captioner
espnet
| captioner | espnet | |
|---|---|---|
| 1 | 15 | |
| 4 | 7,961 | |
| - | 2.4% | |
| 10.0 | 9.9 | |
| almost 2 years ago | about 19 hours ago | |
| TypeScript | Python | |
| - | Apache License 2.0 |
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captioner
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
I'd be interested if you ever dig anything up for this. I hacked together a kind of crude tool to snapshot audio and translate / caption it on the fly:
https://captioner.richardson.co.nz/
I would very much like to improve on this but the live translation / captioning still has some more work to go in this space.
Source was here: https://github.com/Rodeoclash/captioner
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):
https://github.com/espnet/espnet/blob/master/egs2/README.md
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[D] What's stopping you from working on speech and voice?
- https://github.com/espnet/espnet
- Íslensk talgervilsrödd sem hægt er að nota á Macca
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High quality, fast performing, local text to speech generation
This link has instructions for doing this for a Japanese model. It would have to be altered to work with ljspeech and the fine tune dataset.
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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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How to get Job in NLP?
The reason I'm saying this is to point out that having and in-depth knowledge on speech processing/generation requires a lot of information about signal processing and human speech in general (eg. acoustics and phonetics). However, if you're not into learning everything there is to know about a subject, just take one state-of-the-art example and study that as best as you can. Pick one environment/toolkit, for example espnet and simply go with that.
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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
What are some alternatives?
speechbrain - A PyTorch-based Speech Toolkit
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
k2 - FSA/FST algorithms, differentiable, with PyTorch compatibility.
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
kaldi-gstreamer-server - Real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framwork.
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
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.
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
flowtron - Flowtron is an auto-regressive flow-based generative network for text to speech synthesis with control over speech variation and style transfer
StarGANv2-VC - StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion
Conv-TasNet - A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
Resemblyzer - A python package to analyze and compare voices with deep learning