espnet
Conv-TasNet
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espnet | Conv-TasNet | |
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15 | 2 | |
7,852 | 632 | |
2.1% | - | |
10.0 | 1.3 | |
26 minutes ago | about 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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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
Conv-TasNet
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Conv-tasnet
and that's lien github for the simulation Conv-Tasnet
The makefile in question is here: https://github.com/kaituoxu/Conv-TasNet/blob/master/tools/Makefile
What are some alternatives?
speechbrain - A PyTorch-based Speech Toolkit
NeMo - NeMo: a framework for generative AI
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.
pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers
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
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
StarGANv2-VC - StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion