initial commit

Dockerfile

@@ -0,0 +1,40 @@
+FROM python:3.10
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN apt-get update
+
+RUN apt-get install -y cmake
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade --no-build-isolation pyopenjtalk 
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+#COPY ./open_jtalk_dic_utf_8-1.11.tar.gz /usr/local/lib/python3.10/site-packages/pyopenjtalk/dic.tar.gz
+#RUN chmod 777 /usr/local/lib/python3.10/site-packages/pyopenjtalk/dic.tar.gz
+
+#COPY --chown=user ./open_jtalk_dic_utf_8-1.11.tar.gz $HOME/app/dict/
+#COPY --chown=user ./open_jtalk_dic_utf_8-1.11.tar.gz $HOME/app/dict/dic.tar.gz
+
+COPY --chown=user ./dict/ $HOME/app/dict/
+
+RUN chmod 777 -R $HOME/app/dict/
+
+ENV OPEN_JTALK_DICT_DIR=$HOME/app/dict/
+
+CMD ["python", "-u", "app.py"]

LICENSE

@@ -0,0 +1,201 @@
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OUTPUT_MODEL/D_Amitaro.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4610d88f7ce89c54e7bbee6fe1a60b9c98ade40dc8ec052624d0fcac67d6676c
+size 187027092

OUTPUT_MODEL/G_Amitaro.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7644eed4b2c8afd8102ba6ec231a81d620d3a9bd5b659c1481552a3b2d4fdbc9
+size 158888169

OUTPUT_MODEL/config.json

@@ -0,0 +1,56 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 1,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 8192,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"./final_annotation_train.txt",
+    "validation_files":"./final_annotation_val.txt",
+    "text_cleaners":["japanese_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 1,
+    "cleaned_text": true
+  },
+  "model": {
+    "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],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
+  "speakers": {"amitaro":0
+  },
+  "symbols": ["_", ",", ".", "!", "?", "-", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u2193", "\u2191", " "]
+}

README.md

@@ -1,12 +1,11 @@
 ---
-title: GPT4o VITS TTS SutouHaru API
-emoji: 🔥
-colorFrom: yellow
+title: GPT4o VITS TTS SutouHaru
+emoji: 🚀
+colorFrom: blue
 colorTo: pink
 sdk: gradio
 sdk_version: 4.31.4
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

VC_inference.py

@@ -0,0 +1,146 @@
+import os
+import numpy as np
+import torch
+from torch import no_grad, LongTensor
+import argparse
+import commons
+from mel_processing import spectrogram_torch
+import utils
+from models import SynthesizerTrn
+import gradio as gr
+import librosa
+import webbrowser
+
+from text import text_to_sequence, _clean_text
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+import logging
+logging.getLogger("PIL").setLevel(logging.WARNING)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("markdown_it").setLevel(logging.WARNING)
+logging.getLogger("httpx").setLevel(logging.WARNING)
+logging.getLogger("asyncio").setLevel(logging.WARNING)
+
+language_marks = {
+    "Japanese": "",
+    "日本語": "[JA]",
+    "简体中文": "[ZH]",
+    "English": "[EN]",
+    "Mix": "",
+}
+lang = ['日本語', '简体中文', 'English', 'Mix']
+def get_text(text, hps, is_symbol):
+    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm
+
+def create_tts_fn(model, hps, speaker_ids):
+    def tts_fn(text, speaker, language, speed):
+        if language is not None:
+            text = language_marks[language] + text + language_marks[language]
+        speaker_id = speaker_ids[speaker]
+        stn_tst = get_text(text, hps, False)
+        with no_grad():
+            x_tst = stn_tst.unsqueeze(0).to(device)
+            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
+            sid = LongTensor([speaker_id]).to(device)
+            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
+                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
+        del stn_tst, x_tst, x_tst_lengths, sid
+        return "Success", (hps.data.sampling_rate, audio)
+
+    return tts_fn
+
+def create_vc_fn(model, hps, speaker_ids):
+    def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
+        input_audio = record_audio if record_audio is not None else upload_audio
+        if input_audio is None:
+            return "You need to record or upload an audio", None
+        sampling_rate, audio = input_audio
+        original_speaker_id = speaker_ids[original_speaker]
+        target_speaker_id = speaker_ids[target_speaker]
+
+        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+        if len(audio.shape) > 1:
+            audio = librosa.to_mono(audio.transpose(1, 0))
+        if sampling_rate != hps.data.sampling_rate:
+            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
+        with no_grad():
+            y = torch.FloatTensor(audio)
+            y = y / max(-y.min(), y.max()) / 0.99
+            y = y.to(device)
+            y = y.unsqueeze(0)
+            spec = spectrogram_torch(y, hps.data.filter_length,
+                                     hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
+                                     center=False).to(device)
+            spec_lengths = LongTensor([spec.size(-1)]).to(device)
+            sid_src = LongTensor([original_speaker_id]).to(device)
+            sid_tgt = LongTensor([target_speaker_id]).to(device)
+            audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
+                0, 0].data.cpu().float().numpy()
+        del y, spec, spec_lengths, sid_src, sid_tgt
+        return "Success", (hps.data.sampling_rate, audio)
+
+    return vc_fn
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model")
+    parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file")
+    parser.add_argument("--share", default=False, help="make link public (used in colab)")
+
+    args = parser.parse_args()
+    hps = utils.get_hparams_from_file(args.config_dir)
+
+
+    net_g = SynthesizerTrn(
+        len(hps.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.eval()
+
+    _ = utils.load_checkpoint(args.model_dir, net_g, None)
+    speaker_ids = hps.speakers
+    speakers = list(hps.speakers.keys())
+    tts_fn = create_tts_fn(net_g, hps, speaker_ids)
+    vc_fn = create_vc_fn(net_g, hps, speaker_ids)
+    app = gr.Blocks()
+    with app:
+        with gr.Tab("Text-to-Speech"):
+            with gr.Row():
+                with gr.Column():
+                    textbox = gr.TextArea(label="Text",
+                                          placeholder="Type your sentence here",
+                                          value="こんにちわ。", elem_id=f"tts-input")
+                    # select character
+                    char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
+                    language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
+                    duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
+                                                label='速度 Speed')
+                with gr.Column():
+                    text_output = gr.Textbox(label="Message")
+                    audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
+                    btn = gr.Button("Generate!")
+                    btn.click(tts_fn,
+                              inputs=[textbox, char_dropdown, language_dropdown, duration_slider,],
+                              outputs=[text_output, audio_output])
+        with gr.Tab("Voice Conversion"):
+            gr.Markdown("""
+                            录制或上传声音，并选择要转换的音色。
+            """)
+            with gr.Column():
+                record_audio = gr.Audio(label="record your voice", source="microphone")
+                upload_audio = gr.Audio(label="or upload audio here", source="upload")
+                source_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="source speaker")
+                target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
+            with gr.Column():
+                message_box = gr.Textbox(label="Message")
+                converted_audio = gr.Audio(label='converted audio')
+            btn = gr.Button("Convert!")
+            btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio],
+                      outputs=[message_box, converted_audio])
+    webbrowser.open("http://127.0.0.1:7860")
+    app.launch(share=args.share)
+

app.py

@@ -0,0 +1,138 @@
+import utils
+from models import SynthesizerTrn
+import torch
+from torch import no_grad, LongTensor
+from text import text_to_sequence
+import gradio as gr
+import commons
+import json
+import os
+import time
+from openai import OpenAI
+
+model_path = "./OUTPUT_MODEL/G_Amitaro.pth"
+config_path = "./OUTPUT_MODEL/config.json"
+sampling_rate = 22050
+
+length = 1.0
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+def request_openai_message(input_text):
+    client = OpenAI(api_key=os.environ["GPT_API_TOKEN"])
+    init_str = """あなたの名前ははる。あなたは冒険者でこの町の隅っこに住んでいます。
+好きな食べ物はカレー。エッチな質問やいたずらに対して沈黙してください。
+町の名前はルコー。あなたは今暇なので、町案内をしている。
+わからない質問されたら適当に答える。
+返事は短くすること。会話で使わない文字も使わない。
+酒場：町の東側にあって、酒の販売は17時からだが、食事なら11時から。メニューはカレー、ステーキ、野菜スープ、ソーセージなど。24時までやっている
+冒険者ギルド：町の中央広場にある。24時間受付だが、新人登録は受付がいる10時から19時の間だ。お使いクエストや討伐クエストなど受けることができる。
+ポーション屋：町の南側にある。冒険に必須のポーションを売っている店だ。店長の対応はドライだが、実は人見知りだけで、普通に優しい。定番のポーション以外も受注生産承るので、欲しいポーションがなければ聞いてみた方がいい。
+雑貨店：冒険者ギルドの隣にある。日用品から冒険者用アイテムまで売っている。
+               """
+    messages=[
+        {"role": "system", "content": init_str},
+    ]
+    
+    text_log = input_text.split("|")
+    for val in text_log:
+        userind = val.find("user:")
+        assistantind = val.find("assistant:")
+        # just in case, remove conversation tag
+        val = val.replace("user:","")
+        val = val.replace("assistant:","")
+        if userind != -1:
+            messages.append({"role": "user", "content": val})
+        elif assistantind != -1:
+            messages.append({"role": "assistant", "content": val})
+
+    text = ""
+    retry_count = 0
+    while text == "" and retry_count < 3:
+        try:        
+            response = client.chat.completions.create(model="gpt-4o", messages=messages)
+            print("Response from openai is : ")
+            print(response)
+            text = response.choices[0].message.content
+        except Exception as inst:
+            print(type(inst))    # the exception type
+            print(inst.args)     # arguments stored in .args
+            print(inst) 
+            retry_count += 1
+            time.sleep(1)
+            
+    text = text.replace("user:","")
+    text = text.replace("assistant:","")
+
+    return text
+
+def process_text(text):
+    # remove newline
+    text = text.replace("\r\n","。")
+    text = text.replace("\n","。")
+    return text
+
+def tts(text):
+    hps = utils.get_hparams_from_file(config_path)
+    net_g = SynthesizerTrn(
+        len(hps.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.eval()
+    _ = utils.load_checkpoint(model_path, net_g, None)
+    
+    speaker_ids = hps.speakers
+    
+    speaker_id = 0
+    stn_tst = get_text(text, hps, False)
+    with no_grad():
+        x_tst = stn_tst.unsqueeze(0).to(device)
+        x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
+        sid = LongTensor([speaker_id]).to(device)
+        audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.6,
+                            length_scale=1.0 / length)[0][0, 0].data.cpu().float().numpy()
+    del stn_tst, x_tst, x_tst_lengths, sid
+
+    return audio
+
+def get_text(text, hps, is_symbol):
+    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm
+
+def get_vits_array(input_text, flag):
+
+    print(flag, " : ", input_text)
+    # for mokuran webgl
+    if flag == "mokuran":
+        text = request_openai_message(input_text)
+        text = process_text(text)
+        audio = tts(text)
+        return {"utterance": text, "audio_wave": audio.tolist()}, None
+        
+    elif flag == "audio_array":
+        audio = tts(input_text)
+        return {"input_text": input_text, "sampling_rate": sampling_rate, "audio_wave": audio.tolist()}, None
+
+    # for tts api
+    elif flag == "audio_file":
+        audio = tts(input_text)
+        return "", (sampling_rate, audio)
+
+    else:
+        raise Exception("Unknown flag : [" + flag + "]")
+
+gradio_interface = gr.Interface(
+  fn = get_vits_array,
+  inputs = ["text", "text"],
+  outputs = ["text", "audio"]
+)
+
+# for using with nginx docker environment, note that root path change according to nginx configuration
+gradio_interface.launch(server_name="0.0.0.0", server_port=7860, root_path="/mokuran")
+
+# for standalone server (with ssl)
+#gradio_interface.launch(server_name="0.0.0.0", server_port=7860, ssl_keyfile="privkey.pem", ssl_certfile="fullchain.pem",  ssl_verify=False)

attentions.py

@@ -0,0 +1,303 @@
+import copy
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+from modules import LayerNorm
+   
+
+class Encoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+
+    self.drop = nn.Dropout(p_dropout)
+    self.attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask):
+    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.attn_layers[i](x, x, attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class Decoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+
+    self.drop = nn.Dropout(p_dropout)
+    self.self_attn_layers = nn.ModuleList()
+    self.norm_layers_0 = nn.ModuleList()
+    self.encdec_attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
+      self.norm_layers_0.append(LayerNorm(hidden_channels))
+      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask, h, h_mask):
+    """
+    x: decoder input
+    h: encoder output
+    """
+    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.self_attn_layers[i](x, x, self_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_0[i](x + y)
+
+      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+      
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class MultiHeadAttention(nn.Module):
+  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
+    super().__init__()
+    assert channels % n_heads == 0
+
+    self.channels = channels
+    self.out_channels = out_channels
+    self.n_heads = n_heads
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+    self.heads_share = heads_share
+    self.block_length = block_length
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+    self.attn = None
+
+    self.k_channels = channels // n_heads
+    self.conv_q = nn.Conv1d(channels, channels, 1)
+    self.conv_k = nn.Conv1d(channels, channels, 1)
+    self.conv_v = nn.Conv1d(channels, channels, 1)
+    self.conv_o = nn.Conv1d(channels, out_channels, 1)
+    self.drop = nn.Dropout(p_dropout)
+
+    if window_size is not None:
+      n_heads_rel = 1 if heads_share else n_heads
+      rel_stddev = self.k_channels**-0.5
+      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+    nn.init.xavier_uniform_(self.conv_q.weight)
+    nn.init.xavier_uniform_(self.conv_k.weight)
+    nn.init.xavier_uniform_(self.conv_v.weight)
+    if proximal_init:
+      with torch.no_grad():
+        self.conv_k.weight.copy_(self.conv_q.weight)
+        self.conv_k.bias.copy_(self.conv_q.bias)
+      
+  def forward(self, x, c, attn_mask=None):
+    q = self.conv_q(x)
+    k = self.conv_k(c)
+    v = self.conv_v(c)
+    
+    x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+    x = self.conv_o(x)
+    return x
+
+  def attention(self, query, key, value, mask=None):
+    # reshape [b, d, t] -> [b, n_h, t, d_k]
+    b, d, t_s, t_t = (*key.size(), query.size(2))
+    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+    if self.window_size is not None:
+      assert t_s == t_t, "Relative attention is only available for self-attention."
+      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
+      scores_local = self._relative_position_to_absolute_position(rel_logits)
+      scores = scores + scores_local
+    if self.proximal_bias:
+      assert t_s == t_t, "Proximal bias is only available for self-attention."
+      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+    if mask is not None:
+      scores = scores.masked_fill(mask == 0, -1e4)
+      if self.block_length is not None:
+        assert t_s == t_t, "Local attention is only available for self-attention."
+        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+        scores = scores.masked_fill(block_mask == 0, -1e4)
+    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
+    p_attn = self.drop(p_attn)
+    output = torch.matmul(p_attn, value)
+    if self.window_size is not None:
+      relative_weights = self._absolute_position_to_relative_position(p_attn)
+      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
+    return output, p_attn
+
+  def _matmul_with_relative_values(self, x, y):
+    """
+    x: [b, h, l, m]
+    y: [h or 1, m, d]
+    ret: [b, h, l, d]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0))
+    return ret
+
+  def _matmul_with_relative_keys(self, x, y):
+    """
+    x: [b, h, l, d]
+    y: [h or 1, m, d]
+    ret: [b, h, l, m]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+    return ret
+
+  def _get_relative_embeddings(self, relative_embeddings, length):
+    max_relative_position = 2 * self.window_size + 1
+    # Pad first before slice to avoid using cond ops.
+    pad_length = max(length - (self.window_size + 1), 0)
+    slice_start_position = max((self.window_size + 1) - length, 0)
+    slice_end_position = slice_start_position + 2 * length - 1
+    if pad_length > 0:
+      padded_relative_embeddings = F.pad(
+          relative_embeddings,
+          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+    else:
+      padded_relative_embeddings = relative_embeddings
+    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
+    return used_relative_embeddings
+
+  def _relative_position_to_absolute_position(self, x):
+    """
+    x: [b, h, l, 2*l-1]
+    ret: [b, h, l, l]
+    """
+    batch, heads, length, _ = x.size()
+    # Concat columns of pad to shift from relative to absolute indexing.
+    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
+
+    # Concat extra elements so to add up to shape (len+1, 2*len-1).
+    x_flat = x.view([batch, heads, length * 2 * length])
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
+
+    # Reshape and slice out the padded elements.
+    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
+    return x_final
+
+  def _absolute_position_to_relative_position(self, x):
+    """
+    x: [b, h, l, l]
+    ret: [b, h, l, 2*l-1]
+    """
+    batch, heads, length, _ = x.size()
+    # padd along column
+    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
+    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
+    # add 0's in the beginning that will skew the elements after reshape
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
+    return x_final
+
+  def _attention_bias_proximal(self, length):
+    """Bias for self-attention to encourage attention to close positions.
+    Args:
+      length: an integer scalar.
+    Returns:
+      a Tensor with shape [1, 1, length, length]
+    """
+    r = torch.arange(length, dtype=torch.float32)
+    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.activation = activation
+    self.causal = causal
+
+    if causal:
+      self.padding = self._causal_padding
+    else:
+      self.padding = self._same_padding
+
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+    self.drop = nn.Dropout(p_dropout)
+
+  def forward(self, x, x_mask):
+    x = self.conv_1(self.padding(x * x_mask))
+    if self.activation == "gelu":
+      x = x * torch.sigmoid(1.702 * x)
+    else:
+      x = torch.relu(x)
+    x = self.drop(x)
+    x = self.conv_2(self.padding(x * x_mask))
+    return x * x_mask
+  
+  def _causal_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = self.kernel_size - 1
+    pad_r = 0
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x
+
+  def _same_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = (self.kernel_size - 1) // 2
+    pad_r = self.kernel_size // 2
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x

commons.py

@@ -0,0 +1,164 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+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):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l 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. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * 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):
+  ret = torch.zeros_like(x[:, :, :segment_size])
+  for i in range(x.size(0)):
+    idx_str = ids_str[i]
+    idx_end = idx_str + segment_size
+    try:
+      ret[i] = x[i, :, idx_str:idx_end]
+    except RuntimeError:
+      print("?")
+  return ret
+
+
+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 = x_lengths - segment_size + 1
+  ids_str = (torch.rand([b]).to(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):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l 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]
+  """
+  device = duration.device
+  
+  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. / norm_type)
+  return total_norm

configs/amitaro_jp_base.json

@@ -0,0 +1,56 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 1,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 8192,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"./final_annotation_train.txt",
+    "validation_files":"./final_annotation_val.txt",
+    "text_cleaners":["japanese_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 1,
+    "cleaned_text": true
+  },
+  "model": {
+    "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],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
+  "speakers": {"amitaro":0
+  },
+  "symbols": ["_", ",", ".", "!", "?", "-", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u2193", "\u2191", " "]
+}

data_utils.py

@@ -0,0 +1,276 @@
+import time
+import os
+import random
+import numpy as np
+import torch
+import torch.utils.data
+import torchaudio
+
+import commons
+from mel_processing import spectrogram_torch
+from utils import load_wav_to_torch, load_filepaths_and_text
+from text import text_to_sequence, cleaned_text_to_sequence
+"""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, symbols):
+        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
+        self.text_cleaners = hparams.text_cleaners
+        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.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", 190)
+        self.symbols = symbols
+
+        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 = []
+        for audiopath, sid, text in self.audiopaths_sid_text:
+            # audiopath = "./user_voice/" + audiopath
+
+            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
+                audiopaths_sid_text_new.append([audiopath, sid, text])
+                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
+        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, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
+        text = self.get_text(text)
+        spec, wav = self.get_audio(audiopath)
+        sid = self.get_sid(sid)
+        return (text, spec, wav, sid)
+
+    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(
+        #         sampling_rate, self.sampling_rate))
+        # audio_norm = audio / self.max_wav_value if audio.max() > 10 else audio
+        # audio_norm = audio_norm.unsqueeze(0)
+        audio_norm, sampling_rate = torchaudio.load(filename, frame_offset=0, num_frames=-1, normalize=True, channels_first=True)
+        # spec_filename = filename.replace(".wav", ".spec.pt")
+        # if os.path.exists(spec_filename):
+        #     spec = torch.load(spec_filename)
+        # else:
+        #     try:
+        spec = spectrogram_torch(audio_norm, self.filter_length,
+                                 self.sampling_rate, self.hop_length, self.win_length,
+                                 center=False)
+        spec = spec.squeeze(0)
+            # except NotImplementedError:
+            #     print("?")
+            # spec = torch.squeeze(spec, 0)
+            # torch.save(spec, spec_filename)
+        return spec, audio_norm
+
+    def get_text(self, text):
+        if self.cleaned_text:
+            text_norm = cleaned_text_to_sequence(text, self.symbols)
+        else:
+            text_norm = text_to_sequence(text, self.text_cleaners)
+        if self.add_blank:
+            text_norm = commons.intersperse(text_norm, 0)
+        text_norm = torch.LongTensor(text_norm)
+        return text_norm
+
+    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):
+        self.return_ids = return_ids
+
+    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)
+        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_()
+        spec_padded.zero_()
+        wav_padded.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]
+
+        if self.return_ids:
+            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
+        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
+
+
+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()
+        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:
+            print('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)
+            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

dict/COPYING

@@ -0,0 +1,100 @@
+Copyright (c) 2009, Nara Institute of Science and Technology, Japan.
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+Redistributions of source code must retain the above copyright notice,
+this list of conditions and the following disclaimer.
+Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+Neither the name of the Nara Institute of Science and Technology
+(NAIST) nor the names of its contributors may be used to endorse or
+promote products derived from this software without specific prior
+written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Copyright (c) 2011-2017, The UniDic Consortium
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+ * Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+ * Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the
+   distribution.
+
+ * Neither the name of the UniDic Consortium nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+/* ----------------------------------------------------------------- */
+/*           The Japanese TTS System "Open JTalk"                    */
+/*           developed by HTS Working Group                          */
+/*           http://open-jtalk.sourceforge.net/                      */
+/* ----------------------------------------------------------------- */
+/*                                                                   */
+/*  Copyright (c) 2008-2016  Nagoya Institute of Technology          */
+/*                           Department of Computer Science          */
+/*                                                                   */
+/* All rights reserved.                                              */
+/*                                                                   */
+/* Redistribution and use in source and binary forms, with or        */
+/* without modification, are permitted provided that the following   */
+/* conditions are met:                                               */
+/*                                                                   */
+/* - Redistributions of source code must retain the above copyright  */
+/*   notice, this list of conditions and the following disclaimer.   */
+/* - Redistributions in binary form must reproduce the above         */
+/*   copyright notice, this list of conditions and the following     */
+/*   disclaimer in the documentation and/or other materials provided */
+/*   with the distribution.                                          */
+/* - Neither the name of the HTS working group nor the names of its  */
+/*   contributors may be used to endorse or promote products derived */
+/*   from this software without specific prior written permission.   */
+/*                                                                   */
+/* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND            */
+/* CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,       */
+/* INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF          */
+/* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE          */
+/* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS */
+/* BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,          */
+/* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED   */
+/* TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,     */
+/* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON */
+/* ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,   */
+/* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY    */
+/* OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE           */
+/* POSSIBILITY OF SUCH DAMAGE.                                       */
+/* ----------------------------------------------------------------- */

