From 3ecd556bbecb44feed757550ddb67b47903965bf Mon Sep 17 00:00:00 2001 From: Edresson Date: Sat, 1 May 2021 13:41:56 -0300 Subject: [PATCH] add unit test for extract tts spectrograms script --- TTS/bin/extract_tts_spectrograms.py | 6 +- tests/test_extract_tts_spectrograms.py | 85 ++++++++++++++++++++++++++ 2 files changed, 88 insertions(+), 3 deletions(-) create mode 100644 tests/test_extract_tts_spectrograms.py diff --git a/TTS/bin/extract_tts_spectrograms.py b/TTS/bin/extract_tts_spectrograms.py index 1a77e45a..5e230bbd 100755 --- a/TTS/bin/extract_tts_spectrograms.py +++ b/TTS/bin/extract_tts_spectrograms.py @@ -124,7 +124,7 @@ def format_data(data): ) @torch.no_grad() -def inference(model_name, model, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask=None, speaker_ids=None, speaker_embeddings=None): +def inference(model_name, model, config, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask=None, speaker_ids=None, speaker_embeddings=None): if model_name == "glow_tts": mel_input = mel_input.permute(0, 2, 1) # B x D x T speaker_c = None @@ -139,7 +139,7 @@ def inference(model_name, model, ap, text_input, text_lengths, mel_input, mel_le model_output = model_output.transpose(1, 2).detach().cpu().numpy() elif "tacotron" in model_name: - if c.bidirectional_decoder or c.double_decoder_consistency: + if config.bidirectional_decoder or config.double_decoder_consistency: ( _, postnet_outputs, @@ -186,7 +186,7 @@ def extract_spectrograms(data_loader, model, ap, output_path, quantized_wav=Fals item_idx, ) = format_data(data) - model_output = inference(c.model.lower(), model, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask, speaker_ids, speaker_embeddings) + model_output = inference(c.model.lower(), model, c, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask, speaker_ids, speaker_embeddings) for idx in range(text_input.shape[0]): wav_file_path = item_idx[idx] diff --git a/tests/test_extract_tts_spectrograms.py b/tests/test_extract_tts_spectrograms.py new file mode 100644 index 00000000..41e52229 --- /dev/null +++ b/tests/test_extract_tts_spectrograms.py @@ -0,0 +1,85 @@ +import os +import unittest + +import torch + +from tests import get_tests_input_path + +from TTS.tts.models.tacotron2 import Tacotron2 +from TTS.tts.models.glow_tts import GlowTTS + +from TTS.utils.audio import AudioProcessor +from TTS.utils.io import load_config + +from TTS.bin.extract_tts_spectrograms import inference + +torch.manual_seed(1) +use_cuda = torch.cuda.is_available() +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + +c = load_config(os.path.join(get_tests_input_path(), "test_config.json")) +# set params from tacotron inference +c.bidirectional_decoder = False +c.double_decoder_consistency = False +ap = AudioProcessor(**c.audio) + + +# pylint: disable=protected-access +class TestExtractTTSSpectrograms(unittest.TestCase): + @staticmethod + def test_GlowTTS(): + input_dummy = torch.randint(0, 24, (8, 128)).long().to(device) + input_lengths = torch.randint(100, 129, (8,)).long().to(device) + input_lengths[-1] = 128 + mel_spec = torch.rand(8, c.audio["num_mels"], 30).to(device) + mel_lengths = torch.randint(20, 30, (8,)).long().to(device) + + # create model + model = GlowTTS( + num_chars=32, + hidden_channels_enc=48, + hidden_channels_dec=48, + hidden_channels_dp=32, + out_channels=c.audio["num_mels"], + encoder_type="rel_pos_transformer", + encoder_params={ + "kernel_size": 3, + "dropout_p": 0.1, + "num_layers": 6, + "num_heads": 2, + "hidden_channels_ffn": 16, # 4 times the hidden_channels + "input_length": None, + }, + use_encoder_prenet=True, + num_flow_blocks_dec=12, + kernel_size_dec=5, + dilation_rate=1, + num_block_layers=4, + dropout_p_dec=0.0, + num_speakers=0, + c_in_channels=0, + num_splits=4, + num_squeeze=1, + sigmoid_scale=False, + mean_only=False, + ).to(device) + + model.eval() + _ = inference('glow_tts', model, c, ap, input_dummy, input_lengths, mel_spec.permute(0, 2, 1), mel_lengths) + print("GlowTTS extract tts spectrograms ok !") + + @staticmethod + def test_Tacotron(): + input_dummy = torch.randint(0, 24, (8, 128)).long().to(device) + input_lengths = torch.randint(100, 128, (8,)).long().to(device) + input_lengths = torch.sort(input_lengths, descending=True)[0] + mel_spec = torch.rand(8, 30, c.audio["num_mels"]).to(device) + mel_lengths = torch.randint(20, 30, (8,)).long().to(device) + mel_lengths[0] = 30 + + # create model + model = Tacotron2(num_chars=24, decoder_output_dim=c.audio["num_mels"], r=c.r, num_speakers=1).to(device) + model.eval() + + _ = inference('tacotron2', model, c, ap, input_dummy, input_lengths, mel_spec, mel_lengths) + print("Tacotron extract tts spectrograms ok !")