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app.py

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+from onnx_modules.V230_OnnxInference import OnnxInferenceSession
+import numpy as np
+import torch
+from scipy.io.wavfile import write
+from text import cleaned_text_to_sequence, get_bert
+from text.cleaner import clean_text
+import utils
+import commons
+import uuid
+from flask import Flask, request, jsonify, render_template_string
+from flask_cors import CORS
+import gradio as gr
+import os
+from threading import Thread
+
+hps = utils.get_hparams_from_file('onnx/BangDreamApi.json')
+device = 'cpu'
+
+BandList = {
+        "PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
+        "Afterglow":["蘭","モカ","ひまり","巴","つぐみ"],
+        "HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"],
+        "PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"],
+        "Roselia":["友希那","紗夜","リサ","燐子","あこ"],
+        "RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"],
+        "Morfonica":["ましろ","瑠唯","つくし","七深","透子"],
+        "MyGo":["燈","愛音","そよ","立希","楽奈"],
+        "AveMujica":["祥子","睦","海鈴","にゃむ","初華"],
+        "圣翔音乐学园":["華戀","光","香子","雙葉","真晝","純那","克洛迪娜","真矢","奈奈"],
+        "凛明馆女子学校":["珠緒","壘","文","悠悠子","一愛"],
+        "弗隆提亚艺术学校":["艾露","艾露露","菈樂菲","司","靜羽"],
+        "西克菲尔特音乐学院":["晶","未知留","八千代","栞","美帆"]
+}
+
+Session = OnnxInferenceSession(
+        {
+        "enc" : "onnx/BangDreamApi/BangDreamApi_enc_p.onnx",
+        "emb_g" : "onnx/BangDreamApi/BangDreamApi_emb.onnx",
+        "dp" : "onnx/BangDreamApi/BangDreamApi_dp.onnx",
+        "sdp" : "onnx/BangDreamApi/BangDreamApi_sdp.onnx",
+        "flow" : "onnx/BangDreamApi/BangDreamApi_flow.onnx",
+        "dec" : "onnx/BangDreamApi/BangDreamApi_dec.onnx"
+        },
+        Providers = ["CPUExecutionProvider"]
+    )
+
+def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
+    style_text = None if style_text == "" else style_text
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if True:
+        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, style_text, style_weight
+    )
+    del word2ph
+    assert bert_ori.shape[-1] == len(phone), phone
+
+    if language_str == "ZH":
+        bert = bert_ori
+        ja_bert = torch.randn(1024, len(phone))
+        en_bert = torch.randn(1024, len(phone))
+    elif language_str == "JP":
+        bert = torch.randn(1024, len(phone))
+        ja_bert = bert_ori
+        en_bert = torch.randn(1024, len(phone))
+    elif language_str == "EN":
+        bert = torch.randn(1024, len(phone))
+        ja_bert = torch.randn(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,
+    sid,
+    style_text=None,
+    style_weight=0.7,
+    sdp_ratio=0.5,
+    noise_scale=0.6,
+    noise_scale_w=0.667,
+    length_scale=1,
+    unique_filename = 'temp.wav'
+):
+    language= 'JP' if is_japanese(text) else 'ZH'
+    bert, ja_bert, en_bert, phones, tone, language = get_text(
+        text,
+        language,
+        hps, 
+        device,
+        style_text=style_text,
+        style_weight=style_weight,
+    )
+    with torch.no_grad():
+        x_tst = phones.unsqueeze(0).to(device).numpy()
+        language = np.zeros_like(x_tst)
+        tone = np.zeros_like(x_tst)
+        bert = bert.to(device).transpose(0, 1).numpy()
+        ja_bert = ja_bert.to(device).transpose(0, 1).numpy()
+        en_bert = en_bert.to(device).transpose(0, 1).numpy()
+        del phones
+        sid = np.array([hps.spk2id[sid]])
+        audio = Session(
+                x_tst,
+                tone,
+                language,
+                bert,
+                ja_bert,
+                en_bert,
+                sid,
+                seed=114514,
+                seq_noise_scale=noise_scale_w,
+                sdp_noise_scale=noise_scale,
+                length_scale=length_scale,
+                sdp_ratio=sdp_ratio,
+            )
+        del x_tst, tone, language, bert, ja_bert, en_bert, sid
+        write(unique_filename, 44100, audio)
+        return (44100,gr.processing_utils.convert_to_16_bit_wav(audio))
+
+def is_japanese(string):
+        for ch in string:
+            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
+                return True
+        return False
+
+
+Flaskapp = Flask(__name__)
+CORS(Flaskapp)
+@Flaskapp.route('/')
+
+def tts():
+    global last_text, last_model
+    speaker = request.args.get('speaker')
+    sdp_ratio = float(request.args.get('sdp_ratio', 0.2))
+    noise_scale = float(request.args.get('noise_scale', 0.6))
+    noise_scale_w = float(request.args.get('noise_scale_w', 0.8))
+    length_scale = float(request.args.get('length_scale', 1))
+    style_weight = float(request.args.get('style_weight', 0.7))
+    style_text = request.args.get('style_text', 'happy')
+    text = request.args.get('text')
+    is_chat = request.args.get('is_chat', 'false').lower() == 'true'
+    #model = request.args.get('model',modelPaths[-1])
+    
+    if not speaker or not text:
+        return render_template_string("""
+            <!DOCTYPE html>
+            <html>
+            <head>
+                <title>TTS API Documentation</title>
+            </head>
+            <body>
+                <iframe src="http://127.0.0.1:7860" style="width:100%; height:100vh; border:none;"></iframe>
+            </body>
+            </html>