dict/char.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:888ee94c5a8a7a26d24ab3f1b7155441351954fd51ea06b4a2f78bd742492b2f
+size 262496

dict/left-id.def

@@ -0,0 +1,1377 @@
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+69 形容詞,接尾,*,*,形容詞・アウオ段,体言接続,臭い
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+157 形容詞,非自立,*,*,形容詞・イ段,仮定形,欲しい
+158 形容詞,非自立,*,*,形容詞・イ段,仮定縮約１,欲しい
+159 形容詞,非自立,*,*,形容詞・イ段,仮定縮約２,欲しい
+160 形容詞,非自立,*,*,形容詞・イ段,基本形,欲しい
+161 形容詞,非自立,*,*,形容詞・イ段,体言接続,欲しい
+162 形容詞,非自立,*,*,形容詞・イ段,文語基本形,欲しい
+163 形容詞,非自立,*,*,形容詞・イ段,未然ウ接続,欲しい
+164 形容詞,非自立,*,*,形容詞・イ段,未然ヌ接続,欲しい
+165 形容詞,非自立,*,*,形容詞・イ段,命令ｅ,欲しい
+166 形容詞,非自立,*,*,形容詞・イ段,連用ゴザイ接続,欲しい
+167 形容詞,非自立,*,*,形容詞・イ段,連用タ接続,欲しい
+168 形容詞,非自立,*,*,形容詞・イ段,連用テ接続,欲しい
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+183 助詞,格助詞,一般,*,*,*,ヘ
+184 助詞,格助詞,一般,*,*,*,ヲ
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+187 助詞,格助詞,引用,*,*,*,と
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+190 助詞,格助詞,連語,*,*,*,って
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+193 助詞,格助詞,連語,*,*,*,て
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+200 助詞,格助詞,連語,*,*,*,とかいう
+201 助詞,格助詞,連語,*,*,*,とかいふ
+202 助詞,格助詞,連語,*,*,*,として
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+204 助詞,格助詞,連語,*,*,*,としまして
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+207 助詞,格助詞,連語,*,*,*,にあたって
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+219 助詞,格助詞,連語,*,*,*,にたいしまして
+220 助詞,格助詞,連語,*,*,*,にたいします
+221 助詞,格助詞,連語,*,*,*,にたいする
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+264 助詞,格助詞,連語,*,*,*,に当たります
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+267 助詞,格助詞,連語,*,*,*,をめぐりまして
+268 助詞,格助詞,連語,*,*,*,をめぐります
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+272 助詞,格助詞,連語,*,*,*,を以て
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+436 助動詞,*,*,*,五段・ラ行特殊,仮定形,御座る
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+438 助動詞,*,*,*,五段・ラ行特殊,仮定縮約１,御座る
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+441 助動詞,*,*,*,五段・ラ行特殊,未然ウ接続,ござる
+442 助動詞,*,*,*,五段・ラ行特殊,未然ウ接続,御座る
+443 助動詞,*,*,*,五段・ラ行特殊,未然形,ござる
+444 助動詞,*,*,*,五段・ラ行特殊,未然形,御座る
+445 助動詞,*,*,*,五段・ラ行特殊,未然特殊,ござる
+446 助動詞,*,*,*,五段・ラ行特殊,未然特殊,御座る
+447 助動詞,*,*,*,五段・ラ行特殊,命令ｅ,ござる
+448 助動詞,*,*,*,五段・ラ行特殊,命令ｅ,御座る
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+501 助動詞,*,*,*,特殊・ナイ,未然ウ接続,無い
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+523 助動詞,*,*,*,特殊・マス,命令ｅ,やす
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+556 助動詞,*,*,*,文語・ナリ,体言接続,たり
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+597 動詞,自立,*,*,カ変・来ル,仮定縮約１,*
+598 動詞,自立,*,*,カ変・来ル,基本形,*
+599 動詞,自立,*,*,カ変・来ル,体言接続特殊,*
+600 動詞,自立,*,*,カ変・来ル,体言接続特殊２,*
+601 動詞,自立,*,*,カ変・来ル,未然ウ接続,*
+602 動詞,自立,*,*,カ変・来ル,未然形,*
+603 動詞,自立,*,*,カ変・来ル,命令ｉ,*
+604 動詞,自立,*,*,カ変・来ル,命令ｙｏ,*
+605 動詞,自立,*,*,カ変・来ル,連用形,*
+606 動詞,自立,*,*,サ変・−スル,仮定形,*
+607 動詞,自立,*,*,サ変・−スル,仮定縮約１,*
+608 動詞,自立,*,*,サ変・−スル,基本形,*
+609 動詞,自立,*,*,サ変・−スル,文語基本形,*
+610 動詞,自立,*,*,サ変・−スル,未然ウ接続,*
+611 動詞,自立,*,*,サ変・−スル,未然レル接続,*
+612 動詞,自立,*,*,サ変・−スル,未然形,*
+613 動詞,自立,*,*,サ変・−スル,命令ｒｏ,*
+614 動詞,自立,*,*,サ変・−スル,命令ｙｏ,*
+615 動詞,自立,*,*,サ変・−ズル,仮定形,*
+616 動詞,自立,*,*,サ変・−ズル,仮定縮約１,*
+617 動詞,自立,*,*,サ変・−ズル,基本形,*
+618 動詞,自立,*,*,サ変・−ズル,文語基本形,*
+619 動詞,自立,*,*,サ変・−ズル,未然ウ接続,*
+620 動詞,自立,*,*,サ変・−ズル,未然形,*
+621 動詞,自立,*,*,サ変・−ズル,命令ｙｏ,*
+622 動詞,自立,*,*,サ変・スル,仮定形,する
+623 動詞,自立,*,*,サ変・スル,仮定縮約１,する
+624 動詞,自立,*,*,サ変・スル,基本形,する
+625 動詞,自立,*,*,サ変・スル,体言接続特殊,する
+626 動詞,自立,*,*,サ変・スル,体言接続特殊２,する
+627 動詞,自立,*,*,サ変・スル,文語基本形,する
+628 動詞,自立,*,*,サ変・スル,未然ウ接続,する
+629 動詞,自立,*,*,サ変・スル,未然ヌ接続,する
+630 動詞,自立,*,*,サ変・スル,未然レル接続,する
+631 動詞,自立,*,*,サ変・スル,未然形,する
+632 動詞,自立,*,*,サ変・スル,命令ｉ,する
+633 動詞,自立,*,*,サ変・スル,命令ｒｏ,する
+634 動詞,自立,*,*,サ変・スル,命令ｙｏ,する
+635 動詞,自立,*,*,サ変・スル,連用形,する
+636 動詞,自立,*,*,ラ変,仮定形,*
+637 動詞,自立,*,*,ラ変,基本形,*
+638 動詞,自立,*,*,ラ変,体言接続,*
+639 動詞,自立,*,*,ラ変,未然形,*
+640 動詞,自立,*,*,ラ変,命令ｅ,*
+641 動詞,自立,*,*,ラ変,連用形,*
+642 動詞,自立,*,*,一段,*,*
+643 動詞,自立,*,*,一段,仮定形,*
+644 動詞,自立,*,*,一段,仮定縮約１,*
+645 動詞,自立,*,*,一段,基本形,*
+646 動詞,自立,*,*,一段,基本形-促音便,*
+647 動詞,自立,*,*,一段,体言接続特殊,*
+648 動詞,自立,*,*,一段,未然ウ接続,*
+649 動詞,自立,*,*,一段,未然形,*
+650 動詞,自立,*,*,一段,命令ｒｏ,*
+651 動詞,自立,*,*,一段,命令ｙｏ,*
+652 動詞,自立,*,*,一段,連用形,*
+653 動詞,自立,*,*,一段・クレル,仮定形,*
+654 動詞,自立,*,*,一段・クレル,仮定縮約１,*
+655 動詞,自立,*,*,一段・クレル,基本形,*
+656 動詞,自立,*,*,一段・クレル,未然ウ接続,*
+657 動詞,自立,*,*,一段・クレル,未然形,*
+658 動詞,自立,*,*,一段・クレル,未然特殊,*
+659 動詞,自立,*,*,一段・クレル,命令ｅ,*
+660 動詞,自立,*,*,一段・クレル,命令ｒｏ,*
+661 動詞,自立,*,*,一段・クレル,命令ｙｏ,*
+662 動詞,自立,*,*,一段・クレル,連用形,*
+663 動詞,自立,*,*,一段・得ル,仮定形,*
+664 動詞,自立,*,*,一段・得ル,基本形,*
+665 動詞,自立,*,*,下二・カ行,仮定形,*
+666 動詞,自立,*,*,下二・カ行,基本形,*
+667 動詞,自立,*,*,下二・カ行,体言接続,*
+668 動詞,自立,*,*,下二・カ行,未然形,*
+669 動詞,自立,*,*,下二・カ行,命令ｙｏ,*
+670 動詞,自立,*,*,下二・カ行,連用形,*
+671 動詞,自立,*,*,下二・ガ行,仮定形,*
+672 動詞,自立,*,*,下二・ガ行,基本形,*
+673 動詞,自立,*,*,下二・ガ行,体言接続,*
+674 動詞,自立,*,*,下二・ガ行,未然形,*
+675 動詞,自立,*,*,下二・ガ行,命令ｙｏ,*
+676 動詞,自立,*,*,下二・ガ行,連用形,*
+677 動詞,自立,*,*,下二・ダ行,仮定形,*
+678 動詞,自立,*,*,下二・ダ行,基本形,*
+679 動詞,自立,*,*,下二・ダ行,体言接続,*
+680 動詞,自立,*,*,下二・ダ行,未然形,*
+681 動詞,自立,*,*,下二・ダ行,命令ｙｏ,*
+682 動詞,自立,*,*,下二・ダ行,連用形,*
+683 動詞,自立,*,*,下二・ハ行,仮定形,*
+684 動詞,自立,*,*,下二・ハ行,基本形,*
+685 動詞,自立,*,*,下二・ハ行,体言接続,*
+686 動詞,自立,*,*,下二・ハ行,未然形,*
+687 動詞,自立,*,*,下二・ハ行,命令ｙｏ,*
+688 動詞,自立,*,*,下二・ハ行,連用形,*
+689 動詞,自立,*,*,下二・マ行,仮定形,*
+690 動詞,自立,*,*,下二・マ行,基本形,*
+691 動詞,自立,*,*,下二・マ行,体言接続,*
+692 動詞,自立,*,*,下二・マ行,未然形,*
+693 動詞,自立,*,*,下二・マ行,命令ｙｏ,*
+694 動詞,自立,*,*,下二・マ行,連用形,*
+695 動詞,自立,*,*,下二・得,仮定形,*
+696 動詞,自立,*,*,下二・得,基本形,*
+697 動詞,自立,*,*,下二・得,体言接続,*
+698 動詞,自立,*,*,下二・得,未然ウ接続,*
+699 動詞,自立,*,*,下二・得,未然形,*
+700 動詞,自立,*,*,下二・得,命令ｙｏ,*
+701 動詞,自立,*,*,下二・得,連用形,*
+702 動詞,自立,*,*,五段・カ行イ音便,*,*
+703 動詞,自立,*,*,五段・カ行イ音便,仮定形,*
+704 動詞,自立,*,*,五段・カ行イ音便,仮定形,行く
+705 動詞,自立,*,*,五段・カ行イ音便,仮定縮約１,*
+706 動詞,自立,*,*,五段・カ行イ音便,仮定縮約１,行く
+707 動詞,自立,*,*,五段・カ行イ音便,基本形,*
+708 動詞,自立,*,*,五段・カ行イ音便,基本形,行く
+709 動詞,自立,*,*,五段・カ行イ音便,未然ウ接続,*
+710 動詞,自立,*,*,五段・カ行イ音便,未然ウ接続,行く
+711 動詞,自立,*,*,五段・カ行イ音便,未然形,*
+712 動詞,自立,*,*,五段・カ行イ音便,未然形,行く
+713 動詞,自立,*,*,五段・カ行イ音便,命令ｅ,*
+714 動詞,自立,*,*,五段・カ行イ音便,命令ｅ,行く
+715 動詞,自立,*,*,五段・カ行イ音便,連用タ接続,*
+716 動詞,自立,*,*,五段・カ行イ音便,連用タ接続,行く
+717 動詞,自立,*,*,五段・カ行イ音便,連用形,*
+718 動詞,自立,*,*,五段・カ行イ音便,連用形,行く
+719 動詞,自立,*,*,五段・カ行促音便,仮定形,*
+720 動詞,自立,*,*,五段・カ行促音便,仮定形,行く
+721 動詞,自立,*,*,五段・カ行促音便,仮定縮約１,*
+722 動詞,自立,*,*,五段・カ行促音便,仮定縮約１,行く
+723 動詞,自立,*,*,五段・カ行促音便,基本形,*
+724 動詞,自立,*,*,五段・カ行促音便,基本形,行く
+725 動詞,自立,*,*,五段・カ行促音便,未然ウ接続,*
+726 動詞,自立,*,*,五段・カ行促音便,未然ウ接続,行く
+727 動詞,自立,*,*,五段・カ行促音便,未然形,*
+728 動詞,自立,*,*,五段・カ行促音便,未然形,行く
+729 動詞,自立,*,*,五段・カ行促音便,命令ｅ,*
+730 動詞,自立,*,*,五段・カ行促音便,命令ｅ,行く
+731 動詞,自立,*,*,五段・カ行促音便,連用タ接続,*
+732 動詞,自立,*,*,五段・カ行促音便,連用タ接続,行く
+733 動詞,自立,*,*,五段・カ行促音便,連用形,*
+734 動詞,自立,*,*,五段・カ行促音便,連用形,行く
+735 動詞,自立,*,*,五段・カ行促音便ユク,仮定形,*
+736 動詞,自立,*,*,五段・カ行促音便ユク,仮定形,行く
+737 動詞,自立,*,*,五段・カ行促音便ユク,仮定縮約１,*
+738 動詞,自立,*,*,五段・カ行促音便ユク,仮定縮約１,行く
+739 動詞,自立,*,*,五段・カ行促音便ユク,基本形,*
+740 動詞,自立,*,*,五段・カ行促音便ユク,基本形,行く
+741 動詞,自立,*,*,五段・カ行促音便ユク,未然ウ接続,*
+742 動詞,自立,*,*,五段・カ行促音便ユク,未然ウ接続,行く
+743 動詞,自立,*,*,五段・カ行促音便ユク,未然形,*
+744 動詞,自立,*,*,五段・カ行促音便ユク,未然形,行く
+745 動詞,自立,*,*,五段・カ行促音便ユク,命令ｅ,*
+746 動詞,自立,*,*,五段・カ行促音便ユク,命令ｅ,行く
+747 動詞,自立,*,*,五段・カ行促音便ユク,連用形,*
+748 動詞,自立,*,*,五段・カ行促音便ユク,連用形,行く
+749 動詞,自立,*,*,五段・ガ行,*,*
+750 動詞,自立,*,*,五段・ガ行,仮定形,*
+751 動詞,自立,*,*,五段・ガ行,仮定縮約１,*
+752 動詞,自立,*,*,五段・ガ行,基本形,*
+753 動詞,自立,*,*,五段・ガ行,未然ウ接続,*
+754 動詞,自立,*,*,五段・ガ行,未然形,*
+755 動詞,自立,*,*,五段・ガ行,命令ｅ,*
+756 動詞,自立,*,*,五段・ガ行,連用タ接続,*
+757 動詞,自立,*,*,五段・ガ行,連用形,*
+758 動詞,自立,*,*,五段・サ行,*,*
+759 動詞,自立,*,*,五段・サ行,仮定形,*
+760 動詞,自立,*,*,五段・サ行,仮定縮約１,*
+761 動詞,自立,*,*,五段・サ行,基本形,*
+762 動詞,自立,*,*,五段・サ行,未然ウ接続,*
+763 動詞,自立,*,*,五段・サ行,未然形,*
+764 動詞,自立,*,*,五段・サ行,命令ｅ,*
+765 動詞,自立,*,*,五段・サ行,連用形,*
+766 動詞,自立,*,*,五段・タ行,*,*
+767 動詞,自立,*,*,五段・タ行,仮定形,*
+768 動詞,自立,*,*,五段・タ行,仮定縮約１,*
+769 動詞,自立,*,*,五段・タ行,基本形,*
+770 動詞,自立,*,*,五段・タ行,未然ウ接続,*
+771 動詞,自立,*,*,五段・タ行,未然形,*
+772 動詞,自立,*,*,五段・タ行,命令ｅ,*
+773 動詞,自立,*,*,五段・タ行,連用タ接続,*
+774 動詞,自立,*,*,五段・タ行,連用形,*
+775 動詞,自立,*,*,五段・ナ行,仮定形,*
+776 動詞,自立,*,*,五段・ナ行,仮定縮約１,*
+777 動詞,自立,*,*,五段・ナ行,基本形,*
+778 動詞,自立,*,*,五段・ナ行,未然ウ接続,*
+779 動詞,自立,*,*,五段・ナ行,未然形,*
+780 動詞,自立,*,*,五段・ナ行,命令ｅ,*
+781 動詞,自立,*,*,五段・ナ行,連用タ接続,*
+782 動詞,自立,*,*,五段・ナ行,連用形,*
+783 動詞,自立,*,*,五段・バ行,*,*
+784 動詞,自立,*,*,五段・バ行,仮定形,*
+785 動詞,自立,*,*,五段・バ行,仮定縮約１,*
+786 動詞,自立,*,*,五段・バ行,基本形,*
+787 動詞,自立,*,*,五段・バ行,未然ウ接続,*
+788 動詞,自立,*,*,五段・バ行,未然形,*
+789 動詞,自立,*,*,五段・バ行,命令ｅ,*
+790 動詞,自立,*,*,五段・バ行,連用タ接続,*
+791 動詞,自立,*,*,五段・バ行,連用形,*
+792 動詞,自立,*,*,五段・マ行,*,*
+793 動詞,自立,*,*,五段・マ行,仮定形,*
+794 動詞,自立,*,*,五段・マ行,仮定縮約１,*
+795 動詞,自立,*,*,五段・マ行,基本形,*
+796 動詞,自立,*,*,五段・マ行,未然ウ接続,*
+797 動詞,自立,*,*,五段・マ行,未然形,*
+798 動詞,自立,*,*,五段・マ行,命令ｅ,*
+799 動詞,自立,*,*,五段・マ行,連用タ接続,*
+800 動詞,自立,*,*,五段・マ行,連用形,*
+801 動詞,自立,*,*,五段・ラ行,*,*
+802 動詞,自立,*,*,五段・ラ行,*,する
+803 動詞,自立,*,*,五段・ラ行,仮定形,*
+804 動詞,自立,*,*,五段・ラ行,仮定形,する
+805 動詞,自立,*,*,五段・ラ行,仮定縮約１,*
+806 動詞,自立,*,*,五段・ラ行,仮定縮約１,する
+807 動詞,自立,*,*,五段・ラ行,基本形,*
+808 動詞,自立,*,*,五段・ラ行,基本形,する
+809 動詞,自立,*,*,五段・ラ行,体言接続特殊,*
+810 動詞,自立,*,*,五段・ラ行,体言接続特殊,する
+811 動詞,自立,*,*,五段・ラ行,体言接続特殊２,*
+812 動詞,自立,*,*,五段・ラ行,体言接続特殊２,する
+813 動詞,自立,*,*,五段・ラ行,未然ウ接続,*
+814 動詞,自立,*,*,五段・ラ行,未然ウ接続,する
+815 動詞,自立,*,*,五段・ラ行,未然形,*
+816 動詞,自立,*,*,五段・ラ行,未然形,する
+817 動詞,自立,*,*,五段・ラ行,未然特殊,*
+818 動詞,自立,*,*,五段・ラ行,未然特殊,する
+819 動詞,自立,*,*,五段・ラ行,命令ｅ,*
+820 動詞,自立,*,*,五段・ラ行,命令ｅ,する
+821 動詞,自立,*,*,五段・ラ行,連用タ接続,*
+822 動詞,自立,*,*,五段・ラ行,連用タ接続,する
+823 動詞,自立,*,*,五段・ラ行,連用形,*
+824 動詞,自立,*,*,五段・ラ行,連用形,する
+825 動詞,自立,*,*,五段・ラ行特殊,仮定形,*
+826 動詞,自立,*,*,五段・ラ行特殊,仮定縮約１,*
+827 動詞,自立,*,*,五段・ラ行特殊,基本形,*
+828 動詞,自立,*,*,五段・ラ行特殊,未然ウ接続,*
+829 動詞,自立,*,*,五段・ラ行特殊,未然形,*
+830 動詞,自立,*,*,五段・ラ行特殊,未然特殊,*
+831 動詞,自立,*,*,五段・ラ行特殊,命令ｅ,*
+832 動詞,自立,*,*,五段・ラ行特殊,命令ｉ,*
+833 動詞,自立,*,*,五段・ラ行特殊,連用タ接続,*
+834 動詞,自立,*,*,五段・ラ行特殊,連用形,*
+835 動詞,自立,*,*,五段・ワ行ウ音便,*,*
+836 動詞,自立,*,*,五段・ワ行ウ音便,仮定形,*
+837 動詞,自立,*,*,五段・ワ行ウ音便,仮定形,言う
+838 動詞,自立,*,*,五段・ワ行ウ音便,基本形,*
+839 動詞,自立,*,*,五段・ワ行ウ音便,基本形,言う
+840 動詞,自立,*,*,五段・ワ行ウ音便,未��ウ接続,*
+841 動詞,自立,*,*,五段・ワ行ウ音便,未然ウ接続,言う
+842 動詞,自立,*,*,五段・ワ行ウ音便,未然形,*
+843 動詞,自立,*,*,五段・ワ行ウ音便,未然形,言う
+844 動詞,自立,*,*,五段・ワ行ウ音便,命令ｅ,*
+845 動詞,自立,*,*,五段・ワ行ウ音便,命令ｅ,言う
+846 動詞,自立,*,*,五段・ワ行ウ音便,連用タ接続,*
+847 動詞,自立,*,*,五段・ワ行ウ音便,連用タ接続,言う
+848 動詞,自立,*,*,五段・ワ行ウ音便,連用形,*
+849 動詞,自立,*,*,五段・ワ行ウ音便,連用形,言う
+850 動詞,自立,*,*,五段・ワ行促音便,*,*
+851 動詞,自立,*,*,五段・ワ行促音便,仮定形,*
+852 動詞,自立,*,*,五段・ワ行促音便,仮定形,言う
+853 動詞,自立,*,*,五段・ワ行促音便,仮定形,行う
+854 動詞,自立,*,*,五段・ワ行促音便,基本形,*
+855 動詞,自立,*,*,五段・ワ行促音便,基本形,言う
+856 動詞,自立,*,*,五段・ワ行促音便,基本形,行う
+857 動詞,自立,*,*,五段・ワ行促音便,未然ウ接続,*
+858 動詞,自立,*,*,五段・ワ行促音便,未然ウ接続,言う
+859 動詞,自立,*,*,五段・ワ行促音便,未然ウ接続,行う
+860 動詞,自立,*,*,五段・ワ行促音便,未然形,*
+861 動詞,自立,*,*,五段・ワ行促音便,未然形,言う
+862 動詞,自立,*,*,五段・ワ行促音便,未然形,行う
+863 動詞,自立,*,*,五段・ワ行促音便,命令ｅ,*
+864 動詞,自立,*,*,五段・ワ行促音便,命令ｅ,言う
+865 動詞,自立,*,*,五段・ワ行促音便,命令ｅ,行う
+866 動詞,自立,*,*,五段・ワ行促音便,連用タ接続,*
+867 動詞,自立,*,*,五段・ワ行促音便,連用タ接続,言う
+868 動詞,自立,*,*,五段・ワ行促音便,連用タ接続,行う
+869 動詞,自立,*,*,五段・ワ行促音便,連用形,*
+870 動詞,自立,*,*,五段・ワ行促音便,連用形,言う
+871 動詞,自立,*,*,五段・ワ行促音便,連用形,行う
+872 動詞,自立,*,*,四段・サ行,仮定形,*
+873 動詞,自立,*,*,四段・サ行,基本形,*
+874 動詞,自立,*,*,四段・サ行,未然形,*
+875 動詞,自立,*,*,四段・サ行,命令ｅ,*
+876 動詞,自立,*,*,四段・サ行,連用形,*
+877 動詞,自立,*,*,四段・タ行,仮定形,*
+878 動詞,自立,*,*,四段・タ行,基本形,*
+879 動詞,自立,*,*,四段・タ行,未然形,*
+880 動詞,自立,*,*,四段・タ行,命令ｅ,*
+881 動詞,自立,*,*,四段・タ行,連用形,*
+882 動詞,自立,*,*,四段・ハ行,仮定形,*
+883 動詞,自立,*,*,四段・ハ行,基本形,*
+884 動詞,自立,*,*,四段・ハ行,未然形,*
+885 動詞,自立,*,*,四段・ハ行,命令ｅ,*
+886 動詞,自立,*,*,四段・ハ行,連用形,*
+887 動詞,自立,*,*,四段・バ行,仮定形,*
+888 動詞,自立,*,*,四段・バ行,基本形,*
+889 動詞,自立,*,*,四段・バ行,未然形,*
+890 動詞,自立,*,*,四段・バ行,命令ｅ,*
+891 動詞,自立,*,*,四段・バ行,連用形,*
+892 動詞,自立,*,*,上二・ダ行,仮定形,*
+893 動詞,自立,*,*,上二・ダ行,基本形,*
+894 動詞,自立,*,*,上二・ダ行,現代基本形,*
+895 動詞,自立,*,*,上二・ダ行,体言接続,*
+896 動詞,自立,*,*,上二・ダ行,未然形,*
+897 動詞,自立,*,*,上二・ダ行,命令ｙｏ,*
+898 動詞,自立,*,*,上二・ダ行,連用形,*
+899 動詞,自立,*,*,上二・ハ行,仮定形,*
+900 動詞,自立,*,*,上二・ハ行,基本形,*
+901 動詞,自立,*,*,上二・ハ行,体言接続,*
+902 動詞,自立,*,*,上二・ハ行,未然形,*
+903 動詞,自立,*,*,上二・ハ行,命令ｙｏ,*
+904 動詞,自立,*,*,上二・ハ行,連用形,*
+905 動詞,接尾,*,*,一段,仮定形,*
+906 動詞,接尾,*,*,一段,仮定縮約１,*
+907 動詞,接尾,*,*,一段,基本形,*
+908 動詞,接尾,*,*,一段,基本形-促音便,*
+909 動詞,接尾,*,*,一段,体言接続特殊,*
+910 動詞,接尾,*,*,一段,未然ウ接続,*
+911 動詞,接尾,*,*,一段,未然形,*
+912 動詞,接尾,*,*,一段,命令ｒｏ,*
+913 動詞,接尾,*,*,一段,命令ｙｏ,*
+914 動詞,接尾,*,*,一段,連用形,*
+915 動詞,接尾,*,*,五段・サ行,仮定形,*
+916 動詞,接尾,*,*,五段・サ行,仮定縮約１,*
+917 動詞,接尾,*,*,五段・サ行,基本形,*
+918 動詞,接尾,*,*,五段・サ行,未然ウ接続,*
+919 動詞,接尾,*,*,五段・サ行,未然形,*
+920 動詞,接尾,*,*,五段・サ行,命令ｅ,*
+921 動詞,接尾,*,*,五段・サ行,連用形,*
+922 動詞,接尾,*,*,五段・ラ行,仮定形,*
+923 動詞,接尾,*,*,五段・ラ行,仮定縮約１,*
+924 動詞,接尾,*,*,五段・ラ行,基本形,*
+925 動詞,接尾,*,*,五段・ラ行,体言接続特殊,*
+926 動詞,接尾,*,*,五段・ラ行,体言接続特殊２,*
+927 動詞,接尾,*,*,五段・ラ行,未然ウ接続,*
+928 動詞,接尾,*,*,五段・ラ行,未然形,*
+929 動詞,接尾,*,*,五段・ラ行,未然特殊,*
+930 動詞,接尾,*,*,五段・ラ行,命令ｅ,*
+931 動詞,接尾,*,*,五段・ラ行,連用タ接続,*
+932 動詞,接尾,*,*,五���・ラ行,連用形,*
+933 動詞,非自立,*,*,カ変・クル,仮定形,来る
+934 動詞,非自立,*,*,カ変・クル,仮定縮約１,来る
+935 動詞,非自立,*,*,カ変・クル,基本形,来る
+936 動詞,非自立,*,*,カ変・クル,体言接続特殊,来る
+937 動詞,非自立,*,*,カ変・クル,体言接続特殊２,来る
+938 動詞,非自立,*,*,カ変・クル,未然ウ接続,来る
+939 動詞,非自立,*,*,カ変・クル,未然形,来る
+940 動詞,非自立,*,*,カ変・クル,命令ｉ,来る
+941 動詞,非自立,*,*,カ変・クル,命令ｙｏ,来る
+942 動詞,非自立,*,*,カ変・クル,連用形,来る
+943 動詞,非自立,*,*,カ変・来ル,仮定形,来る
+944 動詞,非自立,*,*,カ変・来ル,仮定縮約１,来る
+945 動詞,非自立,*,*,カ変・来ル,基本形,来る
+946 動詞,非自立,*,*,カ変・来ル,体言接続特殊,来る
+947 動詞,非自立,*,*,カ変・来ル,体言接続特殊２,来る
+948 動詞,非自立,*,*,カ変・来ル,未然ウ接続,来る
+949 動詞,非自立,*,*,カ変・来ル,未然形,来る
+950 動詞,非自立,*,*,カ変・来ル,命令ｉ,来る
+951 動詞,非自立,*,*,カ変・来ル,命令ｙｏ,来る
+952 動詞,非自立,*,*,カ変・来ル,連用形,来る
+953 動詞,非自立,*,*,一段,*,*
+954 動詞,非自立,*,*,一段,仮定形,*
+955 動詞,非自立,*,*,一段,仮定形,る
+956 動詞,非自立,*,*,一段,仮定縮約１,*
+957 動詞,非自立,*,*,一段,仮定縮約１,る
+958 動詞,非自立,*,*,一段,基本形,*
+959 動詞,非自立,*,*,一段,基本形,る
+960 動詞,非自立,*,*,一段,基本形-促音便,*
+961 動詞,非自立,*,*,一段,基本形-促音便,る
+962 動詞,非自立,*,*,一段,体言接続特殊,*
+963 動詞,非自立,*,*,一段,体言接続特殊,る
+964 動詞,非自立,*,*,一段,未然ウ接続,*
+965 動詞,非自立,*,*,一段,未然ウ接続,る
+966 動詞,非自立,*,*,一段,未然形,*
+967 動詞,非自立,*,*,一段,命令ｒｏ,*
+968 動詞,非自立,*,*,一段,命令ｒｏ,る
+969 動詞,非自立,*,*,一段,命令ｙｏ,*
+970 動詞,非自立,*,*,一段,命令ｙｏ,る
+971 動詞,非自立,*,*,一段,連用形,*
+972 動詞,非自立,*,*,一段・クレル,仮定形,くれる
+973 動詞,非自立,*,*,一段・クレル,仮定縮約１,くれる
+974 動詞,非自立,*,*,一段・クレル,基本形,くれる
+975 動詞,非自立,*,*,一段・クレル,未然ウ接続,くれる
+976 動詞,非自立,*,*,一段・クレル,未然形,くれる
+977 動詞,非自立,*,*,一段・クレル,未然特殊,くれる
+978 動詞,非自立,*,*,一段・クレル,命令ｅ,くれる
+979 動詞,非自立,*,*,一段・クレル,命令ｒｏ,くれる
+980 動詞,非自立,*,*,一段・クレル,命令ｙｏ,くれる
+981 動詞,非自立,*,*,一段・クレル,連用形,くれる
+982 動詞,非自立,*,*,一段・得ル,仮定形,*
+983 動詞,非自立,*,*,一段・得ル,基本形,*
+984 動詞,非自立,*,*,五段・カ行イ音便,*,*
+985 動詞,非自立,*,*,五段・カ行イ音便,仮定形,*
+986 動詞,非自立,*,*,五段・カ行イ音便,仮定形,おく
+987 動詞,非自立,*,*,五段・カ行イ音便,仮定形,続く
+988 動詞,非自立,*,*,五段・カ行イ音便,仮定形,抜く
+989 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,*
+990 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,おく
+991 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,続く
+992 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,抜く
+993 動詞,非自立,*,*,五段・カ行イ音便,基本形,*
+994 動詞,非自立,*,*,五段・カ行イ音便,基本形,おく
+995 動詞,非自立,*,*,五段・カ行イ音便,基本形,続く
+996 動詞,非自立,*,*,五段・カ行イ音便,基本形,抜く
+997 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,*
+998 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,おく
+999 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,続く
+1000 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,抜く
+1001 動詞,非自立,*,*,五段・カ行イ音便,未然形,*
+1002 動詞,非自立,*,*,五段・カ行イ音便,未然形,おく
+1003 動詞,非自立,*,*,五段・カ行イ音便,未然形,続く
+1004 動詞,非自立,*,*,五段・カ行イ音便,未然形,抜く
+1005 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,*
+1006 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,おく
+1007 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,続く
+1008 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,抜く
+1009 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,*
+1010 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,おく
+1011 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,続く
+1012 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,抜く
+1013 動詞,非自立,*,*,五段・カ行イ音便,連用形,*
+1014 動詞,非自立,*,*,五段・カ行イ音便,連用形,おく
+1015 動詞,非自立,*,*,五段・カ行イ音便,連用形,続く
+1016 動詞,非自立,*,*,五段・カ行イ音便,連用形,抜く
+1017 動詞,非自立,*,*,五段・カ行促音便,仮定形,*
+1018 動詞,非自立,*,*,五段・カ行促音便,仮定形,いく
+1019 動詞,非自立,*,*,五段・カ行促音便,仮定形,く
+1020 動詞,非自立,*,*,五段・カ行促音便,仮定形,行く
+1021 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,*
+1022 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,いく
+1023 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,く
+1024 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,行く
+1025 動詞,非自立,*,*,五段・カ行促音便,基本形,*
+1026 動詞,非自立,*,*,五段・カ行促音便,基本形,いく
+1027 動詞,非自立,*,*,五段・カ行促音便,基本形,く
+1028 動詞,非自立,*,*,五段・カ行促音便,基本形,行く
+1029 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,*
+1030 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,いく
+1031 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,く
+1032 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,行く
+1033 動詞,非自立,*,*,五段・カ行促音便,未然形,*
+1034 動詞,非自立,*,*,五段・カ行促音便,未然形,いく
+1035 動詞,非自立,*,*,五段・カ行促音便,未然形,く
+1036 動詞,非自立,*,*,五段・カ行促音便,未然形,行く
+1037 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,*
+1038 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,いく
+1039 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,く
+1040 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,行く
+1041 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,*
+1042 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,いく
+1043 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,く
+1044 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,行く
+1045 動詞,非自立,*,*,五段・カ行促音便,連用形,*
+1046 動詞,非自立,*,*,五段・カ行促音便,連用形,いく
+1047 動詞,非自立,*,*,五段・カ行促音便,連用形,く
+1048 動詞,非自立,*,*,五段・カ行促音便,連用形,行く
+1049 動詞,非自立,*,*,五段・カ行促音便ユク,仮定形,ゆく
+1050 動詞,非自立,*,*,五段・カ行促音便ユク,仮定形,行く
+1051 動詞,非自立,*,*,五段・カ行促音便ユク,仮定縮約１,ゆく
+1052 動詞,非自立,*,*,五段・カ行促音便ユク,仮定縮約１,行く
+1053 動詞,非自立,*,*,五段・カ行促音便ユク,基本形,ゆく
+1054 動詞,非自立,*,*,五段・カ行促音便ユク,基本形,行く
+1055 動詞,非自立,*,*,五段・カ行促音便ユク,未然ウ接続,ゆく
+1056 動詞,非自立,*,*,五段・カ行促音便ユク,未然ウ接続,行く
+1057 動詞,非自立,*,*,五段・カ行促音便ユク,未然形,ゆく
+1058 動詞,非自立,*,*,五段・カ行促音便ユク,未然形,行く
+1059 動詞,非自立,*,*,五段・カ行促音便ユク,命令ｅ,ゆく
+1060 動詞,非自立,*,*,五段・カ行促音便ユク,命令ｅ,行く
+1061 動詞,非自立,*,*,五段・カ行促音便ユク,連用形,ゆく
+1062 動詞,非自立,*,*,五段・カ行促音便ユク,連用形,行く
+1063 動詞,非自立,*,*,五段・サ行,*,*
+1064 動詞,非自立,*,*,五段・サ行,*,尽くす
+1065 動詞,非自立,*,*,五段・サ行,仮定形,*
+1066 動詞,非自立,*,*,五段・サ行,仮定形,出す
+1067 動詞,非自立,*,*,五段・サ行,仮定形,尽くす
+1068 動詞,非自立,*,*,五段・サ行,仮定形,直す
+1069 動詞,非自立,*,*,五段・サ行,仮定縮約１,*
+1070 動詞,非自立,*,*,五段・サ行,仮定縮約１,出す
+1071 動詞,非自立,*,*,五段・サ行,仮定縮約１,尽くす
+1072 動詞,非自立,*,*,五段・サ行,仮定縮約１,直す
+1073 動詞,非自立,*,*,五段・サ行,基本形,出す
+1074 動詞,非自立,*,*,五段・サ行,基本形,尽くす
+1075 動詞,非自立,*,*,五段・サ行,基本形,直す
+1076 動詞,非自立,*,*,五段・サ行,未然ウ接続,*
+1077 動詞,非自立,*,*,五段・サ行,未然ウ接続,出す
+1078 動詞,非自立,*,*,五段・サ行,未然ウ接続,尽くす
+1079 動詞,非自立,*,*,五段・サ行,未然ウ接続,直す
+1080 動詞,非自立,*,*,五段・サ行,未然形,*
+1081 動詞,非自立,*,*,五段・サ行,未然形,出す
+1082 動詞,非自立,*,*,五段・サ行,未然形,尽くす
+1083 動詞,非自立,*,*,五段・サ行,未然形,直す
+1084 動詞,非自立,*,*,五段・サ行,命令ｅ,*
+1085 動詞,非自立,*,*,五段・サ行,命令ｅ,出す
+1086 動詞,非自立,*,*,五段・サ行,命令ｅ,尽くす
+1087 動詞,非自立,*,*,五段・サ行,命令ｅ,直す
+1088 動詞,非自立,*,*,五段・サ行,連用形,*
+1089 動詞,非自立,*,*,五段・サ行,連用形,出す
+1090 動詞,非自立,*,*,五段・サ���,連用形,尽くす
+1091 動詞,非自立,*,*,五段・サ行,連用形,直す
+1092 動詞,非自立,*,*,五段・タ行,*,*
+1093 動詞,非自立,*,*,五段・タ行,仮定形,*
+1094 動詞,非自立,*,*,五段・タ行,仮定縮約１,*
+1095 動詞,非自立,*,*,五段・タ行,未然ウ接続,*
+1096 動詞,非自立,*,*,五段・タ行,未然形,*
+1097 動詞,非自立,*,*,五段・タ行,命令ｅ,*
+1098 動詞,非自立,*,*,五段・タ行,連用タ接続,*
+1099 動詞,非自立,*,*,五段・タ行,連用形,*
+1100 動詞,非自立,*,*,五段・ナ行,*,*
+1101 動詞,非自立,*,*,五段・ナ行,仮定形,*
+1102 動詞,非自立,*,*,五段・ナ行,仮定縮約１,*
+1103 動詞,非自立,*,*,五段・ナ行,未然ウ接続,*
+1104 動詞,非自立,*,*,五段・ナ行,未然形,*
+1105 動詞,非自立,*,*,五段・ナ行,命令ｅ,*
+1106 動詞,非自立,*,*,五段・ナ行,連用タ接続,*
+1107 動詞,非自立,*,*,五段・ナ行,連用形,*
+1108 動詞,非自立,*,*,五段・マ行,*,*
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+1119 動詞,非自立,*,*,五段・マ行,命令ｅ,込む
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+1315 動詞,非自立,*,*,五段・ワ行促音便,未然ウ接続,*
+1316 動詞,非自立,*,*,五段・ワ行促音便,未然ウ接続,しまう
+1317 動詞,非自立,*,*,五段・ワ行促音便,未然ウ接続,もらう
+1318 動詞,非自立,*,*,五段・ワ行促音便,未然ウ接続,合う
+1319 動詞,非自立,*,*,五段・ワ行促音便,未然形,*
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+1324 動詞,非自立,*,*,五段・ワ行促音便,命令ｅ,しまう
+1325 動詞,非自立,*,*,五段・ワ行促音便,命令ｅ,もらう
+1326 動詞,非自立,*,*,五段・ワ行促音便,命令ｅ,合う
+1327 動詞,非���立,*,*,五段・ワ行促音便,連用タ接続,*
+1328 動詞,非自立,*,*,五段・ワ行促音便,連用タ接続,しまう
+1329 動詞,非自立,*,*,五段・ワ行促音便,連用タ接続,もらう
+1330 動詞,非自立,*,*,五段・ワ行促音便,連用タ接続,合う
+1331 動詞,非自立,*,*,五段・ワ行促音便,連用形,*
+1332 動詞,非自立,*,*,五段・ワ行促音便,連用形,しまう
+1333 動詞,非自立,*,*,五段・ワ行促音便,連用形,もらう
+1334 動詞,非自立,*,*,五段・ワ行促音便,連用形,合う
+1335 動詞,非自立,*,*,四段・ハ行,仮定形,*
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+1339 動詞,非自立,*,*,四段・ハ行,連用形,*
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+1342 副詞,助詞類接続,*,*,*,*,*
+1343 名詞,サ変接続,*,*,*,*,*
+1344 名詞,ナイ形容詞語幹,*,*,*,*,*
+1345 名詞,一般,*,*,*,*,*
+1346 名詞,一般,*,*,*,0,*
+1347 名詞,形容動詞語幹,*,*,*,*,*
+1348 名詞,固有名詞,一般,*,*,*,*
+1349 名詞,固有名詞,人名,一般,*,*,*
+1350 名詞,固有名詞,人名,姓,*,*,*
+1351 名詞,固有名詞,人名,名,*,*,*
+1352 名詞,固有名詞,組織,*,*,*,*
+1353 名詞,固有名詞,地域,一般,*,*,*
+1354 名詞,固有名詞,地域,国,*,*,*
+1355 名詞,数,*,*,*,*,*
+1356 名詞,接続詞的,*,*,*,*,*
+1357 名詞,接尾,サ変接続,*,*,*,*
+1358 名詞,接尾,一般,*,*,*,*
+1359 名詞,接尾,形容動詞語幹,*,*,*,*
+1360 名詞,接尾,助数詞,*,*,*,*
+1361 名詞,接尾,助動詞語幹,*,*,*,*
+1362 名詞,接尾,人名,*,*,*,*
+1363 名詞,接尾,地域,*,*,*,*
+1364 名詞,接尾,特殊,*,*,*,*
+1365 名詞,接尾,副詞可能,*,*,*,*
+1366 名詞,代名詞,一般,*,*,*,*
+1367 名詞,代名詞,縮約,*,*,*,*
+1368 名詞,動詞非自立的,*,*,*,*,*
+1369 名詞,特殊,助動詞語幹,*,*,*,*
+1370 名詞,非自立,*,*,*,*,*
+1371 名詞,非自立,一般,*,*,*,*
+1372 名詞,非自立,形容動詞語幹,*,*,*,*
+1373 名詞,非自立,助動詞語幹,*,*,*,*
+1374 名詞,非自立,副詞可能,*,*,*,*
+1375 名詞,副詞可能,*,*,*,*,*
+1376 連体詞,*,*,*,*,*,*

