NeMo
tortoise-tts
NeMo | tortoise-tts | |
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29 | 145 | |
10,128 | 11,819 | |
3.1% | - | |
9.8 | 8.0 | |
1 day ago | 5 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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NeMo
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[P] Making a TTS voice, HK-47 from Kotor using Tortoise (Ideally WaveRNN)
I don't test WaveRNN but from the ones that I know the best that is open source is FastPitch. And it's easy to use, here is the tutorial for voice cloning.
- [N] Huggingface/nvidia release open source GPT-2B trained on 1.1T tokens
- [D] What is the best open source text to speech model?
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[D] JAX vs PyTorch in 2023
Nowadays... bigger repos like https://github.com/NVIDIA/NeMo are all pytorch, lots of work also published by Meta and Microsoft is all torch. I check new work on GitHub all the time and I haven't seen a Tensorflow repo in years except one.
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[D] What's stopping you from working on speech and voice?
- https://github.com/NVIDIA/NeMo
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Can I use PyTorch to build a fast capitalization recoverer?
Can’t you use the NeMo model and just strip the punctuation from the output again if you don’t want it? You can also fine tune the the model with capitalization only if you look at the examples https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb The capitalization and punctuation are annotated separately (U indicates that the word should be upper cased, and O - no capitalization ). The model seems to be a token level classifier not seq to seq so there should also be a way to get just the capitalization part but you would have to look into the model as it’s not shown in the examples.
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I made a free transcription service powered by Whisper AI
I think there's been talk to do speaker diarization with whisper-asr-webservice[0] which is also written in python and should be able to make use of goodies such as pyannote-audio, py-webrtcvad, etc.
Whisper is great but at the point we get to kludging various things together it starts to make more sense to use something like Nvidia NeMo[1] which was built with all of this in mind and more
[0] - https://github.com/ahmetoner/whisper-asr-webservice
[1] - https://github.com/NVIDIA/NeMo
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Mozilla Common Voice - Korean Language is live - Help Build a Korean Corpus for Training AI/Navi/etc
[커먼보이스 전자우편](mailto:[email protected]) || Common Voice || Korean Language Homepage || FAQs || Speaking Aloud and Reviewing Recordings || Sentence Collector || NVidia/NeMo
- Whisper – open source speech recognition by OpenAI
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Using Edge Biometrics For Better AI Security System Development
The final security grain was added with speech-to-text anti-spoofing built on QuartzNet from the Nemo framework. This model provides a decent quality user experience and is suitable for real-time scenarios. To measure how close what the person says to what the system expects, requires calculation of the Levenshtein distance between them.
tortoise-tts
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ESpeak-ng: speech synthesizer with more than one hundred languages and accents
The quality also depends on the type of model. I'm not really sure what ESpeak-ng actually uses? The classical TTS approaches often use some statistical model (e.g. HMM) + some vocoder. You can get to intelligible speech pretty easily but the quality is bad (w.r.t. how natural it sounds).
There are better open source TTS models. E.g. check https://github.com/neonbjb/tortoise-tts or https://github.com/NVIDIA/tacotron2. Or here for more: https://www.reddit.com/r/MachineLearning/comments/12kjof5/d_...
- 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.
https://github.com/neonbjb/tortoise-tts
- [discussion] text to voice generation for textbooks
- DALL-E 3: Improving image generation with better captions [pdf]
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Open Source Libraries
neonbjb/tortoise-tts
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Running Tortoise-TTS - IndexError: List out of range
EDIT: It appears to be the exact same issue as this
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My Deep Learning Rig
It was primarily being used to train TTS models (see https://github.com/neonbjb/tortoise-tts), which largely fit into a single GPUs memory. So, for data parallelism, x8 PCIe isn't that much of a concern.
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PlayHT2.0: State-of-the-Art Generative Voice AI Model for Conversational Speech
Previously TortoiseTTS was associated with PlayHT in some way, although the exact connection is a bit vague [0].
From the descriptions here it sounds a lot like AudioLM / SPEAR TTS / some of Meta's recent multilingual TTS approaches, although those models are not open source, sounds like PlayHT's approach is in a similar spirit. The discussion of "mel tokens" is closer to what I would call the classic TTS pipeline in many ways... PlayHT has generally been kind of closed about what they used, would be interesting to know more.
I assume the key factor here is high quality, emotive audio with good data cleaning processes. Probably not even a lot of data, at least in the scale of "a lot" in speech, e.g. ASR (millions of hours) or TTS (hundreds to thousands). As opposed to some radically new architectural piece never before seen in the literature, there are lots of really nice tools for emotive and expressive TTS buried in recent years of publications.
Tacotron 2 is perfectly capable of this type of stuff as well, as shown by Dessa [1] a few years ago (this writeup is a nice intro to TTS concepts). With the limit largely being, at some point you haven't heard certain phonetic sounds before in a voice, and need to do something to get plausible outcomes for new voices.
[0] Discussion here https://github.com/neonbjb/tortoise-tts/issues/182#issuecomm...
[1] https://medium.com/dessa-news/realtalk-how-it-works-94c1afda...
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Comparing Tortoise and Bark for Voice Synthesis
Tortoise GitHub repo - Source code, documentation, and usage guide
What are some alternatives?
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
bark - 🔊 Text-Prompted Generative Audio Model
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
espnet - End-to-End Speech Processing Toolkit
piper - A fast, local neural text to speech system
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
larynx - End to end text to speech system using gruut and onnx