Table of Contents
- https://github.com/openai/whisper
- the --initial_prompt CLI arg: For my use, I put a bunch of industry jargon and names that are commonly misspelled in there and that fixes 1/3 to 1/2 of the errors.
- https://freesubtitles.ai/ (hangs my browser when i try it)
- https://github.com/mayeaux/generate-subtitles
- theory: whisper is a way to get more tokens from youtube for gpt4
- Real time whisper https://github.com/shirayu/whispering
- whisper running on $300 device https://twitter.com/drjimfan/status/1616471309961269250?s=46&t=4t17Fxog8a65leEnHNZwVw
- whisper can be hosted on https://deepinfra.com/
- whisperX with diarization https://twitter.com/maxhbain/status/1619698716914622466 https://github.com/m-bain/whisperX Improved timestamps and speaker identification
- whisper as a service self hosting GUI and queueing https://github.com/schibsted/WAAS
- Live microphone demo (not real time, it still does it in chunks) https://github.com/mallorbc/whisper_mic
- Whisper webservice (https://github.com/ahmetoner/whisper-asr-webservice) - via this thread
- Whisper UI https://github.com/hayabhay/whisper-ui
- Streamlit UI https://github.com/hayabhay/whisper-ui
- Whisper playground https://github.com/saharmor/whisper-playground
- Transcribe-anything https://github.com/zackees/transcribe-anything automates video fetching and uses whisper to generate .srt, .vtt and .txt files
- MacWhisper https://goodsnooze.gumroad.com/l/macwhisper
- Crossplatform desktop Whisper that supports semi-realtime https://github.com/chidiwilliams/buzz
- https://github.com/ggerganov/whisper.cpp
High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model:
- Plain C/C++ implementation without dependencies
- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
- AVX intrinsics support for x86 architectures
- Mixed F16 / F32 precision
- Low memory usage (Flash Attention + Flash Forward)
- Zero memory allocations at runtime
- Runs on the CPU
- C-style API
- a fork of whisper.cpp that uses DirectCompute to run it on GPUs without Cuda on Windows: https://github.com/Const-me/Whisper
- Whisper.cpp small model is best traadeoff of performance vs accuracy https://blog.lopp.net/open-source-transcription-software-comparisons/
- whisper openai api https://twitter.com/calumbirdo/status/1614826199527690240?s=46&t=-lurfKb2OVOpdzSMz0juIw
- speech separation model openai/whisper#264 (comment)
https://news.ycombinator.com/item?id=33663486
- https://whispermemos.com pressing button on my Lock Screen and getting a perfect transcription in my inbox.
- whisper on AWS - the g4dn machines are the sweet spot of price/performance.
- https://simonsaysai.com to generate subtitles and they had the functionality to input specialized vocabulary,
- https://skyscraper.ai/ using assemblyai
- Read.ai - https://www.read.ai/transcription Provides transcription & diarization and the bot integrates into your calendar. It joins all your meetings for zoom, teams, meet, webex, tracks talk time, gives recommendations, etc.
- https://huggingface.co/spaces/vumichien/whisper-speaker-diarization This space uses Whisper models from OpenAI to recoginze the speech and ECAPA-TDNN model from SpeechBrain to encode and clasify speakers
- https://github.com/Majdoddin/nlp pyannote diarization
- https://news.ycombinator.com/item?id=33665692
- Podcast summarization
- Teleprompter
- https://github.com/danielgross/teleprompter
- Everything happens privately on your computer. In order to achieve fast latency locally, we use embeddings or a small fine-tuned model.
- The data is from Kaggle's quotes database, and the embeddings were computed using SentenceTransformer, which then runs locally on ASR. I also finetuned a small T5 model that sorta works (but goes crazy a lot).
- https://twitter.com/ggerganov/status/1605322535930941441
- https://github.com/danielgross/teleprompter
- language teacher
- services
- OSS
- pyttsx3 https://pyttsx3.readthedocs.io/en/latest/engine.html
- https://github.com/lucidrains/audiolm-pytorch Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch It also extends the work for conditioning with classifier free guidance with T5. This allows for one to do text-to-audio or TTS, not offered in the paper.