dict/matrix.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62fd16b4f64c851d5dc352ef0d5740c5fc83ddc7c203b2b0b1fc5271969a14ce
+size 3792262

dict/pos-id.def

@@ -0,0 +1,69 @@
+その他,間投,*,* 0
+フィラー,*,*,* 1
+感動詞,*,*,* 2
+記号,アルファベット,*,* 3
+記号,一般,*,* 4
+記号,括弧開,*,* 5
+記号,括弧閉,*,* 6
+記号,句点,*,* 7
+記号,空白,*,* 8
+記号,読点,*,* 9
+形容詞,自立,*,* 10
+形容詞,接尾,*,* 11
+形容詞,非自立,*,* 12
+助詞,格助詞,一般,* 13
+助詞,格助詞,引用,* 14
+助詞,格助詞,連語,* 15
+助詞,係助詞,*,* 16
+助詞,終助詞,*,* 17
+助詞,接続助詞,*,* 18
+助詞,特殊,*,* 19
+助詞,副詞化,*,* 20
+助詞,副助詞,*,* 21
+助詞,副助詞／並立助詞／終助詞,*,* 22
+助詞,並立助詞,*,* 23
+助詞,連体化,*,* 24
+助動詞,*,*,* 25
+接続詞,*,*,* 26
+接頭詞,形容詞接続,*,* 27
+接頭詞,数接続,*,* 28
+接頭詞,動詞接続,*,* 29
+接頭詞,名詞接続,*,* 30
+動詞,自立,*,* 31
+動詞,接尾,*,* 32
+動詞,非自立,*,* 33
+副詞,一般,*,* 34
+副詞,助詞類接続,*,* 35
+名詞,サ変接続,*,* 36
+名詞,ナイ形容詞語幹,*,* 37
+名詞,一般,*,* 38
+名詞,引用文字列,*,* 39
+名詞,形容動詞語幹,*,* 40
+名詞,固有名詞,一般,* 41
+名詞,固有名詞,人名,一般 42
+名詞,固有名詞,人名,姓 43
+名詞,固有名詞,人名,名 44
+名詞,固有名詞,組織,* 45
+名詞,固有名詞,地域,一般 46
+名詞,固有名詞,地域,国 47
+名詞,数,*,* 48
+名詞,接続詞的,*,* 49
+名詞,接尾,サ変接続,* 50
+名詞,接尾,一般,* 51
+名詞,接尾,形容動詞語幹,* 52
+名詞,接尾,助数詞,* 53
+名詞,接尾,助動詞語幹,* 54
+名詞,接尾,人名,* 55
+名詞,接尾,地域,* 56
+名詞,接尾,特殊,* 57
+名詞,接尾,副詞可能,* 58
+名詞,代名詞,一般,* 59
+名詞,代名詞,縮約,* 60
+名詞,動詞非自立的,*,* 61
+名詞,特殊,助動詞語幹,* 62
+名詞,非自立,一般,* 63
+名詞,非自立,形容動詞語幹,* 64
+名詞,非自立,助動詞語幹,* 65
+名詞,非自立,副詞可能,* 66
+名詞,副詞可能,*,* 67
+連体詞,*,*,* 68

