diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -48,6 +48,7 @@ Please use our dedicated channels for questions and discussion. Help is much mor
| 💻 **Docker Image** | [Repository by @synesthesiam](https://github.com/synesthesiam/docker-coqui-aitts)|
| 🖥️ **Demo Server** | [TTS/server](https://github.com/coqui-ai/TTS/tree/master/TTS/server)|
| 🤖 **Synthesize speech** | [TTS/README.md](https://github.com/coqui-ai/TTS#example-synthesizing-speech-on-terminal-using-the-released-models)|
+| 🛠️ **Implementing a New Model** | [TTS/Wiki][https://github.com/coqui-ai/TTS/wiki/Implementing-a-New-Model-in-%F0%9F%90%B8TTS]|
## 🥇 TTS Performance
@@ -176,6 +177,7 @@ Some of the public datasets that we successfully applied 🐸TTS:
- [Spanish](https://drive.google.com/file/d/1Sm_zyBo67XHkiFhcRSQ4YaHPYM0slO_e/view?usp=sharing) - thx! @carlfm01
## Example: Synthesizing Speech on Terminal Using the Released Models.
+
After the installation, 🐸TTS provides a CLI interface for synthesizing speech using pre-trained models. You can either use your own model or the release models under 🐸TTS.
@@ -185,7 +187,15 @@ Listing released 🐸TTS models.
tts --list_models
```
-Run a tts and a vocoder model from the released model list. (Simply copy and paste the full model names from the list as arguments for the command below.)
+Run a TTS model, from the release models list, with its default vocoder. (Simply copy and paste the full model names from the list as arguments for the command below.)
+
+```bash
+tts --text "Text for TTS" \
+ --model_name "///" \
+ --out_path folder/to/save/output.wav
+```
+
+Run a tts and a vocoder model from the released model list. Note that not every vocoder is compatible with every TTS model.
```bash
tts --text "Text for TTS" \
@@ -214,8 +224,46 @@ tts --text "Text for TTS" \
--vocoder_config_path path/to/vocoder_config.json
```
+Run a multi-speaker TTS model from the released models list.
+
+```bash
+tts --model_name "///" --list_speaker_idxs # list the possible speaker IDs.
+tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "//" --speaker_idx ""
+```
+
**Note:** You can use ```./TTS/bin/synthesize.py``` if you prefer running ```tts``` from the TTS project folder.
+## Example: Using the Demo Server for Synthesizing Speech
+
+
+ 
+
+You can boot up a demo 🐸TTS server to run inference with your models. Note that the server is not optimized for performance
+but gives you an easy way to interact with the models.
+
+The demo server provides pretty much the same interface as the CLI command.
+
+```bash
+tts-server -h # see the help
+tts-server --list_models # list the available models.
+```
+
+Run a TTS model, from the release models list, with its default vocoder.
+If the model you choose is a multi-speaker TTS model, you can select different speakers on the Web interface and synthesize
+speech.
+
+```bash
+tts-server --model_name "///"
+```
+
+Run a TTS and a vocoder model from the released model list. Note that not every vocoder is compatible with every TTS model.
+
+```bash
+tts-server --model_name "///" \
+ --vocoder_name "///"
+```
+
+
## Example: Training and Fine-tuning LJ-Speech Dataset
Here you can find a [CoLab](https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b) notebook for a hands-on example, training LJSpeech. Or you can manually follow the guideline below.
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