New Mandarin TTS dataset

https://www.openslr.org/138/

SHALCAS22A
Identifier: SLR138

Summary: A Chinese Mandarin corpus by Shanghai Acoustics Laboratory, CAS and Wuxi Sandu Intelligent Technology Co., Ltd.

Category: Speech

License: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

Downloads (use a mirror closer to you):
SHALCAS22A.tgz [3.9G] ( Corpus ) Mirrors: [US] [EU] [CN]


About this resource:

SHALCAS22A is a 1-channel Chinese Mandarin speech corpus by Shanghai Acoustics Laboratory, CAS and Wuxi Sandu Intelligent Technology Co., Ltd. It was collected over a Hi-Fi microphone in a quiet environment. The corpus contains 14,580 utterances from 60 speakers. Each speaker has 243 utterances.
The contents include number passwords, short Chinese words, and long Chinese sentences. The mapping between the content and utterance is given in content.txt.

This corpus can be used in text-dependent speaker verification on number passwords, text-independent speaker verification on short utterances, and other speech-related fields. Please cite the corpus as "SHALCAS22A, a free Chinese Mandarin corpus by Shanghai Acoustics Laboratory, CAS and Wuxi Sandu Intelligent Technology Co., Ltd., 2022".

Contact: Feng Hong, hongfeng@mail.ioa.ac.cn

Encodec has just changed to an MIT license. Great news for anyone working on LM approaches to audio or just looking for a high-quality audio codec.

No training code but still a really significant change.

https://github.com/facebookresearch/encodec/commit/349b72939f57cb3bc7b60906c0ee8228c849485d

Open Preview for #ICASSP2023 is now available on
@IEEEXplore
! Available through June 10, you can now browse all the papers that were accepted to ICASSP 2023, free of charge. Browse research here: https://hubs.la/Q01N_PdX0

Good VS quality

https://quickvc.github.io/quickvc-demo/

https://github.com/quickvc/QuickVC-VoiceConversion

People report Whisper PEFT + LORA tuning gives quite good results:

https://github.com/huggingface/peft/blob/main/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb

EfficientSpeech, or ES for short, is an efficient neural text to speech (TTS) model. It generates mel spectrogram at a speed of 104 (mRTF) or 104 secs of speech per sec on an RPi4. Its tiny version has a footprint of just 266k parameters. Generating 6 secs of speech consumes 90 MFLOPS only.

https://github.com/roatienza/efficientspeech

https://roatienza.github.io/efficientspeech-demo/

May 12, 2023: Challenge announcement
May 19, 2023: Leaderboard is online and accepting submissions
June 26, 2023: New Language Track Submission Deadline
July 07, 2023: Paper / Model Submission Deadline
July 10, 2023: Paper Revision Deadline

🌍🗣️SUPERB benchmark is back with ML-SUPERB, its multilingual version! The challenge, as one of the #ASRU2023 challenges, includes 3 tracks:
1️⃣ML-SUPERB: For multilingual SSL
2️⃣New language: To new languages!
3️⃣Research: For research papers

More to see 👉 https://multilingual.superbbenchmark.org

Universal Source Separation with Weakly Labelled Data

abs: https://arxiv.org/abs/2305.07447
paper page: https://huggingface.co/papers/2305.07447
github: https://github.com/bytedance/uss

Some people implement streaming speaker diarization manually

https://github.com/pyannote/pyannote-audio/commit/4a6ea9c825b9447a7d03cb9bd94f5f81d661ca16

others just ask ChatGPT to write it

https://github.com/huseinzol05/malaya-speech/commit/564f50c0d91528126fe3b410f387d1b4ff33d364

ChatGPT version is not that bad

The first Arabic TTS Challenge - QASR TTS 1.0 is on!! Register and build your own Arabic Anchor Voice and contribute to enriching #ArabicAI #ASRU2023Challege
More details: https://arabicspeech.org/qasr-challenge/

https://twitter.com/shammur_absar/status/1658429029483986944

Some nice things from industry, autoscaling with Triton and Kubernetes

https://www.speechmatics.com/company/articles-and-news/autoscaling-with-gpu-transcription-models

Multilingual TTS from ElevenLabs

https://twitter.com/radamar/status/1658540025611685888

https://huggingface.co/spaces/elevenlabs/tts

Recent advances in the AudioLM family: 100x higher speed, better consistency, no quality hit - a new paper from and the AudioLM team.

Give it a listen: https://google-research.github.io/seanet/soundstorm/examples/

Arxiv:
https://arxiv.org/abs/2305.09636

Final VoxCeleb Challenge

https://mm.kaist.ac.kr/datasets/voxceleb/voxsrc/competition2023.html

Timeline
May 20th Development set for verification tracks released.
May 31rd Development set for diarisation tracks released.
June 1st Test set released and evaluation server open.
Early August Deadline for submission of results; invitation to workshop speakers.
August 20th Challenge workshop

3 nice Persian TTS datasets

https://www.kaggle.com/magnoliasis/datasets

Whisper is essentially an audio-conditioned LLM. Can we prompt it to do unseen tasks? Introducing PromptingWhisper!

We use simple prompts to adapt Whisper to unseen tasks zero-shot without any finetuning.

📄 Paper: http://arxiv.org/abs/2305.11095
💻 Code: https://github.com/jasonppy/PromptingWhisper

https://twitter.com/csteinmetz1/status/1659458441197355008

I was complaining that LLMs don't have ears... This paper is a solid attempt to try to make that happen.

abs: https://arxiv.org/abs/2305.10790
Work from Yuan Gong et al. at MIT