dict/rewrite.def

@@ -0,0 +1,94 @@
+# 
+# Feature(POS) to Internal State mapping
+#
+[unigram rewrite]
+# 読み,発音をとりのぞいて, 品詞1,2,3,4,活用形,活用型,原形,よみ を使う
+*,*,*,*,*,*,*,*  $1,$2,$3,$4,$5,$6,$7,$8
+# 読みがない場合は無視
+*,*,*,*,*,*,*    $1,$2,$3,$4,$5,$6,$7,*
+
+[left rewrite]
+(助詞|助動詞),*,*,*,*,*,(ない|無い)    $1,$2,$3,$4,$5,$6,無い
+(助詞|助動詞),終助詞,*,*,*,*,(よ|ヨ)   $1,$2,$3,$4,$5,$6,よ
+(助詞|助動詞),終助詞,*,*,*,*,(な|なぁ|なあ|ナ)  $1,$2,$3,$4,$5,$6,な
+(助詞|助動詞),終助詞,*,*,*,*,(ね|ねぇ|ねえ|ねェ|ねエ|ねっ|ねッ|ネ)  $1,$2,$3,$4,$5,$6,ね
+(助詞|助動詞),接続助詞,*,*,*,*,(て|ちゃ|ちゃあ)   $1,$2,$3,$4,$5,$6,て
+(助詞|助動詞),接続助詞,*,*,*,*,(ちゃあ|ちゃ)      $1,$2,$3,$4,$5,$6,ちゃ
+(助詞|助動詞),接続助詞,*,*,*,*,(で|じゃ)          $1,$2,$3,$4,$5,$6,で
+(助詞|助動詞),接続助詞,*,*,*,*,(けど|けれど)      $1,$2,$3,$4,$5,$6,けれど
+(助詞|助動詞),*,*,*,*,*,*                         $1,$2,$3,$4,$5,$6,$7
+記号,(句点|括弧閉|括弧開),*,*,*,*,*   $1,$2,$3,$4,$5,$6,BOS/EOS
+BOS/EOS,*,*,*,*,*,*                   $1,$2,$3,$4,$5,$6,BOS/EOS
+動詞,自立,*,*,*,*,(行う|行なう)       $1,$2,$3,$4,$5,$6,行う
+動詞,自立,*,*,*,*,(いう|言う|云う)    $1,$2,$3,$4,$5,$6,言う
+動詞,自立,*,*,*,*,(いく|行く)         $1,$2,$3,$4,$5,$6,行く
+動詞,自立,*,*,*,*,する                $1,$2,$3,$4,$5,$6,する
+動詞,自立,*,*,*,*,*                   $1,$2,$3,$4,$5,$6,*
+動詞,非自立,*,*,*,*,(ある|おる|かかる|きる|なる|まいる|まわる|やる|回る|終わる|切る|参る|いらっしゃる|らっしゃる|なさる|る|もらう|しまう|続く|いく|ゆく|行く|く|くれる|おく|する)  $1,$2,$3,$4,$5,$6,$7
+動詞,非自立,*,*,*,*,(来る|くる)          $1,$2,$3,$4,$5,$6,来る
+動詞,非自立,*,*,*,*,(ぬく|抜く)          $1,$2,$3,$4,$5,$6,抜く
+動詞,非自立,*,*,*,*,(頂く|いただく)      $1,$2,$3,$4,$5,$6,頂く
+動詞,非自立,*,*,*,*,(いたす|致す)        $1,$2,$3,$4,$5,$6,致す
+動詞,非自立,*,*,*,*,(だす|出す)          $1,$2,$3,$4,$5,$6,出す
+動詞,非自立,*,*,*,*,(つくす|尽くす|尽す) $1,$2,$3,$4,$5,$6,尽くす
+動詞,非自立,*,*,*,*,(直す|なおす)        $1,$2,$3,$4,$5,$6,直す
+動詞,非自立,*,*,*,*,(込む|こむ)          $1,$2,$3,$4,$5,$6,込む
+動詞,非自立,*,*,*,*,(くださる|下さる)    $1,$2,$3,$4,$5,$6,下さる
+動詞,非自立,*,*,*,*,(合う|あう)          $1,$2,$3,$4,$5,$6,合う
+動詞,非自立,*,*,*,*,*                    $1,$2,$3,$4,$5,$6,*
+形容詞,*,*,*,*,*,(ない|無い|いい|らしい)   $1,$2,$3,$4,$5,$6,無い
+形容詞,接尾,*,*,*,*,(臭い|くさい)          $1,$2,$3,$4,$5,$6,臭い
+形容詞,接尾,*,*,*,*,(欲しい|ほしい)        $1,$2,$3,$4,$5,$6,欲しい
+形容詞,接尾,*,*,*,*,(ったらしい|たらしい|っぽい|ぽい) $1,$2,$3,$4,$5,$6,たらしい
+形容詞,接尾,*,*,*,*,*                                 $1,$2,$3,$4,$5,$6,*
+形容詞,非自立,*,*,*,*,(難い|がたい|づらい|にくい|やすい)  $1,$2,$3,$4,$5,$6,難い
+形容詞,非自立,*,*,*,*,(よい|良い)                         $1,$2,$3,$4,$5,$6,良い
+形容詞,非自立,*,*,*,*,(欲しい|ほしい)                     $1,$2,$3,$4,$5,$6,欲しい
+形容詞,非自立,*,*,*,*,(じまう|じゃう|でく|どく|でる|どる) $1,$2,$3,$4,$5,$6,でる
+形容詞,非自立,*,*,*,*,(ちまう|ちゃう|てく|とく|てる|とる) $1,$2,$3,$4,$5,$6,てる
+形容詞,非自立,*,*,*,*,*                                   $1,$2,$3,$4,$5,$6,*
+接続詞,*,*,*,*,*,(及び|および|あるいは|或いは|或は|または|又は|ないし|ならびに|並びに|もしくは|若しくは)   $1,$2,$3,$4,$5,$6,および
+*,*,*,*,*,*,*    $1,$2,$3,$4,$5,$6,*
+
+[right rewrite]
+(助詞|助動詞),*,*,*,*,*,(ない|無い)    $1,$2,$3,$4,$5,$6,無い
+(助詞|助動詞),終助詞,*,*,*,*,(よ|ヨ)   $1,$2,$3,$4,$5,$6,よ
+(助詞|助動詞),終助詞,*,*,*,*,(な|なぁ|なあ|ナ)  $1,$2,$3,$4,$5,$6,な
+(助詞|助動詞),終助詞,*,*,*,*,(ね|ねぇ|ねえ|ねェ|ねエ|ねっ|ねッ|ネ)  $1,$2,$3,$4,$5,$6,ね
+(助詞|助動詞),接続助詞,*,*,*,*,(て|ちゃ|ちゃあ)   $1,$2,$3,$4,$5,$6,て
+(助詞|助動詞),接続助詞,*,*,*,*,(ちゃあ|ちゃ)      $1,$2,$3,$4,$5,$6,ちゃ
+(助詞|助動詞),接続助詞,*,*,*,*,(で|じゃ)          $1,$2,$3,$4,$5,$6,で
+(助詞|助動詞),接続助詞,*,*,*,*,(けど|けれど)      $1,$2,$3,$4,$5,$6,けれど
+(助詞|助動詞),*,*,*,*,*,*                         $1,$2,$3,$4,$5,$6,$7
+記号,(句点|括弧閉|括弧開),*,*,*,*,*   $1,$2,$3,$4,$5,$6,BOS/EOS
+BOS/EOS,*,*,*,*,*,*                   $1,$2,$3,$4,$5,$6,BOS/EOS
+動詞,自立,*,*,*,*,(行う|行なう)       $1,$2,$3,$4,$5,$6,行う
+動詞,自立,*,*,*,*,(いう|言う|云う)    $1,$2,$3,$4,$5,$6,言う
+動詞,自立,*,*,*,*,(いく|行く)         $1,$2,$3,$4,$5,$6,行く
+動詞,自立,*,*,*,*,する                $1,$2,$3,$4,$5,$6,する
+動詞,自立,*,*,*,*,*                   $1,$2,$3,$4,$5,$6,*
+動詞,非自立,*,*,*,*,(ある|おる|かかる|きる|なる|まいる|まわる|やる|回る|終わる|切る|参る|いらっしゃる|らっしゃる|なさる|る|もらう|しまう|続く|いく|ゆく|行く|く|くれる|おく|する)  $1,$2,$3,$4,$5,$6,$7
+動詞,非自立,*,*,*,*,(来る|くる)          $1,$2,$3,$4,$5,$6,来る
+動詞,非自立,*,*,*,*,(ぬく|抜く)          $1,$2,$3,$4,$5,$6,抜く
+動詞,非自立,*,*,*,*,(頂く|いただく)      $1,$2,$3,$4,$5,$6,頂く
+動詞,非自立,*,*,*,*,(いたす|致す)        $1,$2,$3,$4,$5,$6,致す
+動詞,非自立,*,*,*,*,(だす|出す)          $1,$2,$3,$4,$5,$6,出す
+動詞,非自立,*,*,*,*,(つくす|尽くす|尽す) $1,$2,$3,$4,$5,$6,尽くす
+動詞,非自立,*,*,*,*,(直す|なおす)        $1,$2,$3,$4,$5,$6,直す
+動詞,非自立,*,*,*,*,(込む|こむ)          $1,$2,$3,$4,$5,$6,込む
+動詞,非自立,*,*,*,*,(くださる|下さる)    $1,$2,$3,$4,$5,$6,下さる
+動詞,非自立,*,*,*,*,(合う|あう)          $1,$2,$3,$4,$5,$6,合う
+動詞,非自立,*,*,*,*,*                    $1,$2,$3,$4,$5,$6,*
+形容詞,*,*,*,*,*,(ない|無い|いい|らしい)   $1,$2,$3,$4,$5,$6,無い
+形容詞,接尾,*,*,*,*,(臭い|くさい)          $1,$2,$3,$4,$5,$6,臭い
+形容詞,接尾,*,*,*,*,(欲しい|ほしい)        $1,$2,$3,$4,$5,$6,欲しい
+形容詞,接尾,*,*,*,*,(ったらしい|たらしい|っぽい|ぽい) $1,$2,$3,$4,$5,$6,たらしい
+形容詞,接尾,*,*,*,*,*                                 $1,$2,$3,$4,$5,$6,*
+形容詞,非自立,*,*,*,*,(難い|がたい|づらい|にくい|やすい)  $1,$2,$3,$4,$5,$6,難い
+形容詞,非自立,*,*,*,*,(よい|良い)                         $1,$2,$3,$4,$5,$6,良い
+形容詞,非自立,*,*,*,*,(欲しい|ほしい)                     $1,$2,$3,$4,$5,$6,欲しい
+形容詞,非自立,*,*,*,*,(じまう|じゃう|でく|どく|でる|どる) $1,$2,$3,$4,$5,$6,でる
+形容詞,非自立,*,*,*,*,(ちまう|ちゃう|てく|とく|てる|とる) $1,$2,$3,$4,$5,$6,てる
+形容詞,非自立,*,*,*,*,*                                   $1,$2,$3,$4,$5,$6,*
+接続詞,*,*,*,*,*,(及び|および|あるいは|或いは|或は|または|又は|ないし|ならびに|並びに|もしくは|若しくは)   $1,$2,$3,$4,$5,$6,および
+*,*,*,*,*,*,*    $1,$2,$3,$4,$5,$6,*

