espnet
tortoise-tts
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espnet | tortoise-tts | |
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15 | 144 | |
7,769 | 11,440 | |
2.4% | - | |
10.0 | 8.2 | |
about 22 hours ago | 14 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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espnet
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
You might check out this list from espnet. They list the different corpuses they use to train their models sorted by language and task (ASR, TTS etc):
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[D] What's stopping you from working on speech and voice?
- https://github.com/espnet/espnet
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Text to speech generation
This work is made possible by the excellent advancements in text to speech modeling. ESPnet is a great project and should be checked out for more advanced and a wider range of use cases. This pipeline was also made possible by the great work from espnet_onnx in building a framework to export models to ONNX.
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[P] TorToiSe - a true zero-shot multi-voice TTS engine
CMU WavLab has ESPNet https://espnet.github.io/espnet/ which includes a number of high quality TTS models including VITS (which in my subjective experience is just as good as what is demonstrated here). Also the inference on various ESPNet pretrained TTS models is reasonable and sentences take on average 5 seconds per word to generate the waveform on my totally mid PC setup.
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Help picking a good speech recognition library
https://github.com/espnet/espnet (kind of like a newer Kaldi, but also not beginner friendly)
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speechbrain VS espnet - a user suggested alternative
2 projects | 13 Oct 2021
both provide e2e ASR support but espnet does have more utilities where as speechbarain is clean
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Need help with training ASR model from scratch.
This is relatively small amount of speech to train the model from scratch, but you can train using another pre-trained model for initialization. There are numbers of end-to-end ASR toolkits which can be used for this: https://github.com/NVIDIA/NeMo and https://github.com/espnet/espnet
You actually dont need to have phone level alignment for your data. Both hybrid and end-2-end approaches can work with utterance level alignment. For the hybrid approach, you would need a lexicon which maps each unique word in your training transcription to its phone sequence. You can obtain this with CMU's tool. For end-2-end approach you will need a byte pair encoder to tokenize the words in the transcriptions to its sub-words.
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Is there a python based speaker diarization system you would recommend?
Have a look at this PR at ESPnet. It might be useful.
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What are some good speech recognition papers I can implement?
espnet
tortoise-tts
- FLaNK Stack Weekly 12 February 2024
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OpenVoice: Versatile Instant Voice Cloning
I use Tortoise TTS. It's slow, a little clunky, and sometimes the output gets downright weird. But it's the best quality-oriented TTS I've found that I can run locally.
- [discussion] text to voice generation for textbooks
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Open Source Libraries
neonbjb/tortoise-tts
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Comparing Tortoise and Bark for Voice Synthesis
Tortoise GitHub repo - Source code, documentation, and usage guide
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Show HN: Gdańsk AI – full stack AI voice chatbot (STT, LLM, TTS, auth, payments)
TorToiSe (https://github.com/neonbjb/tortoise-tts) produces the best quality speech of any freely available model. However, its long inference times makes it impractical for voice chatbots like Gdansk.
- Are there any AI resources to help create audiobooks from text to speech?
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I found a youtube tutorial voiceover made by AI, and I'm blown away by its quality. Can you help me figure out which tool did the author use?
For free then tortoise is very good (Not sure but I think that elevenlabs started out with a fork of tortoise). - Can be tricky to install if you not are in to computers. But there is lots of forks and tutorials on the net.
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1) That's why you should avoid using proprietary software. Nobody can screw around with your SD running locally with all your settings 2) A photographer also can't take photos without a camera, so luddites should really get down off their high horses "oh i don't need tools to create"
For what it's worth, I haven't tried them yet, but there are also open-source large-language models and text-to-speech models.
- Why this scene hurt so much when you watch it back as an adult?
What are some alternatives?
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
bark - 🔊 Text-Prompted Generative Audio Model
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
speechbrain - A PyTorch-based Speech Toolkit
NeMo - NeMo: a framework for generative AI
piper - A fast, local neural text to speech system
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
k2 - FSA/FST algorithms, differentiable, with PyTorch compatibility.
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
larynx - End to end text to speech system using gruut and onnx
TensorFlowTTS - :stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
kaldi-gstreamer-server - Real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framwork.