To understand the reality, training times on F5. On the other hand, GAN-based TTS like VITS take about the same time.

And you could simply train your own model for a new language:

* Leverage Emilia Dataset (DE EN FR JA KO ZH), as we have include script for it (NOTE. download the mentioned version of Emilia in script, cuz it's currently updated to a WebDataset ver.)
or prepare your own data pairs if not covered, just tailor a Dataset Class in model/dataset.py to your need
* For Base model (multilingual, ~300M), we use <50K hours for each language
* For Small model (e.g. Chinese-only, ~150M), we have made it work with just 1K hours data, config. mentioned in our paper also

Just one thing, the training would take a long time, especially for E2 TTS (if you choose)
And be patient, 8xRTX3090 small model for one week (200~400K updates to hear something reasonable) 8xA100 for base model similarly.

https://github.com/SWivid/F5-TTS/issues/5#issuecomment-2404160945

Pretty simple approach to transfer knowledge from existing task-specific models to audio LLM, however, it is interesting that careful data construction can make good results

https://github.com/kehanlu/DeSTA2

https://arxiv.org/abs/2409.20007

Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data

Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu, Chao-Han Huck Yang, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Hung-yi Lee

Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) by incorporating pre-trained speech models. However, these SLMs often undergo extensive speech instruction-tuning to bridge the gap between speech and text modalities. This requires significant annotation efforts and risks catastrophic forgetting of the original language capabilities. In this work, we present a simple yet effective automatic process for creating speech-text pair data that carefully injects speech paralinguistic understanding abilities into SLMs while preserving the inherent language capabilities of the text-based LLM. Our model demonstrates general capabilities for speech-related tasks without the need for speech instruction-tuning data, achieving impressive performance on Dynamic-SUPERB and AIR-Bench-Chat benchmarks. Furthermore, our model exhibits the ability to follow complex instructions derived from LLMs, such as specific output formatting and chain-of-thought reasoning. Our approach not only enhances the versatility and effectiveness of SLMs but also reduces reliance on extensive annotated datasets, paving the way for more efficient and capable speech understanding systems.

Audio tokens are not that simple, doesn't feel modern models work easily with them

https://arxiv.org/abs/2409.19283

Analyzing and Mitigating Inconsistency in Discrete Audio Tokens for Neural Codec Language Models
Wenrui Liu, Zhifang Guo, Jin Xu, Yuanjun Lv, Yunfei Chu, Zhou Zhao, Junyang Lin

Building upon advancements in Large Language Models (LLMs), the field of audio processing has seen increased interest in training audio generation tasks with discrete audio token sequences. However, directly discretizing audio by neural audio codecs often results in sequences that fundamentally differ from text sequences. Unlike text, where text token sequences are deterministic, discrete audio tokens can exhibit significant variability based on contextual factors, while still producing perceptually identical audio segments. We refer to this phenomenon as \textbf{Discrete Representation Inconsistency (DRI)}. This inconsistency can lead to a single audio segment being represented by multiple divergent sequences, which creates confusion in neural codec language models and results in omissions and repetitions during speech generation. In this paper, we quantitatively analyze the DRI phenomenon within popular audio tokenizers such as EnCodec. Our approach effectively mitigates the DRI phenomenon of the neural audio codec. Furthermore, extensive experiments on the neural codec language model over LibriTTS and large-scale MLS datases (44,000 hours) demonstrate the effectiveness and generality of our method. The demo of audio samples is available online~\footnote{\url{this https URL}}.

A new paper from StyleTTS author. This trick is kind of the same as genetic programming though.

https://dmdspeech.github.io/

https://arxiv.org/abs/2410.11097

DMDSpeech: Distilled Diffusion Model Surpassing The Teacher in Zero-shot Speech Synthesis via Direct Metric Optimization

Yingahao Aaron Li, Rithesh Kumar, Zeyu Jin

Diffusion models have demonstrated significant potential in speech synthesis tasks, including text-to-speech (TTS) and voice cloning. However, their iterative denoising processes are inefficient and hinder the application of end-to-end optimization with perceptual metrics. In this paper, we propose a novel method of distilling TTS diffusion models with direct end-to-end evaluation metric optimization, achieving state-of-the-art performance. By incorporating Connectionist Temporal Classification (CTC) loss and Speaker Verification (SV) loss, our approach optimizes perceptual evaluation metrics, leading to notable improvements in word error rate and speaker similarity. Our experiments show that DMDSpeech consistently surpasses prior state-of-the-art models in both naturalness and speaker similarity while being significantly faster. Moreover, our synthetic speech has a higher level of voice similarity to the prompt than the ground truth in both human evaluation and objective speaker similarity metric. This work highlights the potential of direct metric optimization in speech synthesis, allowing models to better align with human auditory preferences. The audio samples are available at this https URL.

