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prepare_data.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4f6ad8a0-2fbf-4732-adab-88acc36814d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 准备训练和测试数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cffb3f58-7324-4825-9fc2-fdae98d0749d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load phonemizer\n",
+    "import phonemizer\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "\n",
+    "global_phonemizer_en = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True,  with_stress=True)\n",
+    "global_phonemizer_es = phonemizer.backend.EspeakBackend(language='es', preserve_punctuation=True,  with_stress=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8a7bb6c1-9ce9-4d43-958f-112d0df55cce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess(text, language):\n",
+    "    text = text.strip()\n",
+    "    if language == 'en-us':\n",
+    "        ps = global_phonemizer_en.phonemize([text])\n",
+    "    elif language == 'es':\n",
+    "        ps = global_phonemizer_es.phonemize([text])\n",
+    "    ps = word_tokenize(ps[0])\n",
+    "    ps = [p for p in ps if p not in ['``', '`', '(', ')']]\n",
+    "    ps = ' '.join(ps)\n",
+    "\n",
+    "    return ps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dde01040-55aa-4408-9544-507a56a27b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # test\n",
+    "# text = '''¡Usuario! ¿De qué estás hablando? Ese tipo de conversación es inapropiada... Supongo que no puedo decir que no... sólo por esta vez. Mmm... me haces sentir tan bien.'''\n",
+    "# preprocess(text, 'es')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "35366d0d-149e-4cf6-8780-be6631dc3f3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip3 install pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "da973b2e-4520-4647-8451-a83bf0c412bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import wave\n",
+    "import soundfile as sf\n",
+    "\n",
+    "def get_wav_duration(file_path):\n",
+    "    try:\n",
+    "        with wave.open(file_path, 'rb') as wf:\n",
+    "            # 获取音频文件的帧数\n",
+    "            frames = wf.getnframes()\n",
+    "            # 获取帧速率（每秒的帧数）\n",
+    "            frame_rate = wf.getframerate()\n",
+    "            # 计算音频文件的持续时间（秒数）\n",
+    "            duration = frames / float(frame_rate)\n",
+    "            return duration\n",
+    "    except:\n",
+    "        wave_array, sr = sf.read(file_path)\n",
+    "        return len(wave_array) / sr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8b93b418-d2f6-46c9-a6b7-71fa37c063ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1151/1151 [00:08<00:00, 137.66it/s]\n",
+      "100%|██████████| 40/40 [00:00<00:00, 124.61it/s]\n",
+      "100%|██████████| 39/39 [00:00<00:00, 130.81it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "142731 5491 4619\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# libritts\n",
+    "def prepare_libritts_data(dir_path, id1_list):\n",
+    "    final_data = []\n",
+    "    data = {}\n",
+    "    \n",
+    "    for id1_index in tqdm(range(len(id1_list))):\n",
+    "        id1 = id1_list[id1_index]\n",
+    "        # id1是speaker id\n",
+    "        for id2 in os.listdir(dir_path+'/'+id1):\n",
+    "            for fname in os.listdir(dir_path+'/'+id1+'/'+id2):\n",
+    "                if 'txt' in fname or 'wav' in fname:\n",
+    "                    whole_id = fname.split('.')[0]\n",
+    "                    if whole_id not in data:\n",
+    "                        data[whole_id] = {}\n",
+    "    \n",
+    "                    if 'wav' in fname:\n",
+    "                        wav_path = dir_path+'/'+id1+'/'+id2+'/'+fname\n",
+    "                        wav_duration = get_wav_duration(wav_path)\n",
+    "                        data[whole_id]['wav'] = wav_path\n",
+    "                        data[whole_id]['wav_dur'] = wav_duration\n",
+    "                        \n",
+    "                    elif 'normalized' in fname: # 使用normalized版本，或是original\n",
+    "                        with open(dir_path+'/'+id1+'/'+id2+'/'+fname, 'r') as f:\n",
+    "                            data[whole_id]['text'] = f.read().strip()\n",
+    "                    \n",
+    "                    data[whole_id]['speaker_id'] = id1\n",
+    "\n",
+    "    for d in data:\n",
+    "        if data[d]['wav_dur'] > 1:\n",
+    "            # 去除太短的\n",
+    "            final_data.append(f'''{data[d]['wav']}|{data[d]['text']}|{data[d]['speaker_id']}''')\n",
+    "    \n",
+    "    return final_data\n",
+    "\n",
+    "train_path = '/workspace/TTS/data/LibriTTS/train-clean-460'\n",
+    "train_ids = os.listdir(train_path)\n",
+    "\n",
+    "val_path = '/workspace/TTS/data/LibriTTS/dev-clean'\n",
+    "val_ids = os.listdir(val_path)\n",
+    "\n",
+    "test_path = '/workspace/TTS/data/LibriTTS/test-clean'\n",
+    "test_ids = os.listdir(test_path)\n",
+    "\n",
+    "libritts_train_data = prepare_libritts_data(train_path, train_ids)\n",
+    "libritts_val_data = prepare_libritts_data(val_path, val_ids)\n",
+    "libritts_test_data = prepare_libritts_data(test_path, test_ids)\n",
+    "\n",
+    "print(len(libritts_train_data), len(libritts_val_data), len(libritts_test_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a9f1c3cf-16ae-4b48-9b96-0440c1056db4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 分析一下libritts的数据分布，给个参考\n",
+    "# id2num = {}\n",
+    "# for d in libritts_train_data:\n",
+    "#     _id = d.split('|')[2]\n",
+    "#     if _id not in id2num:\n",
+    "#         id2num[_id] = 1\n",
+    "#     else:\n",
+    "#         id2num[_id] += 1\n",
+    "\n",
+    "# sorted_dict = sorted(id2num.items(), key=lambda x: x[1], reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c93c9b4f-4cee-42f6-8fa2-c3cac47220c3",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# for i, d in enumerate(sorted_dict):\n",
+    "#     print(d, i/len(id2num))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d357def5-b0af-4a73-a04e-cc1084c83a83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "153150 1897 1662\n"
+     ]
+    }
+   ],
+   "source": [
+    "# cml-tts\n",
+    "def prepare_cml_data():\n",
+    "    # 准备 cml-tts数据，直接读取csv数据即可\n",
+    "    # 去掉transcription不太对的数据\n",
+    "    # +100000是为了和libritts错开speaker id\n",
+    "\n",
+    "    df_train = pd.read_csv('/workspace/TTS/data/cml_tts_dataset_spanish_v0.1/train.csv', delimiter='|')\n",
+    "    train = []\n",
+    "    for index, row in df_train.iterrows():\n",
+    "        if row['levenshtein'] >= 0.9 and row['duration'] > 1 and row['duration'] < 30:\n",
