Thats something new:
https://arxiv.org/abs/2301.08730
Novel-View Acoustic Synthesis
Changan Chen, Alexander Richard, Roman Shapovalov, Vamsi Krishna Ithapu, Natalia Neverova, Kristen Grauman, Andrea Vedaldi
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint? We propose a neural rendering approach: Visually-Guided Acoustic Synthesis (ViGAS) network that learns to synthesize the sound of an arbitrary point in space by analyzing the input audio-visual cues. To benchmark this task, we collect two first-of-their-kind large-scale multi-view audio-visual datasets, one synthetic and one real. We show that our model successfully reasons about the spatial cues and synthesizes faithful audio on both datasets. To our knowledge, this work represents the very first formulation, dataset, and approach to solve the novel-view acoustic synthesis task, which has exciting potential applications ranging from AR/VR to art and design. Unlocked by this work, we believe that the future of novel-view synthesis is in multi-modal learning from videos.
https://arxiv.org/abs/2301.08730
Novel-View Acoustic Synthesis
Changan Chen, Alexander Richard, Roman Shapovalov, Vamsi Krishna Ithapu, Natalia Neverova, Kristen Grauman, Andrea Vedaldi
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint? We propose a neural rendering approach: Visually-Guided Acoustic Synthesis (ViGAS) network that learns to synthesize the sound of an arbitrary point in space by analyzing the input audio-visual cues. To benchmark this task, we collect two first-of-their-kind large-scale multi-view audio-visual datasets, one synthetic and one real. We show that our model successfully reasons about the spatial cues and synthesizes faithful audio on both datasets. To our knowledge, this work represents the very first formulation, dataset, and approach to solve the novel-view acoustic synthesis task, which has exciting potential applications ranging from AR/VR to art and design. Unlocked by this work, we believe that the future of novel-view synthesis is in multi-modal learning from videos.
https://sites.google.com/view/merlion-ccs-challenge/
About
The inaugural MERLIon CCS Challenge focuses on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom
About
The inaugural MERLIon CCS Challenge focuses on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom
Respected guys
https://arxiv.org/abs/2301.13341
Neural Target Speech Extraction: An Overview
Katerina Zmolikova, Marc Delcroix, Tsubasa Ochiai, Keisuke Kinoshita, Jan Černocký, Dong Yu
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail-party effect. For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and non-target speech signals share similar characteristics, complicating their discrimination. Target speech/speaker extraction (TSE) isolates the speech signal of a target speaker from a mixture of several speakers with or without noises and reverberations using clues that identify the speaker in the mixture. Such clues might be a spatial clue indicating the direction of the target speaker, a video of the speaker's lips, or a pre-recorded enrollment utterance from which their voice characteristics can be derived. TSE is an emerging field of research that has received increased attention in recent years because it offers a practical approach to the cocktail-party problem and involves such aspects of signal processing as audio, visual, array processing, and deep learning. This paper focuses on recent neural-based approaches and presents an in-depth overview of TSE. We guide readers through the different major approaches, emphasizing the similarities among frameworks and discussing potential future directions.
https://arxiv.org/abs/2301.13341
Neural Target Speech Extraction: An Overview
Katerina Zmolikova, Marc Delcroix, Tsubasa Ochiai, Keisuke Kinoshita, Jan Černocký, Dong Yu
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail-party effect. For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and non-target speech signals share similar characteristics, complicating their discrimination. Target speech/speaker extraction (TSE) isolates the speech signal of a target speaker from a mixture of several speakers with or without noises and reverberations using clues that identify the speaker in the mixture. Such clues might be a spatial clue indicating the direction of the target speaker, a video of the speaker's lips, or a pre-recorded enrollment utterance from which their voice characteristics can be derived. TSE is an emerging field of research that has received increased attention in recent years because it offers a practical approach to the cocktail-party problem and involves such aspects of signal processing as audio, visual, array processing, and deep learning. This paper focuses on recent neural-based approaches and presents an in-depth overview of TSE. We guide readers through the different major approaches, emphasizing the similarities among frameworks and discussing potential future directions.
https://twitter.com/alphacep/status/1621612504840273928
NeMo 1.15 is out now! Theres a whole bunch of powerful ASR features added in this release including Hybrid CTC-RNNT models, Multiblank Transducer, Multi Head Attention Adapters, Conformer longformer inference, and a Beam Search API!
First, we dicuss Hybrid CTC-RNNT models. We can train a single model with both losses, and then perform inference with either decoder. It turns out, we can attain better CTC results, and converge 40-50% faster for CTC head when jointly trained.
Next up, we have Multiblank Transducers supported in NeMo. It is an extension of RNNT loss - in which tokens can jump multiple timesteps per predicted token, allowing for highly efficient inference - even at sample level ! Refer to the paper here
With this change, you can now easily train a multi blank RNNT model and obtain better WER but also much faster inference than regular RNNT models.
Next up, we now support Multi Head Attention Adapters in NeMo ASR. With this approach, now any NeMo module can be retrofitted into an adapter module. We see significant parameter efficiency when compared to Houlsby Adapter. With the newly updated scripts for adapter training, we can now easily train either Linear adapters or MHA adapters from the same script. More details can be found in the PR
Long form audio transcription has long been a challange for Conformer based ASR models, because of the attention component. So we now support Longformer based transcriptions - even for pre-trained models ! You can use the transcribe_speech script for this! We find that if you further finetune the model after conversion to Longformer attention, you can recover most of the WER and still get excellent long audio transcription of up to 30-40 minutes in one shot forward pass.
A long-asked feature is to support beam search in NeMo ASR in a easy to use way. So we unified the way we do CTC beam search with external libraries with the simple
We also begin support for AIStore as a framework for terabyte-scale datasets as a scalable solution to train ASR models on enormous real world datasets.
