mirror of https://github.com/coqui-ai/TTS.git
Update notebooks
Eren Golge
parent
f29d1840f9
commit
11d5e5bae5
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@ -105,21 +105,21 @@
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"outputs": [],
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"source": [
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"# Set constants\n",
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"ROOT_PATH = '/media/erogol/data_ssd/Data/models/mozilla_models/4842/'\n",
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"ROOT_PATH = '/media/erogol/data_ssd/Data/models/mozilla_models/4845/'\n",
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"MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n",
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"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
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"OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
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"CONFIG = load_config(CONFIG_PATH)\n",
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"VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-4841-May-26-2019_01+50PM-df8cfe1/model_checkpoints/best_model.pth.tar\"\n",
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"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-4841-May-26-2019_04+23AM-df8cfe1/config.json\"\n",
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"VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/model_checkpoints/best_model.pth.tar\"\n",
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"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/wavernn/mozilla/mozilla-May24-4763/config.json\"\n",
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"VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)\n",
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"use_cuda = False\n",
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"\n",
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"# Set some config fields manually for testing\n",
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"CONFIG.windowing = False\n",
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"CONFIG.prenet_dropout = False\n",
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"CONFIG.separate_stopnet = True\n",
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"CONFIG.stopnet = True\n",
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"# CONFIG.windowing = False\n",
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"# CONFIG.prenet_dropout = False\n",
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"# CONFIG.separate_stopnet = True\n",
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"# CONFIG.stopnet = True\n",
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"\n",
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"# Set the vocoder\n",
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"use_gl = True # use GL if True\n",
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@ -22,6 +22,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"import os\n",
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"import sys\n",
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"sys.path.append(TTS_PATH)\n",
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@ -34,12 +36,12 @@
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"from TTS.datasets.TTSDataset import MyDataset\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.visual import plot_spectrogram\n",
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"from TTS.utils.generic_utils import load_config\n",
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"from TTS.utils.generic_utils import load_config, setup_model\n",
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"from TTS.datasets.preprocess import ljspeech\n",
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"%matplotlib inline\n",
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"\n",
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"import os\n",
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"os.environ['CUDA_VISIBLE_DEVICES']='0'"
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"os.environ['CUDA_VISIBLE_DEVICES']='1'"
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]
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},
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{
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@ -66,18 +68,22 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"OUT_PATH = \"/home/erogol/Data/LJSpeech-1.1/wavernn_4152/\"\n",
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"DATA_PATH = \"/home/erogol/Data/LJSpeech-1.1/\"\n",
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"METADATA_FILE = \"metadata_train.csv\"\n",
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"CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/ljspeech_models/4258_nancy/config.json\"\n",
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"MODEL_FILE = \"/home/erogol/checkpoint_92000.pth.tar\"\n",
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"DRY_RUN = True # if False, does not generate output files, only computes loss and visuals.\n",
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"BATCH_SIZE = 16\n",
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"OUT_PATH = \"/home/erogol/Data/Mozilla/wavernn/4841/\"\n",
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"DATA_PATH = \"/home/erogol/Data/Mozilla/\"\n",
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"DATASET = \"mozilla\"\n",
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"METADATA_FILE = \"metadata.txt\"\n",
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"CONFIG_PATH = \"/media/erogol/data_ssd/Data/models/mozilla_models/4841/config.json\"\n",
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"MODEL_FILE = \"/media/erogol/data_ssd/Data/models/mozilla_models/4841/best_model.pth.tar\"\n",
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"DRY_RUN = False # if False, does not generate output files, only computes loss and visuals.\n",
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"BATCH_SIZE = 32\n",
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"\n",
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"use_cuda = torch.cuda.is_available()\n",
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"print(\" > CUDA enabled: \", use_cuda)\n",
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"\n",
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"C = load_config(CONFIG_PATH)\n",
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"ap = AudioProcessor(bits=9, **C.audio)"
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"ap = AudioProcessor(bits=9, **C.audio)\n",
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"C.prenet_dropout = False\n",
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"C.separate_stopnet = True"
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]
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},
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{
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@ -86,7 +92,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = MyDataset(DATA_PATH, METADATA_FILE, C.r, C.text_cleaner, ap, ljspeech, use_phonemes=C.use_phonemes, phoneme_cache_path=C.phoneme_cache_path)\n",
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"preprocessor = importlib.import_module('datasets.preprocess')\n",
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"preprocessor = getattr(preprocessor, DATASET.lower())\n",
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"\n",
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"dataset = MyDataset(DATA_PATH, METADATA_FILE, C.r, C.text_cleaner, ap, preprocessor, use_phonemes=C.use_phonemes, phoneme_cache_path=C.phoneme_cache_path)\n",
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"loader = DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False)"
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]
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},