+    "            train.append(f'''/workspace/TTS/data/cml_tts_dataset_spanish_v0.1/{row['wav_filename']}|{row['transcript']}|{10000+row['client_id']}''')\n",
+    "          \n",
+    "    df_val = pd.read_csv('/workspace/TTS/data/cml_tts_dataset_spanish_v0.1/dev.csv', delimiter='|')\n",
+    "    val = []\n",
+    "    for index, row in df_val.iterrows():\n",
+    "        if row['levenshtein'] >= 0.9 and row['duration'] > 1 and row['duration'] < 30:\n",
+    "            val.append(f'''/workspace/TTS/data/cml_tts_dataset_spanish_v0.1/{row['wav_filename']}|{row['transcript']}|{10000+int(row['client_id'])}''')\n",
+    "\n",
+    "    df_test = pd.read_csv('/workspace/TTS/data/cml_tts_dataset_spanish_v0.1/test.csv', delimiter='|')\n",
+    "    test = []\n",
+    "    for index, row in df_test.iterrows():\n",
+    "        if row['levenshtein'] >= 0.9 and row['duration'] > 1 and row['duration'] < 30:\n",
+    "            test.append(f'''/workspace/TTS/data/cml_tts_dataset_spanish_v0.1/{row['wav_filename']}|{row['transcript']}|{10000+int(row['client_id'])}''')\n",
+    "\n",
+    "    return train, val, test\n",
+    "    \n",
+    "cml_train_data, cml_val_data, cml_test_data = prepare_cml_data()\n",
+    "print(len(cml_train_data), len(cml_val_data), len(cml_test_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f25a81d5-d032-4d6d-a981-9f866c5a14c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.masked_spec_embed']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC\n",
+    "import torch\n",
+    "import soundfile as sf\n",
+    "import librosa\n",
+    "\n",
+    "# load model and tokenizer\n",
+    "wav2vec_processor = Wav2Vec2Processor.from_pretrained(\"facebook/wav2vec2-base-960h\", cache_dir = '/workspace/hf_resource/')\n",
+    "wav2vec_model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-base-960h\", cache_dir = '/workspace/hf_resource/')\n",
+    "\n",
+    "def wav2vec_asr(wav_path, end_second = None):\n",
+    "    wave, sr = sf.read(wav_path)\n",
+    "    if wave.shape[-1] == 2:\n",
+    "        wave = wave[:, 0].squeeze()\n",
+    "    if sr != 16000:\n",
+    "        wave = librosa.resample(wave, orig_sr=sr, target_sr=16000)\n",
+    "    \n",
+    "    wave = torch.from_numpy(wave).float()\n",
+    "\n",
+    "    if end_second is not None:\n",
+    "       wave = wave[:int(16000 * end_second)]\n",
+    "\n",
+    "    # tokenize\n",
+    "    input_values = wav2vec_processor(wave, return_tensors=\"pt\", padding=\"longest\", sampling_rate=16000).input_values  # Batch size 1\n",
+    "    \n",
+    "    # retrieve logits\n",
+    "    logits = wav2vec_model(input_values).logits\n",
+    "    \n",
+    "    # take argmax and decode\n",
+    "    predicted_ids = torch.argmax(logits, dim=-1)\n",
+    "    transcription = wav2vec_processor.batch_decode(predicted_ids)\n",
+    "\n",
+    "    return transcription"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f2dcd822-6d1d-41de-babd-a966ef0d1da1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'whisper'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwhisper\u001b[39;00m\n\u001b[1;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m whisper\u001b[38;5;241m.\u001b[39mload_model(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlarge-v3\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21masr\u001b[39m(wav_path):\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'whisper'"
+     ]
+    }
+   ],
+   "source": [
+    "import whisper\n",
+    "\n",
+    "model = whisper.load_model(\"large-v3\")\n",
+    "\n",
+    "def asr(wav_path):\n",
+    "    result = model.transcribe(wav_path)\n",
+    "    return result[\"text\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d86ed416-9110-4e02-a605-1ca645f258bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 本质是，第一个单词不能太晚说出来\n",
+    "import whisperx\n",
+    "\n",
+    "model_a, metadata = whisperx.load_align_model(language_code='en', device='cuda')\n",
+    "\n",
+    "def alignment(wav_path):\n",
+    "    result = model.transcribe(wav_path)\n",
+    "    \n",
+    "    result = whisperx.align(result[\"segments\"], model_a, metadata, wav_path, 'cuda', return_char_alignments=False)\n",
+    "    \n",
+    "    return result[\"segments\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a62e3c95-c1e5-48e1-82b4-56836e7c2bd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加载已经处理好的数据，主要为得到id list，为重新采样做准备\n",
+    "game_train_ids, game_val_ids, game_test_ids = [], [], []\n",
+    "\n",
+    "with open('/workspace/TTS/tts/StyleTTS2/Data/train_list_en_20k_0318.txt', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.strip()\n",
+    "        if 'game_en' in line:\n",
+    "            game_train_ids.append('/'.join(line.split('|')[0].split('/')[:-1]))\n",
+    "\n",
+    "with open('/workspace/TTS/tts/StyleTTS2/Data/val_list_en_20k_0318.txt', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.strip()\n",
+    "        if 'game_en' in line:\n",
+    "            game_val_ids.append('/'.join(line.split('|')[0].split('/')[:-1]))\n",
+    "\n",
+    "with open('/workspace/TTS/tts/StyleTTS2/Data/OOD_texts_en_2k_0318.txt', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.strip()\n",
+    "        if 'game_en' in line:\n",
+    "            game_test_ids.append('/'.join(line.split('|')[0].split('/')[:-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e45ae4c5-5163-4c48-85b0-0b84055468d6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "for d in game_train_ids:\n",
+    "    print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebfd5f74-2796-41fd-8cc6-52464776dc33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加入游戏数据（英文）\n",
+    "# 过滤游戏数据\n",
+    "# 短于1s的不要、长度太短的不要、太长的也不要（30s为限）\n",
+    "game_en = []\n",
+    "\n",
+    "def get_game_data(path, speaker_id):\n",
+    "    role_names = os.listdir(path)\n",
+    "    for role_name in role_names:\n",
+    "        if '#Unknown' in role_name or ' ' in role_name:\n",
+    "            continue\n",
+    "    \n",
+    "        file_names = os.listdir(os.path.join(path, role_name))\n",
+    "        file_names = [n for n in file_names if 'lab' in n or 'wav' in n]\n",
+    "        id2wav, id2text = {}, {}\n",
+    "        for file_name in file_names:\n",
+    "            id = file_name.split('.')[0]\n",
+    "            \n",
+    "            if 'lab' in file_name:\n",
+    "                try:\n",
+    "                    with open(path+'/'+role_name+'/'+file_name, 'r') as f:\n",
+    "                        text = f.read().strip()\n",
+    "                        id2text[id] = text\n",
+    "                except:\n",
+    "                    continue\n",
