228 lines
6.6 KiB
Markdown
228 lines
6.6 KiB
Markdown
<div align="center">
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<div> </div>
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<img src="logo.png" width="200"/>
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</div>
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## Introduction
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MeloTTS is a **high-quality multi-lingual** text-to-speech library by [MyShell.ai](https://myshell.ai). Supported languages include:
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| Language | Example |
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| --- | --- |
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| English | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-Default/speed_1.0/sent_000.wav) |
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| English (American) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-US/speed_1.0/sent_000.wav) |
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| English (British) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-BR/speed_1.0/sent_000.wav) |
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| English (Indian) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN_INDIA/speed_1.0/sent_000.wav) |
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| English (Australian) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-AU/speed_1.0/sent_000.wav) |
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| Spanish | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/es/ES/speed_1.0/sent_000.wav) |
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| French | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/fr/FR/speed_1.0/sent_000.wav) |
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| Chinese (mix EN) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/zh/ZH/speed_1.0/sent_008.wav) |
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| Japanese | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/jp/JP/speed_1.0/sent_000.wav) |
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| Korean | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/kr/KR/speed_1.0/sent_000.wav) |
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Some other features include:
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- The Chinese speaker supports `mixed Chinese and English`.
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- Fast enough for `CPU real-time inference`.
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## Install on Linux or macOS
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```bash
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git clone git@github.com:myshell-ai/MeloTTS.git
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cd MeloTTS
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pip install -e .
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python -m unidic download
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```
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We welcome the open-source community to make this repo `Windows` compatible. If you find this repo useful, please consider contributing to the repo.
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## Usage
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An unofficial [live demo](https://huggingface.co/spaces/mrfakename/MeloTTS) is hosted on Hugging Face Spaces.
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### WebUI
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The WebUI supports muliple languages and voices. First, follow the installation steps. Then, simply run:
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```bash
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python app.py
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```
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### CLI
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You may use the MeloTTS CLI to interact with MeloTTS. The CLI may be invoked using either `melotts` or `melo`. Here are some examples:
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**Read English text:**
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```bash
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melo "Text to read" output.wav
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```
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**Specify a language:**
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```bash
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melo "Text to read" output.wav --language EN
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```
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**Specify a speaker:**
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```bash
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melo "Text to read" output.wav --language EN --speaker EN-US
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melo "Text to read" output.wav --language EN --speaker EN-AU
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```
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The available speakers are: `EN-Default`, `EN-US`, `EN-BR`, `EN-INDIA` `EN-AU`.
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**Specify a speed:**
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```bash
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melo "Text to read" output.wav --language EN --speaker EN-US --speed 1.5
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melo "Text to read" output.wav --speed 1.5
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```
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**Use a different language:**
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```bash
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melo "语音合成领域近年来发展迅速" zh.wav -l ZH
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```
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The full API documentation may be found using:
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```bash
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melo --help
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```
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### Python API
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#### English with Multi Accents
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```python
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from melo.api import TTS
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# Speed is adjustable
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speed = 1.0
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# CPU is sufficient for real-time inference.
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# You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps'
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device = 'auto' # Will automatically use GPU if available
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# English
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text = "Did you ever hear a folk tale about a giant turtle?"
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model = TTS(language='EN', device=device)
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speaker_ids = model.hps.data.spk2id
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# Default accent
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output_path = 'en-default.wav'
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model.tts_to_file(text, speaker_ids['EN-Default'], output_path, speed=speed)
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# American accent
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output_path = 'en-us.wav'
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model.tts_to_file(text, speaker_ids['EN-US'], output_path, speed=speed)
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# British accent
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output_path = 'en-br.wav'
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model.tts_to_file(text, speaker_ids['EN-BR'], output_path, speed=speed)
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# Indian accent
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output_path = 'en-india.wav'
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model.tts_to_file(text, speaker_ids['EN_INDIA'], output_path, speed=speed)
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# Australian accent
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output_path = 'en-au.wav'
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model.tts_to_file(text, speaker_ids['EN-AU'], output_path, speed=speed)
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```
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### Spanish
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```python
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from melo.api import TTS
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# Speed is adjustable
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speed = 1.0
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# CPU is sufficient for real-time inference.
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# You can also change to cuda:0
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device = 'cpu'
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text = "El resplandor del sol acaricia las olas, pintando el cielo con una paleta deslumbrante."
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model = TTS(language='ES', device=device)
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speaker_ids = model.hps.data.spk2id
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output_path = 'es.wav'
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model.tts_to_file(text, speaker_ids['ES'], output_path, speed=speed)
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```
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#### French
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```python
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from melo.api import TTS
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# Speed is adjustable
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speed = 1.0
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device = 'cpu' # or cuda:0
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text = "La lueur dorée du soleil caresse les vagues, peignant le ciel d'une palette éblouissante."
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model = TTS(language='FR', device=device)
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speaker_ids = model.hps.data.spk2id
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output_path = 'fr.wav'
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model.tts_to_file(text, speaker_ids['FR'], output_path, speed=speed)
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```
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#### Chinese
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```python
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from melo.api import TTS
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# Speed is adjustable
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speed = 1.0
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device = 'cpu' # or cuda:0
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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。"
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model = TTS(language='ZH', device=device)
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speaker_ids = model.hps.data.spk2id
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output_path = 'zh.wav'
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model.tts_to_file(text, speaker_ids['ZH'], output_path, speed=speed)
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```
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#### Japanese
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```python
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from melo.api import TTS
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# Speed is adjustable
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speed = 1.0
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device = 'cpu' # or cuda:0
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text = "彼は毎朝ジョギングをして体を健康に保っています。"
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model = TTS(language='JP', device=device)
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speaker_ids = model.hps.data.spk2id
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output_path = 'jp.wav'
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model.tts_to_file(text, speaker_ids['JP'], output_path, speed=speed)
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```
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#### Korean
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```python
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from melo.api import TTS
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# Speed is adjustable
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speed = 1.0
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device = 'cpu' # or cuda:0
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text = "안녕하세요! 오늘은 날씨가 정말 좋네요."
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model = TTS(language='KR', device=device)
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speaker_ids = model.hps.data.spk2id
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output_path = 'kr.wav'
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model.tts_to_file(text, speaker_ids['KR'], output_path, speed=speed)
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```
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## License
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This library is under MIT License, which means it is free for both commercial and non-commercial use.
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## Acknowledgements
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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!
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