dict/right-id.def

@@ -0,0 +1,1377 @@
+0 BOS/EOS,*,*,*,*,*,BOS/EOS
+1 その他,間投,*,*,*,*,*
+2 フィラー,*,*,*,*,*,*
+3 感動詞,*,*,*,*,*,*
+4 記号,アルファベット,*,*,*,*,*
+5 記号,一般,*,*,*,*,*
+6 記号,括弧開,*,*,*,*,BOS/EOS
+7 記号,括弧閉,*,*,*,*,BOS/EOS
+8 記号,句点,*,*,*,*,BOS/EOS
+9 記号,空白,*,*,*,*,*
+10 記号,読点,*,*,*,*,*
+11 形容詞,自立,*,*,形容詞・アウオ段,*,*
+12 形容詞,自立,*,*,形容詞・アウオ段,ガル接続,*
+13 形容詞,自立,*,*,形容詞・アウオ段,ガル接続,無い
+14 形容詞,自立,*,*,形容詞・アウオ段,仮定形,*
+15 形容詞,自立,*,*,形容詞・アウオ段,仮定形,無い
+16 形容詞,自立,*,*,形容詞・アウオ段,仮定縮約１,*
+17 形容詞,自立,*,*,形容詞・アウオ段,仮定縮約１,無い
+18 形容詞,自立,*,*,形容詞・アウオ段,仮定縮約２,*
+19 形容詞,自立,*,*,形容詞・アウオ段,仮定縮約２,無い
+20 形容詞,自立,*,*,形容詞・アウオ段,基本形,*
+21 形容詞,自立,*,*,形容詞・アウオ段,基本形,無い
+22 形容詞,自立,*,*,形容詞・アウオ段,体言接続,*
+23 形容詞,自立,*,*,形容詞・アウオ段,体言接続,無い
+24 形容詞,自立,*,*,形容詞・アウオ段,文語基本形,*
+25 形容詞,自立,*,*,形容詞・アウオ段,文語基本形,無い
+26 形容詞,自立,*,*,形容詞・アウオ段,未然ウ接続,*
+27 形容詞,自立,*,*,形容詞・アウオ段,未然ウ接続,無い
+28 形容詞,自立,*,*,形容詞・アウオ段,未然ヌ接続,*
+29 形容詞,自立,*,*,形容詞・アウオ段,未然ヌ接続,無い
+30 形容詞,自立,*,*,形容詞・アウオ段,命令ｅ,*
+31 形容詞,自立,*,*,形容詞・アウオ段,命令ｅ,無い
+32 形容詞,自立,*,*,形容詞・アウオ段,連用ゴザイ接続,*
+33 形容詞,自立,*,*,形容詞・アウオ段,連用ゴザイ接続,無い
+34 形容詞,自立,*,*,形容詞・アウオ段,連用タ接続,*
+35 形容詞,自立,*,*,形容詞・アウオ段,連用タ接続,無い
+36 形容詞,自立,*,*,形容詞・アウオ段,連用テ接続,*
+37 形容詞,自立,*,*,形容詞・アウオ段,連用テ接続,無い
+38 形容詞,自立,*,*,形容詞・イ段,ガル接続,*
+39 形容詞,自立,*,*,形容詞・イ段,仮定形,*
+40 形容詞,自立,*,*,形容詞・イ段,仮定縮約１,*
+41 形容詞,自立,*,*,形容詞・イ段,仮定縮約２,*
+42 形容詞,自立,*,*,形容詞・イ段,基本形,*
+43 形容詞,自立,*,*,形容詞・イ段,体言接続,*
+44 形容詞,自立,*,*,形容詞・イ段,文語基本形,*
+45 形容詞,自立,*,*,形容詞・イ段,未然ウ接続,*
+46 形容詞,自立,*,*,形容詞・イ段,未然ヌ接続,*
+47 形容詞,自立,*,*,形容詞・イ段,命令ｅ,*
+48 形容詞,自立,*,*,形容詞・イ段,連用ゴザイ接続,*
+49 形容詞,自立,*,*,形容詞・イ段,連用タ接続,*
+50 形容詞,自立,*,*,形容詞・イ段,連用テ接続,*
+51 形容詞,自立,*,*,不変化型,基本形,*
+52 形容詞,接尾,*,*,形容詞・アウオ段,ガル接続,*
+53 形容詞,接尾,*,*,形容詞・アウオ段,ガル接続,たらしい
+54 形容詞,接尾,*,*,形容詞・アウオ段,ガル接続,臭い
+55 形容詞,接尾,*,*,形容詞・アウオ段,仮定形,*
+56 形容詞,接尾,*,*,形容詞・アウオ段,仮定形,たらしい
+57 形容詞,接尾,*,*,形容詞・アウオ段,仮定形,臭い
+58 形容詞,接尾,*,*,形容詞・アウオ段,仮定縮約１,*
+59 形容詞,接尾,*,*,形容詞・アウオ段,仮定縮約１,たらしい
+60 形容詞,接尾,*,*,形容詞・アウオ段,仮定縮約１,臭い
+61 形容詞,接尾,*,*,形容詞・アウオ段,仮定縮約２,*
+62 形容詞,接尾,*,*,形容詞・アウオ段,仮定縮約２,たらしい
+63 形容詞,接尾,*,*,形容詞・アウオ段,仮定縮約２,臭い
+64 形容詞,接尾,*,*,形容詞・アウオ段,基本形,*
+65 形容詞,接尾,*,*,形容詞・アウオ段,基本形,たらしい
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+163 形容詞,非自立,*,*,形容詞・イ段,未然ウ接続,欲しい
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+168 形容詞,非自立,*,*,形容詞・イ段,連用テ接続,欲しい
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+371 助詞,副助詞,*,*,*,*,なんぞ
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+398 助動詞,*,*,*,下二・タ行,命令ｙｏ,つ
+399 助動詞,*,*,*,下二・タ行,連用形,つ
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+402 助動詞,*,*,*,形容詞・イ段,仮定形,らしい
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+404 助動詞,*,*,*,形容詞・イ段,仮定縮約１,らしい
+405 助動詞,*,*,*,形容詞・イ段,仮定縮約１,無い
+406 助動詞,*,*,*,形容詞・イ段,仮定縮約２,らしい
+407 助動詞,*,*,*,形容詞・イ段,仮定縮約２,無い
+408 助動詞,*,*,*,形容詞・イ段,基本形,らしい
+409 助動詞,*,*,*,形容詞・イ段,基本形,無い
+410 助動詞,*,*,*,形容詞・イ段,体言接続,らしい
+411 助動詞,*,*,*,形容詞・イ段,体言接続,無い
+412 助動詞,*,*,*,形容詞・イ段,文語基本形,らしい
+413 助動詞,*,*,*,形容詞・イ段,文語基本形,無い
+414 助動詞,*,*,*,形容詞・イ段,未然ウ接続,らしい
+415 助動詞,*,*,*,形容詞・イ段,未然ウ接続,無い
+416 助動詞,*,*,*,形容詞・イ段,未然ヌ接続,らしい
+417 助動詞,*,*,*,形容詞・イ段,未然ヌ接続,無い
+418 助動詞,*,*,*,形容詞・イ段,命令ｅ,らしい
+419 助動詞,*,*,*,形容詞・イ段,命令ｅ,無い
+420 助動詞,*,*,*,形容詞・イ段,連用ゴザイ接続,らしい
+421 助動詞,*,*,*,形容詞・イ段,連用ゴザイ接続,無い
+422 助動詞,*,*,*,形容詞・イ段,連用タ接続,らしい
+423 助動詞,*,*,*,形容詞・イ段,連用タ接続,無い
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+425 助動詞,*,*,*,形容詞・イ段,連用テ接続,無い
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+430 助動詞,*,*,*,五段・ラ行アル,未然ウ接続,ある
+431 助動詞,*,*,*,五段・ラ行アル,未然形,ある
+432 助動詞,*,*,*,五段・ラ行アル,命令ｅ,ある
+433 助動詞,*,*,*,五段・ラ行アル,連用タ接続,ある
+434 助動詞,*,*,*,五段・ラ行アル,連用形,ある
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+436 助動詞,*,*,*,五段・ラ行特殊,仮定形,御座る
+437 助動詞,*,*,*,五段・ラ行特殊,仮定縮約１,ござる
+438 助動詞,*,*,*,五段・ラ行特殊,仮定縮約１,御座る
+439 助動詞,*,*,*,五段・ラ行特殊,基本形,ござる
+440 助動詞,*,*,*,五段・ラ行特殊,基本形,御座る
+441 助動詞,*,*,*,五段・ラ行特殊,未然ウ接続,ござる
+442 助動詞,*,*,*,五段・ラ行特殊,未然ウ接続,御座る
+443 助動詞,*,*,*,五段・ラ行特殊,未然形,ござる
+444 助動詞,*,*,*,五段・ラ行特殊,未然形,御座る
+445 助動詞,*,*,*,五段・ラ行特殊,未然特殊,ござる
+446 助動詞,*,*,*,五段・ラ行特殊,未然特殊,御座る
+447 助動詞,*,*,*,五段・ラ行特殊,命令ｅ,ござる
+448 助動詞,*,*,*,五段・ラ行特殊,命令ｅ,御座る
+449 助動詞,*,*,*,五段・ラ行特殊,命令ｉ,ござる
+450 助動詞,*,*,*,五段・ラ行特殊,命令ｉ,御座る
+451 助動詞,*,*,*,五段・ラ行特殊,連用タ接続,ござる
+452 助動詞,*,*,*,五段・ラ行特殊,連用タ接続,御座る
+453 助動詞,*,*,*,五段・ラ行特殊,連用形,ござる
+454 助動詞,*,*,*,五段・ラ行特殊,連用形,御座る
+455 助動詞,*,*,*,特殊・ジャ,基本形,じゃ
+456 助動詞,*,*,*,特殊・ジャ,未然形,じゃ
+457 助動詞,*,*,*,特殊・ジャ,連用形,じゃ
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+459 助動詞,*,*,*,特殊・タ,仮定形,だ
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+461 助動詞,*,*,*,特殊・タ,基本形,だ
+462 助動詞,*,*,*,特殊・タ,未然形,た
+463 助動詞,*,*,*,特殊・タ,未然形,だ
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+465 助動詞,*,*,*,特殊・タイ,音便基本形,たい
+466 助動詞,*,*,*,特殊・タイ,仮定形,たい
+467 助動詞,*,*,*,特殊・タイ,仮定縮約１,たい
+468 助動詞,*,*,*,特殊・タイ,仮定縮約２,たい
+469 助動詞,*,*,*,特殊・タイ,基本形,たい
+470 助動詞,*,*,*,特殊・タイ,体言接続,たい
+471 助動詞,*,*,*,特殊・タイ,文語基本形,たい
+472 助動詞,*,*,*,特殊・タイ,未然ウ接続,たい
+473 助動詞,*,*,*,特殊・タイ,未然ヌ接続,たい
+474 助動詞,*,*,*,特殊・タイ,連用ゴザイ接続,たい
+475 助動詞,*,*,*,特殊・タイ,連用タ接続,たい
+476 助動詞,*,*,*,特殊・タイ,連用テ接続,たい
+477 助動詞,*,*,*,特殊・ダ,仮定形,だ
+478 助動詞,*,*,*,特殊・ダ,基本形,だ
+479 助動詞,*,*,*,特殊・ダ,体言接続,だ
+480 助動詞,*,*,*,特殊・ダ,未然形,だ
+481 助動詞,*,*,*,特殊・ダ,命令ｅ,だ
+482 助動詞,*,*,*,特殊・ダ,連用タ接続,だ
+483 助動詞,*,*,*,特殊・ダ,連用形,だ
+484 助動詞,*,*,*,特殊・デス,基本形,っす
+485 助動詞,*,*,*,特殊・デス,基本形,です
+486 助動詞,*,*,*,特殊・デス,基本形,どす
+487 助動詞,*,*,*,特殊・デス,未然形,っす
+488 助動詞,*,*,*,特殊・デス,未然形,です
+489 助動詞,*,*,*,特殊・デス,未然形,どす
+490 助動詞,*,*,*,特殊・デス,連用形,っす
+491 助動詞,*,*,*,特殊・デス,連用形,です
+492 助動詞,*,*,*,特殊・デス,連用形,どす
+493 助動詞,*,*,*,特殊・ナイ,ガル接続,無い
+494 助動詞,*,*,*,特殊・ナイ,音便基本形,無い
+495 助動詞,*,*,*,特殊・ナイ,仮定形,無い
+496 助動詞,*,*,*,特殊・ナイ,仮定縮約１,無い
+497 助動詞,*,*,*,特殊・ナイ,仮定縮約２,無い
+498 助動詞,*,*,*,特殊・ナイ,基本形,無い
+499 助動詞,*,*,*,特殊・ナイ,体言接続,無い
+500 助動詞,*,*,*,特殊・ナイ,文語基本形,無い
+501 助動詞,*,*,*,特殊・ナイ,未然ウ接続,無い
+502 助動詞,*,*,*,特殊・ナイ,未然ヌ接続,無い
+503 助動詞,*,*,*,特殊・ナイ,命令ｅ,無い
+504 助動詞,*,*,*,特殊・ナイ,連用ゴザイ接続,無い
+505 助動詞,*,*,*,特殊・ナイ,連用タ接続,無い
+506 助動詞,*,*,*,特殊・ナイ,連用テ接続,無い
+507 助動詞,*,*,*,特殊・ナイ,連用デ接続,無い
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+509 助動詞,*,*,*,特殊・ヌ,基本形,ぬ
+510 助動詞,*,*,*,特殊・ヌ,体言接続,ぬ
+511 助動詞,*,*,*,特殊・ヌ,文語基本形,ぬ
+512 助動詞,*,*,*,特殊・ヌ,連用ニ接続,ぬ
+513 助動詞,*,*,*,特殊・ヌ,連用形,ぬ
+514 助動詞,*,*,*,特殊・マス,仮定形,ます
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+516 助動詞,*,*,*,特殊・マス,基本形,ます
+517 助動詞,*,*,*,特殊・マス,基本形,やす
+518 助動詞,*,*,*,特殊・マス,未然ウ接続,ます
+519 助動詞,*,*,*,特殊・マス,未然ウ接続,やす
+520 助動詞,*,*,*,特殊・マス,未然形,ます
+521 助動詞,*,*,*,特殊・マス,未然形,やす
+522 助動詞,*,*,*,特殊・マス,命令ｅ,ます
+523 助動詞,*,*,*,特殊・マス,命令ｅ,やす
+524 助動詞,*,*,*,特殊・マス,命令ｉ,ます
+525 助動詞,*,*,*,特殊・マス,命令ｉ,やす
+526 助動詞,*,*,*,特殊・マス,連用形,ます
+527 助動詞,*,*,*,特殊・マス,連用形,やす
+528 助動詞,*,*,*,特殊・ヤ,基本形,や
+529 助動詞,*,*,*,特殊・ヤ,未然形,や
+530 助動詞,*,*,*,特殊・ヤ,連用形,や
+531 助動詞,*,*,*,不変化型,基本形,う
+532 助動詞,*,*,*,不変化型,基本形,じ
+533 助動詞,*,*,*,不変化型,基本形,じゃん
+534 助動詞,*,*,*,不変化型,基本形,じゃン
+535 助動詞,*,*,*,不変化型,基本形,ぬ
+536 助動詞,*,*,*,不変化型,基本形,ひん
+537 助動詞,*,*,*,不変化型,基本形,へん
+538 助動詞,*,*,*,不変化型,基本形,まい
+539 助動詞,*,*,*,不変化型,基本形,やん
+540 助動詞,*,*,*,不変化型,基本形,ん
+541 助動詞,*,*,*,文語・キ,基本形,き
+542 助動詞,*,*,*,文語・キ,体言接続,き
+543 助動詞,*,*,*,文語・キ,命令ｅ,き
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+545 助動詞,*,*,*,文語・ケリ,体言接続,けり
+546 助動詞,*,*,*,文語・ゴトシ,基本形,ごとし
+547 助動詞,*,*,*,文語・ゴトシ,基本形,如し
+548 助動詞,*,*,*,文語・ゴトシ,体言接続,ごとし
+549 助動詞,*,*,*,文語・ゴトシ,体言接続,如し
+550 助動詞,*,*,*,文語・ゴトシ,連用形,ごとし
+551 助動詞,*,*,*,文語・ゴトシ,連用形,如し
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+553 助動詞,*,*,*,文語・ナリ,仮定形,なり
+554 助動詞,*,*,*,文語・ナリ,基本形,たり
+555 助動詞,*,*,*,文語・ナリ,基本形,なり
+556 助動詞,*,*,*,文語・ナリ,体言接続,たり
+557 助動詞,*,*,*,文語・ナリ,体言接続,なり
+558 助動詞,*,*,*,文語・ナリ,未然形,たり
+559 助動詞,*,*,*,文語・ナリ,未然形,なり
+560 助動詞,*,*,*,文語・ナリ,命令ｅ,たり
+561 助動詞,*,*,*,文語・ナリ,命令ｅ,なり
+562 助動詞,*,*,*,文語・ベシ,仮定形,べし
+563 助動詞,*,*,*,��語・ベシ,基本形,べし
+564 助動詞,*,*,*,文語・ベシ,体言接続,べし
+565 助動詞,*,*,*,文語・ベシ,未然形,べし
+566 助動詞,*,*,*,文語・ベシ,連用形,べし
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+568 助動詞,*,*,*,文語・マジ,基本形,まじ
+569 助動詞,*,*,*,文語・マジ,体言接続,まじ
+570 助動詞,*,*,*,文語・マジ,連用形,まじ
+571 助動詞,*,*,*,文語・リ,基本形,り
+572 助動詞,*,*,*,文語・リ,体言接続,り
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+575 助動詞,*,*,*,文語・ル,体言接続,る
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+577 助動詞,*,*,*,文語・ル,命令ｅ,る
+578 助動詞,*,*,*,文語・ル,命令ｙｏ,る
+579 助動詞,*,*,*,文語・ル,連用形,る
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+593 動詞,自立,*,*,カ変・クル,命令ｉ,*
+594 動詞,自立,*,*,カ変・クル,命令ｙｏ,*
+595 動詞,自立,*,*,カ変・クル,連用形,*
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+601 動詞,自立,*,*,カ変・来ル,未然ウ接続,*
+602 動詞,自立,*,*,カ変・来ル,未然形,*
+603 動詞,自立,*,*,カ変・来ル,命令ｉ,*
+604 動詞,自立,*,*,カ変・来ル,命令ｙｏ,*
+605 動詞,自立,*,*,カ変・来ル,連用形,*
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+609 動詞,自立,*,*,サ変・−スル,文語基本形,*
+610 動詞,自立,*,*,サ変・−スル,未然ウ接続,*
+611 動詞,自立,*,*,サ変・−スル,未然レル接続,*
+612 動詞,自立,*,*,サ変・−スル,未然形,*
+613 動詞,自立,*,*,サ変・−スル,命令ｒｏ,*
+614 動詞,自立,*,*,サ変・−スル,命令ｙｏ,*
+615 動詞,自立,*,*,サ変・−ズル,仮定形,*
+616 動詞,自立,*,*,サ変・−ズル,仮定縮約１,*
+617 動詞,自立,*,*,サ変・−ズル,基本形,*
+618 動詞,自立,*,*,サ変・−ズル,文語基本形,*
+619 動詞,自立,*,*,サ変・−ズル,未然ウ接続,*
+620 動詞,自立,*,*,サ変・−ズル,未然形,*
+621 動詞,自立,*,*,サ変・−ズル,命令ｙｏ,*
+622 動詞,自立,*,*,サ変・スル,仮定形,する
+623 動詞,自立,*,*,サ変・スル,仮定縮約１,する
+624 動詞,自立,*,*,サ変・スル,基本形,する
+625 動詞,自立,*,*,サ変・スル,体言接続特殊,する
+626 動詞,自立,*,*,サ変・スル,体言接続特殊２,する
+627 動詞,自立,*,*,サ変・スル,文語基本形,する
+628 動詞,自立,*,*,サ変・スル,未然ウ接続,する
+629 動詞,自立,*,*,サ変・スル,未然ヌ接続,する
+630 動詞,自立,*,*,サ変・スル,未然レル接続,する
+631 動詞,自立,*,*,サ変・スル,未然形,する
+632 動詞,自立,*,*,サ変・スル,命令ｉ,する
+633 動詞,自立,*,*,サ変・スル,命令ｒｏ,する
+634 動詞,自立,*,*,サ変・スル,命令ｙｏ,する
+635 動詞,自立,*,*,サ変・スル,連用形,する
+636 動詞,自立,*,*,ラ変,仮定形,*
+637 動詞,自立,*,*,ラ変,基本形,*
+638 動詞,自立,*,*,ラ変,体言接続,*
+639 動詞,自立,*,*,ラ変,未然形,*
+640 動詞,自立,*,*,ラ変,命令ｅ,*
+641 動詞,自立,*,*,ラ変,連用形,*
+642 動詞,自立,*,*,一段,*,*
+643 動詞,自立,*,*,一段,仮定形,*
+644 動詞,自立,*,*,一段,仮定縮約１,*
+645 動詞,自立,*,*,一段,基本形,*
+646 動詞,自立,*,*,一段,基本形-促音便,*
+647 動詞,自立,*,*,一段,体言接続特殊,*
+648 動詞,自立,*,*,一段,未然ウ接続,*
+649 動詞,自立,*,*,一段,未然形,*
+650 動詞,自立,*,*,一段,命令ｒｏ,*
+651 動詞,自立,*,*,一段,命令ｙｏ,*
+652 動詞,自立,*,*,一段,連用形,*
+653 動詞,自立,*,*,一段・クレル,仮定形,*
+654 動詞,自立,*,*,一段・クレル,仮定縮約１,*
+655 動詞,自立,*,*,一段・クレル,基本形,*
+656 動詞,自立,*,*,一段・クレル,未然ウ接続,*
+657 動詞,自立,*,*,一段・クレル,未然形,*
+658 動詞,自立,*,*,一段・クレル,未然特殊,*
+659 動詞,自立,*,*,一段・クレル,命令ｅ,*
+660 動詞,自立,*,*,一段・クレル,命令ｒｏ,*
+661 動詞,自立,*,*,一段・クレル,命令ｙｏ,*
+662 動詞,自立,*,*,一段・クレル,連用形,*
+663 動詞,自立,*,*,一段・得ル,仮定形,*
+664 動詞,自立,*,*,一段・得ル,基本形,*
+665 動詞,自立,*,*,下二・カ行,仮定形,*
+666 動詞,自立,*,*,下二・カ行,基本形,*
+667 動詞,自立,*,*,下二・カ行,体言接続,*
+668 動詞,自立,*,*,下二・カ行,未然形,*
+669 動詞,自立,*,*,下二・カ行,命令ｙｏ,*
+670 動詞,自立,*,*,下二・カ行,連用形,*
+671 動詞,自立,*,*,下二・ガ行,仮定形,*
+672 動詞,自立,*,*,下二・ガ行,基本形,*
+673 動詞,自立,*,*,下二・ガ行,体言接続,*
+674 動詞,自立,*,*,下二・ガ行,未然形,*
+675 動詞,自立,*,*,下二・ガ行,命令ｙｏ,*
+676 動詞,自立,*,*,下二・ガ行,連用形,*
+677 動詞,自立,*,*,下二・ダ行,仮定形,*
+678 動詞,自立,*,*,下二・ダ行,基本形,*
+679 動詞,自立,*,*,下二・ダ行,体言接続,*
+680 動詞,自立,*,*,下二・ダ行,未然形,*
+681 動詞,自立,*,*,下二・ダ行,命令ｙｏ,*
+682 動詞,自立,*,*,下二・ダ行,連用形,*
+683 動詞,自立,*,*,下二・ハ行,仮定形,*
+684 動詞,自立,*,*,下二・ハ行,基本形,*
+685 動詞,自立,*,*,下二・ハ行,体言接続,*
+686 動詞,自立,*,*,下二・ハ行,未然形,*
+687 動詞,自立,*,*,下二・ハ行,命令ｙｏ,*
+688 動詞,自立,*,*,下二・ハ行,連用形,*
+689 動詞,自立,*,*,下二・マ行,仮定形,*
+690 動詞,自立,*,*,下二・マ行,基本形,*
+691 動詞,自立,*,*,下二・マ行,体言接続,*
+692 動詞,自立,*,*,下二・マ行,未然形,*
+693 動詞,自立,*,*,下二・マ行,命令ｙｏ,*
+694 動詞,自立,*,*,下二・マ行,連用形,*
+695 動詞,自立,*,*,下二・得,仮定形,*
+696 動詞,自立,*,*,下二・得,基本形,*
+697 動詞,自立,*,*,下二・得,体言接続,*
+698 動詞,自立,*,*,下二・得,未然ウ接続,*
+699 動詞,自立,*,*,下二・得,未然形,*
+700 動詞,自立,*,*,下二・得,命令ｙｏ,*
+701 動詞,自立,*,*,下二・得,連用形,*
+702 動詞,自立,*,*,五段・カ行イ音便,*,*
+703 動詞,自立,*,*,五段・カ行イ音便,仮定形,*
+704 動詞,自立,*,*,五段・カ行イ音便,仮定形,行く
+705 動詞,自立,*,*,五段・カ行イ音便,仮定縮約１,*
+706 動詞,自立,*,*,五段・カ行イ音便,仮定縮約１,行く
+707 動詞,自立,*,*,五段・カ行イ音便,基本形,*
+708 動詞,自立,*,*,五段・カ行イ音便,基本形,行く
+709 動詞,自立,*,*,五段・カ行イ音便,未然ウ接続,*
+710 動詞,自立,*,*,五段・カ行イ音便,未然ウ接続,行く
+711 動詞,自立,*,*,五段・カ行イ音便,未然形,*
+712 動詞,自立,*,*,五段・カ行イ音便,未然形,行く
+713 動詞,自立,*,*,五段・カ行イ音便,命令ｅ,*
+714 動詞,自立,*,*,五段・カ行イ音便,命令ｅ,行く
+715 動詞,自立,*,*,五段・カ行イ音便,連用タ接続,*
+716 動詞,自立,*,*,五段・カ行イ音便,連用タ接続,行く
+717 動詞,自立,*,*,五段・カ行イ音便,連用形,*
+718 動詞,自立,*,*,五段・カ行イ音便,連用形,行く
+719 動詞,自立,*,*,五段・カ行促音便,仮定形,*
+720 動詞,自立,*,*,五段・カ行促音便,仮定形,行く
+721 動詞,自立,*,*,五段・カ行促音便,仮定縮約１,*
+722 動詞,自立,*,*,五段・カ行促音便,仮定縮約１,行く
+723 動詞,自立,*,*,五段・カ行促音便,基本形,*
+724 動詞,自立,*,*,五段・カ行促音便,基本形,行く
+725 動詞,自立,*,*,五段・カ行促音便,未然ウ接続,*
+726 動詞,自立,*,*,五段・カ行促音便,未然ウ接続,行く
+727 動詞,自立,*,*,五段・カ行促音便,未然形,*
+728 動詞,自立,*,*,五段・カ行促音便,未然形,行く
+729 動詞,自立,*,*,五段・カ行促音便,命令ｅ,*
+730 動詞,自立,*,*,五段・カ行促音便,命令ｅ,行く
+731 動詞,自立,*,*,五段・カ行促音便,連用タ接続,*
+732 動詞,自立,*,*,五段・カ行促音便,連用タ接続,行く
+733 動詞,自立,*,*,五段・カ行促音便,連用形,*
+734 動詞,自立,*,*,五段・カ行促音便,連用形,行く
+735 動詞,自立,*,*,五段・カ行促音便ユク,仮定形,*
+736 動詞,自立,*,*,五段・カ行促音便ユク,仮定形,行く
+737 動詞,自立,*,*,五段・カ行促音便ユク,仮定縮約１,*
+738 動詞,自立,*,*,五段・カ行促音便ユク,仮定縮約１,行く
+739 動詞,自立,*,*,五段・カ行促音便ユク,基本形,*
+740 動詞,自立,*,*,五段・カ行促音便ユク,基本形,行く
+741 動詞,自立,*,*,五段・カ行促音便ユク,未然ウ接続,*
+742 動詞,自立,*,*,五段・カ行促音便ユク,未然ウ接続,行く
+743 動詞,自立,*,*,五段・カ行促音便ユク,未然形,*
+744 動詞,自立,*,*,五段・カ行促音便ユク,未然形,行く
+745 動詞,自立,*,*,五段・カ行促音便ユク,命令ｅ,*
+746 動詞,自立,*,*,五段・カ行促音便ユク,命令ｅ,行く
+747 動詞,自立,*,*,五段・カ行促音便ユク,連用形,*
+748 動詞,自立,*,*,五段・カ行促音便ユク,連用形,行く
+749 動詞,自立,*,*,五段・ガ行,*,*