+        """)
+    '''
+    if model != last_model:
+        unique_filename  = loadmodel(model)
+        last_model = model
+    '''
+    if is_chat and text == last_text:
+        # Generate 1 second of silence and return
+        unique_filename = 'blank.wav'
+        silence = np.zeros(44100, dtype=np.int16)
+        write(unique_filename , 44100, silence)
+    else:
+        last_text = text
+        unique_filename = f"temp{uuid.uuid4()}.wav"
+        infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale,sid = speaker, style_text=style_text, style_weight=style_weight,unique_filename=unique_filename)
+    with open(unique_filename ,'rb') as bit:
+        wav_bytes = bit.read()
+    os.remove(unique_filename)
+    headers = {
+            'Content-Type': 'audio/wav',
+            'Text': unique_filename .encode('utf-8')}
+    return wav_bytes, 200, headers
+
+def gradio_interface():
+    return app.launch(share=False)
+
+if __name__ == "__main__":
+    speaker_ids = hps.spk2id
+    speakers = list(speaker_ids.keys())
+    last_text = ""
+    with gr.Blocks() as app:
+        for band in BandList:
+            with gr.TabItem(band):
+                for name in BandList[band]:
+                    with gr.TabItem(name):
+                        with gr.Row():
+                            with gr.Column():
+                                with gr.Row():
+                                    gr.Markdown(
+                                        '<div align="center">'
+                                        f'<img style="width:auto;height:400px;" src="https://mahiruoshi-bangdream-bert-vits2.hf.space/file/image/{name}.png">' 
+                                        '</div>'
+                                    )
+                                length_scale = gr.Slider(
+                                        minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
+                                    )
+                                with gr.Accordion(label="参数设定", open=False):
+                                    sdp_ratio = gr.Slider(
+                                    minimum=0, maximum=1, value=0.5, step=0.01, label="SDP/DP混合比"
+                                    )
+                                    noise_scale = gr.Slider(
+                                        minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
+                                    )
+                                    noise_scale_w = gr.Slider(
+                                        minimum=0.1, maximum=2, value=0.667, step=0.01, label="音素长度"
+                                    )
+                                    speaker = gr.Dropdown(
+                                        choices=speakers, value=name, label="说话人"
+                                    ) 
+                                    '''
+                                with gr.Accordion(label="切换模型", open=False):
+                                    modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
+                                    btnMod = gr.Button("载入模型")
+                                    statusa = gr.TextArea()
+                                    btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa])
+                            '''
+                            with gr.Column():
+                                text = gr.TextArea(
+                                    label="输入纯日语或者中文",
+                                    placeholder="输入纯日语或者中文",
+                                    value="为什么要演奏春日影!",
+                                )
+                                style_text = gr.Textbox(label="辅助文本")
+                                style_weight = gr.Slider(
+                                        minimum=0,
+                                        maximum=1,
+                                        value=0.7,
+                                        step=0.1,
+                                        label="Weight",
+                                        info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
+                                    )
+                                btn = gr.Button("点击生成", variant="primary")
+                                audio_output = gr.Audio(label="Output Audio")
+                                '''
+                                btntran = gr.Button("快速中翻日")
+                                translateResult = gr.TextArea("从这复制翻译后的文本")
+                                btntran.click(translate, inputs=[text], outputs = [translateResult])
+                                '''
+                    btn.click(
+                        infer,
+                        inputs=[
+                            text,
+                            speaker,
+                            style_text,
+                            style_weight,
+                            sdp_ratio,
+                            noise_scale,
+                            noise_scale_w,
+                            length_scale,
+                        ],
+                        outputs=[audio_output],
+                    )
+
+    api_thread = Thread(target=Flaskapp.run, args=("0.0.0.0", 5000))
+    gradio_thread = Thread(target=gradio_interface)
+    gradio_thread.start()
+    print("推理页面已开启!")
+    api_thread.start()
+    print("api页面已开启!运行在5000端口")    