+    "    \n",
+    "            elif 'wav' in file_name:\n",
+    "                wav = path+'/'+role_name+'/'+file_name\n",
+    "                id2wav[id] = wav\n",
+    "        \n",
+    "        for id in id2wav:\n",
+    "            if id in id2text:\n",
+    "                wav_path, text = id2wav[id], id2text[id]\n",
+    "                wav_len = get_wav_duration(wav_path) \n",
+    "                if wav_len > 1 and wav_len < 30 and len(text.split(' ')) >= 3 and len(text.split(' ')) <= 50 and '{' not in text and '<' not in text:\n",
+    "                    game_en.append(f'{wav_path}|{text}|{speaker_id}')\n",
+    "\n",
+    "        speaker_id += 1\n",
+    "    \n",
+    "    return speaker_id\n",
+    "\n",
+    "sid = get_game_data('/workspace/TTS/data/game_en', 30000)\n",
+    "print(sid)\n",
+    "sid = get_game_data('/workspace/TTS/data/game_en1', sid)\n",
+    "print(sid)\n",
+    "\n",
+    "print(len(game_en))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "776c404e-9865-4f92-b593-272e4d065372",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# # 按照speaker id，统计一下分布\n",
+    "# sid2game = {}\n",
+    "# for d in game_en:\n",
+    "#     d = d.strip().split('|')\n",
+    "#     sid = '/'.join(d[0].split('/')[:-1])\n",
+    "#     if sid not in sid2game:\n",
+    "#         sid2game[sid] = [d]\n",
+    "#     else:\n",
+    "#         sid2game[sid].append(d)\n",
+    "\n",
+    "# sid2game = sorted(sid2game.items(), key=lambda x: len(x[1]), reverse=True)\n",
+    "\n",
+    "# whole_num = sum([len(wavs) for sid, wavs in sid2game])\n",
+    "# print(whole_num)\n",
+    "# for sid, wavs in sid2game:\n",
+    "#     print(sid, len(wavs), len(wavs)/whole_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6a5abdd0-2d6e-433d-8c9e-cd64a1e512dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# len(sid2game)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "20133137-f9b1-444e-a4e2-477d858b0af9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 对游戏数据，再次进行处理\n",
+    "# 处理1：每个sid，设定样本上限\n",
+    "# 处理2：按照sid，划分train/val/test\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "# 隐藏seed，可以更充分利用data\n",
+    "random.seed(42)\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "sid2game = {}\n",
+    "for d in game_en:\n",
+    "    sid = d.split('|')[2]\n",
+    "    if sid not in sid2game:\n",
+    "        sid2game[sid] = [d]\n",
+    "    else:\n",
+    "        sid2game[sid].append(d)\n",
+    "\n",
+    "max_sample = 150\n",
+    "filter_sid2game = {}\n",
+    "for sid in sid2game:\n",
+    "    wavs = sid2game[sid]\n",
+    "    if len(wavs) > max_sample:\n",
+    "        np.random.shuffle(wavs)\n",
+    "        wavs = wavs[:max_sample]\n",
+    "    filter_sid2game[sid] = wavs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ec591e2c-2f84-44b6-9bbe-9e511c16cc84",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# print(len([d for d in filter_sid2game if len(filter_sid2game[d]) > 1]))\n",
+    "# print(len(filter_sid2game))\n",
+    "# filter_sid2game = sorted(filter_sid2game.items(), key=lambda x: len(x[1]), reverse=True)\n",
+    "\n",
+    "# whole_num = sum([len(wavs) for sid, wavs in filter_sid2game])\n",
+    "# print(whole_num)\n",
+    "# for sid, wavs in filter_sid2game:\n",
+    "#     print(sid, len(wavs), len(wavs)/whole_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "10dc4e4f-f3c3-4bcd-9330-413636f4e638",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# index = random.choice(range(len(game_en_train)))\n",
+    "# tmp = game_en_train[index]\n",
+    "# print(tmp)\n",
+    "# import IPython.display as ipd\n",
+    "# display(ipd.Audio(tmp.split('|')[0], rate=24000, normalize=False))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cee4bad1-4d05-45bd-8140-0ee8e677dc92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# game_en_train, game_en_val, game_en_test = [], [], []\n",
+    "# # whole_sids = list(filter_sid2game.keys())\n",
+    "# # np.random.shuffle(whole_sids)\n",
+    "\n",
+    "# # train_sids = whole_sids[:int(0.8 * len(whole_sids))]\n",
+    "# # val_sids = whole_sids[int(0.8 * len(whole_sids)):int(0.9 * len(whole_sids))]\n",
+    "# # test_sids = whole_sids[int(0.9 * len(whole_sids)):]\n",
+    "\n",
+    "# train_sids = set()\n",
+    "# val_sids = set()\n",
+    "# test_sids = set()\n",
+    "# for sid in filter_sid2game:\n",
+    "#     wavs = filter_sid2game[sid]\n",
+    "#     if '/'.join(wavs[0].split('/')[:-1]) in game_train_ids and '男声' not in wavs[0] and '女声' not in wavs:\n",
+    "        \n",
+    "#         train_sids.add(sid)\n",
+    "#     elif '/'.join(wavs[0].split('/')[:-1]) in game_val_ids:\n",
+    "#         val_sids.add(sid)\n",
+    "#     elif '/'.join(wavs[0].split('/')[:-1]) in game_test_ids:\n",
+    "#         test_sids.add(sid)\n",
+    "\n",
+    "# for sid in train_sids:\n",
+    "#     game_en_train += filter_sid2game[sid]\n",
+    "\n",
+    "# for sid in val_sids:\n",
+    "#     game_en_val += filter_sid2game[sid]\n",
+    "\n",
+    "# for sid in test_sids:\n",
+    "#     game_en_test += filter_sid2game[sid]\n",
+    "\n",
+    "# np.random.shuffle(game_en_train)\n",
+    "# np.random.shuffle(game_en_val)\n",
+    "# np.random.shuffle(game_en_test)\n",
+    "\n",
+    "# print(len(game_en_train), len(game_en_val), len(game_en_test))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8351bf48-26f7-4111-b0ea-4ac055953fca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加入emotional tts数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "bba721d0-0c35-4388-968f-ef5b7a96d91e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip3 install langdetect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f3955304-af9e-471c-8fd9-e760b0796202",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from langdetect import detect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5a735bbe-628d-487d-9692-2c11e6ce1909",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import re\n",
+    "\n",
+    "def is_english_string(s):\n",
+    "    return bool(re.match('^[a-zA-Z\\s]+$', s))\n",
+    "    \n",
+    "esd = []\n",
+    "emov = []\n",
+    "jl = []\n",
+    "rav = []\n",
+    "\n",
+    "esd_path = '/workspace/TTS/data/ESD/dataset/'\n",
+    "esd_trans = []\n",
+    "for f1_name in os.listdir(esd_path):\n",
+    "    if '.' not in f1_name:\n",
+    "        with open(esd_path + f1_name + '/' + f1_name + '.txt', 'r') as f:\n",
+    "            for line in f:\n",
+    "                line = line.strip().split('\\t')\n",
+    "                id, trans, emo = line[0], line[1], line[2]\n",
+    "                if is_english_string(emo):\n",
