Update Tacotron models

TTS/tts/models/base_tacotron.py

@@ -17,43 +17,12 @@ from TTS.utils.io import load_fsspec
 from TTS.utils.training import gradual_training_scheduler
 
 
-@dataclass
-class BaseTacotronArgs(Coqpit):
-    """TODO: update Tacotron configs using it"""
-
-    num_chars: int = MISSING
-    num_speakers: int = MISSING
-    r: int = MISSING
-    out_channels: int = 80
-    decoder_output_dim: int = 80
-    attn_type: str = "original"
-    attn_win: bool = False
-    attn_norm: str = "softmax"
-    prenet_type: str = "original"
-    prenet_dropout: bool = True
-    prenet_dropout_at_inference: bool = False
-    forward_attn: bool = False
-    trans_agent: bool = False
-    forward_attn_mask: bool = False
-    location_attn: bool = True
-    attn_K: int = 5
-    separate_stopnet: bool = True
-    bidirectional_decoder: bool = False
-    double_decoder_consistency: bool = False
-    ddc_r: int = None
-    encoder_in_features: int = 512
-    decoder_in_features: int = 512
-    d_vector_dim: int = None
-    use_gst: bool = False
-    gst: bool = None
-    gradual_training: bool = None
-
-
 class BaseTacotron(BaseTTS):
     def __init__(self, config: Coqpit):
         """Abstract Tacotron class"""
-        super().__init__()
+        super().__init__(config)
 
+        # pass all config fields as class attributes
         for key in config:
             setattr(self, key, config[key])
 
@@ -133,22 +102,6 @@ class BaseTacotron(BaseTTS):
     def get_criterion(self) -> nn.Module:
         return TacotronLoss(self.config)
 
-    @staticmethod
-    def get_characters(config: Coqpit) -> str:
-        # TODO: implement CharacterProcessor
-        if config.characters is not None:
-            symbols, phonemes = make_symbols(**config.characters)
-        else:
-            from TTS.tts.utils.text.symbols import (  # pylint: disable=import-outside-toplevel
-                parse_symbols,
-                phonemes,
-                symbols,
-            )
-
-            config.characters = parse_symbols()
-        model_characters = phonemes if config.use_phonemes else symbols
-        return model_characters, config
-
     @staticmethod
     def get_speaker_manager(config: Coqpit, restore_path: str, data: List, out_path: str = None) -> SpeakerManager:
         return get_speaker_manager(config, restore_path, data, out_path)

TTS/tts/models/tacotron.py

@@ -23,7 +23,7 @@ class Tacotron(BaseTacotron):
     def __init__(self, config: Coqpit):
         super().__init__(config)
 
-        chars, self.config = self.get_characters(config)
+        chars, self.config, _ = self.get_characters(config)
         config.num_chars = self.num_chars = len(chars)
 
         # pass all config fields to `self`
@@ -264,7 +264,7 @@ class Tacotron(BaseTacotron):
         loss_dict["align_error"] = align_error
         return outputs, loss_dict
 
-    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict) -> Tuple[Dict, Dict]:
+    def _create_logs(self, batch, outputs, ap):
         postnet_outputs = outputs["model_outputs"]
         alignments = outputs["alignments"]
         alignments_backward = outputs["alignments_backward"]
@@ -284,11 +284,22 @@ class Tacotron(BaseTacotron):
             figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy(), output_fig=False)
 
         # Sample audio
-        train_audio = ap.inv_spectrogram(pred_spec.T)
-        return figures, {"audio": train_audio}
+        audio = ap.inv_spectrogram(pred_spec.T)
+        return figures, {"audio": audio}
 
-    def eval_step(self, batch, criterion):
+    def train_log(
+        self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
+    ) -> None:  # pylint: disable=no-self-use
+        ap = assets["audio_processor"]
+        figures, audios = self._create_logs(batch, outputs, ap)
+        logger.train_figures(steps, figures)
+        logger.train_audios(steps, audios, ap.sample_rate)
+
+    def eval_step(self, batch: dict, criterion: nn.Module):
         return self.train_step(batch, criterion)
 
-    def eval_log(self, ap, batch, outputs):
-        return self.train_log(ap, batch, outputs)
+    def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None:
+        ap = assets["audio_processor"]
+        figures, audios = self._create_logs(batch, outputs, ap)
+        logger.eval_figures(steps, figures)
+        logger.eval_audios(steps, audios, ap.sample_rate)

TTS/tts/models/tacotron2.py

@@ -22,7 +22,7 @@ class Tacotron2(BaseTacotron):
     def __init__(self, config: Coqpit):
         super().__init__(config)
 