bert/bert-base-japanese-v3/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt 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
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz 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
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl 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
+*.safetensors 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
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

bert/bert-base-japanese-v3/README.md

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+---
+license: apache-2.0
+datasets:
+- cc100
+- wikipedia
+language:
+- ja
+widget:
+- text: 東北大学で[MASK]の研究をしています。
+---
+
+# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
+
+This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
+
+This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
+Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
+
+The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
+
+## Model architecture
+
+The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
+
+## Training Data
+
+The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
+For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
+The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
+
+For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
+
+## Tokenization
+
+The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
+The vocabulary size is 32768.
+
+We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
+
+## Training
+
+We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
+For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
+
+For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
+
+## Licenses
+
+The pretrained models are distributed under the Apache License 2.0.
+
+## Acknowledgments
+
+This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.

bert/bert-base-japanese-v3/config.json

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+{
+    "architectures": [
+        "BertForPreTraining"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-12,
+    "max_position_embeddings": 512,
+    "model_type": "bert",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "pad_token_id": 0,
+    "type_vocab_size": 2,
+    "vocab_size": 32768
+}

bert/bert-base-japanese-v3/tokenizer_config.json

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+{
+    "tokenizer_class": "BertJapaneseTokenizer",
+    "model_max_length": 512,
+    "do_lower_case": false,
+    "word_tokenizer_type": "mecab",
+    "subword_tokenizer_type": "wordpiece",
+    "mecab_kwargs": {
+        "mecab_dic": "unidic_lite"
+    }
+}

bert/bert-large-japanese-v2/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt 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
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz 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
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl 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
+*.safetensors 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
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

bert/bert-large-japanese-v2/README.md

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+---
+license: apache-2.0
+datasets:
+- cc100
+- wikipedia
+language:
+- ja
+widget:
+- text: 東北大学で[MASK]の研究をしています。
+---
+
+# BERT large Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
+
+This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
+
+This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
+Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
+
+The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
+
+## Model architecture
+
+The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.
+
+## Training Data
+
+The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
+For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
+The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
+
+For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
+
+## Tokenization
+
+The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
+The vocabulary size is 32768.
+
+We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
+
+## Training
+
+We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
+For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
+
+For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
+
+## Licenses
+
+The pretrained models are distributed under the Apache License 2.0.
+
+## Acknowledgments
+
+This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.

bert/bert-large-japanese-v2/config.json

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+{
+    "architectures": [
+        "BertForPreTraining"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-12,
+    "max_position_embeddings": 512,
+    "model_type": "bert",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "pad_token_id": 0,
+    "type_vocab_size": 2,
+    "vocab_size": 32768
+}

bert/bert-large-japanese-v2/tokenizer_config.json

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+{
+    "tokenizer_class": "BertJapaneseTokenizer",
+    "model_max_length": 512,
+    "do_lower_case": false,
+    "word_tokenizer_type": "mecab",
+    "subword_tokenizer_type": "wordpiece",
+    "mecab_kwargs": {
+        "mecab_dic": "unidic_lite"
+    }
+}

bert/bert_models.json

@@ -0,0 +1,14 @@
+{
+    "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"]
+    }
+}

bert/chinese-roberta-wwm-ext-large/.gitattributes

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bert/chinese-roberta-wwm-ext-large/README.md

@@ -0,0 +1,57 @@
+---
+language:
+- zh
+tags:
+- bert
+license: "apache-2.0"
+---
+
+# Please use 'Bert' related functions to load this model!
+
+## Chinese BERT with Whole Word Masking
+For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
+
+**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
+Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
+
+This repository is developed based on：https://github.com/google-research/bert
+
+You may also interested in,
+- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
+- Chinese MacBERT: https://github.com/ymcui/MacBERT
+- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
+- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
+- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
+
+More resources by HFL: https://github.com/ymcui/HFL-Anthology
+
+## Citation
+If you find the technical report or resource is useful, please cite the following technical report in your paper.
+- Primary: https://arxiv.org/abs/2004.13922
+```
+@inproceedings{cui-etal-2020-revisiting,
+    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
+    author = "Cui, Yiming  and
+      Che, Wanxiang  and
+      Liu, Ting  and
+      Qin, Bing  and
+      Wang, Shijin  and
+      Hu, Guoping",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
+    pages = "657--668",
+}
+```
+- Secondary: https://arxiv.org/abs/1906.08101
+```
+@article{chinese-bert-wwm,
+  title={Pre-Training with Whole Word Masking for Chinese BERT},
+  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
+  journal={arXiv preprint arXiv:1906.08101},
+  year={2019}
+ }
+```