SANE 2024 workshop ended today

https://www.saneworkshop.org/sane2024/

topics are somewhat interesting. For example, Google attempt to use LLM for diarization

https://www.saneworkshop.org/sane2024/#quan

Hopefully videos will be here:

https://www.youtube.com/@speechandaudiointhenortheast

After spending some hours on F5, I found passion to finalize this small post. I'm telling this for quite some time already though.

https://alphacephei.com/nsh/2024/10/18/tts-design.html

Meta shared their SpiritLM

https://github.com/facebookresearch/spiritlm

https://twitter.com/AIatMeta/status/1847383580269510670

A good Chinese MLLM

https://github.com/westlake-baichuan-mllm/bc-omni

https://arxiv.org/abs/2410.08565

Baichuan-Omni Technical Report

The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpart. In this paper, we introduce Baichuan-Omni, the first open-source 7B Multimodal Large Language Model (MLLM) adept at concurrently processing and analyzing modalities of image, video, audio, and text, while delivering an advanced multimodal interactive experience and strong performance. We propose an effective multimodal training schema starting with 7B model...

Quite in-depth paper on continuous vs discrete representation

https://arxiv.org/abs/2410.16048

Continuous Speech Synthesis using per-token Latent Diffusion

Arnon Turetzky, Nimrod Shabtay, Slava Shechtman, Hagai Aronowitz, David Haws, Ron Hoory, Avihu Dekel

The success of autoregressive transformer models with discrete tokens has inspired quantization-based approaches for continuous modalities, though these often limit reconstruction quality. We therefore introduce SALAD, a per-token latent diffusion model for zero-shot text-to-speech, that operates on continuous representations. SALAD builds upon the recently proposed expressive diffusion head for image generation, and extends it to generate variable-length outputs. Our approach utilizes semantic tokens for providing contextual information and determining the stopping condition. We suggest three continuous variants for our method, extending popular discrete speech synthesis techniques. Additionally, we implement discrete baselines for each variant and conduct a comparative analysis of discrete versus continuous speech modeling techniques. Our results demonstrate that both continuous and discrete approaches are highly competent, and that SALAD achieves a superior intelligibility score while obtaining speech quality and speaker similarity on par with the ground-truth audio.

F5 made a splash. This is a bit more complicated but also a better version (more reasonable audio codec for example)

https://maskgct.github.io

MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer

Yuancheng Wang, Haoyue Zhan, Liwei Liu, Ruihong Zeng, Haotian Guo, Jiachen Zheng, Qiang Zhang, Xueyao Zhang, Shunsi Zhang, Zhizheng Wu

The recent large-scale text-to-speech (TTS) systems are usually grouped as autoregressive and non-autoregressive systems. The autoregressive systems implicitly model duration but exhibit certain deficiencies in robustness and lack of duration controllability. Non-autoregressive systems require explicit alignment information between text and speech during training and predict durations for linguistic units (e.g. phone), which may compromise their naturalness. In this paper, we introduce Masked Generative Codec Transformer (MaskGCT), a fully non-autoregressive TTS model that eliminates the need for explicit alignment information between text and speech supervision, as well as phone-level duration prediction. MaskGCT is a two-stage model: in the first stage, the model uses text to predict semantic tokens extracted from a speech self-supervised learning (SSL) model, and in the second stage, the model predicts acoustic tokens conditioned on these semantic tokens. MaskGCT follows the mask-and-predict learning paradigm. During training, MaskGCT learns to predict masked semantic or acoustic tokens based on given conditions and prompts. During inference, the model generates tokens of a specified length in a parallel manner. Experiments with 100K hours of in-the-wild speech demonstrate that MaskGCT outperforms the current state-of-the-art zero-shot TTS systems in terms of quality, similarity, and intelligibility. Audio samples are available at this https URL. We release our code and model checkpoints at this https URL.

"We don't want 200ms latency, that's just not useful"

Will Williams is CTO of Speechmatics in Cambridge. In this sponsored episode - he shares deep technical insights into modern speech recognition technology and system architecture. The episode covers several key technical areas:

Speechmatics' hybrid approach to ASR, which focusses on unsupervised learning methods, achieving comparable results with 100x less data than fully supervised approaches. Williams explains why this is more efficient and generalizable than end-to-end models like Whisper.

Their production architecture implementing multiple operating points for different latency-accuracy trade-offs, with careful latency padding (up to 1.8 seconds) to ensure consistent user experience. The system uses lattice-based decoding with language model integration for improved accuracy.

The challenges and solutions in real-time ASR, including their approach to diarization (speaker identification), handling cross-talk, and implicit source separation. Williams explains why these problems remain difficult even with modern deep learning approaches.

Their testing and deployment infrastructure, including the use of mirrored environments for catching edge cases in production, and their strategy of maintaining global models rather than allowing customer-specific fine-tuning.