-        chars, self.config = self.get_characters(config)
+        chars, self.config, _ = self.get_characters(config)
         config.num_chars = len(chars)
         self.decoder_output_dim = config.out_channels
 
@@ -269,7 +269,7 @@ class Tacotron2(BaseTacotron):
         loss_dict["align_error"] = align_error
         return outputs, loss_dict
 
-    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict) -> Tuple[Dict, Dict]:
+    def _create_logs(self, batch, outputs, ap):
         postnet_outputs = outputs["model_outputs"]
         alignments = outputs["alignments"]
         alignments_backward = outputs["alignments_backward"]
@@ -289,11 +289,22 @@ class Tacotron2(BaseTacotron):
             figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy(), output_fig=False)
 
         # Sample audio
-        train_audio = ap.inv_melspectrogram(pred_spec.T)
-        return figures, {"audio": train_audio}
+        audio = ap.inv_melspectrogram(pred_spec.T)
+        return figures, {"audio": audio}
 
-    def eval_step(self, batch, criterion):
+    def train_log(
+        self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
+    ) -> None:  # pylint: disable=no-self-use
+        ap = assets["audio_processor"]
+        figures, audios = self._create_logs(batch, outputs, ap)
+        logger.train_figures(steps, figures)
+        logger.train_audios(steps, audios, ap.sample_rate)
+
+    def eval_step(self, batch: dict, criterion: nn.Module):
         return self.train_step(batch, criterion)
 
-    def eval_log(self, ap, batch, outputs):
-        return self.train_log(ap, batch, outputs)
+    def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None:
+        ap = assets["audio_processor"]
+        figures, audios = self._create_logs(batch, outputs, ap)
+        logger.eval_figures(steps, figures)
+        logger.eval_audios(steps, audios, ap.sample_rate)

recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py

@@ -0,0 +1,75 @@
+import os
+
+from TTS.config.shared_configs import BaseAudioConfig
+from TTS.trainer import Trainer, TrainingArgs
+from TTS.tts.configs import BaseDatasetConfig, Tacotron2Config
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.tacotron2 import Tacotron2
+from TTS.utils.audio import AudioProcessor
+
+# from TTS.tts.datasets.tokenizer import Tokenizer
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+# init configs
+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,
+    do_trim_silence=True,
+    trim_db=60.0,
+    signal_norm=False,
+    mel_fmin=0.0,
+    mel_fmax=8000,
+    spec_gain=1.0,
+    log_func="np.log",
+    ref_level_db=20,
+    preemphasis=0.0,
+)
+
+config = Tacotron2Config(  # This is the config that is saved for the future use
+    audio=audio_config,
+    batch_size=64,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=-1,
+    ga_alpha=5.0,
+    r=2,
+    attention_type="dynamic_convolution",
+    double_decoder_consistency=True,
+    epochs=1000,
+    text_cleaner="phoneme_cleaners",
+    use_phonemes=True,
+    phoneme_language="en-us",
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    print_step=25,
+    print_eval=True,
+    mixed_precision=False,
+    output_path=output_path,
+    datasets=[dataset_config],
+)
+
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
+
+# init model
+model = Tacotron2(config)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainingArgs(),
+    config,
+    output_path,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+    training_assets={"audio_processor": ap},
+)
+trainer.fit()

recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py

@@ -0,0 +1,74 @@
+import os
+
+from TTS.config.shared_configs import BaseAudioConfig
+from TTS.trainer import Trainer, TrainingArgs
+from TTS.tts.configs import BaseDatasetConfig, Tacotron2Config
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.tacotron2 import Tacotron2
+from TTS.utils.audio import AudioProcessor
+
+# from TTS.tts.datasets.tokenizer import Tokenizer
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+# init configs
+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,
+    do_trim_silence=True,
+    trim_db=60.0,
+    signal_norm=False,
+    mel_fmin=0.0,
+    mel_fmax=8000,
+    spec_gain=1.0,
+    log_func="np.log",
+    ref_level_db=20,
+    preemphasis=0.0,
+)
+
+config = Tacotron2Config(  # This is the config that is saved for the future use
+    audio=audio_config,
+    batch_size=64,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=-1,
+    r=6,
+    gradual_training=[[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
+    double_decoder_consistency=True,
+    epochs=1000,
+    text_cleaner="phoneme_cleaners",
+    use_phonemes=True,
+    phoneme_language="en-us",
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    print_step=25,
+    print_eval=True,
+    mixed_precision=False,
+    output_path=output_path,
+    datasets=[dataset_config],
+)
+
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
+
+# init model
+model = Tacotron2(config)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainingArgs(),
+    config,
+    output_path,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+    training_assets={"audio_processor": ap},
+)
+trainer.fit()