bert/chinese-roberta-wwm-ext-large/added_tokens.json

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+{}

bert/chinese-roberta-wwm-ext-large/config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}

bert/chinese-roberta-wwm-ext-large/pytorch_model.bin

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bert/chinese-roberta-wwm-ext-large/special_tokens_map.json

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bert/chinese-roberta-wwm-ext-large/tokenizer_config.json

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bert/deberta-v2-large-japanese-char-wwm/README.md

@@ -0,0 +1,89 @@
+---
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+tags:
+  - deberta
+  - deberta-v2
+  - fill-mask
+  - character
+  - wwm
+datasets:
+  - wikipedia
+  - cc100
+  - oscar
+metrics:
+  - accuracy
+mask_token: "[MASK]"
+widget:
+    - text: "京都大学で自然言語処理を[MASK][MASK]する。"
+---
+
+# Model Card for Japanese character-level DeBERTa V2 large
+
+## Model description
+
+This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
+This model is trained with character-level tokenization and whole word masking.
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+
+sentence = '京都大学で自然言語処理を[MASK][MASK]する。'
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can also fine-tune this model on downstream tasks.
+
+## Tokenization
+
+There is no need to tokenize texts in advance, and you can give raw texts to the tokenizer.
+The texts are tokenized into character-level tokens by [sentencepiece](https://github.com/google/sentencepiece).
+
+## Training data
+
+We used the following corpora for pre-training:
+
+- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
+- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
+- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
+
+Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
+Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
+
+## Training procedure
+
+We first segmented texts in the corpora into words using [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) for whole word masking.
+Then, we built a sentencepiece model with 22,012 tokens including all characters that appear in the training corpus.
+
+We tokenized raw corpora into character-level subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library.
+The training took 26 days using 16 NVIDIA A100-SXM4-40GB GPUs.
+
+The following hyperparameters were used during pre-training:
+
+- learning_rate: 1e-4
+- per_device_train_batch_size: 26
+- distributed_type: multi-GPU
+- num_devices: 16
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 3,328
+- max_seq_length: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup (lr = 0 at 300k steps)
+- training_steps: 260,000
+- warmup_steps: 10,000
+
+The accuracy of the trained model on the masked language modeling task was 0.795.
+The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
+
+## Acknowledgments
+
+This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models".
+For training models, we used the mdx: a platform for the data-driven future.

bert/deberta-v2-large-japanese-char-wwm/config.json

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+{
+  "architectures": [
+    "DebertaV2ForMaskedLM"
+  ],
+  "attention_head_size": 64,
+  "attention_probs_dropout_prob": 0.1,
+  "conv_act": "gelu",
+  "conv_kernel_size": 3,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
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+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 0,
+  "vocab_size": 22012
+}

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "do_subword_tokenize": true,
+  "do_word_tokenize": true,
+  "jumanpp_kwargs": null,
+  "mask_token": "[MASK]",
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+  "special_tokens_map_file": null,
+  "subword_tokenizer_type": "character",
+  "sudachi_kwargs": null,
+  "tokenizer_class": "BertJapaneseTokenizer",
+  "unk_token": "[UNK]",
+  "word_tokenizer_type": "basic"
+}

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bert/deberta-v2-large-japanese/README.md

