwarnings("ignore") - -mos_result_dir = Path("mos_results") -mos_result_dir.mkdir(exist_ok=True) - -test_texts = [ - # JVNVコーパスのテキスト - # https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus - # CC BY-SA 4.0 - "ああ？どうしてこんなに荒々しい態度をとるんだ？落ち着いて話を聞けばいいのに。", - "いや、あんな醜い人間を見るのは本当に嫌だ。", - "うわ、不景気の影響で失業してしまうかもしれない。どうしよう、心配で眠れない。", - "今日の山登りは最高だった！山頂で見た景色は言葉に表せないほど美しかった！あはは、絶頂の喜びが胸に溢れるよ！", - "あーあ、昨日の事故で大切な車が全損になっちゃった。もうどうしようもないよ。", - "ああ、彼は本当に速い！ダッシュの速さは尋常じゃない！", - # 以下app.pyの説明文章 - "音声合成は、機械学習を活用して、テキストから人の声を再現する技術です。この技術は、言語の構造を解析し、それに基づいて音声を生成します。", - "この分野の最新の研究成果を使うと、より自然で表現豊かな音声の生成が可能である。深層学習の応用により、感情やアクセントを含む声質の微妙な変化も再現することが出来る。", -] - -predictor = torch.hub.load( - "tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True -) - -parser = argparse.ArgumentParser() -parser.add_argument("--model_name", "-m", type=str, required=True) -parser.add_argument("--device", "-d", type=str, default="cuda") - -args = parser.parse_args() - -model_name: str = args.model_name -device: str = args.device - -model_path = Path(config.assets_root) / model_name - -# .safetensorsファイルを検索 -safetensors_files = model_path.glob("*.safetensors") - - -def get_model(model_file: Path): - return Model( - model_path=str(model_file), - config_path=str(model_file.parent / "config.json"), - style_vec_path=str(model_file.parent / "style_vectors.npy"), - device=device, - ) - - -results = [] - -safetensors_files = list(safetensors_files) - -logger.info(f"There are {len(safetensors_files)} models.") - -for model_file in tqdm(safetensors_files): - # `test_e10_s1000.safetensors`` -> 1000を取り出す - match = re.search(r"_s(\d+)\.safetensors$", model_file.name) - if match: - step = int(match.group(1)) - else: - logger.warning(f"Step count not found in {model_file.name}, so skip it.") - continue - model = get_model(model_file) - scores = [] - for i, text in enumerate(test_texts): - sr, audio = model.infer(text) - audio = audio.astype("float32") - score = predictor(torch.from_numpy(audio).unsqueeze(0), sr).item() - scores.append(score) - logger.info(f"score: {score}") - results.append((model_file.name, step, scores)) - del model - -logger.success("All models have been evaluated:") -# meanを計算 -results = [ - (model_file, step, scores + [np.mean(scores)]) - for model_file, step, scores in results -] -# meanでソートして表示 -results = sorted(results, key=lambda x: x[2][-1], reverse=True) -for model_file, step, scores in results: - logger.info(f"{model_file}: {scores[-1]}") - -with open( - mos_result_dir / f"mos_{model_name}.csv", "w", encoding="utf_8_sig", newline="" -) as f: - writer = csv.writer(f) - writer.writerow(["model_path"] + ["step"] + test_texts + ["mean"]) - for model_file, step, scores in results: - writer.writerow([model_file] + [step] + scores) - -logger.info(f"mos_{model_name}.csv has been saved.") - -# step countと各MOSの値を格納するリストを初期化 -steps = [] -mos_values = [] - -# resultsからデータを抽出 -for _, step, scores in results: - steps.append(step) - mos_values.append(scores) # scores は MOS1, MOS2, MOS3,..., mean のリスト - -# DataFrame形式に変換 -df = pd.DataFrame(mos_values, index=steps) -# ステップ数でソート -df = df.sort_index() - -plt.figure(figsize=(10, 5)) - -# 各MOSについての折れ線グラフを描画（最後の平均値の列は除外） -for col in range(len(df.columns) - 1): - plt.plot(df.index, df.iloc[:, col], label=f"MOS{col + 1}") - -# 既存の平均値の列を使用 -plt.plot(df.index, df.iloc[:, -1], label="Mean", color="black", linewidth=2) - - -# グラフのタイトルと軸ラベルを設定 -plt.title("TTS Model Naturalness MOS") -plt.xlabel("Step Count") -plt.ylabel("MOS") - -# ステップ数の軸ラベルを1000単位で表示するように調整 -plt.xticks( - ticks=np.arange(0, max(steps) + 1000, 2000), - labels=[f"{int(x/1000)}" for x in np.arange(0, max(steps) + 1000, 2000)], -) - -# 縦の補助線を追加 -plt.grid(True, axis="x") - -# 凡例をグラフの右外側に配置 -plt.legend(loc="upper left", bbox_to_anchor=(1, 1)) - -plt.tight_layout() - -# グラフを画像として保存（plt.show() の前に実行する） -plt.savefig(mos_result_dir / f"mos_{model_name}.png") - -# グラフを表示 -plt.show() diff --git a/static/404.html b/static/404.html new file mode 100644 index 0000000000000000000000000000000000000000..923048411ec5f5b82c7a6030e13c27328ca87472 --- /dev/null +++ b/static/404.html @@ -0,0 +1 @@ +404: This page could not be found.Style-Bert-VITS2 エディター

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