Add preliminary sphinx documentation

2026-09-09 11:16:00 +08:00 · 2021-06-28 17:03:47 +02:00
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+# Tutorial For Nervous Beginners
+
+## Installation
+
+User friendly installation. Recommended only for synthesizing voice.
+
+```bash
+$ pip install TTS
+```
+
+Developer friendly installation.
+
+```bash
+$ git clone https://github.com/coqui-ai/TTS
+$ cd TTS
+$ pip install -e .
+```
+
+## Training a `tts` Model
+
+A breakdown of a simple script training a GlowTTS model on LJspeech dataset. See the comments for the explanation of
+each line.
+
+### Pure Python Way
+
+```python
+import os
+
+# GlowTTSConfig: all model related values for training, validating and testing.
+from TTS.tts.configs import GlowTTSConfig
+
+# BaseDatasetConfig: defines name, formatter and path of the dataset.
+from TTS.tts.configs import BaseDatasetConfig
+
+# init_training: Initialize and setup the training environment.
+# Trainer: Where the ✨️ happens.
+# TrainingArgs: Defines the set of arguments of the Trainer.
+from TTS.trainer import init_training, Trainer, TrainingArgs
+
+# we use the same path as this script as our training folder.
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+# set LJSpeech as our target dataset and define its path so that the Trainer knows what data formatter it needs.
+dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/"))
+
+# Configure the model. Every config class inherits the BaseTTSConfig to have all the fields defined for the Trainer.
+config = GlowTTSConfig(
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    text_cleaner="english_cleaners",
+    use_phonemes=False,
+    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]
+)
+
+# Take the config and the default Trainer arguments, setup the training environment and override the existing
+# config values from the terminal. So you can do the following.
+# >>> python train.py --coqpit.batch_size 128
+args, config, output_path, _, _, _= init_training(TrainingArgs(), config)
+
+# Initiate the Trainer.
+# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
+# distributed training etc.
+trainer = Trainer(args, config, output_path)
+
+# And kick it 🚀
+trainer.fit()
+```
+
+### CLI Way
+
+We still support running training from CLI like in the old days. The same training can be started as follows.
+
+1. Define your `config.json`
+
+    ```json
+    {
+        "model": "glow_tts",
+        "batch_size": 32,
+        "eval_batch_size": 16,
+        "num_loader_workers": 4,
+        "num_eval_loader_workers": 4,
+        "run_eval": true,
+        "test_delay_epochs": -1,
+        "epochs": 1000,
+        "text_cleaner": "english_cleaners",
+        "use_phonemes": false,
+        "phoneme_language": "en-us",
+        "phoneme_cache_path": "phoneme_cache",
+        "print_step": 25,
+        "print_eval": true,
+        "mixed_precision": false,
+        "output_path": "recipes/ljspeech/glow_tts/",
+        "datasets":[{"name": "ljspeech", "meta_file_train":"metadata.csv", "path": "recipes/ljspeech/LJSpeech-1.1/"}]
+    }
+    ```
+
+2. Start training.
+    ```bash
+    $ CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py --config_path config.json
+    ```
+
+
+
+## Training a `vocoder` Model
+
+```python
+import os
+
+from TTS.vocoder.configs import HifiganConfig
+from TTS.trainer import init_training, Trainer, TrainingArgs
+
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+config = HifiganConfig(
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    seq_len=8192,
+    pad_short=2000,
+    use_noise_augment=True,
+    eval_split_size=10,
+    print_step=25,
+    print_eval=True,
+    mixed_precision=False,
+    lr_gen=1e-4,
+    lr_disc=1e-4,
+    # `vocoder` only needs a data path and they read recursively all the `.wav` files underneath.
+    data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
+    output_path=output_path,
+)
+args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
+trainer = Trainer(args, config, output_path, c_logger, tb_logger)
+trainer.fit()
+```
+
+❗️ Note that you can also start the training run from CLI as the `tts` model above.
+
+## Synthesizing Speech
+
+You can run `tts` and synthesize speech directly on the terminal.
+
+```bash
+$ tts -h # see the help
+$ tts --list_models  # list the available models.
+```
+
+![cli.gif](https://github.com/coqui-ai/TTS/raw/main/images/tts_cli.gif)
+
+
+You can call `tts-server` to start a local demo server that you can open it on
+your favorite web browser and 🗣️.
+
+```bash
+$ tts-server -h # see the help
+$ tts-server --list_models  # list the available models.
+```
+![server.gif](https://github.com/coqui-ai/TTS/raw/main/images/demo_server.gif)
+
+
+