@@ -0,0 +1,111 @@
+---
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+tags:
+  - deberta
+  - deberta-v2
+  - fill-mask
+datasets:
+  - wikipedia
+  - cc100
+  - oscar
+metrics:
+  - accuracy
+mask_token: "[MASK]"
+widget:
+  - text: "京都 大学 で 自然 言語 処理 を [MASK] する 。"
+---
+
+# Model Card for Japanese DeBERTa V2 large
+
+## Model description
+
+This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the
+Japanese portion of OSCAR.
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese')
+model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese')
+
+sentence = '京都 大学 で 自然 言語 処理 を [MASK] する 。'  # input should be segmented into words by Juman++ in advance
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can also fine-tune this model on downstream tasks.
+
+## Tokenization
+
+The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in
+advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each
+word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece).
+
+## Training data
+
+We used the following corpora for pre-training:
+
+- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
+- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
+- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
+
+Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
+Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of
+CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
+
+## Training procedure
+
+We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp).
+Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC))
+and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
+
+We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model
+using [transformers](https://github.com/huggingface/transformers) library.
+The training took 36 days using 8 NVIDIA A100-SXM4-40GB GPUs.
+
+The following hyperparameters were used during pre-training:
+
+- learning_rate: 1e-4
+- per_device_train_batch_size: 18
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 2,304
+- max_seq_length: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup
+- training_steps: 300,000
+- warmup_steps: 10,000
+
+The accuracy of the trained model on the masked language modeling task was 0.799.
+The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
+
+## Fine-tuning on NLU tasks
+
+We fine-tuned the following models and evaluated them on the dev set of JGLUE.
+We tuned learning rate and training epochs for each model and task
+following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
+
+| Model                         | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
+|-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------|
+| Waseda RoBERTa base           | 0.965       | 0.913        | 0.876         | 0.905    | 0.853     | 0.916     | 0.853      |
+| Waseda RoBERTa large (seq512) | 0.969       | 0.925        | 0.890         | 0.928    | 0.910     | 0.955     | 0.900      |
+| LUKE Japanese base*           | 0.965       | 0.916        | 0.877         | 0.912    | -         | -         | 0.842      |
+| LUKE Japanese large*          | 0.965       | 0.932        | 0.902         | 0.927    | -         | -         | 0.893      |
+| DeBERTaV2 base                | 0.970       | 0.922        | 0.886         | 0.922    | 0.899     | 0.951     | 0.873      |
+| DeBERTaV2 large               | 0.968       | 0.925        | 0.892         | 0.924    | 0.912     | 0.959     | 0.890      |
+
+*The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke).
+
+## Acknowledgments
+
+This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (
+JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of
+Large-Scale Japanese Language Models".
+For training models, we used the mdx: a platform for the data-driven future.

bert/deberta-v2-large-japanese/config.json

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+{
+  "_name_or_path": "configs/deberta_v2_large.json",
+  "architectures": [
+    "DebertaV2ForMaskedLM"
+  ],
+  "attention_head_size": 64,
+  "attention_probs_dropout_prob": 0.1,
+  "conv_act": "gelu",
+  "conv_kernel_size": 3,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.23.1",
+  "type_vocab_size": 0,
+  "vocab_size": 32000
+}

bert/deberta-v2-large-japanese/special_tokens_map.json

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

bert/deberta-v2-large-japanese/tokenizer_config.json

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+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "keep_accents": true,
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "special_tokens_map_file": null,
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]"
+}

bert/deberta-v3-large/.gitattributes

@@ -0,0 +1,27 @@
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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
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bert/deberta-v3-large/README.md

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+---
+language: en
+tags:
+  - deberta
+  - deberta-v3
+  - fill-mask
+thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
+license: mit
+---
+
+## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
+
+[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
+
+In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa,  our V3 version significantly improves the model performance on downstream tasks.  You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
+
+Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
+
+The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters  with a vocabulary containing 128K tokens which introduces 131M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.
+
+
+#### Fine-tuning on NLU tasks
+
+We present the dev results on SQuAD 2.0 and MNLI tasks.
+
+| Model             |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)|
+|-------------------|----------|-------------------|-----------|----------|
+| RoBERTa-large     |50     |304                | 89.4/86.5 | 90.2   |
+| XLNet-large       |32     |-                  | 90.6/87.9 | 90.8   |
+| DeBERTa-large     |50     |-                  | 90.7/88.0 | 91.3   |
+| **DeBERTa-v3-large**|128|304                  |  **91.5/89.0**| **91.8/91.9**|
+
+
+#### Fine-tuning with HF transformers
+
+```bash
+#!/bin/bash
+
+cd transformers/examples/pytorch/text-classification/
+
+pip install datasets
+export TASK_NAME=mnli
+
+output_dir="ds_results"
+
+num_gpus=8
+
+batch_size=8
+
+python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
+  run_glue.py \
+  --model_name_or_path microsoft/deberta-v3-large \
+  --task_name $TASK_NAME \
+  --do_train \
+  --do_eval \
+  --evaluation_strategy steps \
+  --max_seq_length 256 \
+  --warmup_steps 50 \
+  --per_device_train_batch_size ${batch_size} \
+  --learning_rate 6e-6 \
+  --num_train_epochs 2 \
+  --output_dir $output_dir \
+  --overwrite_output_dir \
+  --logging_steps 1000 \
+  --logging_dir $output_dir
+
+```
+
+### Citation
+
+If you find DeBERTa useful for your work, please cite the following papers:
+
+``` latex
+@misc{he2021debertav3,
+      title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
+      author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
+      year={2021},
+      eprint={2111.09543},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+``` latex
+@inproceedings{
+he2021deberta,
+title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
+author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
+booktitle={International Conference on Learning Representations},
+year={2021},
+url={https://openreview.net/forum?id=XPZIaotutsD}
+}
+```

