Implement VitsAudioConfig (#1556)

* Implement VitsAudioConfig

* Update VITS LJSpeech recipe

* Update VITS VCTK recipe

* Make style

* Add missing decorator

* Add missing param

* Make style

* Update recipes

* Fix test

* Bug fix

* Exclude tests folder

* Make linter

* Make style

MANIFEST.in

@@ -11,4 +11,5 @@ recursive-include TTS *.md
 recursive-include TTS *.py
 recursive-include TTS *.pyx
 recursive-include images *.png
-
+recursive-exclude tests *
+prune tests*

TTS/tts/configs/vits_config.py

@@ -2,7 +2,7 @@ from dataclasses import dataclass, field
 from typing import List
 
 from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.vits import VitsArgs
+from TTS.tts.models.vits import VitsArgs, VitsAudioConfig
 
 
 @dataclass
@@ -16,6 +16,9 @@ class VitsConfig(BaseTTSConfig):
         model_args (VitsArgs):
             Model architecture arguments. Defaults to `VitsArgs()`.
 
+        audio (VitsAudioConfig):
+            Audio processing configuration. Defaults to `VitsAudioConfig()`.
+
         grad_clip (List):
             Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
 
@@ -94,6 +97,7 @@ class VitsConfig(BaseTTSConfig):
     model: str = "vits"
     # model specific params
     model_args: VitsArgs = field(default_factory=VitsArgs)
+    audio: VitsAudioConfig = VitsAudioConfig()
 
     # optimizer
     grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])

TTS/tts/layers/losses.py

@@ -137,7 +137,7 @@ class SSIMLoss(torch.nn.Module):
 
         if ssim_loss.item() < 0.0:
             print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0")
-            ssim_loss =  torch.tensor([0.0])
+            ssim_loss = torch.tensor([0.0])
 
         return ssim_loss
 

TTS/tts/models/vits.py

@@ -200,6 +200,22 @@ def wav_to_mel(y, n_fft, num_mels, sample_rate, hop_length, win_length, fmin, fm
     return spec
 
 
+#############################
+# CONFIGS
+#############################
+
+
+@dataclass
+class VitsAudioConfig(Coqpit):
+    fft_size: int = 1024
+    sample_rate: int = 22050
+    win_length: int = 1024
+    hop_length: int = 256
+    num_mels: int = 80
+    mel_fmin: int = 0
+    mel_fmax: int = None
+
+
 ##############################
 # DATASET
 ##############################

TTS/tts/utils/ssim.py

@@ -16,9 +16,9 @@ def _reduce(x: torch.Tensor, reduction: str = "mean") -> torch.Tensor:
     """
     if reduction == "none":
         return x
-    elif reduction == "mean":
+    if reduction == "mean":
         return x.mean(dim=0)
-    elif reduction == "sum":
+    if reduction == "sum":
         return x.sum(dim=0)
     raise ValueError("Unknown reduction. Expected one of {'none', 'mean', 'sum'}")
 

TTS/utils/synthesizer.py

@@ -307,7 +307,7 @@ class Synthesizer(object):
                 waveform = waveform.squeeze()
 
                 # trim silence
-                if self.tts_config.audio["do_trim_silence"] is True:
+                if "do_trim_silence" in self.tts_config.audio and self.tts_config.audio["do_trim_silence"]:
                     waveform = trim_silence(waveform, self.tts_model.ap)
 
                 wavs += list(waveform)

recipes/ljspeech/fast_pitch/train_fast_pitch.py

@@ -54,7 +54,6 @@ config = FastPitchConfig(
     print_step=50,
     print_eval=False,
     mixed_precision=False,
-    sort_by_audio_len=True,
     max_seq_len=500000,
     output_path=output_path,
     datasets=[dataset_config],

recipes/ljspeech/fast_speech/train_fast_speech.py

@@ -53,7 +53,6 @@ config = FastSpeechConfig(
     print_step=50,
     print_eval=False,
     mixed_precision=False,
-    sort_by_audio_len=True,
     max_seq_len=500000,
     output_path=output_path,
     datasets=[dataset_config],

recipes/ljspeech/speedy_speech/train_speedy_speech.py

@@ -46,7 +46,6 @@ config = SpeedySpeechConfig(
     print_step=50,
     print_eval=False,
     mixed_precision=False,
-    sort_by_audio_len=True,
     max_seq_len=500000,
     output_path=output_path,
     datasets=[dataset_config],