+750 動詞,自立,*,*,五段・ガ行,仮定形,*
+751 動詞,自立,*,*,五段・ガ行,仮定縮約１,*
+752 動詞,自立,*,*,五段・ガ行,基本形,*
+753 動詞,自立,*,*,五段・ガ行,未然ウ接続,*
+754 動詞,自立,*,*,五段・ガ行,未然形,*
+755 動詞,自立,*,*,五段・ガ行,命令ｅ,*
+756 動詞,自立,*,*,五段・ガ行,連用タ接続,*
+757 動詞,自立,*,*,五段・ガ行,連用形,*
+758 動詞,自立,*,*,五段・サ行,*,*
+759 動詞,自立,*,*,五段・サ行,仮定形,*
+760 動詞,自立,*,*,五段・サ行,仮定縮約１,*
+761 動詞,自立,*,*,五段・サ行,基本形,*
+762 動詞,自立,*,*,五段・サ行,未然ウ接続,*
+763 動詞,自立,*,*,五段・サ行,未然形,*
+764 動詞,自立,*,*,五段・サ行,命令ｅ,*
+765 動詞,自立,*,*,五段・サ行,連用形,*
+766 動詞,自立,*,*,五段・タ行,*,*
+767 動詞,自立,*,*,五段・タ行,仮定形,*
+768 動詞,自立,*,*,五段・タ行,仮定縮約１,*
+769 動詞,自立,*,*,五段・タ行,基本形,*
+770 動詞,自立,*,*,五段・タ行,未然ウ接続,*
+771 動詞,自立,*,*,五段・タ行,未然形,*
+772 動詞,自立,*,*,五段・タ行,命令ｅ,*
+773 動詞,自立,*,*,五段・タ行,連用タ接続,*
+774 動詞,自立,*,*,五段・タ行,連用形,*
+775 動詞,自立,*,*,五段・ナ行,仮定形,*
+776 動詞,自立,*,*,五段・ナ行,仮定縮約１,*
+777 動詞,自立,*,*,五段・ナ行,基本形,*
+778 動詞,自立,*,*,五段・ナ行,未然ウ接続,*
+779 動詞,自立,*,*,五段・ナ行,未然形,*
+780 動詞,自立,*,*,五段・ナ行,命令ｅ,*
+781 動詞,自立,*,*,五段・ナ行,連用タ接続,*
+782 動詞,自立,*,*,五段・ナ行,連用形,*
+783 動詞,自立,*,*,五段・バ行,*,*
+784 動詞,自立,*,*,五段・バ行,仮定形,*
+785 動詞,自立,*,*,五段・バ行,仮定縮約１,*
+786 動詞,自立,*,*,五段・バ行,基本形,*
+787 動詞,自立,*,*,五段・バ行,未然ウ接続,*
+788 動詞,自立,*,*,五段・バ行,未然形,*
+789 動詞,自立,*,*,五段・バ行,命令ｅ,*
+790 動詞,自立,*,*,五段・バ行,連用タ接続,*
+791 動詞,自立,*,*,五段・バ行,連用形,*
+792 動詞,自立,*,*,五段・マ行,*,*
+793 動詞,自立,*,*,五段・マ行,仮定形,*
+794 動詞,自立,*,*,五段・マ行,仮定縮約１,*
+795 動詞,自立,*,*,五段・マ行,基本形,*
+796 動詞,自立,*,*,五段・マ行,未然ウ接続,*
+797 動詞,自立,*,*,五段・マ行,未然形,*
+798 動詞,自立,*,*,五段・マ行,命令ｅ,*
+799 動詞,自立,*,*,五段・マ行,連用タ接続,*
+800 動詞,自立,*,*,五段・マ行,連用形,*
+801 動詞,自立,*,*,五段・ラ行,*,*
+802 動詞,自立,*,*,五段・ラ行,*,する
+803 動詞,自立,*,*,五段・ラ行,仮定形,*
+804 動詞,自立,*,*,五段・ラ行,仮定形,する
+805 動詞,自立,*,*,五段・ラ行,仮定縮約１,*
+806 動詞,自立,*,*,五段・ラ行,仮定縮約１,する
+807 動詞,自立,*,*,五段・ラ行,基本形,*
+808 動詞,自立,*,*,五段・ラ行,基本形,する
+809 動詞,自立,*,*,五段・ラ行,体言接続特殊,*
+810 動詞,自立,*,*,五段・ラ行,体言接続特殊,する
+811 動詞,自立,*,*,五段・ラ行,体言接続特殊２,*
+812 動詞,自立,*,*,五段・ラ行,体言接続特殊２,する
+813 動詞,自立,*,*,五段・ラ行,未然ウ接続,*
+814 動詞,自立,*,*,五段・ラ行,未然ウ接続,する
+815 動詞,自立,*,*,五段・ラ行,未然形,*
+816 動詞,自立,*,*,五段・ラ行,未然形,する
+817 動詞,自立,*,*,五段・ラ行,未然特殊,*
+818 動詞,自立,*,*,五段・ラ行,未然特殊,する
+819 動詞,自立,*,*,五段・ラ行,命令ｅ,*
+820 動詞,自立,*,*,五段・ラ行,命令ｅ,する
+821 動詞,自立,*,*,五段・ラ行,連用タ接続,*
+822 動詞,自立,*,*,五段・ラ行,連用タ接続,する
+823 動詞,自立,*,*,五段・ラ行,連用形,*
+824 動詞,自立,*,*,五段・ラ行,連用形,する
+825 動詞,自立,*,*,五段・ラ行特殊,仮定形,*
+826 動詞,自立,*,*,五段・ラ行特殊,仮定縮約１,*
+827 動詞,自立,*,*,五段・ラ行特殊,基本形,*
+828 動詞,自立,*,*,五段・ラ行特殊,未然ウ接続,*
+829 動詞,自立,*,*,五段・ラ行特殊,未然形,*
+830 動詞,自立,*,*,五段・ラ行特殊,未然特殊,*
+831 動詞,自立,*,*,五段・ラ行特殊,命令ｅ,*
+832 動詞,自立,*,*,五段・ラ行特殊,命令ｉ,*
+833 動詞,自立,*,*,五段・ラ行特殊,連用タ接続,*
+834 動詞,自立,*,*,五段・ラ行特殊,連用形,*
+835 動詞,自立,*,*,五段・ワ行ウ音便,*,*
+836 動詞,自立,*,*,五段・ワ行ウ音便,仮定形,*
+837 動詞,自立,*,*,五段・ワ行ウ音便,仮定形,言う
+838 動詞,自立,*,*,五段・ワ行ウ音便,基本形,*
+839 動詞,自立,*,*,五段・ワ行ウ音便,基本形,言う
+840 動詞,自立,*,*,五段・ワ行ウ音便,未��ウ接続,*
+841 動詞,自立,*,*,五段・ワ行ウ音便,未然ウ接続,言う
+842 動詞,自立,*,*,五段・ワ行ウ音便,未然形,*
+843 動詞,自立,*,*,五段・ワ行ウ音便,未然形,言う
+844 動詞,自立,*,*,五段・ワ行ウ音便,命令ｅ,*
+845 動詞,自立,*,*,五段・ワ行ウ音便,命令ｅ,言う
+846 動詞,自立,*,*,五段・ワ行ウ音便,連用タ接続,*
+847 動詞,自立,*,*,五段・ワ行ウ音便,連用タ接続,言う
+848 動詞,自立,*,*,五段・ワ行ウ音便,連用形,*
+849 動詞,自立,*,*,五段・ワ行ウ音便,連用形,言う
+850 動詞,自立,*,*,五段・ワ行促音便,*,*
+851 動詞,自立,*,*,五段・ワ行促音便,仮定形,*
+852 動詞,自立,*,*,五段・ワ行促音便,仮定形,言う
+853 動詞,自立,*,*,五段・ワ行促音便,仮定形,行う
+854 動詞,自立,*,*,五段・ワ行促音便,基本形,*
+855 動詞,自立,*,*,五段・ワ行促音便,基本形,言う
+856 動詞,自立,*,*,五段・ワ行促音便,基本形,行う
+857 動詞,自立,*,*,五段・ワ行促音便,未然ウ接続,*
+858 動詞,自立,*,*,五段・ワ行促音便,未然ウ接続,言う
+859 動詞,自立,*,*,五段・ワ行促音便,未然ウ接続,行う
+860 動詞,自立,*,*,五段・ワ行促音便,未然形,*
+861 動詞,自立,*,*,五段・ワ行促音便,未然形,言う
+862 動詞,自立,*,*,五段・ワ行促音便,未然形,行う
+863 動詞,自立,*,*,五段・ワ行促音便,命令ｅ,*
+864 動詞,自立,*,*,五段・ワ行促音便,命令ｅ,言う
+865 動詞,自立,*,*,五段・ワ行促音便,命令ｅ,行う
+866 動詞,自立,*,*,五段・ワ行促音便,連用タ接続,*
+867 動詞,自立,*,*,五段・ワ行促音便,連用タ接続,言う
+868 動詞,自立,*,*,五段・ワ行促音便,連用タ接続,行う
+869 動詞,自立,*,*,五段・ワ行促音便,連用形,*
+870 動詞,自立,*,*,五段・ワ行促音便,連用形,言う
+871 動詞,自立,*,*,五段・ワ行促音便,連用形,行う
+872 動詞,自立,*,*,四段・サ行,仮定形,*
+873 動詞,自立,*,*,四段・サ行,基本形,*
+874 動詞,自立,*,*,四段・サ行,未然形,*
+875 動詞,自立,*,*,四段・サ行,命令ｅ,*
+876 動詞,自立,*,*,四段・サ行,連用形,*
+877 動詞,自立,*,*,四段・タ行,仮定形,*
+878 動詞,自立,*,*,四段・タ行,基本形,*
+879 動詞,自立,*,*,四段・タ行,未然形,*
+880 動詞,自立,*,*,四段・タ行,命令ｅ,*
+881 動詞,自立,*,*,四段・タ行,連用形,*
+882 動詞,自立,*,*,四段・ハ行,仮定形,*
+883 動詞,自立,*,*,四段・ハ行,基本形,*
+884 動詞,自立,*,*,四段・ハ行,未然形,*
+885 動詞,自立,*,*,四段・ハ行,命令ｅ,*
+886 動詞,自立,*,*,四段・ハ行,連用形,*
+887 動詞,自立,*,*,四段・バ行,仮定形,*
+888 動詞,自立,*,*,四段・バ行,基本形,*
+889 動詞,自立,*,*,四段・バ行,未然形,*
+890 動詞,自立,*,*,四段・バ行,命令ｅ,*
+891 動詞,自立,*,*,四段・バ行,連用形,*
+892 動詞,自立,*,*,上二・ダ行,仮定形,*
+893 動詞,自立,*,*,上二・ダ行,基本形,*
+894 動詞,自立,*,*,上二・ダ行,現代基本形,*
+895 動詞,自立,*,*,上二・ダ行,体言接続,*
+896 動詞,自立,*,*,上二・ダ行,未然形,*
+897 動詞,自立,*,*,上二・ダ行,命令ｙｏ,*
+898 動詞,自立,*,*,上二・ダ行,連用形,*
+899 動詞,自立,*,*,上二・ハ行,仮定形,*
+900 動詞,自立,*,*,上二・ハ行,基本形,*
+901 動詞,自立,*,*,上二・ハ行,体言接続,*
+902 動詞,自立,*,*,上二・ハ行,未然形,*
+903 動詞,自立,*,*,上二・ハ行,命令ｙｏ,*
+904 動詞,自立,*,*,上二・ハ行,連用形,*
+905 動詞,接尾,*,*,一段,仮定形,*
+906 動詞,接尾,*,*,一段,仮定縮約１,*
+907 動詞,接尾,*,*,一段,基本形,*
+908 動詞,接尾,*,*,一段,基本形-促音便,*
+909 動詞,接尾,*,*,一段,体言接続特殊,*
+910 動詞,接尾,*,*,一段,未然ウ接続,*
+911 動詞,接尾,*,*,一段,未然形,*
+912 動詞,接尾,*,*,一段,命令ｒｏ,*
+913 動詞,接尾,*,*,一段,命令ｙｏ,*
+914 動詞,接尾,*,*,一段,連用形,*
+915 動詞,接尾,*,*,五段・サ行,仮定形,*
+916 動詞,接尾,*,*,五段・サ行,仮定縮約１,*
+917 動詞,接尾,*,*,五段・サ行,基本形,*
+918 動詞,接尾,*,*,五段・サ行,未然ウ接続,*
+919 動詞,接尾,*,*,五段・サ行,未然形,*
+920 動詞,接尾,*,*,五段・サ行,命令ｅ,*
+921 動詞,接尾,*,*,五段・サ行,連用形,*
+922 動詞,接尾,*,*,五段・ラ行,仮定形,*
+923 動詞,接尾,*,*,五段・ラ行,仮定縮約１,*
+924 動詞,接尾,*,*,五段・ラ行,基本形,*
+925 動詞,接尾,*,*,五段・ラ行,体言接続特殊,*
+926 動詞,接尾,*,*,五段・ラ行,体言接続特殊２,*
+927 動詞,接尾,*,*,五段・ラ行,未然ウ接続,*
+928 動詞,接尾,*,*,五段・ラ行,未然形,*
+929 動詞,接尾,*,*,五段・ラ行,未然特殊,*
+930 動詞,接尾,*,*,五段・ラ行,命令ｅ,*
+931 動詞,接尾,*,*,五段・ラ行,連用タ接続,*
+932 動詞,接尾,*,*,五���・ラ行,連用形,*
+933 動詞,非自立,*,*,カ変・クル,仮定形,来る
+934 動詞,非自立,*,*,カ変・クル,仮定縮約１,来る
+935 動詞,非自立,*,*,カ変・クル,基本形,来る
+936 動詞,非自立,*,*,カ変・クル,体言接続特殊,来る
+937 動詞,非自立,*,*,カ変・クル,体言接続特殊２,来る
+938 動詞,非自立,*,*,カ変・クル,未然ウ接続,来る
+939 動詞,非自立,*,*,カ変・クル,未然形,来る
+940 動詞,非自立,*,*,カ変・クル,命令ｉ,来る
+941 動詞,非自立,*,*,カ変・クル,命令ｙｏ,来る
+942 動詞,非自立,*,*,カ変・クル,連用形,来る
+943 動詞,非自立,*,*,カ変・来ル,仮定形,来る
+944 動詞,非自立,*,*,カ変・来ル,仮定縮約１,来る
+945 動詞,非自立,*,*,カ変・来ル,基本形,来る
+946 動詞,非自立,*,*,カ変・来ル,体言接続特殊,来る
+947 動詞,非自立,*,*,カ変・来ル,体言接続特殊２,来る
+948 動詞,非自立,*,*,カ変・来ル,未然ウ接続,来る
+949 動詞,非自立,*,*,カ変・来ル,未然形,来る
+950 動詞,非自立,*,*,カ変・来ル,命令ｉ,来る
+951 動詞,非自立,*,*,カ変・来ル,命令ｙｏ,来る
+952 動詞,非自立,*,*,カ変・来ル,連用形,来る
+953 動詞,非自立,*,*,一段,*,*
+954 動詞,非自立,*,*,一段,仮定形,*
+955 動詞,非自立,*,*,一段,仮定形,る
+956 動詞,非自立,*,*,一段,仮定縮約１,*
+957 動詞,非自立,*,*,一段,仮定縮約１,る
+958 動詞,非自立,*,*,一段,基本形,*
+959 動詞,非自立,*,*,一段,基本形,る
+960 動詞,非自立,*,*,一段,基本形-促音便,*
+961 動詞,非自立,*,*,一段,基本形-促音便,る
+962 動詞,非自立,*,*,一段,体言接続特殊,*
+963 動詞,非自立,*,*,一段,体言接続特殊,る
+964 動詞,非自立,*,*,一段,未然ウ接続,*
+965 動詞,非自立,*,*,一段,未然ウ接続,る
+966 動詞,非自立,*,*,一段,未然形,*
+967 動詞,非自立,*,*,一段,命令ｒｏ,*
+968 動詞,非自立,*,*,一段,命令ｒｏ,る
+969 動詞,非自立,*,*,一段,命令ｙｏ,*
+970 動詞,非自立,*,*,一段,命令ｙｏ,る
+971 動詞,非自立,*,*,一段,連用形,*
+972 動詞,非自立,*,*,一段・クレル,仮定形,くれる
+973 動詞,非自立,*,*,一段・クレル,仮定縮約１,くれる
+974 動詞,非自立,*,*,一段・クレル,基本形,くれる
+975 動詞,非自立,*,*,一段・クレル,未然ウ接続,くれる
+976 動詞,非自立,*,*,一段・クレル,未然形,くれる
+977 動詞,非自立,*,*,一段・クレル,未然特殊,くれる
+978 動詞,非自立,*,*,一段・クレル,命令ｅ,くれる
+979 動詞,非自立,*,*,一段・クレル,命令ｒｏ,くれる
+980 動詞,非自立,*,*,一段・クレル,命令ｙｏ,くれる
+981 動詞,非自立,*,*,一段・クレル,連用形,くれる
+982 動詞,非自立,*,*,一段・得ル,仮定形,*
+983 動詞,非自立,*,*,一段・得ル,基本形,*
+984 動詞,非自立,*,*,五段・カ行イ音便,*,*
+985 動詞,非自立,*,*,五段・カ行イ音便,仮定形,*
+986 動詞,非自立,*,*,五段・カ行イ音便,仮定形,おく
+987 動詞,非自立,*,*,五段・カ行イ音便,仮定形,続く
+988 動詞,非自立,*,*,五段・カ行イ音便,仮定形,抜く
+989 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,*
+990 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,おく
+991 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,続く
+992 動詞,非自立,*,*,五段・カ行イ音便,仮定縮約１,抜く
+993 動詞,非自立,*,*,五段・カ行イ音便,基本形,*
+994 動詞,非自立,*,*,五段・カ行イ音便,基本形,おく
+995 動詞,非自立,*,*,五段・カ行イ音便,基本形,続く
+996 動詞,非自立,*,*,五段・カ行イ音便,基本形,抜く
+997 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,*
+998 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,おく
+999 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,続く
+1000 動詞,非自立,*,*,五段・カ行イ音便,未然ウ接続,抜く
+1001 動詞,非自立,*,*,五段・カ行イ音便,未然形,*
+1002 動詞,非自立,*,*,五段・カ行イ音便,未然形,おく
+1003 動詞,非自立,*,*,五段・カ行イ音便,未然形,続く
+1004 動詞,非自立,*,*,五段・カ行イ音便,未然形,抜く
+1005 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,*
+1006 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,おく
+1007 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,続く
+1008 動詞,非自立,*,*,五段・カ行イ音便,命令ｅ,抜く
+1009 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,*
+1010 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,おく
+1011 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,続く
+1012 動詞,非自立,*,*,五段・カ行イ音便,連用タ接続,抜く
+1013 動詞,非自立,*,*,五段・カ行イ音便,連用形,*
+1014 動詞,非自立,*,*,五段・カ行イ音便,連用形,おく
+1015 動詞,非自立,*,*,五段・カ行イ音便,連用形,続く
+1016 動詞,非自立,*,*,五段・カ行イ音便,連用形,抜く
+1017 動詞,非自立,*,*,五段・カ行促音便,仮定形,*
+1018 動詞,非自立,*,*,五段・カ行促音便,仮定形,いく
+1019 動詞,非自立,*,*,五段・カ行促音便,仮定形,く
+1020 動詞,非自立,*,*,五段・カ行促音便,仮定形,行く
+1021 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,*
+1022 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,いく
+1023 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,く
+1024 動詞,非自立,*,*,五段・カ行促音便,仮定縮約１,行く
+1025 動詞,非自立,*,*,五段・カ行促音便,基本形,*
+1026 動詞,非自立,*,*,五段・カ行促音便,基本形,いく
+1027 動詞,非自立,*,*,五段・カ行促音便,基本形,く
+1028 動詞,非自立,*,*,五段・カ行促音便,基本形,行く
+1029 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,*
+1030 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,いく
+1031 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,く
+1032 動詞,非自立,*,*,五段・カ行促音便,未然ウ接続,行く
+1033 動詞,非自立,*,*,五段・カ行促音便,未然形,*
+1034 動詞,非自立,*,*,五段・カ行促音便,未然形,いく
+1035 動詞,非自立,*,*,五段・カ行促音便,未然形,く
+1036 動詞,非自立,*,*,五段・カ行促音便,未然形,行く
+1037 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,*
+1038 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,いく
+1039 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,く
+1040 動詞,非自立,*,*,五段・カ行促音便,命令ｅ,行く
+1041 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,*
+1042 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,いく
+1043 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,く
+1044 動詞,非自立,*,*,五段・カ行促音便,連用タ接続,行く
+1045 動詞,非自立,*,*,五段・カ行促音便,連用形,*
+1046 動詞,非自立,*,*,五段・カ行促音便,連用形,いく
+1047 動詞,非自立,*,*,五段・カ行促音便,連用形,く
+1048 動詞,非自立,*,*,五段・カ行促音便,連用形,行く
+1049 動詞,非自立,*,*,五段・カ行促音便ユク,仮定形,ゆく
+1050 動詞,非自立,*,*,五段・カ行促音便ユク,仮定形,行く
+1051 動詞,非自立,*,*,五段・カ行促音便ユク,仮定縮約１,ゆく
+1052 動詞,非自立,*,*,五段・カ行促音便ユク,仮定縮約１,行く
+1053 動詞,非自立,*,*,五段・カ行促音便ユク,基本形,ゆく
+1054 動詞,非自立,*,*,五段・カ行促音便ユク,基本形,行く
+1055 動詞,非自立,*,*,五段・カ行促音便ユク,未然ウ接続,ゆく
+1056 動詞,非自立,*,*,五段・カ行促音便ユク,未然ウ接続,行く
+1057 動詞,非自立,*,*,五段・カ行促音便ユク,未然形,ゆく
+1058 動詞,非自立,*,*,五段・カ行促音便ユク,未然形,行く
+1059 動詞,非自立,*,*,五段・カ行促音便ユク,命令ｅ,ゆく
+1060 動詞,非自立,*,*,五段・カ行促音便ユク,命令ｅ,行く
+1061 動詞,非自立,*,*,五段・カ行促音便ユク,連用形,ゆく
+1062 動詞,非自立,*,*,五段・カ行促音便ユク,連用形,行く
+1063 動詞,非自立,*,*,五段・サ行,*,*
+1064 動詞,非自立,*,*,五段・サ行,*,尽くす
+1065 動詞,非自立,*,*,五段・サ行,仮定形,*
+1066 動詞,非自立,*,*,五段・サ行,仮定形,出す
+1067 動詞,非自立,*,*,五段・サ行,仮定形,尽くす
+1068 動詞,非自立,*,*,五段・サ行,仮定形,直す
+1069 動詞,非自立,*,*,五段・サ行,仮定縮約１,*
+1070 動詞,非自立,*,*,五段・サ行,仮定縮約１,出す
+1071 動詞,非自立,*,*,五段・サ行,仮定縮約１,尽くす
+1072 動詞,非自立,*,*,五段・サ行,仮定縮約１,直す
+1073 動詞,非自立,*,*,五段・サ行,基本形,出す
+1074 動詞,非自立,*,*,五段・サ行,基本形,尽くす
+1075 動詞,非自立,*,*,五段・サ行,基本形,直す
+1076 動詞,非自立,*,*,五段・サ行,未然ウ接続,*
+1077 動詞,非自立,*,*,五段・サ行,未然ウ接続,出す
+1078 動詞,非自立,*,*,五段・サ行,未然ウ接続,尽くす
+1079 動詞,非自立,*,*,五段・サ行,未然ウ接続,直す
+1080 動詞,非自立,*,*,五段・サ行,未然形,*
+1081 動詞,非自立,*,*,五段・サ行,未然形,出す
+1082 動詞,非自立,*,*,五段・サ行,未然形,尽くす
+1083 動詞,非自立,*,*,五段・サ行,未然形,直す
+1084 動詞,非自立,*,*,五段・サ行,命令ｅ,*
+1085 動詞,非自立,*,*,五段・サ行,命令ｅ,出す
+1086 動詞,非自立,*,*,五段・サ行,命令ｅ,尽くす
+1087 動詞,非自立,*,*,五段・サ行,命令ｅ,直す
+1088 動詞,非自立,*,*,五段・サ行,連用形,*
+1089 動詞,非自立,*,*,五段・サ行,連用形,出す
+1090 動詞,非自立,*,*,五段・サ���,連用形,尽くす
+1091 動詞,非自立,*,*,五段・サ行,連用形,直す
+1092 動詞,非自立,*,*,五段・タ行,*,*
+1093 動詞,非自立,*,*,五段・タ行,仮定形,*
+1094 動詞,非自立,*,*,五段・タ行,仮定縮約１,*
+1095 動詞,非自立,*,*,五段・タ行,未然ウ接続,*
+1096 動詞,非自立,*,*,五段・タ行,未然形,*
+1097 動詞,非自立,*,*,五段・タ行,命令ｅ,*
+1098 動詞,非自立,*,*,五段・タ行,連用タ接続,*
+1099 動詞,非自立,*,*,五段・タ行,連用形,*
+1100 動詞,非自立,*,*,五段・ナ行,*,*
+1101 動詞,非自立,*,*,五段・ナ行,仮定形,*
+1102 動詞,非自立,*,*,五段・ナ行,仮定縮約１,*
+1103 動詞,非自立,*,*,五段・ナ行,未然ウ接続,*
+1104 動詞,非自立,*,*,五段・ナ行,未然形,*
+1105 動詞,非自立,*,*,五段・ナ行,命令ｅ,*
+1106 動詞,非自立,*,*,五段・ナ行,連用タ接続,*
+1107 動詞,非自立,*,*,五段・ナ行,連用形,*
+1108 動詞,非自立,*,*,五段・マ行,*,*
+1109 動詞,非自立,*,*,五段・マ行,仮定形,*
+1110 動詞,非自立,*,*,五段・マ行,仮定形,込む
+1111 動詞,非自立,*,*,五段・マ行,仮定縮約１,*
+1112 動詞,非自立,*,*,五段・マ行,仮定縮約１,込む
+1113 動詞,非自立,*,*,五段・マ行,基本形,込む
+1114 動詞,非自立,*,*,五段・マ行,未然ウ接続,*
+1115 動詞,非自立,*,*,五段・マ行,未然ウ接続,込む
+1116 動詞,非自立,*,*,五段・マ行,未然形,*
+1117 動詞,非自立,*,*,五段・マ行,未然形,込む
+1118 動詞,非自立,*,*,五段・マ行,命令ｅ,*
+1119 動詞,非自立,*,*,五段・マ行,命令ｅ,込む
+1120 動詞,非自立,*,*,五段・マ行,連用タ接続,*
+1121 動詞,非自立,*,*,五段・マ行,連用タ接続,込む
+1122 動詞,非自立,*,*,五段・マ行,連用形,*
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+1361 名詞,接尾,助動詞語幹,*,*,*,*
+1362 名詞,接尾,人名,*,*,*,*
+1363 名詞,接尾,地域,*,*,*,*
+1364 名詞,接尾,特殊,*,*,*,*
+1365 名詞,接尾,副詞可能,*,*,*,*
+1366 名詞,代名詞,一般,*,*,*,*
+1367 名詞,代名詞,縮約,*,*,*,*
+1368 名詞,動詞非自立的,*,*,*,*,*
+1369 名詞,特殊,助動詞語幹,*,*,*,*
+1370 名詞,非自立,*,*,*,*,*
+1371 名詞,非自立,一般,*,*,*,*
+1372 名詞,非自立,形容動詞語幹,*,*,*,*
+1373 名詞,非自立,助動詞語幹,*,*,*,*
+1374 名詞,非自立,副詞可能,*,*,*,*
+1375 名詞,副詞可能,*,*,*,*,*
+1376 連体詞,*,*,*,*,*,*