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@ -99,11 +108,11 @@
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"from utils.text.symbols import symbols, phonemes\n",
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"from utils.generic_utils import sequence_mask\n",
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"from layers.losses import L1LossMasked\n",
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"from utils.text.symbols import symbols, phonemes\n",
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"\n",
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"# load the model\n",
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"MyModel = importlib.import_module('TTS.models.'+C.model.lower())\n",
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"MyModel = getattr(MyModel, C.model)\n",
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"num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
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"model = MyModel(num_chars, C.r, attn_win=False)\n",
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"model = setup_model(num_chars, C)\n",
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"checkpoint = torch.load(MODEL_FILE)\n",
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"model.load_state_dict(checkpoint['model'])\n",
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"print(checkpoint['step'])\n",
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@ -151,10 +160,19 @@
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" stop_targets = stop_targets.cuda()\n",
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" \n",
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" mask = sequence_mask(text_lengths)\n",
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" mel_outputs, mel_postnet_outputs, alignments, stop_tokens = model.forward(text_input, text_lengths, mel_input, mask)\n",
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" mel_outputs, postnet_outputs, alignments, stop_tokens = model.forward(text_input, text_lengths, mel_input)\n",
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" \n",
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" # compute mel specs from linear spec if model is Tacotron\n",
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" mel_specs = []\n",
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" if C.model == \"Tacotron\":\n",
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" postnet_outputs = postnet_outputs.data.cpu().numpy()\n",
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" for b in range(postnet_outputs.shape[0]):\n",
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" postnet_output = postnet_outputs[b]\n",
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" mel_specs.append(torch.FloatTensor(ap.out_linear_to_mel(postnet_output.T).T).cuda())\n",
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" postnet_outputs = torch.stack(mel_specs)\n",
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" \n",
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" loss = criterion(mel_outputs, mel_input, mel_lengths)\n",
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" loss_postnet = criterion(mel_postnet_outputs, mel_input, mel_lengths)\n",
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" loss_postnet = criterion(postnet_outputs, mel_input, mel_lengths)\n",
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" losses.append(loss.item())\n",
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" postnet_losses.append(loss_postnet.item())\n",
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" if not DRY_RUN:\n",
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@ -164,12 +182,12 @@
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" file_name, wavq_path, mel_path, wav_path = set_filename(wav_file_path, OUT_PATH)\n",
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" file_idxs.append(file_name)\n",
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"\n",
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" # quantize and save wav\n",
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" wavq = ap.quantize(wav)\n",
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" np.save(wavq_path, wavq)\n",
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"# # quantize and save wav\n",
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"# wavq = ap.quantize(wav)\n",
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"# np.save(wavq_path, wavq)\n",
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"\n",
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" # save TTS mel\n",
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" mel = mel_postnet_outputs[idx]\n",
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" mel = postnet_outputs[idx]\n",
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" mel = mel.data.cpu().numpy()\n",
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" mel_length = mel_lengths[idx]\n",
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" mel = mel[:mel_length, :].T\n",
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@ -202,7 +220,18 @@
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"outputs": [],
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"source": [
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"idx = 1\n",
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"mel_example = mel_postnet_outputs[idx].data.cpu().numpy()\n",
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"mel_example = postnet_outputs[idx].data.cpu().numpy()\n",
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"plot_spectrogram(mel_example[:mel_lengths[idx], :], ap);\n",
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"print(mel_example[:mel_lengths[1], :].shape)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"mel_example = mel_outputs[idx].data.cpu().numpy()\n",
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"plot_spectrogram(mel_example[:mel_lengths[idx], :], ap);\n",
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"print(mel_example[:mel_lengths[1], :].shape)"
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]
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@ -225,8 +254,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# postnet, decoder diff\n",
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"from matplotlib import pylab as plt\n",
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"mel_diff = mel_outputs[idx] - mel_postnet_outputs[idx]\n",
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"mel_diff = mel_outputs[idx] - postnet_outputs[idx]\n",
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"plt.figure(figsize=(16, 10))\n",
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"plt.imshow(abs(mel_diff.detach().cpu().numpy()[:mel_lengths[idx],:]).T,aspect=\"auto\", origin=\"lower\");\n",
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"plt.colorbar()\n",
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@ -241,13 +271,20 @@
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"source": [
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"from matplotlib import pylab as plt\n",
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"# mel = mel_poutputs[idx].detach().cpu().numpy()\n",
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"mel = mel_postnet_outputs[idx].detach().cpu().numpy()\n",
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"mel = postnet_outputs[idx].detach().cpu().numpy()\n",
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"mel_diff2 = melt.T - mel[:melt.shape[1]]\n",
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"plt.figure(figsize=(16, 10))\n",
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"plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n",
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"plt.colorbar()\n",
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"plt.tight_layout()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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