Speech Technology

A bit more data on cross-language codecs

https://github.com/jishengpeng/WavChat

https://arxiv.org/abs/2411.13577

WavChat: A Survey of Spoken Dialogue Models
Shengpeng Ji, Yifu Chen, Minghui Fang, Jialong Zuo, Jingyu Lu, Hanting Wang, Ziyue Jiang, Long Zhou, Shujie Liu, Xize Cheng, Xiaoda Yang, Zehan Wang, Qian Yang, Jian Li, Yidi Jiang, Jingzhen He, Yunfei Chu, Jin Xu, Zhou Zhao
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. Compared to traditional three-tier cascaded spoken dialogue models that comprise speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS), modern spoken dialogue models exhibit greater intelligence. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Moreover, they generate high-quality, multi-turn speech responses with low latency, enabling real-time interaction through simultaneous listening and speaking capability. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems and the underlying technologies. To address this, we have first compiled existing spoken dialogue systems in the chronological order and categorized them into the cascaded and end-to-end paradigms. We then provide an in-depth overview of the core technologies in spoken dialogue models, covering aspects such as speech representation, training paradigm, streaming, duplex, and interaction capabilities. Each section discusses the limitations of these technologies and outlines considerations for future research. Additionally, we present a thorough review of relevant datasets, evaluation metrics, and benchmarks from the perspectives of training and evaluating spoken dialogue systems. We hope this survey will contribute to advancing both academic research and industrial applications in the field of spoken dialogue systems. The related material is available at this https URL.

SANE 2024 Videos, interesting things

https://www.youtube.com/playlist?list=PLBJWRPcgwk7vVzKLPnTrqm831VohoLMmy

https://www.youtube.com/watch?v=pRUrO0x637A

https://github.com/john852517791/awesome-fake-audio-detection

Some numbers for codec quality for Russian audio dataset

BigVGAN2 is good, but very slow (112M parameters). MEL-Vocos is not perfect. Encodec-Vocos is probably good.

Should we test something else like SNAC?

https://github.com/fixie-ai/ultravox/releases/tag/v0.4.1

Apple's papers are always very practical. This one is also good, many in-depth experiments and practical cases. Note that biasing effect is minimal (usually WER goes down a little 17% -> 15%).

https://arxiv.org/abs/2411.00664

Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval

Nikolaos Flemotomos, Roger Hsiao, Pawel Swietojanski, Takaaki Hori, Dogan Can, Xiaodan Zhuang

Neural contextual biasing allows speech recognition models to leverage contextually relevant information, leading to improved transcription accuracy. However, the biasing mechanism is typically based on a cross-attention module between the audio and a catalogue of biasing entries, which means computational complexity can pose severe practical limitations on the size of the biasing catalogue and consequently on accuracy improvements. This work proposes an approximation to cross-attention scoring based on vector quantization and enables compute- and memory-efficient use of large biasing catalogues. We propose to use this technique jointly with a retrieval based contextual biasing approach. First, we use an efficient quantized retrieval module to shortlist biasing entries by grounding them on audio. Then we use retrieved entries for biasing. Since the proposed approach is agnostic to the biasing method, we investigate using full cross-attention, LLM prompting, and a combination of the two. We show that retrieval based shortlisting allows the system to efficiently leverage biasing catalogues of several thousands of entries, resulting in up to 71% relative error rate reduction in personal entity recognition. At the same time, the proposed approximation algorithm reduces compute time by 20% and memory usage by 85-95%, for lists of up to one million entries, when compared to standard dot-product cross-attention.

It is simply bad

https://arxiv.org/abs/2411.03866

Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward

Shashi Kumar, Iuliia Thorbecke, Sergio Burdisso, Esaú Villatoro-Tello, Manjunath K E, Kadri Hacioğlu, Pradeep Rangappa, Petr Motlicek, Aravind Ganapathiraju, Andreas Stolcke

Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions, such as domain shifts and different speech perturbations. In this paper, we address these questions by conducting various ablation experiments using a recent and widely adopted approach called SLAM-ASR. We present novel empirical findings that offer insights on how to effectively utilize the SLAM-ASR architecture across a wide range of settings. Our main findings indicate that the SLAM-ASR exhibits poor performance in cross-domain evaluation settings. Additionally, speech perturbations within in-domain data, such as changes in speed or the presence of additive noise, can significantly impact performance. Our findings offer critical insights for fine-tuning and configuring robust LLM-based ASR models, tailored to different data characteristics and computational resources.