+    "                    esd_trans.append([id, trans, emo])\n",
+    "\n",
+    "emov_path = '/workspace/TTS/data/emov_db/'\n",
+    "# 没有trans，需要过whisper\n",
+    "for f1_name in os.listdir(emov_path):\n",
+    "    for f2_name in os.listdir(emov_path + f1_name):\n",
+    "        if 'wav' in f2_name:\n",
+    "            emov.append(f'{emov_path}{f1_name}/{f2_name}')\n",
+    "\n",
+    "rav_path = '/workspace/TTS/data/ravdess/'\n",
+    "# 也没有trans，需要过whisper\n",
+    "for f1_name in os.listdir(rav_path):\n",
+    "    for f2_name in os.listdir(rav_path + f1_name):\n",
+    "        rav.append(f'/workspace/TTS/data/ravdess/{f1_name}/{f2_name}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4ce217b6-09cc-4932-9209-fb213cdde19a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jl_path = '/workspace/TTS/data/jl_corpus/raw_jl/jl/'\n",
+    "jl_sid2wav = {}\n",
+    "jl_sid2txt = {}\n",
+    "# 有trans\n",
+    "for f1_name in os.listdir(jl_path):\n",
+    "    if 'wav' in f1_name:\n",
+    "        sid = f1_name.split('.')[0]\n",
+    "        jl_sid2wav[sid] = jl_path + f1_name\n",
+    "    elif 'txt' in f1_name:\n",
+    "        with open(jl_path + f1_name, 'r') as f:\n",
+    "            jl_sid2txt[sid] = f.read().strip()\n",
+    "\n",
+    "for sid in jl_sid2wav:\n",
+    "    if sid in jl_sid2txt:\n",
+    "        jl.append([jl_sid2wav[sid], jl_sid2txt[sid]])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "cc8666ed-c576-409f-bfeb-03c8827de90c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 进行asr\n",
+    "need_whisper = []\n",
+    "for d in game_en_train + game_en_val + game_en_test:\n",
+    "    need_whisper.append(d.split('|')[0])\n",
+    "\n",
+    "need_whisper += rav\n",
+    "print(len(need_whisper))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2cc9361f-39dd-45d3-a548-0118041c5cb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "asr_res = []\n",
+    "\n",
+    "with open('/workspace/TTS/data/whisper_large_v3_result_0330.txt', 'w') as f:\n",
+    "    for i in tqdm(range(len(need_whisper))):\n",
+    "        wav_path = need_whisper[i]\n",
+    "        trans = ''\n",
+    "        try:\n",
+    "            trans = asr(wav_path)\n",
+    "        except:\n",
+    "            pass\n",
+    "        \n",
+    "        asr_res.append([wav_path, trans])\n",
+    "        f.write(f'{wav_path}|{trans}\\n')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d25b2f6c-6bd1-488d-bb77-7891d5aee5a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# len(emov)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "bc51b44d-57f2-4fc7-bbb2-08e6cbe7bfbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 对emov也进行asr\n",
+    "new_need_whisper = []\n",
+    "new_need_whisper += emov\n",
+    "\n",
+    "with open('/workspace/TTS/data/whisper_large_v3_result_0401_add.txt', 'w') as f:\n",
+    "    for i in tqdm(range(len(new_need_whisper))):\n",
+    "        wav_path = new_need_whisper[i]\n",
+    "        trans = ''\n",
+    "        try:\n",
+    "            trans = asr(wav_path)\n",
+    "        except:\n",
+    "            pass\n",
+    "\n",
+    "        f.write(f'{wav_path}|{trans}\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "13c471aa-0d2a-4050-a438-5bcb02c437c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asr_res = []\n",
+    "with open('/workspace/TTS/data/whisper_large_v3_result_0330.txt', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.strip().split('|')\n",
+    "        asr_res.append([line[0], line[1]])\n",
+    "\n",
+    "with open('/workspace/TTS/data/whisper_large_v3_result_0401_add.txt', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.strip().split('|')\n",
+    "        asr_res.append([line[0], line[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "77210c33-9e55-476e-891c-edb0de0b0ad1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# len(asr_res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "3c3afd1a-ad41-4c8f-a48f-6c75202024e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install editdistance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "eea2bb15-613a-4f85-8fc2-9995f94df5e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 过滤游戏数据\n",
+    "import string\n",
+    "import IPython.display as ipd\n",
+    "import editdistance\n",
+    "\n",
+    "wav2asr = {}\n",
+    "for items in asr_res:\n",
+    "    wav2asr[items[0]] = items[1]\n",
+    "\n",
+    "def remove_punctuation(text):\n",
+    "    # 定义标点符号集合\n",
+    "    punctuation = string.punctuation\n",
+    "    # 遍历字符串，将标点符号替换为空格\n",
+    "    result = ''.join([char if char not in punctuation else '' for char in text])\n",
+    "    return result\n",
+    "\n",
+    "def jaccard_similarity(s1, s2):\n",
+    "    set1 = set(s1)\n",
+    "    set2 = set(s2)\n",
+    "    intersection = len(set1.intersection(set2))\n",
+    "    union = len(set1.union(set2))\n",
+    "    return intersection / union if union != 0 else 0\n",
+    "    \n",
+    "def is_game_en_valid(wav_path, text, log = False):\n",
+    "    aha_words = ['ahem', 'argh', 'sigh', 'sobs', 'uh', 'um', 'hmm', 'ugh', 'woo', 'haha', 'hehe', 'huh', 'hmph', 'hm', 'whoa', 'yay', 'ah', 'mmhmm', 'ha', 'oh', 'hu', 'mmm']\n",
+    "\n",
+    "    if wav_path in wav2asr:\n",
+    "        whisper_trans = remove_punctuation(wav2asr[wav_path].lower().strip())\n",
+    "    else:\n",
+    "        whisper_trans = asr(wav_path).lower().strip()\n",
+    "\n",
+    "    original_text = text\n",
+    "        \n",
+    "    text = remove_punctuation(text.lower().strip())\n",
+    "\n",
+    "    duration = get_wav_duration(wav_path)\n",
+    "    mean_dur = duration / len(text.split(' '))\n",
+    "\n",
+    "    jaccard_score = jaccard_similarity(whisper_trans.split(' '), text.split(' '))\n",
+    "    edit_distance = editdistance.eval(whisper_trans.split(' '), text.split(' '))\n",
+    "\n",
+    "    length_ratio = len(whisper_trans.split(' ')) / len(text.split(' '))\n",
+    "\n",
+    "    if '*' in original_text \\\n",
+    "        or (edit_distance >= 3 and length_ratio > 1.5) \\\n",
+    "        or whisper_trans.split(' ')[0] in aha_words \\\n",
+    "        or text.split(' ')[0] in aha_words \\\n",
+    "        or ('男声' in wav_path or '女声' in wav_path or '会场广播' in wav_path):\n",
+    "    # if '*' in text or len(whisper_trans) == 0 or length_ratio > 1.5 or mean_dur > 1.0 or jaccard_score < 0.3 or (whisper_trans.split(' ')[0] != text.split(' ')[0] and whisper_trans.split(' ')[0] in aha_words) or text.split(' ')[0] in aha_words or 'sigh' in text or len(start_wav2vec_trans0) == 0 or start_wav2vec_trans0.split(' ')[0] in aha_words:\n",
+    "        if log:\n",
