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https://github.com/wassname/TTS.git
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Added a notbook for d-vector multilingual VITS
This commit is contained in:
WeberJulian authored and Eren Gölge committed
1 parent
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commit
6ac31e4152
1 file changed
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"source": [
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"import IPython\n",
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"import torch\n",
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"\n",
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"from IPython.display import Audio\n",
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"\n",
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"from TTS.config import load_config\n",
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"from TTS.tts.models import setup_model\n",
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"from TTS.tts.utils.synthesis import synthesis\n",
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"from TTS.utils.audio import AudioProcessor"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"source": [
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"GENERAL_PATH = '/home/julian/workspace/train/VITS-pt-en-fr-lr/vits-August-29-2021_01+20PM-c68d7fa25/'\n",
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"MODEL_PATH = GENERAL_PATH + 'best_model.pth.tar'\n",
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"CONFIG_PATH = GENERAL_PATH + 'config.json'\n",
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"TTS_LANGUAGES = GENERAL_PATH + \"language_ids.json\"\n",
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"TTS_SPEAKERS = GENERAL_PATH + \"speakers.json\"\n",
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"USE_CUDA = torch.cuda.is_available()"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"source": [
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"# load the config\n",
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"C = load_config(CONFIG_PATH)\n",
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"\n",
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"# load the audio processor\n",
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"ap = AudioProcessor(**C.audio)\n",
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"\n",
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"speaker_embedding = None\n",
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"\n",
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"C.model_args['d_vector_file'] = TTS_SPEAKERS\n",
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"\n",
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"model = setup_model(C)\n",
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"model.language_manager.set_language_ids_from_file(TTS_LANGUAGES)\n",
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"cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))\n",
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"model.load_state_dict(cp['model'])\n",
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"\n",
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"\n",
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"model.eval()\n",
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"\n",
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"if USE_CUDA:\n",
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" model = model.cuda()\n",
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"\n",
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"use_griffin_lim = True"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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" > Setting up Audio Processor...\n",
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" | > sample_rate:16000\n",
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" | > resample:False\n",
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" | > num_mels:80\n",
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" | > min_level_db:-100\n",
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" | > frame_shift_ms:None\n",
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" | > frame_length_ms:None\n",
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" | > ref_level_db:20\n",
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" | > fft_size:1024\n",
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" | > power:1.5\n",
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" | > preemphasis:0.0\n",
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" | > griffin_lim_iters:60\n",
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" | > signal_norm:False\n",
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" | > symmetric_norm:True\n",
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" | > mel_fmin:0\n",
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" | > mel_fmax:None\n",
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" | > spec_gain:1.0\n",
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" | > stft_pad_mode:reflect\n",
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" | > max_norm:4.0\n",
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" | > clip_norm:True\n",
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" | > do_trim_silence:True\n",