Overall, we find no evidence that multiscale aspects of MR-HuBERT lead to improved acquisition of high level concepts. The question now is how to build an architecture that does leverage this hierarchy?🤔 (4/5)

https://twitter.com/theo_clark_/status/1852299593272131874

https://arxiv.org/abs/2410.23955

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

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/

https://huggingface.co/nvidia/stt_uz_fastconformer_hybrid_large_pc

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.

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.

Name speaks for itself

https://github.com/yakami129/VirtualWife

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

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.

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.

"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

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.

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.

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...

https://petewarden.com/2024/10/21/introducing-moonshine-the-new-state-of-the-art-for-speech-to-text/

Meta shared their SpiritLM

https://github.com/facebookresearch/spiritlm

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

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

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

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.

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}}.

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.

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

5Hz tokenization for better performance of speech LM

SyllableLM

https://twitter.com/BaadeAlan/status/1844148297562538479

Q: Why can't we get GPT-level understanding from language models on speech?
A: We need better speech tokens!

SyllableLM beats kyutai_labs Moshi on semantic understanding in 70 hours of training by making speech tokens at 5 frames/s

https://github.com/AlanBaade/SyllableLM
https://arxiv.org/abs/2410.04029

SyllableLM: Learning Coarse Semantic Units for Speech Language Models

Alan Baade, Puyuan Peng, David Harwath

Language models require tokenized inputs. However, tokenization strategies for continuous data like audio and vision are often based on simple heuristics such as fixed sized convolutions or discrete clustering, which do not necessarily align with the semantic structure of the data. For speech in particular, the high resolution of waveforms (16,000 samples/second or more) presents a significant challenge as speech-based language models have had to use several times more tokens per word than text-based language models. In this work, we introduce a controllable self-supervised technique to merge speech representations into coarser syllable-like units while still preserving semantic information. We do this by 1) extracting noisy boundaries through analyzing correlations in pretrained encoder losses and 2) iteratively improving model representations with a novel distillation technique. Our method produces controllable-rate semantic units at as low as 5Hz and 60bps and achieves SotA in syllabic segmentation and clustering. Using these coarse tokens, we successfully train SyllableLM, a Speech Language Model (SpeechLM) that matches or outperforms current SotA SpeechLMs on a range of spoken language modeling tasks. SyllableLM also achieves significant improvements in efficiency with a 30x reduction in training compute and a 4x wall-clock inference speedup.

https://github.com/SWivid/F5-TTS

A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching

This paper introduces F5-TTS, a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT). Without requiring complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then the denoising is performed for speech generation, which was originally proved feasible by E2 TTS. However, the original design of E2 TTS makes it hard to follow due to its slow convergence and low robustness. To address these issues, we first model the input with ConvNeXt to refine the text representation, making it easy to align with the speech. We further propose an inference-time Sway Sampling strategy, which significantly improves our model’s performance and efficiency. This sampling strategy for flow step can be easily applied to existing flow matching based models without retraining. Our design allows faster training and achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based TTS models. Trained on a public 100K hours multilingual dataset, our Fairytaler Fakes Fluent and Faithful speech with Flow matching (F5-TTS) exhibits highly natural and expressive zero-shot ability, seamless code-switching capability, and speed control efficiency. Demo samples can be found at https://SWivid.github.io/F5-TTS. We will release all code and checkpoints to promote community development.

This is an important paper for training audio LLM. One can keep LM aligned with synthetic TTS data.

https://arxiv.org/abs/2309.00916

BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing

Chen Wang, Minpeng Liao, Zhongqiang Huang, Jinliang Lu, Junhong Wu, Yuchen Liu, Chengqing Zong, Jiajun Zhang

The emergence of large language models (LLMs) has sparked significant interest in extending their remarkable language capabilities to speech. However, modality alignment between speech and text still remains an open problem. Current solutions can be categorized into two strategies. One is a cascaded approach where outputs (tokens or states) of a separately trained speech recognition system are used as inputs for LLMs, which limits their potential in modeling alignment between speech and text. The other is an end-to-end approach that relies on speech instruction data, which is very difficult to collect in large quantities. In this paper, we address these issues and propose the BLSP approach that Bootstraps Language-Speech Pre-training via behavior alignment of continuation writing. We achieve this by learning a lightweight modality adapter between a frozen speech encoder and an LLM, ensuring that the LLM exhibits the same generation behavior regardless of the modality of input: a speech segment or its transcript. The training process can be divided into two steps. The first step prompts an LLM to generate texts with speech transcripts as prefixes, obtaining text continuations. In the second step, these continuations are used as supervised signals to train the modality adapter in an end-to-end manner. We demonstrate that this straightforward process can extend the capabilities of LLMs to speech, enabling speech recognition, speech translation, spoken language understanding, and speech conversation, even in zero-shot cross-lingual scenarios.

