add docker

Dockerfile

@@ -10,4 +10,4 @@ RUN pip install -e .
 RUN python -m unidic download
 RUN python melo/init_downloads.py
 
-CMD ["python", "./melo/app.py", "--port", "8800"]
+CMD ["python", "./melo/app.py", "--host", "0.0.0.0", "--port", "8888"]

README.md

@@ -23,221 +23,16 @@ Some other features include:
 - The Chinese speaker supports `mixed Chinese and English`.
 - Fast enough for `CPU real-time inference`.
 
-## Install on Linux or macOS
-
-**Installation:**
-
-```bash
-pip install git+https://github.com/myshell-ai/MeloTTS.git
-python -m unidic download
-```
-
-**Manual installation:**
-
-```bash
-git clone https://github.com/myshell-ai/MeloTTS.git
-cd MeloTTS
-pip install -e .
-python -m unidic download
-```
-
-We welcome the open-source community to make this repo `Windows` compatible. If you find this repo useful, please consider contributing to the repo.
-
 ## Usage
-
-An unofficial [live demo](https://huggingface.co/spaces/mrfakename/MeloTTS) is hosted on Hugging Face Spaces.
-
-### WebUI
-
-The WebUI supports muliple languages and voices. First, follow the installation steps. Then, simply run:
-
-```bash
-melo-ui
-# Or: python melo/app.py
-```
-
-### CLI
-
-You may use the MeloTTS CLI to interact with MeloTTS. The CLI may be invoked using either `melotts` or `melo`. Here are some examples:
-
-**Read English text:**
-
-```bash
-melo "Text to read" output.wav
-```
-
-**Specify a language:**
-
-```bash
-melo "Text to read" output.wav --language EN
-```
-
-**Specify a speaker:**
-
-```bash
-melo "Text to read" output.wav --language EN --speaker EN-US
-melo "Text to read" output.wav --language EN --speaker EN-AU
-```
-
-The available speakers are: `EN-Default`, `EN-US`, `EN-BR`, `EN-INDIA` `EN-AU`.
-
-**Specify a speed:**
-
-```bash
-melo "Text to read" output.wav --language EN --speaker EN-US --speed 1.5
-melo "Text to read" output.wav --speed 1.5
-```
-
-**Use a different language:**
-
-```bash
-melo "text-to-speech 领域近年来发展迅速" zh.wav -l ZH
-```
-
-**Load from a file:**
-
-```bash
-melo file.txt out.wav --file
-```
-
-The full API documentation may be found using:
-
-```bash
-melo --help
-```
-
-### Python API
-
-#### English with Multiple Accents
-
-```python
-from melo.api import TTS
-
-# Speed is adjustable
-speed = 1.0
-
-# CPU is sufficient for real-time inference.
-# You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps'
-device = 'auto' # Will automatically use GPU if available
-
-# English 
-text = "Did you ever hear a folk tale about a giant turtle?"
-model = TTS(language='EN', device=device)
-speaker_ids = model.hps.data.spk2id
-
-# American accent
-output_path = 'en-us.wav'
-model.tts_to_file(text, speaker_ids['EN-US'], output_path, speed=speed)
-
-# British accent
-output_path = 'en-br.wav'
-model.tts_to_file(text, speaker_ids['EN-BR'], output_path, speed=speed)
-
-# Indian accent
-output_path = 'en-india.wav'
-model.tts_to_file(text, speaker_ids['EN_INDIA'], output_path, speed=speed)
-
-# Australian accent
-output_path = 'en-au.wav'
-model.tts_to_file(text, speaker_ids['EN-AU'], output_path, speed=speed)
-
-# Default accent
-output_path = 'en-default.wav'
-model.tts_to_file(text, speaker_ids['EN-Default'], output_path, speed=speed)
-
-```
-
-### Spanish
-```python
-from melo.api import TTS
-
-# Speed is adjustable
-speed = 1.0
-
-# CPU is sufficient for real-time inference.
-# You can also change to cuda:0
-device = 'cpu'
-
-text = "El resplandor del sol acaricia las olas, pintando el cielo con una paleta deslumbrante."
-model = TTS(language='ES', device=device)
-speaker_ids = model.hps.data.spk2id
-
-output_path = 'es.wav'
-model.tts_to_file(text, speaker_ids['ES'], output_path, speed=speed)
-```
-
-#### French
-
-```python
-from melo.api import TTS
-
-# Speed is adjustable
-speed = 1.0
-device = 'cpu' # or cuda:0
-
-text = "La lueur dorée du soleil caresse les vagues, peignant le ciel d'une palette éblouissante."
-model = TTS(language='FR', device=device)
-speaker_ids = model.hps.data.spk2id
-
-output_path = 'fr.wav'
-model.tts_to_file(text, speaker_ids['FR'], output_path, speed=speed)
-```
-
-#### Chinese
-
-```python
-from melo.api import TTS
-
-# Speed is adjustable
-speed = 1.0
-device = 'cpu' # or cuda:0
-
-text = "我最近在学习machine learning，希望能够在未来的artificial intelligence领域有所建树。"
-model = TTS(language='ZH', device=device)
-speaker_ids = model.hps.data.spk2id
-
-output_path = 'zh.wav'
-model.tts_to_file(text, speaker_ids['ZH'], output_path, speed=speed)
-```
-
-#### Japanese
-
-```python
-from melo.api import TTS
-
-# Speed is adjustable
-speed = 1.0
-device = 'cpu' # or cuda:0
-
-text = "彼は毎朝ジョギングをして体を健康に保っています。"
-model = TTS(language='JP', device=device)
-speaker_ids = model.hps.data.spk2id
-
-output_path = 'jp.wav'
-model.tts_to_file(text, speaker_ids['JP'], output_path, speed=speed)
-```
-
-#### Korean
-
-```python
-from melo.api import TTS
-
-# Speed is adjustable
-speed = 1.0
-device = 'cpu' # or cuda:0
-
-text = "안녕하세요! 오늘은 날씨가 정말 좋네요."
-model = TTS(language='KR', device=device)
-speaker_ids = model.hps.data.spk2id
-
-output_path = 'kr.wav'
-model.tts_to_file(text, speaker_ids['KR'], output_path, speed=speed)
-```
+- [Use without Installation](docs/quick_use.md)
+- [Install and Use Locally](docs/install.md)
 
 ## License
 
 This library is under MIT License, which means it is free for both commercial and non-commercial use.
 
 ## Acknowledgements
 
-This implementation is based on several excellent projects, [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work!
+This implementation is based on [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work.
+
+Many thanks to [@fakerybakery](https://github.com/fakerybakery) for adding the Web UI and CLI part.