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" | > trim_db:45\n",
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" | > do_sound_norm:False\n",
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" | > do_amp_to_db_linear:False\n",
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" | > do_amp_to_db_mel:True\n",
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" | > stats_path:None\n",
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" | > base:2.718281828459045\n",
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" | > hop_length:256\n",
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" | > win_length:1024\n",
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" > Using model: vits\n",
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" > Speaker manager is loaded with 421 speakers: ED, MLS_10032, MLS_10058, MLS_10065, MLS_10082, MLS_10087, MLS_10177, MLS_103, MLS_10620, MLS_10827, MLS_10957, MLS_112, MLS_11247, MLS_1127, MLS_115, MLS_11743, MLS_11772, MLS_11795, MLS_11822, MLS_11875, MLS_11954, MLS_12205, MLS_123, MLS_1243, MLS_125, MLS_12501, MLS_12512, MLS_12541, MLS_12709, MLS_12713, MLS_12823, MLS_12899, MLS_12968, MLS_12981, MLS_13142, MLS_13177, MLS_1329, MLS_13611, MLS_13634, MLS_13655, MLS_13658, MLS_14, MLS_1474, MLS_1579, MLS_1590, MLS_1591, MLS_1624, MLS_1649, MLS_1664, MLS_1745, MLS_177, MLS_1770, MLS_1798, MLS_1805, MLS_1817, MLS_1840, MLS_1844, MLS_1869, MLS_1887, MLS_1977, MLS_1989, MLS_2033, MLS_204, MLS_2155, MLS_2284, MLS_2297, MLS_2316, MLS_2506, MLS_2544, MLS_2587, MLS_2596, MLS_2607, MLS_27, MLS_2771, MLS_2776, MLS_28, MLS_2825, MLS_2904, MLS_2926, MLS_2946, MLS_30, MLS_3060, MLS_3182, MLS_3190, MLS_3204, MLS_3267, MLS_3270, MLS_3319, MLS_3344, MLS_3370, MLS_3464, MLS_3503, MLS_3595, MLS_3698, MLS_4018, MLS_4174, MLS_4193, MLS_4336, MLS_4396, MLS_4512, MLS_4609, MLS_4650, MLS_4699, MLS_4724, MLS_4744, MLS_4937, MLS_5021, MLS_5077, MLS_52, MLS_5232, MLS_5295, MLS_5525, MLS_5526, MLS_5553, MLS_5595, MLS_5612, MLS_5764, MLS_577, MLS_579, MLS_5830, MLS_5840, MLS_5968, MLS_6070, MLS_6128, MLS_62, MLS_6249, MLS_6318, MLS_6348, MLS_6362, MLS_6381, MLS_66, MLS_6856, MLS_694, MLS_7032, MLS_707, MLS_7142, MLS_7150, MLS_7193, MLS_7200, MLS_7239, MLS_7377, MLS_7423, MLS_7438, MLS_7439, MLS_753, MLS_7591, MLS_7601, MLS_7614, MLS_7679, MLS_78, MLS_7848, MLS_8102, MLS_8128, MLS_8582, MLS_8778, MLS_9121, MLS_9242, MLS_928, MLS_94, MLS_9804, MLS_9854, VCTK_p225, VCTK_p226, VCTK_p227, VCTK_p228, VCTK_p229, VCTK_p230, VCTK_p231, VCTK_p232, VCTK_p233, VCTK_p234, VCTK_p236, VCTK_p237, VCTK_p238, VCTK_p239, VCTK_p240, VCTK_p241, VCTK_p243, VCTK_p244, VCTK_p245, VCTK_p246, VCTK_p247, VCTK_p248, VCTK_p249, VCTK_p250, VCTK_p251, VCTK_p252, VCTK_p253, VCTK_p254, VCTK_p255, VCTK_p256, VCTK_p257, VCTK_p258, VCTK_p259, VCTK_p260, VCTK_p261, VCTK_p262, VCTK_p263, VCTK_p264, VCTK_p265, VCTK_p266, VCTK_p267, VCTK_p268, VCTK_p269, VCTK_p270, VCTK_p271, VCTK_p272, VCTK_p273, VCTK_p274, VCTK_p275, VCTK_p276, VCTK_p277, VCTK_p278, VCTK_p279, VCTK_p280, VCTK_p281, VCTK_p282, VCTK_p283, VCTK_p284, VCTK_p285, VCTK_p286, VCTK_p287, VCTK_p288, VCTK_p292, VCTK_p293, VCTK_p294, VCTK_p295, VCTK_p297, VCTK_p298, VCTK_p299, VCTK_p300, VCTK_p301, VCTK_p302, VCTK_p303, VCTK_p304, VCTK_p305, VCTK_p306, VCTK_p307, VCTK_p308, VCTK_p310, VCTK_p311, VCTK_p312, VCTK_p313, VCTK_p314, VCTK_p316, VCTK_p317, VCTK_p318, VCTK_p323, VCTK_p326, VCTK_p329, VCTK_p330, VCTK_p333, VCTK_p334, VCTK_p335, VCTK_p336, VCTK_p339, VCTK_p340, VCTK_p341, VCTK_p343, VCTK_p345, VCTK_p347, VCTK_p351, VCTK_p360, VCTK_p361, VCTK_p362, VCTK_p363, VCTK_p364, VCTK_p374, VCTK_p376, bernard, elodie, ezwa, gilles_g_le_blanc, nadine_eckert_boulet, openSLR_afr0184, openSLR_afr1919, openSLR_afr2418, openSLR_afr6590, openSLR_afr7130, openSLR_afr7214, openSLR_afr8148, openSLR_afr8924, openSLR_afr8963, openSLR_jvf00264, openSLR_jvf00658, openSLR_jvf01392, openSLR_jvf02059, openSLR_jvf02884, openSLR_jvf03187, openSLR_jvf04679, openSLR_jvf04715, openSLR_jvf04982, openSLR_jvf05540, openSLR_jvf06207, openSLR_jvf06510, openSLR_jvf06941, openSLR_jvf07335, openSLR_jvf07638, openSLR_jvf08002, openSLR_jvf08305, openSLR_jvf08736, openSLR_jvf09039, openSLR_jvm00027, openSLR_jvm01519, openSLR_jvm01932, openSLR_jvm02326, openSLR_jvm03314, openSLR_jvm03424, openSLR_jvm03727, openSLR_jvm04175, openSLR_jvm04285, openSLR_jvm04588, openSLR_jvm05219, openSLR_jvm05522, openSLR_jvm05667, openSLR_jvm05970, openSLR_jvm06080, openSLR_jvm06383, openSLR_jvm07765, openSLR_jvm07875, openSLR_jvm08178, openSLR_jvm09724, openSLR_sso0145, openSLR_sso0493, openSLR_sso0806, openSLR_sso1266, openSLR_sso1367, openSLR_sso1801, openSLR_sso2388, openSLR_sso2910, openSLR_sso4592, openSLR_sso5945, openSLR_sso6499, openSLR_sso7801, openSLR_sso7821, openSLR_sso7876, openSLR_sso7912, openSLR_sso7934, openSLR_sso8596, openSLR_sso8777, openSLR_sso9892, openSLR_suf00297, openSLR_suf00600, openSLR_suf00691, openSLR_suf00994, openSLR_suf01056, openSLR_suf01359, openSLR_suf02092, openSLR_suf02395, openSLR_suf02953, openSLR_suf03712, openSLR_suf03887, openSLR_suf04190, openSLR_suf04646, openSLR_suf04748, openSLR_suf05051, openSLR_suf05507, openSLR_suf06543, openSLR_suf07302, openSLR_suf08338, openSLR_suf08703, openSLR_sum00060, openSLR_sum00454, openSLR_sum01038, openSLR_sum01552, openSLR_sum01596, openSLR_sum01855, openSLR_sum01899, openSLR_sum02716, openSLR_sum03391, openSLR_sum03650, openSLR_sum03694, openSLR_sum04208, openSLR_sum04511, openSLR_sum05186, openSLR_sum06003, openSLR_sum06047, openSLR_sum07842, openSLR_sum08659, openSLR_sum09243, openSLR_sum09637, openSLR_sum09757, openSLR_tsn0045, openSLR_tsn0378, openSLR_tsn0441, openSLR_tsn1483, openSLR_tsn1498, openSLR_tsn1932, openSLR_tsn2839, openSLR_tsn3342, openSLR_tsn3629, openSLR_tsn4506, oLine truncated