+    "            print(wav_path)\n",
+    "            display(ipd.Audio(wav_path, rate=24000, normalize=False))\n",
+    "            print(f'mean_dur: {mean_dur}')\n",
+    "            print(f'transcript(orignial): {text} \\ntranscript(whisper): {whisper_trans}')\n",
+    "            print(f'length_ratio: {length_ratio}')\n",
+    "            print(f'jaccard_score: {jaccard_score}')\n",
+    "            print(f'editdistance: {edit_distance}')\n",
+    "            print()\n",
+    "        return False\n",
+    "            \n",
+    "    wav2vec_trans0 = wav2vec_asr(wav_path, 0.5)[0].lower().strip()\n",
+    "    if len(wav2vec_trans0) > 0 and wav2vec_trans0.split(' ')[0] in aha_words:\n",
+    "        if log:\n",
+    "            print(f'transcript-0.5s(wav2vec): {wav2vec_trans0}')\n",
+    "        return False\n",
+    "        \n",
+    "    wav2vec_trans1 = wav2vec_asr(wav_path, 2.0)[0].lower().strip()\n",
+    "    if len(wav2vec_trans1) > 0 and wav2vec_trans1.split(' ')[0] in aha_words:\n",
+    "        if log:\n",
+    "            print(f'transcript-2s(wav2vec): {wav2vec_trans1}')\n",
+    "        return False\n",
+    "    \n",
+    "    if len(wav2vec_trans0) == 0:\n",
+    "        alignment_asr = alignment(wav_path)\n",
+    "        if len(alignment_asr) > 0 and alignment_asr[0]['start'] > 0.5:\n",
+    "            if log:\n",
+    "                print(f'alignment: {alignment_asr}')\n",
+    "            return False     \n",
+    "\n",
+    "    return True\n",
+    "\n",
+    "    # # 第二次过滤\n",
+    "    # trans1 = asr(wav_path, 2.0)[0].lower().strip()\n",
+    "    # trans2 = asr(wav_path, 3.0)[0].lower().strip()\n",
+    "    # if (len(trans1) > 0 and trans1.split(' ')[0] in aha_words) or (len(trans2) > 0 and trans2.split(' ')[0] in aha_words):\n",
+    "    #     if log:\n",
+    "    #         print(wav_path, text)\n",
+    "    #         print(trans0)\n",
+    "    #         print(trans1)\n",
+    "    #         print(trans2)\n",
+    "    #         display(ipd.Audio(wav_path, rate=24000, normalize=False))\n",
+    "    #     return False\n",
+    "    # else:\n",
+    "    #     return True\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "7c4a6cc8-3a08-4a97-9658-3cece6c97628",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# # 看过滤规则的准确率【这些样本是不是不想加入训练的】\n",
+    "# sample_size = 200\n",
+    "# game_en_tmp = np.array(game_en_train)\n",
+    "# random_indices = np.random.choice(len(game_en_train), size=sample_size, replace=False)\n",
+    "# game_en_tmp = game_en_tmp[random_indices]\n",
+    "\n",
+    "# game_en_tmp1 = []\n",
+    "# for i in tqdm(range(len(game_en_tmp))):\n",
+    "#     d = game_en_tmp[i]\n",
+    "#     if is_game_en_valid(d.split('|')[0], d.split('|')[1], log=True):\n",
+    "#         game_en_tmp1.append(d)\n",
+    "\n",
+    "# print(len(game_en_tmp1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "7a1c7cc8-c93a-4dfc-b00a-651254a17fe7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 随机查看过滤后的数据【看看是不是都想用来训练】，保证召回率\n",
+    "# tmp = random.choice(game_en_tmp1)\n",
+    "# print(tmp)\n",
+    "# display(ipd.Audio(tmp.split('|')[0], rate=24000, normalize=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "2c3da5b7-3776-4597-a719-4ac51ebdd442",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# same_speaker_data = [d for d in game_en_train if d.split('|')[-1]==tmp.split('|')[-1]]\n",
+    "# for d in same_speaker_data[:3]:\n",
+    "#     print(d)\n",
+    "#     display(ipd.Audio(d.split('|')[0], rate=24000, normalize=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "id": "f2554191-419a-40fc-a2e8-87fffac30fcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2798/2798 [21:25<00:00,  2.18it/s] s]\n",
+      " 95%|█████████▌| 3189/3347 [22:40<01:23,  1.89it/s] "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to align segment (\"风流尽在山水间\"): no characters in this segment found in model dictionary, resorting to original...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 3347/3347 [23:41<00:00,  2.35it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19338 2136 2674\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 过滤数据\n",
+    "def purify_game(data):\n",
+    "    new_data = []\n",
+    "    for i in tqdm(range(len(data))):\n",
+    "        d = data[i]\n",
+    "        if is_game_en_valid(d.split('|')[0], d.split('|')[1], log=False):\n",
+    "            new_data.append(d)\n",
+    "    return new_data\n",
+    "\n",
+    "game_en_train = purify_game(game_en_train)\n",
+    "game_en_val = purify_game(game_en_val)\n",
+    "game_en_test = purify_game(game_en_test)\n",
+    "\n",
+    "print(len(game_en_train), len(game_en_val), len(game_en_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 386,
+   "id": "59d2027f-a23c-448f-873a-6546504e62d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1200 150 150\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6893/6893 [00:00<00:00, 389635.42it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "375 75 75\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 过滤emotional tts数据，包括采样处理\n",
+    "emo_start_id = 35000 # 避开游戏\n",
+    "\n",
+    "emo_id_num = -1\n",
+    "emotion_data = []\n",
+    "\n",
+    "#esd\n",
+    "esd_ids = []\n",
+    "esd_data = {}\n",
+    "for d in esd_trans:\n",
+    "    id, txt, emo = d[0], d[1], d[2]\n",
+    "    role_id = id.split('_')[0] + d[2]\n",
+    "    if role_id not in esd_ids:\n",
+    "        emo_id_num += 1\n",
+    "        esd_ids.append(role_id)\n",
+    "\n",
+    "    wav_path = f'''/workspace/TTS/data/ESD/dataset/{id.split('_')[0]}/{emo}/{id}.wav'''\n",
+    "    if role_id not in esd_data:\n",
+    "        esd_data[role_id] = [[wav_path, txt, emo_start_id + emo_id_num]]\n",
+    "    else:\n",
+    "        esd_data[role_id].append([wav_path, txt, emo_start_id + emo_id_num])\n",
+    "\n",
+    "# 采样\n",
+    "esd_train, esd_val, esd_test = [], [], []\n",
+    "esd_max_num = 30\n",
+    "for role_id in esd_data:\n",
+    "    d = esd_data[role_id]\n",
+    "    np.random.shuffle(d)\n",
+    "    d = d[:esd_max_num]\n",
+    "    if '0011' in role_id:\n",
+    "        esd_val += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "    elif '0017' in role_id:\n",
+    "        esd_test += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "    else:\n",
+    "        esd_train += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "\n",
+    "print(len(esd_train), len(esd_val), len(esd_test))\n",
+    "\n",
+    "# emov\n",
+    "emov_data = {}\n",
+    "for i in tqdm(range(len(emov))):\n",
+    "    d = emov[i]\n",
+    "    role_name = d.split('/')[5]\n",
