新一代开源TTS

Coqui TTS

🐸（青蛙）TTS

https://github.com/coqui-ai/TTS

https://coqui.ai/

文档：https://tts.readthedocs.io/en/latest/tutorial_for_nervous_beginners.html

For the first time, tts need to download  a data model. If the download fails, it will fail for the second time. We need to remove empty data model folder from path below to make it do a retry download:

/home/hgneng/.local/share/tts/

modals are download from here: https://github.com/coqui-ai/TTS/releases/tag/v0.6.1_models

有个论坛，当没有思路的时候可以看看甚至提问：https://github.com/coqui-ai/TTS/discussions

课程

公开课：https://edu.speechhome.com/

收费语音合成课程：https://edu.speechhome.com/p/t_pc/goods_pc_detail/goods_detail/course_29s1VsJQpubTWw2PPPYvbVGyOhE?app_id=appzxw56sw27444

自注意力机制：https://www.youtube.com/watch?v=hYdO9CscNes，找“李宏毅“相关视频可以完成完整的机器学习课程。

训练中文语音

有人正在做这样的尝试，他应该已经成功合成，只是定制的时候出现问题：https://github.com/coqui-ai/TTS/discussions/2488

已经有中文模型，不过不知道为什么声音后面会多了一段奇怪的重复语音（似乎是必须补齐12.05秒）：

tts = TTS(model_name="tts_models/zh-CN/baker/tacotron2-DDC-GST")
tts.tts_to_file("你好")

这个语音有一个TensorFlow的版本（不过我没有运行成功）：https://huggingface.co/tensorspeech/tts-tacotron2-baker-ch

定制语音

Raise your voice - training a model on your very own voice clips with Common Voice and Coqui

YourTTS: Zero-Shot Multi-Speaker Text Synthesis and Voice Conversion

https://github.com/Edresson/YourTTS

Create a custom Speech-to-Text model for 💫 Your Voice 💫 with Common Voice

Best Procedure For Voice Cloning

以下命令可以轻易地克隆声音，耗时11秒。必须使用multi-lingual模型。目前主要问题应该在于性能。如果实在没有办法，就生产基本拼音让Ekho来合成。

from TTS.api import TTS
tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
tts.tts_to_file("This is voice cloning.", speaker_wav="cameron.wav", language="en", file_path="output.wav")

Coqui STT

https://github.com/coqui-ai/STT

Tacotron2

2006年发布的Tacotron是第一批成功的使用深度学习应用于TTS的模型之一。Tacotron mainly is an encoder-decoder model with attention.

2008年发布了Tacotron2。此模型合成Hello World耗时74秒。

2020年Coqui Eren Gölge提出Tacotron2 Double Decoder Consistency模型。此模型合成Hello World耗时9秒。

参考：https://tts.readthedocs.io/en/latest/models/tacotron1-2.html

学习Pytorch关于语音合成的模块Tacotron2: https://pytorch.org/audio/stable/tutorials/tacotron2_pipeline_tutorial.html

We need to fix network issue:

/home/hgneng/miniconda3/envs/tacotron2/lib/python3.10/site-packages/torch/hub.py download_url_to_file from https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/en_us_cmudict_forward.pt  to  /home/hgneng/.cache/torch/hub/checkpoints/en_us_cmudict_forward.pt

Vocoder translating specgrams to wav seems slow

此模型合成Hello World耗时139秒，远远高于Coqui的Tracotron2 DDC模型（9秒）。

https://pytorch.org/audio/stable/generated/torchaudio.pipelines.Tacotron2TTSBundle.Vocoder.html?highlight=vocoder#torchaudio.pipelines.Tacotron2TTSBundle.Vocoder

What's difference between Phoneme-based TTS and Character-based TTS?

Phoneme-based TTS is a text-to-speech system that uses the sounds of a language (phonemes) to generate speech. It is more accurate than character-based TTS because it is based on a more detailed analysis of the language. Character-based TTS, on the other hand, is a text-to-speech system that uses characters (or words) to generate speech. It is less accurate than phoneme-based TTS because it does not take into account the nuances of the language.

https://pytorch.org/audio/2.0.1/pipelines.html

Tacotron2 data Modal

理解其模型，看有没有中文可用的，如果没有想办法自己训练：https://pytorch.org/audio/stable/pipelines.html