bert/deberta-v3-large/config.json

@@ -0,0 +1,22 @@
+{
+		"model_type": "deberta-v2",
+		"attention_probs_dropout_prob": 0.1,
+		"hidden_act": "gelu",
+		"hidden_dropout_prob": 0.1,
+		"hidden_size": 1024,
+		"initializer_range": 0.02,
+		"intermediate_size": 4096,
+		"max_position_embeddings": 512,
+		"relative_attention": true,
+		"position_buckets": 256,
+		"norm_rel_ebd": "layer_norm",
+		"share_att_key": true,
+		"pos_att_type": "p2c|c2p",
+		"layer_norm_eps": 1e-7,
+		"max_relative_positions": -1,
+		"position_biased_input": false,
+		"num_attention_heads": 16,
+		"num_hidden_layers": 24,
+		"type_vocab_size": 0,
+		"vocab_size": 128100
+}

bert/deberta-v3-large/generator_config.json

@@ -0,0 +1,22 @@
+{
+	"model_type": "deberta-v2",
+	"attention_probs_dropout_prob": 0.1,
+	"hidden_act": "gelu",
+	"hidden_dropout_prob": 0.1,
+	"hidden_size": 1024,
+	"initializer_range": 0.02,
+	"intermediate_size": 4096,
+	"max_position_embeddings": 512,
+	"relative_attention": true,
+	"position_buckets": 256,
+	"norm_rel_ebd": "layer_norm",
+	"share_att_key": true,
+	"pos_att_type": "p2c|c2p",
+	"layer_norm_eps": 1e-7,
+	"max_relative_positions": -1,
+	"position_biased_input": false,
+	"num_attention_heads": 16,
+	"num_hidden_layers": 12,
+	"type_vocab_size": 0,
+	"vocab_size": 128100
+}

bert/deberta-v3-large/pytorch_model.bin

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bert/deberta-v3-large/spm.model

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bert/deberta-v3-large/tokenizer_config.json

@@ -0,0 +1,4 @@
+{
+  "do_lower_case": false,
+  "vocab_type": "spm"
+}

commons.py

@@ -0,0 +1,158 @@
+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 convert_pad_shape(pad_shape):
+    layer = pad_shape[::-1]
+    pad_shape = [item for sublist in layer for item in sublist]
+    return pad_shape
+
+
+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