recipes/ljspeech/tacotron2-Capacitron/train_capacitron_t2.py

@@ -68,7 +68,6 @@ config = Tacotron2Config(
     print_step=25,
     print_eval=True,
     mixed_precision=False,
-    sort_by_audio_len=True,
     seq_len_norm=True,
     output_path=output_path,
     datasets=[dataset_config],

recipes/ljspeech/vits_tts/train_vits.py

@@ -2,11 +2,10 @@ import os
 
 from trainer import Trainer, TrainerArgs
 
-from TTS.config.shared_configs import BaseAudioConfig
 from TTS.tts.configs.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
 from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits
+from TTS.tts.models.vits import Vits, VitsAudioConfig
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
@@ -14,21 +13,8 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
     name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
-audio_config = BaseAudioConfig(
-    sample_rate=22050,
-    win_length=1024,
-    hop_length=256,
-    num_mels=80,
-    preemphasis=0.0,
-    ref_level_db=20,
-    log_func="np.log",
-    do_trim_silence=True,
-    trim_db=45,
-    mel_fmin=0,
-    mel_fmax=None,
-    spec_gain=1.0,
-    signal_norm=False,
-    do_amp_to_db_linear=False,
+audio_config = VitsAudioConfig(
+    sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
 )
 
 config = VitsConfig(
@@ -37,7 +23,7 @@ config = VitsConfig(
     batch_size=32,
     eval_batch_size=16,
     batch_group_size=5,
-    num_loader_workers=0,
+    num_loader_workers=8,
     num_eval_loader_workers=4,
     run_eval=True,
     test_delay_epochs=-1,
@@ -52,6 +38,7 @@ config = VitsConfig(
     mixed_precision=True,
     output_path=output_path,
     datasets=[dataset_config],
+    cudnn_benchmark=False,
 )
 
 # INITIALIZE THE AUDIO PROCESSOR

recipes/multilingual/vits_tts/train_vits_tts.py

@@ -3,11 +3,10 @@ from glob import glob
 
 from trainer import Trainer, TrainerArgs
 
-from TTS.config.shared_configs import BaseAudioConfig
 from TTS.tts.configs.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
 from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs
+from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs, VitsAudioConfig
 from TTS.tts.utils.languages import LanguageManager
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
@@ -22,22 +21,13 @@ dataset_config = [
     for path in dataset_paths
 ]
 
-audio_config = BaseAudioConfig(
+audio_config = VitsAudioConfig(
     sample_rate=16000,
     win_length=1024,
     hop_length=256,
     num_mels=80,
-    preemphasis=0.0,
-    ref_level_db=20,
-    log_func="np.log",
-    do_trim_silence=False,
-    trim_db=23.0,
     mel_fmin=0,
     mel_fmax=None,
-    spec_gain=1.0,
-    signal_norm=True,
-    do_amp_to_db_linear=False,
-    resample=False,
 )
 
 vitsArgs = VitsArgs(
@@ -69,7 +59,6 @@ config = VitsConfig(
     use_language_weighted_sampler=True,
     print_eval=False,
     mixed_precision=False,
-    sort_by_audio_len=True,
     min_audio_len=32 * 256 * 4,
     max_audio_len=160000,
     output_path=output_path,

recipes/thorsten_DE/speedy_speech/train_speedy_speech.py

@@ -60,7 +60,6 @@ config = SpeedySpeechConfig(
         "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
         "Vor dem 22. November 1963.",
     ],
-    sort_by_audio_len=True,
     max_seq_len=500000,
     output_path=output_path,
     datasets=[dataset_config],

recipes/thorsten_DE/vits_tts/train_vits.py

@@ -2,11 +2,10 @@ import os
 
 from trainer import Trainer, TrainerArgs
 
-from TTS.config.shared_configs import BaseAudioConfig
 from TTS.tts.configs.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
 from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits
+from TTS.tts.models.vits import Vits, VitsAudioConfig
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.downloaders import download_thorsten_de
@@ -21,21 +20,13 @@ if not os.path.exists(dataset_config.path):
     print("Downloading dataset")
     download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
 
