Update ForwardTTS for Trainer_v2

TTS/tts/models/forward_tts.py

@@ -161,24 +161,7 @@ class ForwardTTS(BaseTTS):
     # pylint: disable=dangerous-default-value
     def __init__(self, config: Coqpit):
 
-        super().__init__()
-
-        # don't use isintance not to import recursively
-        if "Config" in config.__class__.__name__:
-            if "characters" in config:
-                # loading from FasrPitchConfig
-                _, self.config, num_chars = self.get_characters(config)
-                config.model_args.num_chars = num_chars
-                self.args = self.config.model_args
-            else:
-                # loading from ForwardTTSArgs
-                self.config = config
-                self.args = config.model_args
-        elif isinstance(config, ForwardTTSArgs):
-            self.args = config
-            self.config = config
-        else:
-            raise ValueError("config must be either a *Config or ForwardTTSArgs")
+        super().__init__(config)
 
         self.max_duration = self.args.max_duration
         self.use_aligner = self.args.use_aligner
@@ -634,7 +617,8 @@ class ForwardTTS(BaseTTS):
 
         return outputs, loss_dict
 
-    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):  # pylint: disable=no-self-use
+    def _create_logs(self, batch, outputs, ap):
+        """Create common logger outputs."""
         model_outputs = outputs["model_outputs"]
         alignments = outputs["alignments"]
         mel_input = batch["mel_input"]
@@ -674,11 +658,22 @@ class ForwardTTS(BaseTTS):
         train_audio = ap.inv_melspectrogram(pred_spec.T)
         return figures, {"audio": train_audio}
 
+    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: AudioProcessor, batch: dict, outputs: dict):
-        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)
 
     def load_checkpoint(
         self, config, checkpoint_path, eval=False

recipes/ljspeech/fast_pitch/train_fast_pitch.py

@@ -1,8 +1,11 @@
 import os
 
 from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.trainer import Trainer, TrainingArgs, init_training
+from TTS.trainer import Trainer, TrainingArgs
 from TTS.tts.configs import FastPitchConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.forward_tts import ForwardTTS
+from TTS.utils.audio import AudioProcessor
 from TTS.utils.manage import ModelManager
 
 output_path = os.path.dirname(os.path.abspath(__file__))
@@ -64,7 +67,23 @@ if not config.model_args.use_aligner:
         f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/  --use_cuda true"
     )
 
-# train the model
-args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger)
+# init audio processor
+ap = AudioProcessor(**config.audio)
+
+# load training samples
+train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
+
+# init the model
+model = ForwardTTS(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/fast_speech/train_fast_speech.py

@@ -0,0 +1,88 @@
+import os
+
+from TTS.config import BaseAudioConfig, BaseDatasetConfig
+from TTS.trainer import Trainer, TrainingArgs
+from TTS.tts.configs import FastSpeechConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.forward_tts import ForwardTTS
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.manage import ModelManager
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+# init configs
+dataset_config = BaseDatasetConfig(
+    name="ljspeech",
+    meta_file_train="metadata.csv",
+    # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
+    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 = FastSpeechConfig(
+    run_name="fast_speech_ljspeech",
+    audio=audio_config,
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=8,
+    num_eval_loader_workers=4,
+    compute_input_seq_cache=True,
+    compute_f0=False,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    text_cleaner="english_cleaners",
+    use_phonemes=True,
+    use_espeak_phonemes=False,
+    phoneme_language="en-us",
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    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],
+)
+
+# compute alignments
+if not config.model_args.use_aligner:
+    manager = ModelManager()
+    model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
+    # TODO: make compute_attention python callable
+    os.system(
+        f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/  --use_cuda true"
+    )
+
+# init audio processor
+ap = AudioProcessor(**config.audio)
+
+# load training samples
+train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
+
+# init the model
+model = ForwardTTS(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/speedy_speech/train_speedy_speech.py

@@ -1,18 +1,16 @@
 import os
 
 from TTS.config import BaseAudioConfig, BaseDatasetConfig
-from TTS.trainer import Trainer, TrainingArgs, init_training
+from TTS.trainer import Trainer, TrainingArgs
 from TTS.tts.configs import SpeedySpeechConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.forward_tts import ForwardTTS
+from TTS.utils.audio import AudioProcessor
 from TTS.utils.manage import ModelManager
 
 output_path = os.path.dirname(os.path.abspath(__file__))
-
-# init configs
 dataset_config = BaseDatasetConfig(
-    name="ljspeech",
-    meta_file_train="metadata.csv",
-    # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
-    path=os.path.join(output_path, "../LJSpeech-1.1/"),
+    name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
 
 audio_config = BaseAudioConfig(
@@ -53,16 +51,32 @@ config = SpeedySpeechConfig(
     datasets=[dataset_config],
 )
 
-# compute alignments
-if not config.model_args.use_aligner:
-    manager = ModelManager()
-    model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
-    # TODO: make compute_attention python callable
-    os.system(
-        f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/  --use_cuda true"
-    )
+# # compute alignments
+# if not config.model_args.use_aligner:
+#     manager = ModelManager()
+#     model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
+#     # TODO: make compute_attention python callable
+#     os.system(
+#         f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/  --use_cuda true"
+#     )
 
-# train the model
-args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger)
+# 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 = ForwardTTS(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()