- tortoise https://github.com/neonbjb/tortoise-tts
- https://github.com/coqui-ai/TTS
- previously mozilla TTS
- custom voices
- https://github.com/neonbjb/tortoise-tts#voice-customization-guide
- microsoft and google cloud have apis
- twilio maybe
- VallE when it comes out
- research papers
- https://speechresearch.github.io/naturalspeech/
- research paper from very short voice sample https://valle-demo.github.io/
- https://github.com/rhasspy/larynx
- pico2wave with the -l=en-GB flag to get the British lady voice is not too bad for offline free TTS. You can hear it in this video: https://www.youtube.com/watch?v=tfcme7maygw&t=45s
- https://github.com/espeak-ng/espeak-ng (for very specific non-english purposes, and I was willing to wrangle IPA)
- https://github.com/words/syllable and ecosystem
- speaker diarization
- https://news.ycombinator.com/item?id=33892105
- https://github.com/pyannote/pyannote-audio
- https://arxiv.org/abs/2012.00931
- example diarization impl https://colab.research.google.com/drive/1V-Bt5Hm2kjaDb4P1RyMSswsDKyrzc2-3?usp=sharing
- from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
- https://lablab.ai/t/whisper-transcription-and-speaker-identification
- noise cleaning
- adobe enhance speech for cleaning up spoken audio https://news.ycombinator.com/item?id=34047976 https://podcast.adobe.com/enhance
- https://github.com/elanmart/cbp-translate
- Process short video clips (e.g. a single scene)
- Work with multiple characters / speakers
- Detect and transcribe speech in both English and Polish
- Translate the speech to any language
- Assign each phrase to a speaker
- Show the speaker on the screen
- Add subtitles to the original video in a way mimicking the Cyberpunk example
- Have a nice frontend
- Run remotely in the cloud
- https://essentia.upf.edu/
- Extensive collection of reusable algorithms
- Cross-platform
- Fast prototyping
- Industrial applications
- Similarity
- Classification
- Deep learning inference
- Mood detection
- Key detection
- Onset detection
- Segmentation
- Beat tracking
- Melody extraction
- Audio fingerprinting
- Cover song detection
- Spectral analysis
- Loudness metering
- Audio problems detection
- Voice analysis
- Synthesis
- https://github.com/regorxxx/Music-Graph An open source graph representation of most genres and styles found on popular, classical and folk music. Usually used to compute similarity (by distance) between 2 sets of genres/styles.
- https://github.com/regorxxx/Camelot-Wheel-Notation Javascript implementation of the Camelot Wheel, ready to use "harmonic mixing" rules and translations for standard key notations.
- youtube whisper (large-v2 support) https://twitter.com/jeffistyping/status/1600549658949931008
- list of audio editing ai apps https://twitter.com/ramsri_goutham/status/1592754049719603202?s=20&t=49HqYD7DyViRl_T5foZAxA
- https://beta.elevenlabs.io/ techmeme ridehome - voice generation in your own voice from existing samples (not reading script)
- https://github.com/deezer/spleeter (and bpm detection)
general consensus is that it's just not very good right now
- disco diffusion?
- img-to-music via CLIP interrogator => Mubert (HF space, tweet)
- https://soundraw.io/ https://news.ycombinator.com/item?id=33727550
- Riffusion https://news.ycombinator.com/item?id=33999162
- Google AudioLM https://www.technologyreview.com/2022/10/07/1060897/ai-audio-generation/ Google’s new AI can hear a snippet of song—and then keep on playing
- AudioLDM https://github.com/haoheliu/AudioLDM speech, soud effects, music
- MusicLM https://google-research.github.io/seanet/musiclm/examples/
- reactions https://twitter.com/JacquesThibs/status/1618839343661203456
- implementation https://github.com/lucidrains/musiclm-pytorch
- https://arxiv.org/abs/2301.12662 singsong voice generation