config.py

@@ -0,0 +1,248 @@
+"""
+@Desc: 全局配置文件读取
+"""
+import argparse
+import yaml
+from typing import Dict, List
+import os
+import shutil
+import sys
+
+
+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
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, 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"])
+
+        return cls(**data)
+
+
+class Preprocess_text_config:
+    """数据预处理配置"""
+
+    def __init__(
+        self,
+        transcription_path: str,
+        cleaned_path: str,
+        train_path: str,
+        val_path: str,
+        config_path: str,
+        val_per_lang: int = 5,
+        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
+
+    @classmethod
+    def from_dict(cls, dataset_path: str, data: Dict[str, any]):
+        """从字典中生成实例"""
+
+        data["transcription_path"] = os.path.join(
+            dataset_path, data["transcription_path"]
+        )
+        if data["cleaned_path"] == "" or data["cleaned_path"] is None:
+            data["cleaned_path"] = None
+        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"])
+
+        return cls(**data)
+
+
+class Bert_gen_config:
+    """bert_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 2,
+        device: str = "cuda",
+        use_multi_device: bool = False,
+    ):
+        self.config_path = config_path
+        self.num_processes = num_processes
+        self.device = device
+        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"])
+
+        return cls(**data)
+
+
+class Emo_gen_config:
+    """emo_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 2,
+        device: str = "cuda",
+        use_multi_device: bool = False,
+    ):
+        self.config_path = config_path
+        self.num_processes = num_processes
+        self.device = device
+        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"])
+
+        return cls(**data)
+
+
+class Train_ms_config:
+    """训练配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        env: Dict[str, any],
+        base: Dict[str, any],
+        model: str,
+        num_workers: int,
+        spec_cache: bool,
+        keep_ckpts: int,
+    ):
+        self.env = env  # 需要加载的环境变量
+        self.base = base  # 底模配置
+        self.model = model  # 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.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]):
+        # data["model"] = os.path.join(dataset_path, data["model"])
+        data["config_path"] = os.path.join(dataset_path, data["config_path"])
+
+        return cls(**data)
+
+
+class Webui_config:
+    """webui 配置"""
+
+    def __init__(
+        self,
+        device: str,
+        model: str,
+        config_path: str,
+        language_identification_library: str,
+        port: int = 7860,
+        share: bool = False,
+        debug: bool = False,
+    ):
+        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
+        )
+
+    @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"])
+        return cls(**data)
+
+
+class Server_config:
+    def __init__(
+        self, models: List[Dict[str, any]], port: int = 5000, device: str = "cuda"
+    ):
+        self.models: List[Dict[str, any]] = models  # 需要加载的所有模型的配置
+        self.port: int = port  # 端口号
+        self.device: str = device  # 模型默认使用设备
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, any]):
+        return cls(**data)
+
+
+class Translate_config:
+    """翻译api配置"""
+
+    def __init__(self, app_key: str, secret_key: str):
+        self.app_key = app_key
+        self.secret_key = secret_key
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, any]):
+        return cls(**data)
+
+
+class Config:
+    def __init__(self, config_path: str):
+        if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"):
+            shutil.copy(src="default_config.yml", dst=config_path)
+            print(
+                f"已根据默认配置文件default_config.yml生成配置文件{config_path}。请按该配置文件的说明进行配置后重新运行。"
+            )
+            print("如无特殊需求，请勿修改default_config.yml或备份该文件。")
+            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())
+            dataset_path: str = yaml_config["dataset_path"]
+            openi_token: str = yaml_config["openi_token"]
+            self.dataset_path: str = dataset_path
+            self.mirror: str = yaml_config["mirror"]
+            self.openi_token: str = openi_token
+            self.resample_config: Resample_config = Resample_config.from_dict(
+                dataset_path, yaml_config["resample"]
+            )
+            self.preprocess_text_config: Preprocess_text_config = (
+                Preprocess_text_config.from_dict(
+                    dataset_path, yaml_config["preprocess_text"]
+                )
+            )
+            self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict(
+                dataset_path, yaml_config["bert_gen"]
+            )
+            self.emo_gen_config: Emo_gen_config = Emo_gen_config.from_dict(
+                dataset_path, yaml_config["emo_gen"]
+            )
+            self.train_ms_config: Train_ms_config = Train_ms_config.from_dict(
+                dataset_path, yaml_config["train_ms"]
+            )
+            self.webui_config: Webui_config = Webui_config.from_dict(
+                dataset_path, yaml_config["webui"]
+            )
+            self.server_config: Server_config = Server_config.from_dict(
+                yaml_config["server"]
+            )
+            self.translate_config: Translate_config = Translate_config.from_dict(
+                yaml_config["translate"]
+            )
+
+
+parser = argparse.ArgumentParser()
+# 为避免与以前的config.json起冲突，将其更名如下
+parser.add_argument("-y", "--yml_config", type=str, default="config.yml")
+args, _ = parser.parse_known_args()
+config = Config(args.yml_config)