-audio_config = BaseAudioConfig(
+audio_config = VitsAudioConfig(
     sample_rate=22050,
     win_length=1024,
     hop_length=256,
     num_mels=80,
-    preemphasis=0.0,
-    ref_level_db=20,
-    log_func="np.log",
-    do_trim_silence=True,
-    trim_db=45,
     mel_fmin=0,
     mel_fmax=None,
-    spec_gain=1.0,
-    signal_norm=False,
-    do_amp_to_db_linear=False,
 )
 
 config = VitsConfig(

recipes/vctk/vits/train_vits.py

@@ -2,11 +2,10 @@ import os
 
 from trainer import Trainer, TrainerArgs
 
-from TTS.config.shared_configs import BaseAudioConfig
 from TTS.tts.configs.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
 from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits, VitsArgs
+from TTS.tts.models.vits import Vits, VitsArgs, VitsAudioConfig
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
@@ -17,22 +16,8 @@ dataset_config = BaseDatasetConfig(
 )
 
 
-audio_config = BaseAudioConfig(
-    sample_rate=22050,
-    win_length=1024,
-    hop_length=256,
-    num_mels=80,
-    preemphasis=0.0,
-    ref_level_db=20,
-    log_func="np.log",
-    do_trim_silence=True,
-    trim_db=23.0,
-    mel_fmin=0,
-    mel_fmax=None,
-    spec_gain=1.0,
-    signal_norm=False,
-    do_amp_to_db_linear=False,
-    resample=True,
+audio_config = VitsAudioConfig(
+    sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
 )
 
 vitsArgs = VitsArgs(
@@ -62,6 +47,7 @@ config = VitsConfig(
     max_text_len=325,  # change this if you have a larger VRAM than 16GB
     output_path=output_path,
     datasets=[dataset_config],
+    cudnn_benchmark=False,
 )
 
 # INITIALIZE THE AUDIO PROCESSOR

setup.py

@@ -90,7 +90,7 @@ setup(
     # ext_modules=find_cython_extensions(),
     # package
     include_package_data=True,
-    packages=find_packages(include=["TTS*"]),
+    packages=find_packages(include=["TTS"], exclude=["*.tests", "*tests.*", "tests.*", "*tests", "tests"]),
     package_data={
         "TTS": [
             "VERSION",

tests/tts_tests/test_vits.py

@@ -9,7 +9,17 @@ from tests import assertHasAttr, assertHasNotAttr, get_tests_data_path, get_test
 from TTS.config import load_config
 from TTS.encoder.utils.generic_utils import setup_encoder_model
 from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.models.vits import Vits, VitsArgs, amp_to_db, db_to_amp, load_audio, spec_to_mel, wav_to_mel, wav_to_spec
+from TTS.tts.models.vits import (
+    Vits,
+    VitsArgs,
+    VitsAudioConfig,
+    amp_to_db,
+    db_to_amp,
+    load_audio,
+    spec_to_mel,
+    wav_to_mel,
+    wav_to_spec,
+)
 from TTS.tts.utils.speakers import SpeakerManager
 
 LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
@@ -421,8 +431,10 @@ class TestVits(unittest.TestCase):
         self._check_parameter_changes(model, model_ref)
 
     def test_train_step_upsampling(self):
+        """Upsampling by the decoder upsampling layers"""
         # setup the model
         with torch.autograd.set_detect_anomaly(True):
+            audio_config = VitsAudioConfig(sample_rate=22050)
             model_args = VitsArgs(
                 num_chars=32,
                 spec_segment_size=10,
@@ -430,7 +442,7 @@ class TestVits(unittest.TestCase):
                 interpolate_z=False,
                 upsample_rates_decoder=[8, 8, 4, 2],
             )
-            config = VitsConfig(model_args=model_args)
+            config = VitsConfig(model_args=model_args, audio=audio_config)
             model = Vits(config).to(device)
             model.train()
             # model to train
@@ -459,10 +471,18 @@ class TestVits(unittest.TestCase):
         self._check_parameter_changes(model, model_ref)
 
     def test_train_step_upsampling_interpolation(self):
+        """Upsampling by interpolation"""
         # setup the model
         with torch.autograd.set_detect_anomaly(True):
-            model_args = VitsArgs(num_chars=32, spec_segment_size=10, encoder_sample_rate=11025, interpolate_z=True)
-            config = VitsConfig(model_args=model_args)
+            audio_config = VitsAudioConfig(sample_rate=22050)
+            model_args = VitsArgs(
+                num_chars=32,
+                spec_segment_size=10,
+                encoder_sample_rate=11025,
+                interpolate_z=True,
+                upsample_rates_decoder=[8, 8, 2, 2],
+            )
+            config = VitsConfig(model_args=model_args, audio=audio_config)
             model = Vits(config).to(device)
             model.train()
             # model to train