+    "    emo = d.split('/')[6].split('_')[0]\n",
+    "    role_id = role_name + '_' + emo\n",
+    "\n",
+    "    if 'neu' in role_id or 'ang' in role_id:\n",
+    "        if role_id not in emov_data:\n",
+    "            emo_id_num += 1\n",
+    "            emov_data[role_id] = [[d, wav2asr[d].strip(), emo_start_id + emo_id_num]]\n",
+    "        else:\n",
+    "            emov_data[role_id].append([d, wav2asr[d].strip(), emo_start_id + emo_id_num])\n",
+    "\n",
+    "emov_train, emov_val, emov_test = [], [], []\n",
+    "emov_max_num = 75 #emo少\n",
+    "for role_id in emov_data:\n",
+    "    d = emov_data[role_id]\n",
+    "    np.random.shuffle(d)\n",
+    "    d = d[:emov_max_num]\n",
+    "    if 'jenie_neutral' in role_id:\n",
+    "        emov_val += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "    elif 'jenie_anger' in role_id:\n",
+    "        emov_test += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "    else:\n",
+    "        emov_train += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "\n",
+    "print(len(emov_train), len(emov_val), len(emov_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 388,
+   "id": "3dbcf791-840c-414f-a557-5d9713dd0407",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2380/2380 [00:00<00:00, 200093.08it/s]\n",
+      "100%|██████████| 40/40 [03:53<00:00,  5.84s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "340 20 40\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1440/1440 [00:00<00:00, 156135.71it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "192 0 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# jl\n",
+    "jl_data = {}\n",
+    "for i in tqdm(range(len(jl))):\n",
+    "    d = jl[i]\n",
+    "    role_name = d[0].split('/')[-1].split('_')[0]\n",
+    "    emo = d[0].split('/')[-1].split('_')[1]\n",
+    "    text = d[1].strip()\n",
+    "    role_id = role_name + '_' + emo\n",
+    "    # role_id = role_name\n",
+    "\n",
+    "    if role_id not in jl_data:\n",
+    "        emo_id_num += 1\n",
+    "        jl_data[role_id] = [[d[0], text, emo_start_id + emo_id_num]]\n",
+    "    else:\n",
+    "        jl_data[role_id].append([d[0], text, emo_start_id + emo_id_num])\n",
+    "\n",
+    "jl_train, jl_val, jl_test = [], [], []\n",
+    "jl_max_num = 10 #emo多\n",
+    "jl_ids = list(jl_data.keys())\n",
+    "for i in tqdm(range(len(jl_ids))):\n",
+    "    role_id = jl_ids[i]\n",
+    "    d = jl_data[role_id]\n",
+    "    np.random.shuffle(d)\n",
+    "    d = d[:jl_max_num]\n",
+    "\n",
+    "    # 去掉transcript没对齐的数据\n",
+    "    new_d = []\n",
+    "    for _d in d:\n",
+    "        wav_path = _d[0]\n",
+    "        text = _d[1]\n",
+    "        whisper_asr = asr(wav_path).strip()\n",
+    "\n",
+    "        new_d.append([_d[0], whisper_asr, _d[2]])\n",
+    "    \n",
+    "        # edit_distance = editdistance.eval(remove_punctuation(text.lower().strip()).split(' '), remove_punctuation(whisper_asr.lower().strip()).split(' '))\n",
+    "        # if edit_distance == 0:\n",
+    "        #     new_d.append(_d)\n",
+    "\n",
+    "    d = new_d\n",
+    "    \n",
+    "    if 'female1_encouraging' in role_id or 'female2_happy' in role_id:\n",
+    "        jl_val += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "    elif 'male1_angry' in role_id or 'male2_anxious' in role_id:\n",
+    "        jl_test += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "    else:\n",
+    "        jl_train += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "\n",
+    "print(len(jl_train), len(jl_val), len(jl_test))\n",
+    "\n",
+    "# rav数据\n",
+    "rav_data = {}\n",
+    "for i in tqdm(range(len(rav))):\n",
+    "    d = rav[i]\n",
+    "    role_name = d.split('/')[5]\n",
+    "    profile = d.split('/')[-1].split('.')[0].split('-')\n",
+    "    emo = profile[2]\n",
+    "    emo_inten = profile[3]\n",
+    "    role_id = role_name + '_' + emo\n",
+    "\n",
+    "    if role_id not in rav_data:\n",
+    "        emo_id_num += 1\n",
+    "        rav_data[role_id] = [[d, wav2asr[d].strip(), emo_start_id + emo_id_num]]\n",
+    "    else:\n",
+    "        rav_data[role_id].append([d, wav2asr[d].strip(), emo_start_id + emo_id_num])\n",
+    "\n",
+    "rav_train, rav_val, rav_test = [], [], []\n",
+    "rav_max_num = 1 #emo少\n",
+    "for role_id in rav_data:\n",
+    "    d = rav_data[role_id]\n",
+    "    np.random.shuffle(d)\n",
+    "    d = d[:rav_max_num]\n",
+    "    \n",
+    "    rav_train += [f'{_d[0]}|{_d[1]}|{_d[2]}' for _d in d]\n",
+    "\n",
+    "print(len(rav_train), len(rav_val), len(rav_test))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 389,
+   "id": "c37279a3-d2a6-4695-99eb-f52c202cb806",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "340 20 40\n",
+      "192 0 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(jl_train), len(jl_val), len(jl_test))\n",
+    "print(len(rav_train), len(rav_val), len(rav_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bc3b899-aff4-4d73-b3eb-f79de84ac568",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 随机查看数据\n",
+    "# # esd\n",
+    "# sample_data = esd_train + esd_val + esd_test\n",
+    "# sample_data = [d for d in sample_data if 'Surprise' in d]\n",
+    "# tmp = random.choice(sample_data)\n",
+    "# print(tmp)\n",
+    "# display(ipd.Audio(tmp.split('|')[0], rate=24000, normalize=False))\n",
+    "\n",
+    "# # emov\n",
+    "# sample_data = [d for d in emov if 'neu' in d]\n",
+    "# # sample_data = emov\n",
+    "# tmp = random.choice(sample_data)\n",
+    "# print(tmp, wav2asr[tmp])\n",
+    "# wave, sr = sf.read(tmp)\n",
+    "# display(ipd.Audio(wave, rate=sr, normalize=False))\n",
+    "\n",
+    "# # jl\n",
+    "# sample_data = [d for d in jl if '' in d[0]]\n",
+    "# tmp = random.choice(sample_data)\n",
+    "# print(tmp)\n",
+    "# display(ipd.Audio(tmp[0], rate=sr, normalize=False))\n",
+    "\n",
+    "# # rav\n",
+    "# sample_data = [d for d in rav]\n",
+    "# # sample_data = emov\n",
+    "# tmp = random.choice(sample_data)\n",
+    "# print(tmp, wav2asr[tmp])\n",
+    "# display(ipd.Audio(tmp, rate=sr, normalize=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 391,
+   "id": "95e602d2-2144-4795-b39a-feec48dd26f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2107 245 265\n"
+     ]
+    }
+   ],
+   "source": [
+    "emo_train = esd_train + emov_train + jl_train + rav_train\n",
+    "emo_val = esd_val + emov_val + jl_val + rav_val\n",
+    "emo_test = esd_test + emov_test + jl_test + rav_test\n",
+    "\n",
+    "print(len(emo_train), len(emo_val), len(emo_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 459,