config.yml

@@ -0,0 +1,177 @@
+# 全局配置
+# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml
+
+# 拟提供通用路径配置，统一存放数据，避免数据放得很乱
+# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径
+# 不填或者填空则路径为相对于项目根目录的路径
+dataset_path: "Data/V23"
+
+# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token
+mirror: ""
+openi_token: ""  # openi token
+
+# resample 音频重采样配置
+# 注意， “:” 后需要加空格
+resample:
+  # 目标重采样率
+  sampling_rate: 44100
+  # 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样
+  # 请填入相对于datasetPath的相对路径
+  in_dir: "" # 相对于根目录的路径为 /datasetPath/in_dir
+  # 音频文件重采样后输出路径
+  out_dir: ""
+
+
+# preprocess_text 数据集预处理相关配置
+# 注意， “:” 后需要加空格
+preprocess_text:
+  # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
+  transcription_path: "filelists/whole.list"
+  # 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
+  cleaned_path: ""
+  # 训练集路径
+  train_path: "filelists/train.list"
+  # 验证集路径
+  val_path: "filelists/val.list"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 每个语言的验证集条数
+  val_per_lang: 4
+  # 验证集最大条数，多于的会被截断并放到训练集中
+  max_val_total: 800
+  # 是否进行数据清洗
+  clean: true
+
+
+# bert_gen 相关配置
+# 注意， “:” 后需要加空格
+bert_gen:
+  # 训练数据集配置文件路径
+  config_path: "configs/config.json"
+  # 并行数
+  num_processes: 4
+  # 使用设备：可选项 "cpu" 显卡推理，"cpu" cpu推理
+  # 该选项同时决定了get_bert_feature的默认设备
+  device: "cpu"
+  # 使用多卡推理
+  use_multi_device: false
+
+# emo_gen 相关配置
+# 注意， “:” 后需要加空格
+emo_gen:
+  # 训练数据集配置文件路径
+  config_path: "configs/config.json"
+  # 并行数
+  num_processes: 16
+  # 使用设备：可选项 "cpu" 显卡推理，"cpu" cpu推理
+  device: "cpu"
+  # 使用多卡推理
+  use_multi_device: false
+
+# train 训练配置
+# 注意， “:” 后需要加空格
+train_ms:
+  env:
+    MASTER_ADDR: "localhost"
+    MASTER_PORT: 10086
+    WORLD_SIZE: 1
+    LOCAL_RANK: 0
+    RANK: 0
+    # 可以填写任意名的环境变量
+    # THE_ENV_VAR_YOU_NEED_TO_USE: "1234567"
+  # 底模设置
+  base:
+    use_base_model: True
+    repo_id: "Stardust_minus/Bert-VITS2"
+    model_image: "Bert-VITS2_2.3底模" # openi网页的模型名
+  # 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下
+  model: "models"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 训练使用的worker，不建议超过CPU核心数
+  num_workers: 22
+  # 关闭此项可以节约接近50%的磁盘空间，但是可能导致实际训练速度变慢和更高的CPU使用率。
+  spec_cache: True
+  # 保存的检查点数量，多于此数目的权重会被删除来节省空间。
+  keep_ckpts: 8
+
+
+# webui webui配置
+# 注意， “:” 后需要加空格
+webui:
+  # 推理设备
+  device: "cpu"
+  # 模型路径
+  model: "models/G_408000.pth"
+  # 配置文件路径
+  config_path: "configs/config.json"
+  # 端口号
+  port: 7860
+  # 是否公开部署，对外网开放
+  share: false
+  # 是否开启debug模式
+  debug: false
+  # 语种识别库，可选langid, fastlid
+  language_identification_library: "langid"
+
+
+# server-fastapi配置
+# 注意， “:” 后需要加空格
+# 注意，本配置下的所有配置均为相对于根目录的路径
+server:
+  # 端口号
+  port: 5000
+  # 模型默认使用设备：但是当前并没有实现这个配置。
+  device: "cpu"
+  # 需要加载的所有模型的配置，可以填多个模型，也可以不填模型，等网页成功后手动加载模型
+  # 不加载模型的配置格式：删除默认给的两个模型配置，给models赋值 [ ]，也就是空列表。参考模型2的speakers 即 models: [ ]
+  # 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。
+  # 也可以不填模型，等网页加载成功后手动填写models。
+  models:
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cpu"
+      # 模型默认使用的语言
+      language: "ZH"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      # 暂时不用填写，当前尚未实现按人区分配置
+      speakers:
+        - speaker: "科比"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1
+        - speaker: "五条悟"
+          sdp_ratio: 0.3
+          noise_scale: 0.7
+          noise_scale_w: 0.8
+          length_scale: 0.5
+        - speaker: "安倍晋三"
+          sdp_ratio: 0.2
+          noise_scale: 0.6
+          noise_scale_w: 0.8
+          length_scale: 1.2
+    - # 模型的路径
+      model: ""
+      # 模型config.json的路径
+      config: ""
+      # 模型使用设备，若填写则会覆盖默认配置
+      device: "cpu"
+      # 模型默认使用的语言
+      language: "JP"
+      # 模型人物默认参数
+      # 不必填写所有人物，不填的使用默认值
+      speakers: [ ] # 也可以不填
+
+# 百度翻译开放平台 api配置
+# api接入文档 https://api.fanyi.baidu.com/doc/21
+# 请不要在github等网站公开分享你的app id 与 key
+translate:
+  # 你的APPID
+  "app_key": "20231117001883321"
+  # 你的密钥
+  "secret_key": "lMQbvZHeJveDceLof2wf"

onnx/BangDreamApi.json

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+    "Folder": "BangDreamApi",
+    "Name": "BangDreamApi",
+    "Type": "BertVits",
+    "Symbol": [
+        "_",
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+    ,
+    "LanguageMap": {
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+    "Dict": "BasicDict",
+    "BertPath": [
+        "chinese-roberta-wwm-ext-large",
+        "deberta-v2-large-japanese",
+        "bert-base-japanese-v3"
+    ],
+    "Clap": "clap-htsat-fused"
+}

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