This is interesting, all open source conformer implementations have bugs:

📢 We have just released open source a bug-free 🚫🪲implementation of the Conformer model.
📌Check it at: https://github.com/hlt-mt/FBK-fairseq/blob/master/fbk_works/BUGFREE_CONFORMER.md
Want to discover what "bug-free" means?
➡ Take a look at our paper: https://arxiv.org/pdf/2303.16166.pdf

#opensource #conformer #speech #bug #bugfree #NLProc

https://twitter.com/sarapapi/status/1641750885524029440

📢The largest 2,000 hours multi-layer annotated corpus QASR is available @ https://arabicspeech.org/qasr/ QASR is suitable for ASR, dialect ID, punctuation, speaker ID-linking, and potentially other NLP modules for spoken data.
#nlproc #speechproc #Arabic #AI
@QatarComputing

@qcrialt

https://twitter.com/ArabicSpeech/status/1641402805951815681

https://www.openslr.org/136/

EMNS
Identifier: SLR136

Summary: An emotive single-speaker dataset for narrative storytelling. EMNS is dataset containing transcriptions, emotion, emotion intensity, and description of acted speech.

Category: Speech, text-to-speech, automatic speech recognition

License: Apache 2.0
About this resource:

Emotive Narrative Storytelling (EMNS) corpus introduces a dataset consisting of a single speaker, British English speech with high-quality labelled utterances tailored to drive interactive experiences with dynamic and expressive language. Each audio-text pairs are reviewed for artefacts and quality. Furthermore, we extract critical features using natural language descriptions, including word emphasis, level of expressiveness and emotion.

EMNS data collection tool: https://github.com/knoriy/EMNS-DCT

EMNS cleaner: https://github.com/knoriy/EMNS-cleaner

https://groups.inf.ed.ac.uk/edacc/

The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR. Ramon Sanabria, Bogoychev, Markl, Carmantini, Klejch, and Bell. ICASSP 2023. Presentation of the EdAcc.

Learning model from Whisper

https://github.com/speechcatcher-asr

NeMo 1.17 is now released and and includes a lot of improvements that users have long requested.

This includes a high level Diarization API, PyCTCDecode support for beam search, InterCTC Loss support, AWS Sagemaker tutorial and more !

https://twitter.com/alphacep/status/1644685634404073472

GPU beam search in pytorch

https://github.com/pytorch/audio/pull/3096

Space is closer than you think. Happy Cosmonautics day my friends.

Laugh is nice, Russian stress is traditionally bad

https://github.com/suno-ai/bark

Not sure about claimed accuracy but numbers are interesting

https://blog.deepgram.com/nova-speech-to-text-whisper-api/


A remarkable 22% reduction in word error rate (WER)

A blazing-fast 23-78x quicker inference time

A budget-friendly 3-7x lower cost starting at only $0.0043/min

AUDIT:
Audio Editing by Following Instructions with Latent Diffusion Models

Yuancheng Wang, Zeqian Ju, Xu Tan, Lei He, Zhizheng Wu, Jiang Bian, Sheng Zhao
Abstract. Audio editing is applicable for various purposes, such as adding background sound effects, replacing a musical instrument, and repairing damaged audio. Recently, some diffusion-based methods achieved zero-shot audio editing by using a diffusion and denoising process conditioned on the text description of the output audio. However, these methods still have some problems: 1) they have not been trained on editing tasks and cannot ensure good editing effects; 2) they can erroneously modify audio segments that do not require editing; 3) they need a complete description of the output audio, which is not always available or necessary in practical scenarios. In this work, we propose AUDIT, an instruction-guided audio editing model based on latent diffusion models. Specifically, AUDIT has three main design features: 1) we construct triplet training data (instruction, input audio, output audio) for different audio editing tasks and train a diffusion model using instruction and input (to be edited) audio as conditions and generating output (edited) audio; 2) it can automatically learn to only modify segments that need to be edited by comparing the difference between the input and output audio; 3) it only needs edit instructions instead of full target audio descriptions as text input. AUDIT achieves state-of-the-art results in both objective and subjective metrics for several audio editing tasks (e.g., adding, dropping, replacement, inpainting, super-resolution).

This research is done in alignment with Microsoft's responsible AI principles.

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

NaturalSpeech 2, a new powerful zero-shot TTS model in NaturaSpeech series🔥
1. Latent diffusion model + continuous codec, avoiding the dilemma in language model + discrete codec;
2. Strong zero-shot speech synthesis with a 3s prompt, singing synthesis with only a speech prompt!

abs: https://arxiv.org/abs/2304.09116
project page: https://speechresearch.github.io/naturalspeech2/

JaX is faster than Pytorch

https://twitter.com/sanchitgandhi99/status/1649046661816287236

Whisper can actually do speaker diarization with a prompt. Magic is:

or do a crude form of speaker turn tracking (e.g. " - Hey how are you doing? - I'm doing good. How are you?", note that the token for " -" is suppressed by default and will need to be enabled manually.)

https://github.com/openai/whisper/discussions/117#discussioncomment-3727051

http://www.asru2023.org/

Taiwan, Taipei

December 16-20, 2023

Regular & Challenge paper submission due: July 3, 2023

LODR decoding in K2

https://mp.weixin.qq.com/s/HJDaZ5BN1TzEa8oWQ9CBhw

Adding LODR to the rescore process only increases the decoding time by 20% compared to beam search, but reduces the word error rate by 13.8%, which is fast and accurate.