+   "id": "65c347f5-bfc8-42a3-8dd7-f93c26b21916",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "def process_text(text):\n",
+    "    new_text = re.sub(r'^\\.\\.\\.', '', text)\n",
+    "    return new_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 470,
+   "id": "5cbf0254-082c-4fcc-b173-005df662c231",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "libritts_val_data hours 3.830820000000008\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2380/2380 [00:01<00:00, 1391.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game_train hours 36.31573596808863\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 19338/19338 [00:15<00:00, 1235.76it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game_val hours 3.908920038225938\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 2136/2136 [00:01<00:00, 1233.00it/s]\n",
+      "100%|██████████| 2107/2107 [00:01<00:00, 1811.55it/s]\n",
+      "100%|██████████| 245/245 [00:00<00:00, 1533.77it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 进行phonemizer, mix data\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "# 隐藏seed，可以更充分利用data\n",
+    "random.seed(42)\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "train, val = [], []\n",
+    "\n",
+    "# np.random.shuffle(libritts_train_data)\n",
+    "# libritts_train_data = libritts_train_data[:10000]\n",
+    "# hours = sum([get_wav_duration(x.split('|')[0]) for x in libritts_train_data]) / 3600\n",
+    "# print('libritts_train_data hours', hours)\n",
+    "\n",
+    "# for i in tqdm(range(len(libritts_train_data))):\n",
+    "#     line = libritts_train_data[i]\n",
+    "#     line = line.split('|')\n",
+    "#     wav, text, sid = line[0], line[1], line[2]\n",
+    "#     ps = preprocess(text, 'en-us')\n",
+    "#     train.append(f'{wav}|{ps}|{sid}')\n",
+    "\n",
+    "np.random.shuffle(libritts_val_data)\n",
+    "sample_libritts_val_data = libritts_val_data[:2380]\n",
+    "hours = sum([get_wav_duration(x.split('|')[0]) for x in sample_libritts_val_data]) / 3600\n",
+    "print('libritts_val_data hours', hours)\n",
+    "for i in tqdm(range(len(sample_libritts_val_data))):\n",
+    "    line = sample_libritts_val_data[i]\n",
+    "    line = line.split('|')\n",
+    "    wav, text, sid = line[0], line[1], line[2]\n",
+    "    ps = preprocess(process_text(text.strip()), 'en-us')\n",
+    "    val.append(f'{wav}|{ps}|{sid}')\n",
+    "\n",
+    "hours = sum([get_wav_duration(x.split('|')[0]) for x in game_en_train]) / 3600\n",
+    "print('game_train hours', hours)\n",
+    "\n",
+    "for i in tqdm(range(len(game_en_train))):\n",
+    "    line = game_en_train[i]\n",
+    "    line = line.split('|')\n",
+    "    wav, text, sid = line[0], line[1], line[2]\n",
+    "    ps = preprocess(process_text(text.strip()), 'en-us')\n",
+    "    train.append(f'{wav}|{ps}|{sid}')\n",
+    "\n",
+    "hours = sum([get_wav_duration(x.split('|')[0]) for x in game_en_val]) / 3600\n",
+    "print('game_val hours', hours)\n",
+    "\n",
+    "for i in tqdm(range(len(game_en_val))):\n",
+    "    line = game_en_val[i]\n",
+    "    line = line.split('|')\n",
+    "    wav, text, sid = line[0], line[1], line[2]\n",
+    "    ps = preprocess(process_text(text.strip()), 'en-us')\n",
+    "    val.append(f'{wav}|{ps}|{sid}')\n",
+    "\n",
+    "for i in tqdm(range(len(emo_train))):\n",
+    "    line = emo_train[i]\n",
+    "    line = line.split('|')\n",
+    "    wav, text, sid = line[0], line[1], line[2]\n",
+    "    second = get_wav_duration(wav)\n",
+    "    if second > 1:\n",
+    "        ps = preprocess(process_text(text.strip()), 'en-us')\n",
+    "        train.append(f'{wav}|{ps}|{sid}')\n",
+    "\n",
+    "for i in tqdm(range(len(emo_val))):\n",
+    "    line = emo_val[i]\n",
+    "    line = line.split('|')\n",
+    "    wav, text, sid = line[0], line[1], line[2]\n",
+    "    second = get_wav_duration(wav)\n",
+    "    if second > 1:\n",
+    "        ps = preprocess(process_text(text.strip()), 'en-us')\n",
+    "        val.append(f'{wav}|{ps}|{sid}')\n",
+    "\n",
+    "# np.random.shuffle(cml_train_data)\n",
+    "# cml_train_data = cml_train_data[:25000]\n",
+    "# hours = sum([get_wav_duration(x.split('|')[0]) for x in cml_train_data]) / 3600\n",
+    "# print('cml_train_data hours', hours)\n",
+    "# for i in tqdm(range(len(cml_train_data))):\n",
+    "#     line = cml_train_data[i]\n",
+    "#     line = line.split('|')\n",
+    "#     wav, text, sid = line[0], line[1], line[2]\n",
+    "#     ps = preprocess(text, 'es')\n",
+    "#     train.append(f'{wav}|{ps}|{sid}')\n",
+    "\n",
+    "# np.random.shuffle(cml_val_data)\n",
+    "# cml_val_data = cml_val_data[:500]\n",
+    "# hours = sum([get_wav_duration(x.split('|')[0]) for x in cml_val_data]) / 3600\n",
+    "# print('cml_val_data hours', hours)\n",
+    "# for i in tqdm(range(len(cml_val_data))):\n",
+    "#     line = cml_val_data[i]\n",
+    "#     line = line.split('|')\n",
+    "#     wav, text, sid = line[0], line[1], line[2]\n",
+    "#     ps = preprocess(text, 'es')\n",
+    "#     val.append(f'{wav}|{ps}|{sid}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 471,
+   "id": "fc762cfc-84c0-4280-b7d7-586c7bc293ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21445\n",
+      "38.01937940192716\n"
+     ]
+    }
+   ],
+   "source": [
+    "# data statistic\n",
+    "hours = sum([get_wav_duration(x.split('|')[0]) for x in train])\n",
+    "print(len(train))\n",
+    "print(hours/3600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 472,
+   "id": "8d4f7db0-b01a-4f3e-9949-73d7d05e12d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4761\n",
+      "7.950606687531487\n"
+     ]
+    }
+   ],
+   "source": [
+    "# data statistic\n",
+    "hours = sum([get_wav_duration(x.split('|')[0]) for x in val])\n",
+    "print(len(val))\n",
+    "print(hours/3600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 473,
+   "id": "1e862965-6b93-40ad-bac6-1340325811e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21445\n",
+      "4761\n",
+      "21440\n",
+      "4752\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 保证长度可以整除batch\n",
+    "# 去除太长的样本\n",
+    "\n",
+    "from meldataset import TextCleaner\n",
+    "\n",
+    "text_cleaner = TextCleaner() # 对齐training\n",
+    "\n",
+    "bs = 16\n",
+    "maxlen = 512-2 # bert最大长度512，并且要考虑开头和结尾的pad\n",
+    "\n",
+    "print(len(train))\n",
+    "print(len(val))\n",
+    "\n",
+    "train = [d for d in train if len(text_cleaner(d.split('|')[1])) < maxlen]\n",
+    "val = [d for d in val if len(text_cleaner(d.split('|')[1])) < maxlen]\n",
+    "\n",
+    "train = train[:int(len(train)/bs) * bs]\n",
