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https://github.com/coqui-ai/TTS.git
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28a7464975
* Fix the bug in split_dataset * Make eval_split_size configurable * Change test_loader to use load_tts_samples function * Change eval_split_portion to eval_split_size and permits to set the absolute number of samples in eval * Fix samplers unit test * Add data unit test on GitHub workflow
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import functools
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import torch
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.utils.languages import get_language_weighted_sampler
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# Fixing random state to avoid random fails
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torch.manual_seed(0)
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dataset_config_en = BaseDatasetConfig(
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name="ljspeech",
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meta_file_train="metadata.csv",
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meta_file_val="metadata.csv",
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path="tests/data/ljspeech",
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language="en",
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)
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dataset_config_pt = BaseDatasetConfig(
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name="ljspeech",
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meta_file_train="metadata.csv",
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meta_file_val="metadata.csv",
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path="tests/data/ljspeech",
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language="pt-br",
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)
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# Adding the EN samples twice to create an unbalanced dataset
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train_samples, eval_samples = load_tts_samples(
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[dataset_config_en, dataset_config_en, dataset_config_pt], eval_split=True
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)
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def is_balanced(lang_1, lang_2):
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return 0.85 < lang_1 / lang_2 < 1.2
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random_sampler = torch.utils.data.RandomSampler(train_samples)
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ids = functools.reduce(lambda a, b: a + b, [list(random_sampler) for i in range(100)])
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en, pt = 0, 0
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for index in ids:
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if train_samples[index]["language"] == "en":
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en += 1
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else:
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pt += 1
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assert not is_balanced(en, pt), "Random sampler is supposed to be unbalanced"
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weighted_sampler = get_language_weighted_sampler(train_samples)
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ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
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en, pt = 0, 0
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for index in ids:
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if train_samples[index]["language"] == "en":
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en += 1
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else:
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pt += 1
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assert is_balanced(en, pt), "Weighted sampler is supposed to be balanced"
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