Technical evolution in ASR, from early days of custom CUDA kernels and manual memory management to modern frameworks, with Williams offering interesting critiques of current PyTorch memory management approaches and arguing for more efficient direct memory allocation in production systems.

https://www.youtube.com/watch?v=k6eXkBtYIHg

https://twitter.com/SamueleCornell/status/1849115845516984758

https://arxiv.org/abs/2408.09215

Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition
Samuele Cornell, Jordan Darefsky, Zhiyao Duan, Shinji Watanabe

Currently, a common approach in many speech processing tasks is to leverage large scale pre-trained models by fine-tuning them on in-domain data for a particular application. Yet obtaining even a small amount of such data can be problematic, especially for sensitive domains and conversational speech scenarios, due to both privacy issues and annotation costs. To address this, synthetic data generation using single speaker datasets has been employed. Yet, for multi-speaker cases, such an approach often requires extensive manual effort and is prone to domain mismatches. In this work, we propose a synthetic data generation pipeline for multi-speaker conversational ASR, leveraging a large language model (LLM) for content creation and a conversational multi-speaker text-to-speech (TTS) model for speech synthesis. We conduct evaluation by fine-tuning the Whisper ASR model for telephone and distant conversational speech settings, using both in-domain data and generated synthetic data. Our results show that the proposed method is able to significantly outperform classical multi-speaker generation approaches that use external, non-conversational speech datasets.

Some notes on Speechmatics interview:

Latency should be dynamic, modern advertising about small latency is not reasonable, but dynamic context-dependent latency is a thing. AudioLLMs enable that.

Lattices are not the optimal way of representation of the search space if you have may aspects of speech (emotion, etc). Vectorized representations suit GPU better, more compact and learnable. By using lattices we have some control over results but restrict ourselves at the same time.

Wav2vec-like learning Speechmatics uses is 100x faster but at the same time it is very hard to learn long distribution tail without lexical information just from the audio. Semi-supervised learning or full e2e approach definitely have an advantage.

Continuous learning (active inference) is something to think about more actively, yes, something very important for the future.

We released new Vosk models for Persian, WER improved significantly

https://alphacephei.com/vosk/models/vosk-model-fa-0.42.zip
https://alphacephei.com/vosk/models/vosk-model-small-fa-0.42.zip

For more details see

https://github.com/alphacep/awesome-speech/blob/main/persian.md#asr-results

Name speaks for itself

https://github.com/yakami129/VirtualWife

https://arxiv.org/abs/2410.18908

A Survey on Speech Large Language Models

Jing Peng, Yucheng Wang, Yu Xi, Xu Li, Xizhuo Zhang, Kai Yu

Large Language Models (LLMs) exhibit strong contextual understanding and remarkable multi-task performance. Therefore, researchers have been seeking to integrate LLMs in the broad sense of Spoken Language Understanding (SLU) field. Different from the traditional method of cascading LLMs to process text generated by Automatic Speech Recognition(ASR), new efforts have focused on designing architectures centered around Audio Feature Extraction - Multimodal Information Fusion - LLM Inference(Speech LLMs). This approach enables richer audio feature extraction while simultaneously facilitating end-to-end fusion of audio and text modalities, thereby achieving deeper understanding and reasoning from audio data. This paper elucidates the development of Speech LLMs, offering an in-depth analysis of system architectures and training strategies. Through extensive research and a series of targeted experiments, the paper assesses Speech LLMs' advancements in Rich Audio Transcription and its potential for Cross-task Integration within the SLU field. Additionally, it indicates key challenges uncovered through experimentation, such as the Dormancy of LLMs under certain conditions. The paper further delves into the training strategies for Speech LLMs, proposing potential solutions based on these findings, and offering valuable insights and references for future research in this domain, as well as LLM applications in multimodal contexts.

Nice paper with few interesting details. Extra CTC head for Whisper stabilization is interesting for example.

https://arxiv.org/abs/2409.09543

Target Speaker ASR with Whisper

Alexander Polok, Dominik Klement, Matthew Wiesner, Sanjeev Khudanpur, Jan Černocký, Lukáš Burget

We propose a novel approach to enable the use of large, single speaker ASR models, such as Whisper, for target speaker ASR. The key insight of this method is that it is much easier to model relative differences among speakers by learning to condition on frame-level diarization outputs, than to learn the space of all speaker embeddings. We find that adding even a single bias term per diarization output type before the first transformer block can transform single speaker ASR models, into target speaker ASR models. Our target-speaker ASR model can be used for speaker attributed ASR by producing, in sequence, a transcript for each hypothesized speaker in a diarization output. This simplified model for speaker attributed ASR using only a single microphone outperforms cascades of speech separation and diarization by 11% absolute ORC-WER on the NOTSOFAR-1 dataset.

Even with our new speech codec, producing a 2-minute dialogue requires generating over 5000 tokens. To model these long sequences, we developed a specialized Transformer architecture that can efficiently handle hierarchies of information, matching the structure of our acoustic tokens.

https://deepmind.google/discover/blog/pushing-the-frontiers-of-audio-generation/

Fish Agent V0.1 3B is a groundbreaking Voice-to-Voice model capable of capturing and generating environmental audio information with unprecedented accuracy. What sets it apart is its semantic-token-free architecture, eliminating the need for traditional semantic encoders/decoders like Whisper and CosyVoice.

Additionally, it stands as a state-of-the-art text-to-speech (TTS) model, trained on an extensive dataset of 700,000 hours of multilingual audio content.

This model is a continue-pretrained version of Qwen-2.5-3B-Instruct for 200B voice & text tokens.

https://huggingface.co/fishaudio/fish-agent-v0.1-3b