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docs/source/inference.md

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+(synthesizing_speech)=
+# Synthesizing Speech
+
+First, you need to install TTS. We recommend using PyPi. You need to call the command below:
+
+```bash
+$ pip install TTS
+```
+
+After the installation, 2 terminal commands are available.
+
+1. TTS Command Line Interface (CLI). - `tts`
+2. Local Demo Server. - `tts-server`
+
+## On the Commandline - `tts`
+![cli.gif](https://github.com/coqui-ai/TTS/raw/main/images/tts_cli.gif)
+
+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.
+
+Listing released 🐸TTS models.
+
+```bash
+tts --list_models
+```
+
+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 "<type>/<language>/<dataset>/<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" \
+    --model_name "<type>/<language>/<dataset>/<model_name>" \
+    --vocoder_name "<type>/<language>/<dataset>/<model_name>" \
+    --out_path folder/to/save/output.wav
+```
+
+Run your own TTS model (Using Griffin-Lim Vocoder)
+
+```bash
+tts --text "Text for TTS" \
+    --model_path path/to/model.pth.tar \
+    --config_path path/to/config.json \
+    --out_path folder/to/save/output.wav
+```
+
+Run your own TTS and Vocoder models
+
+```bash
+tts --text "Text for TTS" \
+    --config_path path/to/config.json \
+    --model_path path/to/model.pth.tar \
+    --out_path folder/to/save/output.wav \
+    --vocoder_path path/to/vocoder.pth.tar \
+    --vocoder_config_path path/to/vocoder_config.json
+```
+
+Run a multi-speaker TTS model from the released models list.
+
+```bash
+tts --model_name "<type>/<language>/<dataset>/<model_name>"  --list_speaker_idxs  # list the possible speaker IDs.
+tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>"  --speaker_idx "<speaker_id>"
+```
+
+**Note:** You can use ```./TTS/bin/synthesize.py``` if you prefer running ```tts``` from the TTS project folder.
+
+## On the Demo Server - `tts-server`
+
+ <!-- <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/demo_server.gif" height="56"/> -->
+![server.gif](https://github.com/coqui-ai/TTS/raw/main/images/demo_server.gif)
+
+You can boot up a demo 🐸TTS server to run an 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 "<type>/<language>/<dataset>/<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 "<type>/<language>/<dataset>/<model_name>" \
+           --vocoder_name "<type>/<language>/<dataset>/<model_name>"
+```
+
+## TorchHub
+You can also use [this simple colab notebook](https://colab.research.google.com/drive/1iAe7ZdxjUIuN6V4ooaCt0fACEGKEn7HW?usp=sharing) using TorchHub to synthesize speech.