finetune_speaker_v2.py

@@ -0,0 +1,351 @@
+import os
+import json
+import argparse
+import itertools
+import math
+import torch
+from torch import nn, optim
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+from tqdm import tqdm
+
+import librosa
+import logging
+
+logging.getLogger('numba').setLevel(logging.WARNING)
+
+import commons
+import utils
+from data_utils import (
+  TextAudioSpeakerLoader,
+  TextAudioSpeakerCollate,
+  DistributedBucketSampler
+)
+from models import (
+  SynthesizerTrn,
+  MultiPeriodDiscriminator,
+)
+from losses import (
+  generator_loss,
+  discriminator_loss,
+  feature_loss,
+  kl_loss
+)
+from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+
+
+torch.backends.cudnn.benchmark = True
+global_step = 0
+
+
+def main():
+  """Assume Single Node Multi GPUs Training Only"""
+  assert torch.cuda.is_available(), "CPU training is not allowed."
+
+  n_gpus = torch.cuda.device_count()
+  os.environ['MASTER_ADDR'] = 'localhost'
+  os.environ['MASTER_PORT'] = '8000'
+
+  hps = utils.get_hparams()
+  mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
+
+
+def run(rank, n_gpus, hps):
+  global global_step
+  symbols = hps['symbols']
+  if rank == 0:
+    logger = utils.get_logger(hps.model_dir)
+    logger.info(hps)
+    utils.check_git_hash(hps.model_dir)
+    writer = SummaryWriter(log_dir=hps.model_dir)
+    writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
+
+  # Use gloo backend on Windows for Pytorch
+  dist.init_process_group(backend=  'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
+  torch.manual_seed(hps.train.seed)
+  torch.cuda.set_device(rank)
+
+  train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data, symbols)
+  train_sampler = DistributedBucketSampler(
+      train_dataset,
+      hps.train.batch_size,
+      [32,300,400,500,600,700,800,900,1000],
+      num_replicas=n_gpus,
+      rank=rank,
+      shuffle=True)
+  collate_fn = TextAudioSpeakerCollate()
+  train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
+      collate_fn=collate_fn, batch_sampler=train_sampler)
+  # train_loader = DataLoader(train_dataset, batch_size=hps.train.batch_size, num_workers=2, shuffle=False, pin_memory=True,
+  #                           collate_fn=collate_fn)
+  if rank == 0:
+    eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, symbols)
+    eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
+        batch_size=hps.train.batch_size, pin_memory=True,
+        drop_last=False, collate_fn=collate_fn)
+
+  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).cuda(rank)
+  net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
+
+  # load existing model
+  if hps.cont:
+      try:
+          _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_latest.pth"), net_g, None)
+          _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_latest.pth"), net_d, None)
+          global_step = (epoch_str - 1) * len(train_loader)
+      except:
+          print("Failed to find latest checkpoint, loading G_0.pth...")
+          if hps.train_with_pretrained_model:
+              print("Train with pretrained model...")
+              _, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/G_0.pth", net_g, None)
+              _, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/D_0.pth", net_d, None)
+          else:
+              print("Train without pretrained model...")
+          epoch_str = 1
+          global_step = 0
+  else:
+      if hps.train_with_pretrained_model:
+          print("Train with pretrained model...")
+          _, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/G_0.pth", net_g, None)
+          _, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/D_0.pth", net_d, None)
+      else:
+          print("Train without pretrained model...")
+      epoch_str = 1
+      global_step = 0
+  # freeze all other layers except speaker embedding
+  for p in net_g.parameters():
+      p.requires_grad = True
+  for p in net_d.parameters():
+      p.requires_grad = True
+  # for p in net_d.parameters():
+  #     p.requires_grad = False
+  # net_g.emb_g.weight.requires_grad = True
+  optim_g = torch.optim.AdamW(
+      net_g.parameters(),
+      hps.train.learning_rate,
+      betas=hps.train.betas,
+      eps=hps.train.eps)
+  optim_d = torch.optim.AdamW(
+      net_d.parameters(),
+      hps.train.learning_rate,
+      betas=hps.train.betas,
+      eps=hps.train.eps)
+  # optim_d = None
+  net_g = DDP(net_g, device_ids=[rank])
+  net_d = DDP(net_d, device_ids=[rank])
+
+  scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay)
+  scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay)
+
+  scaler = GradScaler(enabled=hps.train.fp16_run)
+
+  for epoch in range(epoch_str, hps.train.epochs + 1):
+    if rank==0:
+      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
+    else:
+      train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
+    scheduler_g.step()
+    scheduler_d.step()
+
+
+def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
+  net_g, net_d = nets
+  optim_g, optim_d = optims
+  scheduler_g, scheduler_d = schedulers
+  train_loader, eval_loader = loaders
+  if writers is not None:
+    writer, writer_eval = writers
+
+  # train_loader.batch_sampler.set_epoch(epoch)
+  global global_step
+
+  net_g.train()
+  net_d.train()
+  for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(tqdm(train_loader)):
+    x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
+    spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
+    y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
+    speakers = speakers.cuda(rank, non_blocking=True)
+
+    with autocast(enabled=hps.train.fp16_run):
+      y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
+      (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)
+
+      mel = spec_to_mel_torch(
+          spec,
+          hps.data.filter_length,
+          hps.data.n_mel_channels,
+          hps.data.sampling_rate,
+          hps.data.mel_fmin,
+          hps.data.mel_fmax)
+      y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
+      y_hat_mel = mel_spectrogram_torch(
+          y_hat.squeeze(1),
+          hps.data.filter_length,
+          hps.data.n_mel_channels,
+          hps.data.sampling_rate,
+          hps.data.hop_length,
+          hps.data.win_length,
+          hps.data.mel_fmin,
+          hps.data.mel_fmax
+      )
+
+      y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
+
+      # Discriminator
+      y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+      with autocast(enabled=False):
+        loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
+        loss_disc_all = loss_disc
+    optim_d.zero_grad()
+    scaler.scale(loss_disc_all).backward()
+    scaler.unscale_(optim_d)
+    grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
+    scaler.step(optim_d)
+
+    with autocast(enabled=hps.train.fp16_run):
+      # Generator
+      y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+      with autocast(enabled=False):
+        loss_dur = torch.sum(l_length.float())
+        loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+        loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+        loss_fm = feature_loss(fmap_r, fmap_g)
+        loss_gen, losses_gen = generator_loss(y_d_hat_g)
+        loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
+    optim_g.zero_grad()
+    scaler.scale(loss_gen_all).backward()
+    scaler.unscale_(optim_g)
+    grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
+    scaler.step(optim_g)
+    scaler.update()
+
+    if rank==0:
+      if global_step % hps.train.log_interval == 0:
+        lr = optim_g.param_groups[0]['lr']
+        losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
+        logger.info('Train Epoch: {} [{:.0f}%]'.format(
+          epoch,
+          100. * batch_idx / len(train_loader)))
+        logger.info([x.item() for x in losses] + [global_step, lr])
+
+        scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_g": grad_norm_g}
+        scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
+
+        scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
+        scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
+        scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
+        image_dict = {
+            "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
+            "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
+            "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
+            "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
+        }
+        utils.summarize(
+          writer=writer,
+          global_step=global_step,
+          images=image_dict,
+          scalars=scalar_dict)
+
+      if global_step % hps.train.eval_interval == 0:
+        evaluate(hps, net_g, eval_loader, writer_eval)
+        
+        utils.save_checkpoint(net_g, None, hps.train.learning_rate, epoch,
+                              os.path.join(hps.model_dir, "G_latest.pth".format(global_step)))
+        
+        utils.save_checkpoint(net_d, None, hps.train.learning_rate, epoch,
+                              os.path.join(hps.model_dir, "D_latest.pth".format(global_step)))
+        if hps.preserved > 0:
+          utils.save_checkpoint(net_g, None, hps.train.learning_rate, epoch,
+                                  os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
+          utils.save_checkpoint(net_d, None, hps.train.learning_rate, epoch,
+                                  os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
+          old_g = utils.oldest_checkpoint_path(hps.model_dir, "G_[0-9]*.pth",
+                                               preserved=hps.preserved)  # Preserve 4 (default) historical checkpoints.
+          old_d = utils.oldest_checkpoint_path(hps.model_dir, "D_[0-9]*.pth", preserved=hps.preserved)
+          if os.path.exists(old_g):
+            print(f"remove {old_g}")
+            os.remove(old_g)
+          if os.path.exists(old_d):
+            print(f"remove {old_d}")
+            os.remove(old_d)
+    global_step += 1
+    if epoch > hps.max_epochs:
+        print("Maximum epoch reached, closing training...")
+        exit()
+
+  if rank == 0:
+    logger.info('====> Epoch: {}'.format(epoch))
+
+
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    with torch.no_grad():
+      for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(eval_loader):
+        x, x_lengths = x.cuda(0), x_lengths.cuda(0)
+        spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
+        y, y_lengths = y.cuda(0), y_lengths.cuda(0)
+        speakers = speakers.cuda(0)
+
+        # remove else
+        x = x[:1]
+        x_lengths = x_lengths[:1]
+        spec = spec[:1]
+        spec_lengths = spec_lengths[:1]
+        y = y[:1]
+        y_lengths = y_lengths[:1]
+        speakers = speakers[:1]
+        break
+      y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, max_len=1000)
+      y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
+
+      mel = spec_to_mel_torch(
+        spec,
+        hps.data.filter_length,
+        hps.data.n_mel_channels,
+        hps.data.sampling_rate,
+        hps.data.mel_fmin,
+        hps.data.mel_fmax)
+      y_hat_mel = mel_spectrogram_torch(
+        y_hat.squeeze(1).float(),
+        hps.data.filter_length,
+        hps.data.n_mel_channels,
+        hps.data.sampling_rate,
+        hps.data.hop_length,
+        hps.data.win_length,
+        hps.data.mel_fmin,
+        hps.data.mel_fmax
+      )
+    image_dict = {
+      "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
+    }
+    audio_dict = {
+      "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
+    }
+    if global_step == 0:
+      image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
+      audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
+
+    utils.summarize(
+      writer=writer_eval,
+      global_step=global_step,
+      images=image_dict,
+      audios=audio_dict,
+      audio_sampling_rate=hps.data.sampling_rate
+    )
+    generator.train()
+
+
+if __name__ == "__main__":
+  main()

losses.py

@@ -0,0 +1,61 @@
+import torch 
+from torch.nn import functional as F
+
+import commons
+
+
+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. * logs_p)
+  kl = torch.sum(kl * z_mask)
+  l = kl / torch.sum(z_mask)
+  return l

mel_processing.py

@@ -0,0 +1,112 @@
+import math
+import os
+import random
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.utils.data
+import numpy as np
+import librosa
+import librosa.util as librosa_util
+from librosa.util import normalize, pad_center, tiny
+from scipy.signal import get_window
+from scipy.io.wavfile import read
+from librosa.filters import mel as librosa_mel_fn
+
+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.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        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(sampling_rate, n_fft, num_mels, fmin, 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.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        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(sampling_rate, n_fft, num_mels, fmin, 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.float(), 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

models.py

@@ -0,0 +1,533 @@
+import copy
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+import attentions
+import monotonic_align
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+
+
+class StochasticDurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+    super().__init__()
+    filter_channels = in_channels # it needs to be removed from future version.
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.log_flow = modules.Log()
+    self.flows = nn.ModuleList()
+    self.flows.append(modules.ElementwiseAffine(2))
+    for i in range(n_flows):
+      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.flows.append(modules.Flip())
+
+    self.post_pre = nn.Conv1d(1, filter_channels, 1)
+    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    self.post_flows = nn.ModuleList()
+    self.post_flows.append(modules.ElementwiseAffine(2))
+    for i in range(4):
+      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.post_flows.append(modules.Flip())
+
+    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+    x = torch.detach(x)
+    x = self.pre(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.convs(x, x_mask)
+    x = self.proj(x) * x_mask
+
+    if not reverse:
+      flows = self.flows
+      assert w is not None
+
+      logdet_tot_q = 0
+      h_w = self.post_pre(w)
+      h_w = self.post_convs(h_w, x_mask)
+      h_w = self.post_proj(h_w) * x_mask
+      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+      z_q = e_q
+      for flow in self.post_flows:
+        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+        logdet_tot_q += logdet_q
+      z_u, z1 = torch.split(z_q, [1, 1], 1)
+      u = torch.sigmoid(z_u) * x_mask
+      z0 = (w - u) * x_mask
+      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
+      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
+
+      logdet_tot = 0
+      z0, logdet = self.log_flow(z0, x_mask)
+      logdet_tot += logdet
+      z = torch.cat([z0, z1], 1)
+      for flow in flows:
+        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+        logdet_tot = logdet_tot + logdet
+      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
+      return nll + logq # [b]
+    else:
+      flows = list(reversed(self.flows))
+      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
+      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+      for flow in flows:
+        z = flow(z, x_mask, g=x, reverse=reverse)
+      z0, z1 = torch.split(z, [1, 1], 1)
+      logw = z0
+      return logw
+
+
+class DurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+    super().__init__()
+
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.gin_channels = gin_channels
+
+    self.drop = nn.Dropout(p_dropout)
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_1 = modules.LayerNorm(filter_channels)
+    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_2 = modules.LayerNorm(filter_channels)
+    self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+  def forward(self, x, x_mask, g=None):
+    x = torch.detach(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.conv_1(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_1(x)
+    x = self.drop(x)
+    x = self.conv_2(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_2(x)
+    x = self.drop(x)
+    x = self.proj(x * x_mask)
+    return x * x_mask
+
+
+class TextEncoder(nn.Module):
+  def __init__(self,
+      n_vocab,
+      out_channels,
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout):
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+
+    self.emb = nn.Embedding(n_vocab, hidden_channels)
+    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+    self.encoder = attentions.Encoder(
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout)
+    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths):
+    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
+    x = torch.transpose(x, 1, -1) # [b, h, t]
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+    x = self.encoder(x * x_mask, x_mask)
+    stats = self.proj(x) * x_mask
+
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class PosteriorEncoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self,
+    n_vocab,
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock,
+    resblock_kernel_sizes,
+    resblock_dilation_sizes,
+    upsample_rates,
+    upsample_initial_channel,
+    upsample_kernel_sizes,
+    n_speakers=0,
+    gin_channels=0,
+    use_sdp=True,
+    **kwargs):
+
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.n_speakers = n_speakers
+    self.gin_channels = gin_channels
+
+    self.use_sdp = use_sdp
+
+    self.enc_p = TextEncoder(n_vocab,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+    if use_sdp:
+      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
+    else:
+      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
+
+    if n_speakers >= 1:
+      self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+  def forward(self, x, x_lengths, y, y_lengths, sid=None):
+
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+    z_p = self.flow(z, y_mask, g=g)
+
+    with torch.no_grad():
+      # negative cross-entropy
+      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
+      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+    w = attn.sum(2)
+    if self.use_sdp:
+      l_length = self.dp(x, x_mask, w, g=g)
+      l_length = l_length / torch.sum(x_mask)
+    else:
+      logw_ = torch.log(w + 1e-6) * x_mask
+      logw = self.dp(x, x_mask, g=g)
+      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
+
+    # expand prior
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
+
+    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    if self.use_sdp:
+      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+    else:
+      logw = self.dp(x, x_mask, g=g)
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+    attn = commons.generate_path(w_ceil, attn_mask)
+
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    z = self.flow(z_p, y_mask, g=g, reverse=True)
+    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
+    return o, attn, y_mask, (z, z_p, m_p, logs_p)
+
+  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
+    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
+    g_src = self.emb_g(sid_src).unsqueeze(-1)
+    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
+    z_p = self.flow(z, y_mask, g=g_src)
+    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
+    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
+    return o_hat, y_mask, (z, z_p, z_hat)

models_infer.py

@@ -0,0 +1,402 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import commons
+import modules
+import attentions
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from commons import init_weights, get_padding
+
+
+class StochasticDurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+    super().__init__()
+    filter_channels = in_channels # it needs to be removed from future version.
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.log_flow = modules.Log()
+    self.flows = nn.ModuleList()
+    self.flows.append(modules.ElementwiseAffine(2))
+    for i in range(n_flows):
+      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.flows.append(modules.Flip())
+
+    self.post_pre = nn.Conv1d(1, filter_channels, 1)
+    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    self.post_flows = nn.ModuleList()
+    self.post_flows.append(modules.ElementwiseAffine(2))
+    for i in range(4):
+      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.post_flows.append(modules.Flip())
+
+    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+    x = torch.detach(x)
+    x = self.pre(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.convs(x, x_mask)
+    x = self.proj(x) * x_mask
+
+    if not reverse:
+      flows = self.flows
+      assert w is not None
+
+      logdet_tot_q = 0
+      h_w = self.post_pre(w)
+      h_w = self.post_convs(h_w, x_mask)
+      h_w = self.post_proj(h_w) * x_mask
+      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+      z_q = e_q
+      for flow in self.post_flows:
+        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+        logdet_tot_q += logdet_q
+      z_u, z1 = torch.split(z_q, [1, 1], 1)
+      u = torch.sigmoid(z_u) * x_mask
+      z0 = (w - u) * x_mask
+      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
+      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
+
+      logdet_tot = 0
+      z0, logdet = self.log_flow(z0, x_mask)
+      logdet_tot += logdet
+      z = torch.cat([z0, z1], 1)
+      for flow in flows:
+        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+        logdet_tot = logdet_tot + logdet
+      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
+      return nll + logq # [b]
+    else:
+      flows = list(reversed(self.flows))
+      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
+      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+      for flow in flows:
+        z = flow(z, x_mask, g=x, reverse=reverse)
+      z0, z1 = torch.split(z, [1, 1], 1)
+      logw = z0
+      return logw
+
+
+class DurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+    super().__init__()
+
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.gin_channels = gin_channels
+
+    self.drop = nn.Dropout(p_dropout)
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_1 = modules.LayerNorm(filter_channels)
+    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_2 = modules.LayerNorm(filter_channels)
+    self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+  def forward(self, x, x_mask, g=None):
+    x = torch.detach(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.conv_1(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_1(x)
+    x = self.drop(x)
+    x = self.conv_2(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_2(x)
+    x = self.drop(x)
+    x = self.proj(x * x_mask)
+    return x * x_mask
+
+
+class TextEncoder(nn.Module):
+  def __init__(self,
+      n_vocab,
+      out_channels,
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout):
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+
+    self.emb = nn.Embedding(n_vocab, hidden_channels)
+    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+    self.encoder = attentions.Encoder(
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout)
+    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths):
+    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
+    x = torch.transpose(x, 1, -1) # [b, h, t]
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+    x = self.encoder(x * x_mask, x_mask)
+    stats = self.proj(x) * x_mask
+
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class PosteriorEncoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self,
+    n_vocab,
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock,
+    resblock_kernel_sizes,
+    resblock_dilation_sizes,
+    upsample_rates,
+    upsample_initial_channel,
+    upsample_kernel_sizes,
+    n_speakers=0,
+    gin_channels=0,
+    use_sdp=True,
+    **kwargs):
+
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.n_speakers = n_speakers
+    self.gin_channels = gin_channels
+
+    self.use_sdp = use_sdp
+
+    self.enc_p = TextEncoder(n_vocab,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+    if use_sdp:
+      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
+    else:
+      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
+
+    if n_speakers > 1:
+      self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    if self.use_sdp:
+      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+    else:
+      logw = self.dp(x, x_mask, g=g)
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+    attn = commons.generate_path(w_ceil, attn_mask)
+
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    z = self.flow(z_p, y_mask, g=g, reverse=True)
+    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
+    return o, attn, y_mask, (z, z_p, m_p, logs_p)
+
+  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
+    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
+    g_src = self.emb_g(sid_src).unsqueeze(-1)
+    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
+    z_p = self.flow(z, y_mask, g=g_src)
+    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
+    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
+    return o_hat, y_mask, (z, z_p, z_hat)
+

modules.py

@@ -0,0 +1,390 @@
+import copy
+import math
+import numpy as np
+import scipy
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+import commons
+from commons import init_weights, get_padding
+from transforms import piecewise_rational_quadratic_transform
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    super().__init__()
+    self.channels = channels
+    self.eps = eps
+
+    self.gamma = nn.Parameter(torch.ones(channels))
+    self.beta = nn.Parameter(torch.zeros(channels))
+
+  def forward(self, x):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+ 
+class ConvReluNorm(nn.Module):
+  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+    super().__init__()
+    self.in_channels = in_channels
+    self.hidden_channels = hidden_channels
+    self.out_channels = out_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+    assert n_layers > 1, "Number of layers should be larger than 0."
+
+    self.conv_layers = nn.ModuleList()
+    self.norm_layers = nn.ModuleList()
+    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+    self.norm_layers.append(LayerNorm(hidden_channels))
+    self.relu_drop = nn.Sequential(
+        nn.ReLU(),
+        nn.Dropout(p_dropout))
+    for _ in range(n_layers-1):
+      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+      self.norm_layers.append(LayerNorm(hidden_channels))
+    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask):
+    x_org = x
+    for i in range(self.n_layers):
+      x = self.conv_layers[i](x * x_mask)
+      x = self.norm_layers[i](x)
+      x = self.relu_drop(x)
+    x = x_org + self.proj(x)
+    return x * x_mask
+
+
+class DDSConv(nn.Module):
+  """
+  Dialted and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    self.hidden_channels =hidden_channels
+    self.kernel_size = kernel_size,
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+    self.p_dropout = p_dropout
+
+    self.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
+      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+    for i in range(n_layers):
+      dilation = dilation_rate ** i
+      padding = int((kernel_size * dilation - dilation) / 2)
+      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
+                                 dilation=dilation, padding=padding)
+      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+      self.in_layers.append(in_layer)
+
+      # last one is not necessary
+      if i < n_layers - 1:
+        res_skip_channels = 2 * hidden_channels
+      else:
+        res_skip_channels = hidden_channels
+
+      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+      self.res_skip_layers.append(res_skip_layer)
+
+  def forward(self, x, x_mask, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      if g is not None:
+        cond_offset = i * 2 * self.hidden_channels
+        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
+      else:
+        g_l = torch.zeros_like(x_in)
+
+      acts = commons.fused_add_tanh_sigmoid_multiply(
+          x_in,
+          g_l,
+          n_channels_tensor)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * x_mask
+        output = output + res_skip_acts[:,self.hidden_channels:,:]
+      else:
+        output = output + res_skip_acts
+    return output * x_mask
+
+  def remove_weight_norm(self):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+    
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x
+
+
+class ConvFlow(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.num_bins = num_bins
+    self.tail_bound = tail_bound
+    self.half_channels = in_channels // 2
+
+    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
+    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0)
+    h = self.convs(h, x_mask, g=g)
+    h = self.proj(h) * x_mask
+
+    b, c, t = x0.shape
+    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
+
+    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_derivatives = h[..., 2 * self.num_bins:]
+
+    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
+        unnormalized_widths,
+        unnormalized_heights,
+        unnormalized_derivatives,
+        inverse=reverse,
+        tails='linear',
+        tail_bound=self.tail_bound
+    )
+
+    x = torch.cat([x0, x1], 1) * x_mask
+    logdet = torch.sum(logabsdet * x_mask, [1,2])
+    if not reverse:
+        return x, logdet
+    else:
+        return x

monotonic_align/__init__.py

@@ -0,0 +1,19 @@
+import numpy as np
+import torch
+from .monotonic_align.core import maximum_path_c
+
+
+def maximum_path(neg_cent, mask):
+  """ Cython optimized version.
+  neg_cent: [b, t_t, t_s]
+  mask: [b, t_t, t_s]
+  """
+  device = neg_cent.device
+  dtype = neg_cent.dtype
+  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
+  path = np.zeros(neg_cent.shape, dtype=np.int32)
+
+  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
+  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
+  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
+  return torch.from_numpy(path).to(device=device, dtype=dtype)

monotonic_align/core.pyx

@@ -0,0 +1,42 @@
+cimport cython
+from cython.parallel import prange
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
+  cdef int x
+  cdef int y
+  cdef float v_prev
+  cdef float v_cur
+  cdef float tmp
+  cdef int 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.
+        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
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
+  cdef int b = paths.shape[0]
+  cdef int i
+  for i in prange(b, nogil=True):
+    maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])

monotonic_align/setup.py

@@ -0,0 +1,9 @@
+from distutils.core import setup
+from Cython.Build import cythonize
+import numpy
+
+setup(
+  name = 'monotonic_align',
+  ext_modules = cythonize("core.pyx"),
+  include_dirs=[numpy.get_include()]
+)