+    "val = val[:int(len(val)/bs) * bs]\n",
+    "\n",
+    "print(len(train))\n",
+    "print(len(val))\n",
+    "\n",
+    "np.random.shuffle(train)\n",
+    "\n",
+    "with open('/workspace/TTS/tts/StyleTTS2/Data/train_list_en_21k_0401.txt', 'w') as f:\n",
+    "    for d in train:\n",
+    "        f.write(d+'\\n')\n",
+    "\n",
+    "with open('/workspace/TTS/tts/StyleTTS2/Data/val_list_en_21k_0401.txt', 'w') as f:\n",
+    "    for d in val:\n",
+    "        f.write(d+'\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 474,
+   "id": "14de8383-89fd-4a44-a8e4-377b8bb77180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install langdetect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 475,
+   "id": "f96880cd-ce73-4e69-bc55-c54dd735a260",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # 新增OOD数据\n",
+    "# import json, re, random\n",
+    "# from tqdm import tqdm\n",
+    "# from langdetect import detect\n",
+    "# from nltk.tokenize import sent_tokenize\n",
+    "\n",
+    "# ood_data = []\n",
+    "\n",
+    "# waifu_data = []\n",
+    "# with open('/workspace/TTS/data/sft_test.jsonl', 'r') as f:\n",
+    "#     for line in f:\n",
+    "#         line = json.loads(line.strip())\n",
+    "#         if line['lang']  == 'es':\n",
+    "#             waifu_data.append(line)\n",
+    "\n",
+    "# with open('/workspace/TTS/data/sft_val.jsonl', 'r') as f:\n",
+    "#     for line in f:\n",
+    "#         line = json.loads(line.strip())\n",
+    "#         if line['lang']  == 'es':\n",
+    "#             waifu_data.append(line)\n",
+    "\n",
+    "# def get_ood_data_waifu(text):\n",
+    "\n",
+    "#     sentences = \n",
+    "    \n",
+    "#     sentences = sent_tokenize(text) # 拆分出来，还是太长\n",
+    "#     print('sen1', sentences)\n",
+    "\n",
+    "#     sentences = [preprocess(sen.strip(), 'es') for sen in sentences]\n",
+    "\n",
+    "#     if len(sentences) > 0:\n",
+    "#         sentence = random.choice(sentences)\n",
+    "#         print('sen4', sentence)\n",
+    "#         return sentence\n",
+    "#     else:\n",
+    "#         return None\n",
+    "\n",
+    "# for i in tqdm(range(len(waifu_data[:10]))):\n",
+    "#     d = waifu_data[i]\n",
+    "#     if len(d['context']) < 1:\n",
+    "#         continue\n",
+    "        \n",
+    "#     text = d['context'][0]['translate_content']\n",
+    "\n",
+    "#     # print(text)\n",
+    "    \n",
+    "#     try:\n",
+    "#         if isinstance(text, str) and len(text) > 10 and detect(text[:100]) == 'es':\n",
+    "#             # 去除非西班牙数据\n",
+    "#             print('sen', text)\n",
+    "#             sen = get_ood_data_waifu(text)\n",
+    "#             if sen is not None:\n",
+    "#                 ood_data.append(sen)\n",
+    "#     except:\n",
+    "#         pass\n",
+    "\n",
+    "#     # if len(d['context']) < 2:\n",
+    "#     #     continue\n",
+    "        \n",
+    "#     # text = d['context'][1]['translate_content']\n",
+    "\n",
+    "#     # try:\n",
+    "#     #     if isinstance(text, str) and len(text) > 10 and detect(text[:100]) == 'es':\n",
+    "#     #         # 去除非西班牙数据\n",
+    "#     #         sen = get_ood_data_waifu(text)\n",
+    "#     #         if sen is not None:\n",
+    "#     #             ood_data.append(sen)\n",
+    "#     # except:\n",
+    "#     #     pass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 476,
+   "id": "6227d8c1-40c6-4c5e-98f1-1cd135074adc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2674"
+      ]
+     },
+     "execution_count": 476,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(game_en_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 477,
+   "id": "0f76809d-120c-4083-8f36-d599badf678c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "265"
+      ]
+     },
+     "execution_count": 477,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(emo_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 480,
+   "id": "f262faf8-d7eb-4cc3-bd08-64de847f3ca0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5938\n"
+     ]
+    }
+   ],
+   "source": [
+    "from meldataset import TextCleaner\n",
+    "\n",
+    "text_cleaner = TextCleaner() # 对齐training\n",
+    "\n",
+    "# es_ood_data = []\n",
+    "# with open('/workspace/TTS/tts/StyleTTS2/Data/OOD_texts.txt', 'r') as f:\n",
+    "#     # waifu数据\n",
+    "#     for line in f:\n",
+    "#         line = line.strip().split('|')\n",
+    "#         text, sid = line[0], line[1]\n",
+    "#         es_ood_data.append(preprocess(text, 'es'))\n",
+    "\n",
+    "# # +其他西班牙语\n",
+    "# for d in cml_test_data[:1500]:\n",
+    "#     es_ood_data.append(preprocess(d.split('|')[1], 'es'))\n",
+    "\n",
+    "# +英语，加入wav_path\n",
+    "en_ood_data = []\n",
+    "for d in libritts_test_data[:3000]:\n",
+    "    en_ood_data.append([d.split('|')[0], preprocess(d.split('|')[1], 'en-us')])\n",
+    "\n",
+    "for d in game_en_test:\n",
+    "    # en_ood_data.append([d.split('|')[0], d.split('|')[1]])\n",
+    "    en_ood_data.append([d.split('|')[0], preprocess(process_text(d.split('|')[1].strip()), 'en-us')])\n",
+    "\n",
+    "for d in emo_test:\n",
+    "    en_ood_data.append([d.split('|')[0], preprocess(process_text(d.split('|')[1].strip()), 'en-us')])\n",
+    "    \n",
+    "# es_ood_data = list(set(es_ood_data))\n",
+    "# en_ood_data = list(set(en_ood_data))\n",
+    "\n",
+    "# 去掉text cleaner之后超长的内容\n",
+    "# es_ood_data = [d for d in es_ood_data if len(text_cleaner(d)) < 510]\n",
+    "en_ood_data = [d for d in en_ood_data if len(text_cleaner(d[1])) < 510]\n",
+    "\n",
+    "# print(len(es_ood_data))\n",
+    "print(len(en_ood_data))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 481,
+   "id": "3be67931-2506-44f0-b394-56cb6ecb722f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# es_ood_data = [f'{d}|0|es' for d in es_ood_data]\n",
+    "en_ood_data = [f'{d[0]}|{d[1]}|en' for d in en_ood_data]\n",
+    "ood_data = en_ood_data\n",
+    "# ood_data = es_ood_data + en_ood_data\n",
+    "np.random.shuffle(ood_data)\n",
+    "print(len(ood_data))\n",
+    "\n",
+    "with open('/workspace/TTS/tts/StyleTTS2/Data/OOD_texts_en_6k_0401.txt', 'w') as f:\n",
+    "    for d in ood_data:\n",
+    "        f.write(d+'\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2dda822d-24f7-4bb4-882f-7b9711c8e983",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56ed7106-07d2-45aa-9fc1-1286a3a956bd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51992195-2507-4b56-8dd0-6a7876decf0e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56e4670b-598b-4c08-8137-5475f87e8aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}