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import os |
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import shutil |
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import numpy as np |
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from tests import get_tests_path, get_tests_input_path, get_tests_output_path |
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from torch.utils.data import DataLoader |
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from TTS.utils.audio import AudioProcessor |
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from TTS.utils.io import load_config |
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from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset |
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from TTS.vocoder.datasets.preprocess import load_wav_feat_data, preprocess_wav_files |
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file_path = os.path.dirname(os.path.realpath(__file__)) |
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OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/") |
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os.makedirs(OUTPATH, exist_ok=True) |
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C = load_config(os.path.join(get_tests_input_path(), |
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"test_vocoder_wavernn_config.json")) |
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test_data_path = os.path.join(get_tests_path(), "data/ljspeech/") |
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test_mel_feat_path = os.path.join(test_data_path, "mel") |
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test_quant_feat_path = os.path.join(test_data_path, "quant") |
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ok_ljspeech = os.path.exists(test_data_path) |
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def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, mulaw, num_workers): |
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""" run dataloader with given parameters and check conditions """ |
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ap = AudioProcessor(**C.audio) |
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C.batch_size = batch_size |
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C.mode = mode |
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C.seq_len = seq_len |
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C.data_path = test_data_path |
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preprocess_wav_files(test_data_path, C, ap) |
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_, train_items = load_wav_feat_data( |
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test_data_path, test_mel_feat_path, 5) |
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dataset = WaveRNNDataset(ap=ap, |
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items=train_items, |
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seq_len=seq_len, |
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hop_len=hop_len, |
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pad=pad, |
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mode=mode, |
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mulaw=mulaw |
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) |
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loader = DataLoader(dataset, |
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shuffle=True, |
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collate_fn=dataset.collate, |
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batch_size=batch_size, |
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num_workers=num_workers, |
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pin_memory=True, |
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) |
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max_iter = 10 |
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count_iter = 0 |
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try: |
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for data in loader: |
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x_input, mels, _ = data |
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expected_feat_shape = (ap.num_mels, |
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(x_input.shape[-1] // hop_len) + (pad * 2)) |
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assert np.all( |
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mels.shape[1:] == expected_feat_shape), f" [!] {mels.shape} vs {expected_feat_shape}" |
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assert (mels.shape[2] - pad * 2) * hop_len == x_input.shape[1] |
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count_iter += 1 |
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if count_iter == max_iter: |
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break |
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finally: |
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shutil.rmtree(test_mel_feat_path) |
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shutil.rmtree(test_quant_feat_path) |
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def test_parametrized_wavernn_dataset(): |
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''' test dataloader with different parameters ''' |
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params = [ |
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[16, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, 10, True, 0], |
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[16, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, "mold", False, 4], |
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[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, 9, False, 0], |
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[1, C.audio['hop_length'], C.audio['hop_length'], 2, 10, True, 0], |
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[1, C.audio['hop_length'], C.audio['hop_length'], 2, "mold", False, 0], |
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[1, C.audio['hop_length'] * 5, C.audio['hop_length'], 4, 10, False, 2], |
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[1, C.audio['hop_length'] * 5, C.audio['hop_length'], 2, "mold", False, 0], |
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] |
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for param in params: |
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print(param) |
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wavernn_dataset_case(*param) |
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