FairMOT
TensorFlowTTS
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| FairMOT | TensorFlowTTS | |
|---|---|---|
| 2 | 6 | |
| 3,929 | 3,697 | |
| - | 1.4% | |
| 0.0 | 0.0 | |
| 7 months ago | 5 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
FairMOT
- Does Multi Object Tracking work better (precision/recall) on videos than jury rigging a SOTA image object detection to work on videos?
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[D] Multi object tracking (MOT)
Looks like FairMOT (https://paperswithcode.com/paper/a-simple-baseline-for-multi-object-tracking) which has code available (https://github.com/ifzhang/FairMOT ).
TensorFlowTTS
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Ask HN: On-Device Text to Speech
Hey HN, has anyone found a viable solution for doing this locally and offline on iOS? I'd like to offer a privacy-friendly text to speech feature to my App, and Apple's speech synthesis sounds awful compared to some newer models and TTS engines. The only thing I've found is an older TensorflowTTS example here: https://github.com/TensorSpeech/TensorFlowTTS/tree/master/examples/ios
Any pointers or tips appreciated.
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NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality
I had a lot of success using [FastSpeech2 + MB MelGAN via TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). There are demos for [iOS](https://github.com/TensorSpeech/TensorFlowTTS/tree/master/ex...) and [Android](https://github.com/TensorSpeech/TensorFlowTTS/tree/master/ex...) which will allow you to run pretty convincing, modern TTS models with only a few hundred milliseconds of processing latency.
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TTS mobile help
I need an example of how I would go about it. I've combed through examples but it's just not clicking for me.
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A Working TTS feature has been found (No Google Services Required)
https://github.com/TensorSpeech/TensorFlowTTS was the project. It was pretty much a direct compile and run. I went through and added the required features to enable it as TTS service for Android. I also moved the Tensorflow portion into a separate thread from the TTS service directly, since Android restricts it's TTS service to a single thread, and the Tensorflow service uses five threads to run at a good speed. It's a much much heavier solution than a C/C++ compiled library, but it works out of the box and I will worry about optimizations later
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Free library for text-to-speech
You need to try, it implements most advanced algorithms and not as ad-hoc as nvidia https://github.com/TensorSpeech/TensorFlowTTS
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Reviving the 1973 Unix text to voice translator
For open source offline TTS with more or less recent algorithms you can check
https://github.com/TensorSpeech/TensorFlowTTS
What are some alternatives?
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
yolo_tracking - BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
flowtron - Flowtron is an auto-regressive flow-based generative network for text to speech synthesis with control over speech variation and style transfer
yolact - A simple, fully convolutional model for real-time instance segmentation.
hifi-gan - HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Lip2Speech - A pipeline to read lips and generate speech for the read content, i.e Lip to Speech Synthesis.