Also

https://arxiv.org/abs/2405.19041

BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation
Chen Wang, Minpeng Liao, Zhongqiang Huang, Jiajun Zhang
Recent end-to-end approaches have shown promise in extending large language models (LLMs) to speech inputs, but face limitations in directly assessing and optimizing alignment quality and fail to achieve fine-grained alignment due to speech-text length mismatch. We introduce BLSP-KD, a novel approach for Bootstrapping Language-Speech Pretraining via Knowledge Distillation, which addresses these limitations through two key techniques. First, it optimizes speech-text alignment by minimizing the divergence between the LLM's next-token prediction distributions for speech and text inputs using knowledge distillation. Second, it employs a continuous-integrate-andfire strategy to segment speech into tokens that correspond one-to-one with text tokens, enabling fine-grained alignment. We also introduce Partial LoRA (PLoRA), a new adaptation method supporting LLM finetuning for speech inputs under knowledge distillation. Quantitative evaluation shows that BLSP-KD outperforms previous end-to-end baselines and cascaded systems with comparable scale of parameters, facilitating general instruction-following capabilities for LLMs with speech inputs. This approach provides new possibilities for extending LLMs to spoken language interactions.

SLM adversarial training in styletts is not that useful (we confirmed that on our experiments too). Spectral loss is enough

Its fun how much compute resources were spent on it

Interesting that some teams still claimed it is useful, for example here in FlashSpeech https://arxiv.org/abs/2404.14700. I suppose they used only simple discriminators.

The more LLMs out there the more data leaks. A good example is librispeech asr accuracy of LLM is usually very good given LLM definitely saw all book texts before. Another example here

https://arxiv.org/abs/2409.04927

Just ASR + LLM? A Study on Speech Large Language Models' Ability to Identify and Understand Speaker in Spoken Dialogue

Junkai Wu, Xulin Fan, Bo-Ru Lu, Xilin Jiang, Nima Mesgarani, Mark Hasegawa-Johnson, Mari Ostendorf

In recent years, we have observed a rapid advancement in speech language models (SpeechLLMs), catching up with humans' listening and reasoning abilities. SpeechLLMs have demonstrated impressive spoken dialog question-answering (SQA) performance in benchmarks like Gaokao, the English listening test of the college entrance exam in China, which seemingly requires understanding both the spoken content and voice characteristics of speakers in a conversation. However, after carefully examining Gaokao's questions, we find the correct answers to many questions can be inferred from the conversation transcript alone, i.e.\ without speaker segmentation and identification. Our evaluation of state-of-the-art models Qwen-Audio and WavLLM on both Gaokao and our proposed "What Do You Like?" dataset shows a significantly higher accuracy in these context-based questions than in identity-critical questions, which can only be answered reliably with correct speaker identification. The results and analysis suggest that when solving SQA, the current SpeechLLMs exhibit limited speaker awareness from the audio and behave similarly to an LLM reasoning from the conversation transcription without sound. We propose that tasks focused on identity-critical questions could offer a more accurate evaluation framework of SpeechLLMs in SQA.

https://arxiv.org/abs/2408.13106

NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech Processing Tasks

He Huang, Taejin Park, Kunal Dhawan, Ivan Medennikov, Krishna C. Puvvada, Nithin Rao Koluguri, Weiqing Wang, Jagadeesh Balam, Boris Ginsburg

Self-supervised learning has been proved to benefit a wide range of speech processing tasks, such as speech recognition/translation, speaker verification and diarization, etc. However, most of current approaches are computationally expensive. In this paper, we propose a simplified and more efficient self-supervised learning framework termed as NeMo Encoder for Speech Tasks (NEST). Specifically, we adopt the FastConformer architecture with 8x sub-sampling rate, which is faster than Transformer or Conformer architectures. Instead of clustering-based quantization, we use fixed random projection for its simplicity and effectiveness. We also implement a generalized noisy speech augmentation that teaches the model to disentangle the main speaker from noise or other speakers. Experiments show that \model improves over existing self-supervised models and achieves new state-of-the-art performance on a variety of speech processing tasks, such as speech recognition/translation, speaker diarization, spoken language understanding, etc. Code and checkpoints will be publicly available via NVIDIA NeMo framework.