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]
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}
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"source": [
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"#set speaker\n",
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"d_vector = model.speaker_manager.get_mean_d_vector('VCTK_p260')"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"source": [
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"model.language_manager.language_id_mapping"
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],
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"{'af': 0,\n",
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" 'en': 1,\n",
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" 'fr-fr': 2,\n",
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" 'jv': 3,\n",
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" 'pt-br': 4,\n",
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" 'st': 5,\n",
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" 'su': 6,\n",
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" 'tn': 7,\n",
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" 'xh': 8}"
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]
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},
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"metadata": {},
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"execution_count": 5
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}
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],
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"metadata": {
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"scrolled": true
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}
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"source": [
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"# set scales \n",
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"model.noise_scale = 0.0 # defines the noise variance applied to the random z vector at inference.\n",
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"model.length_scale = 1.0 # scaler for the duration predictor. The larger it is, the slower the speech.\n",
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"model.noise_scale_w = 0.0 # defines the noise variance applied to the duration predictor z vector at inference.\n",
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"model.inference_noise_scale = 0.5 # defines the noise variance applied to the random z vector at inference.\n",
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"model.inference_noise_scale_dp = 0.6 # defines the noise variance applied to the duration predictor z vector at inference."
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"source": [
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"text = \"Il m'a fallu beaucoup de temps pour développer une voix, et maintenant que je l'ai, je ne vais pas me taire.\"\n",
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"language_id = 2\n",
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"wav, alignment, _, _ = synthesis(\n",
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" model,\n",
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" text,\n",
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" C,\n",
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" \"cuda\" in str(next(model.parameters()).device),\n",
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" ap,\n",
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" speaker_id=None,\n",
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" d_vector=d_vector,\n",
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" style_wav=None,\n",
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" language_id=language_id,\n",
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" enable_eos_bos_chars=C.enable_eos_bos_chars,\n",
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" use_griffin_lim=True,\n",
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" do_trim_silence=False,\n",
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" ).values()\n",
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"IPython.display.display(Audio(wav, rate=ap.sample_rate))"
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],
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"\n",
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" <audio controls=\"controls\" >\n",
|
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" <source src=\"data:audio/wav;base64,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 truncated
|
||||
" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.Audio object>"
|
||||
]
|
||||
},
|
||||
"metadata": {}
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "b925b73899c1545aa2d9bbcf4e8e1df4138a367d2daefc2707570579325ca4c0"
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.8.10 64-bit ('TTS': conda)"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in new issue
Block a user