Nice results and finally proper evaluation of the TTS

https://github.com/zhenye234/xcodec

https://arxiv.org/abs/2408.17175

Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model

Zhen Ye, Peiwen Sun, Jiahe Lei, Hongzhan Lin, Xu Tan, Zheqi Dai, Qiuqiang Kong, Jianyi Chen, Jiahao Pan, Qifeng Liu, Yike Guo, Wei Xue

Recent advancements in audio generation have been significantly propelled by the capabilities of Large Language Models (LLMs). The existing research on audio LLM has primarily focused on enhancing the architecture and scale of audio language models, as well as leveraging larger datasets, and generally, acoustic codecs, such as EnCodec, are used for audio tokenization. However, these codecs were originally designed for audio compression, which may lead to suboptimal performance in the context of audio LLM. Our research aims to address the shortcomings of current audio LLM codecs, particularly their challenges in maintaining semantic integrity in generated audio. For instance, existing methods like VALL-E, which condition acoustic token generation on text transcriptions, often suffer from content inaccuracies and elevated word error rates (WER) due to semantic misinterpretations of acoustic tokens, resulting in word skipping and errors. To overcome these issues, we propose a straightforward yet effective approach called X-Codec. X-Codec incorporates semantic features from a pre-trained semantic encoder before the Residual Vector Quantization (RVQ) stage and introduces a semantic reconstruction loss after RVQ. By enhancing the semantic ability of the codec, X-Codec significantly reduces WER in speech synthesis tasks and extends these benefits to non-speech applications, including music and sound generation. Our experiments in text-to-speech, music continuation, and text-to-sound tasks demonstrate that integrating semantic information substantially improves the overall performance of language models in audio generation. Our code and demo are available (Demo: this https URL Code: this https URL)

This is very reasonable architecture for many reasons. I'll write more on this later

https://bytedancespeech.github.io/seedtts_tech_report/

No open source it seems, but there is a new repo which you can try. Voice conversion is quite good even for cross-lingual case

https://huggingface.co/spaces/Plachta/Seed-VC

https://github.com/Plachtaa/seed-vc

https://github.com/nyrahealth/CrisperWhisper

https://huggingface.co/nyrahealth/CrisperWhisper

https://arxiv.org/abs/2408.16589

CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions

Laurin Wagner, Bernhard Thallinger, Mario Zusag

We demonstrate that carefully adjusting the tokenizer of the Whisper speech recognition model significantly improves the precision of word-level timestamps when applying dynamic time warping to the decoder's cross-attention scores. We fine-tune the model to produce more verbatim speech transcriptions and employ several techniques to increase robustness against multiple speakers and background noise. These adjustments achieve state-of-the-art performance on benchmarks for verbatim speech transcription, word segmentation, and the timed detection of filler events, and can further mitigate transcription hallucinations. The code is available open this https URL.

You can automatically convert between creative forms now, paper to podcast, next paper to artwork

https://illuminate.google.com

Illuminate, an experimental AI application made by Google. Convert a paper from PDF to a podcast. The synthetic voice sounds pretty natural. What a great time to be a learner!

Google introduces Think tokens (emulating brain) everywhere

Contemplative Mechanism for Speech Recognition:
Speech Encoders can Think
Tien-Ju Yang, Andrew Rosenberg, Bhuvana Ramabhadran

https://www.isca-archive.org/interspeech_2024/yang24g_interspeech.pdf

Related too

Think before you speak: Training Language Models With Pause Tokens
Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan

https://arxiv.org/abs/2310.02226

CHIME Challenge is way more dense than Interespeech. Chime 8 workshop just ended

https://www.chimechallenge.org/current/workshop/index

Congrats to STC team, as usual they demonstrate top performance on Chime tasks.

No publications yet, but even keynote talk is interesting

Teaching New Skills to Foundation Models: Insights and Experiences
Speaker: Hung-yi Lee
National Taiwan University (NTU)

https://www.chimechallenge.org/current/workshop/CHiME2024_Lee.pdf

And, an interesting NOTSOFAR task, for information to many startups trying to implement meeting transcription

To illustrate, on our newly released NOTSOFAR meeting benchmark, Whisper large-v3 with head-mounted mics achieves 9.3% WER (word-error-rate), yet on audio from a distant mic it climbs to 37.4% WER. The culprits are reverberation, noise, and overlapping speech, which interfere with the source signal.

https://arxiv.org/abs/2401.08887

NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription

Alon Vinnikov, Amir Ivry, Aviv Hurvitz, Igor Abramovski, Sharon Koubi, Ilya Gurvich, Shai Pe`er, Xiong Xiao, Benjamin Martinez Elizalde, Naoyuki Kanda, Xiaofei Wang, Shalev Shaer, Stav Yagev, Yossi Asher, Sunit Sivasankaran, Yifan Gong, Min Tang, Huaming Wang, Eyal Krupka

We introduce the first Natural Office Talkers in Settings of Far-field Audio Recordings (``NOTSOFAR-1'') Challenge alongside datasets and baseline system. The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics. It is recorded across 30 conference rooms, featuring 4-8 attendees and a total of 35 unique speakers. Second, a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions. The tasks focus on single-device DASR, where multi-channel devices always share the same known geometry. This is aligned with common setups in actual conference rooms, and avoids technical complexities associated with multi-device tasks. It also allows for the development of geometry-specific solutions. The NOTSOFAR-1 Challenge aims to advance research in the field of distant conversational speech recognition, providing key resources to unlock the potential of data-driven methods, which we believe are currently constrained by the absence of comprehensive high-quality training and benchmarking datasets.