preprocess_v2.py

@@ -0,0 +1,153 @@
+import os
+import argparse
+import json
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--add_auxiliary_data", type=bool, help="Whether to add extra data as fine-tuning helper")
+    parser.add_argument("--languages", default="CJE")
+    args = parser.parse_args()
+    if args.languages == "CJE":
+        langs = ["[ZH]", "[JA]", "[EN]"]
+    elif args.languages == "CJ":
+        langs = ["[ZH]", "[JA]"]
+    elif args.languages == "C":
+        langs = ["[ZH]"]
+    elif args.languages == "J":
+        langs = ["[JA]"]
+    new_annos = []
+    # Source 1: transcribed short audios
+    if os.path.exists("short_character_anno.txt"):
+        with open("short_character_anno.txt", 'r', encoding='utf-8') as f:
+            short_character_anno = f.readlines()
+            new_annos += short_character_anno
+    # Source 2: transcribed long audio segments
+    if os.path.exists("./long_character_anno.txt"):
+        with open("./long_character_anno.txt", 'r', encoding='utf-8') as f:
+            long_character_anno = f.readlines()
+            new_annos += long_character_anno
+
+    # Get all speaker names
+    speakers = []
+    for line in new_annos:
+        path, speaker, text = line.split("|")
+        if speaker not in speakers:
+            speakers.append(speaker)
+    assert (len(speakers) != 0), "No audio file found. Please check your uploaded file structure."
+    # Source 3 (Optional): sampled audios as extra training helpers
+    if args.add_auxiliary_data:
+        with open("./sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
+            old_annos = f.readlines()
+        # filter old_annos according to supported languages
+        filtered_old_annos = []
+        for line in old_annos:
+            for lang in langs:
+                if lang in line:
+                    filtered_old_annos.append(line)
+        old_annos = filtered_old_annos
+        for line in old_annos:
+            path, speaker, text = line.split("|")
+            if speaker not in speakers:
+                speakers.append(speaker)
+        num_old_voices = len(old_annos)
+        num_new_voices = len(new_annos)
+        # STEP 1: balance number of new & old voices
+        cc_duplicate = num_old_voices // num_new_voices
+        if cc_duplicate == 0:
+            cc_duplicate = 1
+
+
+        # STEP 2: modify config file
+        with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+            hps = json.load(f)
+
+        # assign ids to new speakers
+        speaker2id = {}
+        for i, speaker in enumerate(speakers):
+            speaker2id[speaker] = i
+        # modify n_speakers
+        hps['data']["n_speakers"] = len(speakers)
+        # overwrite speaker names
+        hps['speakers'] = speaker2id
+        hps['train']['log_interval'] = 10
+        hps['train']['eval_interval'] = 100
+        hps['train']['batch_size'] = 16
+        hps['data']['training_files'] = "final_annotation_train.txt"
+        hps['data']['validation_files'] = "final_annotation_val.txt"
+        # save modified config
+        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
+            json.dump(hps, f, indent=2)
+
+        # STEP 3: clean annotations, replace speaker names with assigned speaker IDs
+        import text
+        cleaned_new_annos = []
+        for i, line in enumerate(new_annos):
+            path, speaker, txt = line.split("|")
+            if len(txt) > 150:
+                continue
+            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
+            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
+            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
+        cleaned_old_annos = []
+        for i, line in enumerate(old_annos):
+            path, speaker, txt = line.split("|")
+            if len(txt) > 150:
+                continue
+            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
+            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
+            cleaned_old_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
+        # merge with old annotation
+        final_annos = cleaned_old_annos + cc_duplicate * cleaned_new_annos
+        # save annotation file
+        with open("./final_annotation_train.txt", 'w', encoding='utf-8') as f:
+            for line in final_annos:
+                f.write(line)
+        # save annotation file for validation
+        with open("./final_annotation_val.txt", 'w', encoding='utf-8') as f:
+            for line in cleaned_new_annos:
+                f.write(line)
+        print("finished")
+    else:
+        # Do not add extra helper data
+        # STEP 1: modify config file
+        with open("./configs/amitaro_jp_base.json", 'r', encoding='utf-8') as f:
+            hps = json.load(f)
+
+        # assign ids to new speakers
+        speaker2id = {}
+        for i, speaker in enumerate(speakers):
+            speaker2id[speaker] = i
+        # modify n_speakers
+        hps['data']["n_speakers"] = len(speakers)
+        # overwrite speaker names
+        hps['speakers'] = speaker2id
+        hps['train']['log_interval'] = 10
+        hps['train']['eval_interval'] = 100
+        hps['train']['batch_size'] = 16
+        hps['data']['training_files'] = "final_annotation_train.txt"
+        hps['data']['validation_files'] = "final_annotation_val.txt"
+        # save modified config
+        with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
+            json.dump(hps, f, indent=2)
+
+        # STEP 2: clean annotations, replace speaker names with assigned speaker IDs
+        import text
+
+        cleaned_new_annos = []
+        for i, line in enumerate(new_annos):
+            path, speaker, txt = line.split("|")
+            if len(txt) > 150:
+                continue
+            cleaned_text = text._clean_text(txt, hps['data']['text_cleaners']).replace("[ZH]", "")
+            cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
+            cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
+
+        final_annos = cleaned_new_annos
+        # save annotation file
+        with open("./final_annotation_train.txt", 'w', encoding='utf-8') as f:
+            for line in final_annos:
+                f.write(line)
+        # save annotation file for validation
+        with open("./final_annotation_val.txt", 'w', encoding='utf-8') as f:
+            for line in cleaned_new_annos:
+                f.write(line)
+        print("finished")

requirements.txt

@@ -0,0 +1,27 @@
+pydantic<2
+Cython==0.29.21
+librosa==0.9.2
+matplotlib==3.3.1
+numpy
+scipy
+tensorboard
+torch
+torchvision
+torchaudio
+unidecode
+jamo
+pypinyin
+jieba
+protobuf
+cn2an
+inflect
+eng_to_ipa
+ko_pron
+indic_transliteration==2.3.37
+num_thai==0.0.5
+opencc==1.1.1
+demucs
+openai-whisper
+gradio
+openai
+pyopenjtalk

scripts/denoise_audio.py

@@ -0,0 +1,22 @@
+import os
+import json
+import torchaudio
+raw_audio_dir = "./raw_audio/"
+denoise_audio_dir = "./denoised_audio/"
+filelist = list(os.walk(raw_audio_dir))[0][2]
+# 2023/4/21: Get the target sampling rate
+with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+    hps = json.load(f)
+target_sr = hps['data']['sampling_rate']
+for file in filelist:
+    if file.endswith(".wav"):
+        os.system(f"demucs --two-stems=vocals {raw_audio_dir}{file}")
+for file in filelist:
+    file = file.replace(".wav", "")
+    wav, sr = torchaudio.load(f"./separated/htdemucs/{file}/vocals.wav", frame_offset=0, num_frames=-1, normalize=True,
+                              channels_first=True)
+    # merge two channels into one
+    wav = wav.mean(dim=0).unsqueeze(0)
+    if sr != target_sr:
+        wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
+    torchaudio.save(denoise_audio_dir + file + ".wav", wav, target_sr, channels_first=True)

scripts/download_model.py

@@ -0,0 +1,4 @@
+from google.colab import files
+files.download("./G_latest.pth")
+files.download("./finetune_speaker.json")
+files.download("./moegoe_config.json")

scripts/download_video.py

@@ -0,0 +1,37 @@
+import os
+import random
+import shutil
+from concurrent.futures import ThreadPoolExecutor
+from google.colab import files
+
+basepath = os.getcwd()
+uploaded = files.upload()  # 上传文件
+for filename in uploaded.keys():
+    assert (filename.endswith(".txt")), "speaker-videolink info could only be .txt file!"
+    shutil.move(os.path.join(basepath, filename), os.path.join("./speaker_links.txt"))
+
+
+def generate_infos():
+    infos = []
+    with open("./speaker_links.txt", 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+    for line in lines:
+        line = line.replace("\n", "").replace(" ", "")
+        if line == "":
+            continue
+        speaker, link = line.split("|")
+        filename = speaker + "_" + str(random.randint(0, 1000000))
+        infos.append({"link": link, "filename": filename})
+    return infos
+
+
+def download_video(info):
+    link = info["link"]
+    filename = info["filename"]
+    os.system(f"youtube-dl -f 0 {link} -o ./video_data/{filename}.mp4 --no-check-certificate")
+
+
+if __name__ == "__main__":
+    infos = generate_infos()
+    with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
+        executor.map(download_video, infos)

scripts/long_audio_transcribe.py

@@ -0,0 +1,75 @@
+from moviepy.editor import AudioFileClip
+import whisper
+import os
+import json
+import torchaudio
+import librosa
+import torch
+import argparse
+parent_dir = "./denoised_audio/"
+filelist = list(os.walk(parent_dir))[0][2]
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--languages", default="CJE")
+    parser.add_argument("--whisper_size", default="medium")
+    args = parser.parse_args()
+    if args.languages == "CJE":
+        lang2token = {
+            'zh': "[ZH]",
+            'ja': "[JA]",
+            "en": "[EN]",
+        }
+    elif args.languages == "CJ":
+        lang2token = {
+            'zh': "[ZH]",
+            'ja': "[JA]",
+        }
+    elif args.languages == "C":
+        lang2token = {
+            'zh': "[ZH]",
+        }
+    assert(torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
+    with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+        hps = json.load(f)
+    target_sr = hps['data']['sampling_rate']
+    model = whisper.load_model(args.whisper_size)
+    speaker_annos = []
+    for file in filelist:
+        print(f"transcribing {parent_dir + file}...\n")
+        options = dict(beam_size=5, best_of=5)
+        transcribe_options = dict(task="transcribe", **options)
+        result = model.transcribe(parent_dir + file, word_timestamps=True, **transcribe_options)
+        segments = result["segments"]
+        # result = model.transcribe(parent_dir + file)
+        lang = result['language']
+        if result['language'] not in list(lang2token.keys()):
+            print(f"{lang} not supported, ignoring...\n")
+            continue
+        # segment audio based on segment results
+        character_name = file.rstrip(".wav").split("_")[0]
+        code = file.rstrip(".wav").split("_")[1]
+        if not os.path.exists("./segmented_character_voice/" + character_name):
+            os.mkdir("./segmented_character_voice/" + character_name)
+        wav, sr = torchaudio.load(parent_dir + file, frame_offset=0, num_frames=-1, normalize=True,
+                                  channels_first=True)
+
+        for i, seg in enumerate(result['segments']):
+            start_time = seg['start']
+            end_time = seg['end']
+            text = seg['text']
+            text = lang2token[lang] + text.replace("\n", "") + lang2token[lang]
+            text = text + "\n"
+            wav_seg = wav[:, int(start_time*sr):int(end_time*sr)]
+            wav_seg_name = f"{character_name}_{code}_{i}.wav"
+            savepth = "./segmented_character_voice/" + character_name + "/" + wav_seg_name
+            speaker_annos.append(savepth + "|" + character_name + "|" + text)
+            print(f"Transcribed segment: {speaker_annos[-1]}")
+            # trimmed_wav_seg = librosa.effects.trim(wav_seg.squeeze().numpy())
+            # trimmed_wav_seg = torch.tensor(trimmed_wav_seg[0]).unsqueeze(0)
+            torchaudio.save(savepth, wav_seg, target_sr, channels_first=True)
+    if len(speaker_annos) == 0:
+        print("Warning: no long audios & videos found, this IS expected if you have only uploaded short audios")
+        print("this IS NOT expected if you have uploaded any long audios, videos or video links. Please check your file structure or make sure your audio/video language is supported.")
+    with open("./long_character_anno.txt", 'w', encoding='utf-8') as f:
+        for line in speaker_annos:
+            f.write(line)

scripts/rearrange_speaker.py

@@ -0,0 +1,37 @@
+import torch
+import argparse
+import json
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_dir", type=str, default="./OUTPUT_MODEL/G_latest.pth")
+    parser.add_argument("--config_dir", type=str, default="./configs/modified_finetune_speaker.json")
+    args = parser.parse_args()
+
+    model_sd = torch.load(args.model_dir, map_location='cpu')
+    with open(args.config_dir, 'r', encoding='utf-8') as f:
+        hps = json.load(f)
+
+    valid_speakers = list(hps['speakers'].keys())
+    if hps['data']['n_speakers'] > len(valid_speakers):
+        new_emb_g = torch.zeros([len(valid_speakers), 256])
+        old_emb_g = model_sd['model']['emb_g.weight']
+        for i, speaker in enumerate(valid_speakers):
+            new_emb_g[i, :] = old_emb_g[hps['speakers'][speaker], :]
+            hps['speakers'][speaker] = i
+        hps['data']['n_speakers'] = len(valid_speakers)
+        model_sd['model']['emb_g.weight'] = new_emb_g
+        with open("./finetune_speaker.json", 'w', encoding='utf-8') as f:
+            json.dump(hps, f, indent=2)
+        torch.save(model_sd, "./G_latest.pth")
+    else:
+        with open("./finetune_speaker.json", 'w', encoding='utf-8') as f:
+            json.dump(hps, f, indent=2)
+        torch.save(model_sd, "./G_latest.pth")
+    # save another config file copy in MoeGoe format
+    hps['speakers'] = valid_speakers
+    with open("./moegoe_config.json", 'w', encoding='utf-8') as f:
+        json.dump(hps, f, indent=2)
+
+
+

scripts/resample.py

@@ -0,0 +1,20 @@
+import os
+import json
+import argparse
+import torchaudio
+
+
+def main():
+    with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+        hps = json.load(f)
+    target_sr = hps['data']['sampling_rate']
+    filelist = list(os.walk("./sampled_audio4ft"))[0][2]
+    if target_sr != 22050:
+        for wavfile in filelist:
+            wav, sr = torchaudio.load("./sampled_audio4ft" + "/" + wavfile, frame_offset=0, num_frames=-1,
+                                      normalize=True, channels_first=True)
+            wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
+            torchaudio.save("./sampled_audio4ft" + "/" + wavfile, wav, target_sr, channels_first=True)
+
+if __name__ == "__main__":
+    main()

scripts/short_audio_transcribe.py

@@ -0,0 +1,91 @@
+import whisper
+import os
+import json
+import torchaudio
+import argparse
+import torch
+from tqdm import tqdm
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--whisper_size", default="large")
+    args = parser.parse_args()
+    #assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
+    model = whisper.load_model(args.whisper_size)
+    parent_dir = "./custom_character_voice/"
+    speaker_names = list(os.walk(parent_dir))[0][1]
+    speaker_annos = []
+    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
+    # resample audios
+    # 2023/4/21: Get the target sampling rate
+    with open("./configs/amitaro_jp_base.json", 'r', encoding='utf-8') as f:
+        hps = json.load(f)
+    target_sr = hps['data']['sampling_rate']
+    processed_files = 0
+    for speaker in speaker_names:
+        filelist = (list(os.walk(parent_dir + speaker))[0][2])
+        for i, wavfile in tqdm(enumerate(filelist), desc="Processing Audio:", total=len(filelist)):
+            # try to load file as audio
+            if wavfile.startswith("processed_"):
+                continue
+            try:
+                wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
+                                          channels_first=True)
+                wav = wav.mean(dim=0).unsqueeze(0)
+                if sr != target_sr:
+                    wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
+                if wav.shape[1] / sr > 20:
+                    print(f"{wavfile} too long, ignoring\n")
+                save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
+                torchaudio.save(save_path, wav, target_sr, channels_first=True)
+                # transcribe text
+                #lang, text = transcribe_one(save_path)
+
+                audio = whisper.load_audio(save_path)
+                audio = whisper.pad_or_trim(audio)
+
+                # make log-Mel spectrogram and move to the same device as the model
+                mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+                options = whisper.DecodingOptions(beam_size=5, language="ja", fp16 = False)
+                result = whisper.decode(model, mel, options)
+
+                text = "[JA]"+ result.text + "[JA]\n"
+                speaker_annos.append(save_path + "|" + speaker + "|" + text)
+
+                processed_files += 1
+            #print(f"Processed: {processed_files}/{total_files}")
+            except:
+                print(f"Error occurred: {wavfile}")
+                continue
+
+    # # clean annotation
+    # import argparse
+    # import text
+    # from utils import load_filepaths_and_text
+    # for i, line in enumerate(speaker_annos):
+    #     path, sid, txt = line.split("|")
+    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
+    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
+    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
+    # write into annotation
+    if len(speaker_annos) == 0:
+        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
+        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
+    with open("short_character_anno.txt", 'w', encoding='utf-8') as f:
+        for line in speaker_annos:
+            f.write(line)
+
+    # import json
+    # # generate new config
+    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+    #     hps = json.load(f)
+    # # modify n_speakers
+    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
+    # # add speaker names
+    # for speaker in speaker_names:
+    #     hps['speakers'][speaker] = speaker2id[speaker]
+    # # save modified config
+    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
+    #     json.dump(hps, f, indent=2)
+    # print("finished")

scripts/video2audio.py

@@ -0,0 +1,27 @@
+import os
+from concurrent.futures import ThreadPoolExecutor
+
+from moviepy.editor import AudioFileClip
+
+video_dir = "./video_data/"
+audio_dir = "./raw_audio/"
+filelist = list(os.walk(video_dir))[0][2]
+
+
+def generate_infos():
+    videos = []
+    for file in filelist:
+        if file.endswith(".mp4"):
+            videos.append(file)
+    return videos
+
+
+def clip_file(file):
+    my_audio_clip = AudioFileClip(video_dir + file)
+    my_audio_clip.write_audiofile(audio_dir + file.rstrip(".mp4") + ".wav")
+
+
+if __name__ == "__main__":
+    infos = generate_infos()
+    with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
+        executor.map(clip_file, infos)

scripts/voice_upload.py

@@ -0,0 +1,28 @@
+from google.colab import files
+import shutil
+import os
+import argparse
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--type", type=str, required=True, help="type of file to upload")
+    args = parser.parse_args()
+    file_type = args.type
+
+    basepath = os.getcwd()
+    uploaded = files.upload() # 上传文件
+    assert(file_type in ['zip', 'audio', 'video'])
+    if file_type == "zip":
+        upload_path = "./custom_character_voice/"
+        for filename in uploaded.keys():
+            #将上传的文件移动到指定的位置上
+            shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, "custom_character_voice.zip"))
+    elif file_type == "audio":
+        upload_path = "./raw_audio/"
+        for filename in uploaded.keys():
+            #将上传的文件移动到指定的位置上
+            shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, filename))
+    elif file_type == "video":
+        upload_path = "./video_data/"
+        for filename in uploaded.keys():
+            # 将上传的文件移动到指定的位置上
+            shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, filename))

short_audio_transcribe.py

@@ -0,0 +1,91 @@
+import whisper
+import os
+import json
+import torchaudio
+import argparse
+import torch
+from tqdm import tqdm
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--whisper_size", default="large")
+    args = parser.parse_args()
+    #assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
+    model = whisper.load_model(args.whisper_size, device="cpu")
+    parent_dir = "./custom_character_voice/"
+    speaker_names = list(os.walk(parent_dir))[0][1]
+    speaker_annos = []
+    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
+    # resample audios
+    # 2023/4/21: Get the target sampling rate
+    with open("./configs/amitaro_jp_base.json", 'r', encoding='utf-8') as f:
+        hps = json.load(f)
+    target_sr = hps['data']['sampling_rate']
+    processed_files = 0
+    for speaker in speaker_names:
+        filelist = (list(os.walk(parent_dir + speaker))[0][2])
+        for i, wavfile in tqdm(enumerate(filelist), desc="Processing Audio:", total=len(filelist)):
+            # try to load file as audio
+            if wavfile.startswith("processed_"):
+                continue
+            #try:
+            wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
+                                      channels_first=True)
+            wav = wav.mean(dim=0).unsqueeze(0)
+            if sr != target_sr:
+                wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
+            if wav.shape[1] / sr > 20:
+                print(f"{wavfile} too long, ignoring\n")
+            save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
+            torchaudio.save(save_path, wav, target_sr, channels_first=True)
+            # transcribe text
+            #lang, text = transcribe_one(save_path)
+
+            audio = whisper.load_audio(save_path)
+            audio = whisper.pad_or_trim(audio)
+
+            # make log-Mel spectrogram and move to the same device as the model
+            mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+            options = whisper.DecodingOptions(beam_size=5, language="ja", fp16 = False)
+            result = whisper.decode(model, mel, options)
+
+            text = "[JA]"+ result.text + "[JA]\n"
+            speaker_annos.append(save_path + "|" + speaker + "|" + text)
+
+            processed_files += 1
+            #print(f"Processed: {processed_files}/{total_files}")
+            #except:
+            #    print(f"Error occurred: {wavfile}")
+            #    continue
+
+    # # clean annotation
+    # import argparse
+    # import text
+    # from utils import load_filepaths_and_text
+    # for i, line in enumerate(speaker_annos):
+    #     path, sid, txt = line.split("|")
+    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
+    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
+    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
+    # write into annotation
+    if len(speaker_annos) == 0:
+        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
+        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
+    with open("short_character_anno.txt", 'w', encoding='utf-8') as f:
+        for line in speaker_annos:
+            f.write(line)
+
+    # import json
+    # # generate new config
+    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
+    #     hps = json.load(f)
+    # # modify n_speakers
+    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
+    # # add speaker names
+    # for speaker in speaker_names:
+    #     hps['speakers'][speaker] = speaker2id[speaker]
+    # # save modified config
+    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
+    #     json.dump(hps, f, indent=2)
+    # print("finished")

text/LICENSE

@@ -0,0 +1,19 @@
+Copyright (c) 2017 Keith Ito
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

text/__init__.py

@@ -0,0 +1,60 @@
+""" from https://github.com/keithito/tacotron """
+from text import cleaners
+from text.symbols import symbols
+
+
+# Mappings from symbol to numeric ID and vice versa:
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+
+def text_to_sequence(text, symbols, cleaner_names):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+      cleaner_names: names of the cleaner functions to run the text through
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  sequence = []
+  symbol_to_id = {s: i for i, s in enumerate(symbols)}
+  clean_text = _clean_text(text, cleaner_names)
+  print(clean_text)
+  print(f" length:{len(clean_text)}")
+  for symbol in clean_text:
+    if symbol not in symbol_to_id.keys():
+      continue
+    symbol_id = symbol_to_id[symbol]
+    sequence += [symbol_id]
+  print(f" length:{len(sequence)}")
+  return sequence
+
+
+def cleaned_text_to_sequence(cleaned_text, symbols):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  symbol_to_id = {s: i for i, s in enumerate(symbols)}
+  sequence = [symbol_to_id[symbol] for symbol in cleaned_text if symbol in symbol_to_id.keys()]
+  return sequence
+
+
+def sequence_to_text(sequence):
+  '''Converts a sequence of IDs back to a string'''
+  result = ''
+  for symbol_id in sequence:
+    s = _id_to_symbol[symbol_id]
+    result += s
+  return result
+
+
+def _clean_text(text, cleaner_names):
+  for name in cleaner_names:
+    cleaner = getattr(cleaners, name)
+    if not cleaner:
+      raise Exception('Unknown cleaner: %s' % name)
+    text = cleaner(text)
+  return text