mirror of
https://github.com/wassname/TTS.git
synced 2026-09-20 12:30:22 +08:00
Edresson Casanova
917f417ac4
* Add alphas to control language and speaker balancer * Add docs for speaker and language samplers * Change the Samplers weights to float for save memory * Change the test_samplers to unittest format * Add get_sampler method in BaseTTS * Fix rebase issues * Add language and speaker samplers support for DDP training * Rename distributed sampler wrapper * Remove the DistributedSamplerWrapper and use the one from Trainer * Bugfix after rebase * Move the samplers config to tts config
85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
import functools
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import unittest
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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_balancer_weights
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from TTS.tts.utils.speakers import get_speaker_balancer_weights
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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 a language 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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# gerenate a speaker unbalanced dataset
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for i, sample in enumerate(train_samples):
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if i < 5:
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sample["speaker_name"] = "ljspeech-0"
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else:
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sample["speaker_name"] = "ljspeech-1"
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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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class TestSamplers(unittest.TestCase):
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def test_language_random_sampler(self): # pylint: disable=no-self-use
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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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def test_language_weighted_random_sampler(self): # pylint: disable=no-self-use
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weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(get_language_balancer_weights(train_samples), len(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), "Language Weighted sampler is supposed to be balanced"
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def test_speaker_weighted_random_sampler(self): # pylint: disable=no-self-use
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weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(get_speaker_balancer_weights(train_samples), len(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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spk1, spk2 = 0, 0
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for index in ids:
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if train_samples[index]["speaker_name"] == "ljspeech-0":
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spk1 += 1
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else:
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spk2 += 1
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assert is_balanced(spk1, spk2), "Speaker Weighted sampler is supposed to be balanced"
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