Fun fact, the amount of fake content grows. For example a user just sent me an article which mentions Vosk Indonesian model and even gives a link to it. The problem is we never had one! The article is clearly autogenerated!

https://x.com/FishAudio/status/1833787529595912531

Excited to introduce Fish Speech 1.4 - now open-source and more powerful than ever! Our mission is to make cutting-edge voice tech accessible to everyone.

What's new:
- Trained on 700k hours of multilingual data (up from 200k)
- Now supports 8 languages: English, Chinese, German, Japanese, French, Spanish, Korean, and Arabic
- Fully open-source, empowering developers and researchers worldwide

Key features:
- Lightning-fast TTS with ultra-low latency
- Instant voice cloning
- Self-host or use our cloud service
- Simple, flat-rate pricing

Try it out:
- Playground: https://fish.audio
- GitHub: https://github.com/fishaudio/fish-speech
- HuggingFace Model: https://huggingface.co/fishaudio/fish-speech-1.4
- Demo: https://huggingface.co/spaces/fishaudio/fish-speech-1
- Product Hunt: https://producthunt.com/posts/fish-speech-1-4

Somewhat useful, monotonic align ported to GPU

https://github.com/supertone-inc/super-monotonic-align

Small LLMS are the right thing to go, here is a Chinese LLM with 26M parameters

https://github.com/jingyaogong/minimind/blob/master/README_en.md

New paper from StyleTTS authors. Metrics looks good, and finally proper comparison between systems! But I kind of wonder if algorithms are too focused on read speech. Hard to believe in such a great metrics for conversational dataset with proposed complex algorithms.

https://arxiv.org/abs/2409.10058

StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion
Yinghao Aaron Li, Xilin Jiang, Cong Han, Nima Mesgarani
The rapid development of large-scale text-to-speech (TTS) models has led to significant advancements in modeling diverse speaker prosody and voices. However, these models often face issues such as slow inference speeds, reliance on complex pre-trained neural codec representations, and difficulties in achieving naturalness and high similarity to reference speakers. To address these challenges, this work introduces StyleTTS-ZS, an efficient zero-shot TTS model that leverages distilled time-varying style diffusion to capture diverse speaker identities and prosodies. We propose a novel approach that represents human speech using input text and fixed-length time-varying discrete style codes to capture diverse prosodic variations, trained adversarially with multi-modal discriminators. A diffusion model is then built to sample this time-varying style code for efficient latent diffusion. Using classifier-free guidance, StyleTTS-ZS achieves high similarity to the reference speaker in the style diffusion process. Furthermore, to expedite sampling, the style diffusion model is distilled with perceptual loss using only 10k samples, maintaining speech quality and similarity while reducing inference speed by 90%. Our model surpasses previous state-of-the-art large-scale zero-shot TTS models in both naturalness and similarity, offering a 10-20 faster sampling speed, making it an attractive alternative for efficient large-scale zero-shot TTS systems. The audio demo, code and models are available at this https URL.

Everyone is doing DiT in TTS. Honestly I failed to understand the use of it, but metrics show good values. Examples are

https://github.com/KdaiP/StableTTS

https://github.com/7Xin/DPI-TTS

True anime dataset

https://huggingface.co/datasets/joujiboi/japanese-anime-speech-v2

Its interesting how non-optimal decisions require much more compute. For example, speech events are clearly non-uniformly arranged in time. One has to model time offsets. Given that it is strange modern discrete codecs use large uniform shifts. A proper encoder should have blank tokens or something like that and higher sampling rate.

New Whisper model large v3 turbo

https://github.com/openai/whisper/pull/2361

Linagora released Arabic Tunisian model for Vosk

https://huggingface.co/linagora/linto-asr-ar-tn-0.1

Interesting research on ASR robustness. Nemo Canary model is most accurate but far from being most Robust. Whisper variants and MMS are somewhat better. Training data size matters.

https://github.com/ahmedshah1494/speech_robust_bench

https://arxiv.org/pdf/2403.07937

Speech Robust Bench: A Robustness Benchmark For Speech Recognition

Muhammad A. Shah, David Solans Noguero, Mikko A. Heikkila, Bhiksha Raj, Nicolas Kourtellis

As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose Speech Robust Bench (SRB), a comprehensive benchmark for evaluating the robustness of ASR models to diverse corruptions. SRB is composed of 114 input perturbations which simulate an heterogeneous range of corruptions that ASR models may encounter when deployed in the wild. We use SRB to evaluate the robustness of several state-of-the-art ASR models and observe that model size and certain modeling choices such as the use of discrete representations, or self-training appear to be conducive to robustness. We extend this analysis to measure the robustness of ASR models on data from various demographic subgroups, namely English and Spanish speakers, and males and females. Our results revealed noticeable disparities in the model's robustness across subgroups. We believe that SRB will significantly facilitate future research towards robust ASR models, by making it easier to conduct comprehensive and comparable robustness evaluations.