Update docs

docs/source/training_a_model.md

@@ -5,27 +5,30 @@
     Each model has a different set of pros and cons that define the run-time efficiency and the voice quality. It is up to you to decide what model servers your needs. Other than referring to the papers, one easy way is to test the 🐸TTS
     community models and see how fast and good each of the models. Or you can start a discussion on our communication channels.
 
-2. Understand the configuration class, its fields and values of your model.
+2. Understand the configuration, its fields and values of your model.
 
     For instance, if you want to train a `Tacotron` model then see the `TacotronConfig` class and make sure you understand it.
 
 3. Go to the recipes and check the recipe of your target model.
 
-    Recipes do not promise perfect models but they provide a good start point for `Nervous Beginners`. A recipe script training
-    a `GlowTTS` model on `LJSpeech` dataset looks like below. Let's be creative and call this script `train_glowtts.py`.
+    Recipes do not promise perfect models but they provide a good start point for `Nervous Beginners`. A recipe script for
+    `GlowTTS` using `LJSpeech` dataset looks like below. Let's be creative and call this `train_glowtts.py`.
 
     ```python
     # train_glowtts.py
 
-    import os
-
-    from TTS.tts.configs import GlowTTSConfig
-    from TTS.tts.configs import BaseDatasetConfig
-    from TTS.trainer import init_training, Trainer, TrainingArgs
+   import os
 
+    from TTS.trainer import Trainer, TrainingArgs
+    from TTS.tts.configs import BaseDatasetConfig, GlowTTSConfig
+    from TTS.tts.datasets import load_tts_samples
+    from TTS.tts.models.glow_tts import GlowTTS
+    from TTS.utils.audio import AudioProcessor
 
     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/"))
+    dataset_config = BaseDatasetConfig(
+        name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
+    )
     config = GlowTTSConfig(
         batch_size=32,
         eval_batch_size=16,
@@ -34,33 +37,50 @@
         run_eval=True,
         test_delay_epochs=-1,
         epochs=1000,
-        text_cleaner="english_cleaners",
-        use_phonemes=False,
+        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,
+        print_eval=False,
+        mixed_precision=True,
         output_path=output_path,
-        datasets=[dataset_config]
+        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 = GlowTTS(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},
     )
-    args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
-    trainer = Trainer(args, config, output_path, c_logger, tb_logger)
     trainer.fit()
+
     ```
 
-    You need to change fields of the `BaseDatasetConfig` to match your own dataset and then update `GlowTTSConfig`
+    You need to change fields of the `BaseDatasetConfig` to match your dataset and then update `GlowTTSConfig`
     fields as you need.
 
  4. Run the training.
 
-    You need to run the training script.
-
     ```bash
     $ CUDA_VISIBLE_DEVICES="0" python train_glowtts.py
     ```
 
-    Notice that you set the GPU you want to use on your system by setting `CUDA_VISIBLE_DEVICES` environment variable.
+    Notice that we set the GPU for the training by `CUDA_VISIBLE_DEVICES` environment variable.
     To see available GPUs on your system, you can use `nvidia-smi` command on the terminal.
 
     If you like to run a multi-gpu training using DDP back-end,
@@ -71,7 +91,7 @@
 
     The example above runs a multi-gpu training using GPUs `0, 1, 2`.
 
-    The beginning of a training run looks like below.
+    Beginning of a training log looks like this:
 
     ```console
     > Experiment folder: /your/output_path/-Juni-23-2021_02+52-78899209
@@ -140,11 +160,11 @@
     $ tensorboard --logdir=<path to your training directory>
     ```
 
-6. Check the logs and the Tensorboard and monitor the training.
+6. Monitor the training process.
 
-    On the terminal and Tensorboard, you can monitor the losses and their changes over time. Also Tensorboard provides certain figures and sample outputs.
+    On the terminal and Tensorboard, you can monitor the progress of your model. Also Tensorboard provides certain figures and sample outputs.
 
-    Note that different models have different metrics, visuals and outputs to be displayed.
+    Note that different models have different metrics, visuals and outputs.
 
     You should also check the [FAQ page](https://github.com/coqui-ai/TTS/wiki/FAQ) for common problems and solutions
     that occur in a training.