NeMo 1.15 is out now! Theres a whole bunch of powerful ASR features added in this release including Hybrid CTC-RNNT models, Multiblank Transducer, Multi Head Attention Adapters, Conformer longformer inference, and a Beam Search API!
First, we dicuss Hybrid CTC-RNNT models. We can train a single model with both losses, and then perform inference with either decoder. It turns out, we can attain better CTC results, and converge 40-50% faster for CTC head when jointly trained.
Next up, we have Multiblank Transducers supported in NeMo. It is an extension of RNNT loss - in which tokens can jump multiple timesteps per predicted token, allowing for highly efficient inference - even at sample level ! Refer to the paper here
With this change, you can now easily train a multi blank RNNT model and obtain better WER but also much faster inference than regular RNNT models.
Next up, we now support Multi Head Attention Adapters in NeMo ASR. With this approach, now any NeMo module can be retrofitted into an adapter module. We see significant parameter efficiency when compared to Houlsby Adapter. With the newly updated scripts for adapter training, we can now easily train either Linear adapters or MHA adapters from the same script. More details can be found in the PR
Long form audio transcription has long been a challange for Conformer based ASR models, because of the attention component. So we now support Longformer based transcriptions - even for pre-trained models ! You can use the transcribe_speech script for this! We find that if you further finetune the model after conversion to Longformer attention, you can recover most of the WER and still get excellent long audio transcription of up to 30-40 minutes in one shot forward pass.
A long-asked feature is to support beam search in NeMo ASR in a easy to use way. So we unified the way we do CTC beam search with external libraries with the simple
model.transcribe() method! You can simply update the config, and then transcribe !We also begin support for AIStore as a framework for terabyte-scale datasets as a scalable solution to train ASR models on enormous real world datasets.
Twitter
RT @HaseoX94: NeMo 1.15 is out now! Theres a whole bunch of powerful ASR features added in this release including Hybrid CTC-RNNT models, M…
It is interesting that for things like NER for latest research Google returned to structured prediction instead of pure transformers
https://github.com/lyutyuh/ASP
https://arxiv.org/abs/2210.14698
Autoregressive Structured Prediction with Language Models
Tianyu Liu, Yuchen Jiang, Nicholas Monath, Ryan Cotterell, Mrinmaya Sachan
Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
However, there are many difficult design decisions to represent structures (e.g. tagged text, coreference chains) in a way such that they can be captured by PLMs. Prior work on structured prediction with PLMs typically flattens the structured output into a sequence, which limits the quality of structural information being learned and leads to inferior performance compared to classic discriminative models. In this work, we describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs, allowing in-structure dependencies to be learned without any loss.
Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at, namely, named entity recognition, end-to-end relation extraction, and coreference resolution.
https://github.com/lyutyuh/ASP
https://arxiv.org/abs/2210.14698
Autoregressive Structured Prediction with Language Models
Tianyu Liu, Yuchen Jiang, Nicholas Monath, Ryan Cotterell, Mrinmaya Sachan
Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
However, there are many difficult design decisions to represent structures (e.g. tagged text, coreference chains) in a way such that they can be captured by PLMs. Prior work on structured prediction with PLMs typically flattens the structured output into a sequence, which limits the quality of structural information being learned and leads to inferior performance compared to classic discriminative models. In this work, we describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs, allowing in-structure dependencies to be learned without any loss.
Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at, namely, named entity recognition, end-to-end relation extraction, and coreference resolution.
GitHub
GitHub - lyutyuh/ASP: PyTorch implementation and pre-trained models for ASP - Autoregressive Structured Prediction with Language…
PyTorch implementation and pre-trained models for ASP - Autoregressive Structured Prediction with Language Models, EMNLP 22. https://arxiv.org/pdf/2210.14698.pdf - lyutyuh/ASP
https://twitter.com/DrJimFan/status/1622276293776793600
Looks like many of you are ready to embrace the Year of Sound Waves!
Here’s a big and OPEN dataset for you to get your hands dirty on AI audio modeling: EPIC-SOUNDS, 78k segments of annotated, audible events and actions.
Downloadable here: https://epic-kitchens.github.io/epic-sounds/
Looks like many of you are ready to embrace the Year of Sound Waves!
Here’s a big and OPEN dataset for you to get your hands dirty on AI audio modeling: EPIC-SOUNDS, 78k segments of annotated, audible events and actions.
Downloadable here: https://epic-kitchens.github.io/epic-sounds/
CMU pubs are nice. High quality TTS trained from Youtube
https://github.com/b04901014/MQTTS
https://arxiv.org/abs/2302.04215
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech
Li-Wei Chen, Shinji Watanabe, Alexander Rudnicky
Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness. The diversity of human speech, however, often goes beyond the coverage of these corpora. We believe the ability to handle such diversity is crucial for AI systems to achieve human-level communication. Our work explores the use of more abundant real-world data for building speech synthesizers. We train TTS systems using real-world speech from YouTube and podcasts. We observe the mismatch between training and inference alignments in mel-spectrogram based autoregressive models, leading to unintelligible synthesis, and demonstrate that learned discrete codes within multiple code groups effectively resolves this issue. We introduce our MQTTS system whose architecture is designed for multiple code generation and monotonic alignment, along with the use of a clean silence prompt to improve synthesis quality. We conduct ablation analyses to identify the efficacy of our methods. We show that MQTTS outperforms existing TTS systems in several objective and subjective measures.
https://github.com/b04901014/MQTTS
https://arxiv.org/abs/2302.04215
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech
Li-Wei Chen, Shinji Watanabe, Alexander Rudnicky
Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness. The diversity of human speech, however, often goes beyond the coverage of these corpora. We believe the ability to handle such diversity is crucial for AI systems to achieve human-level communication. Our work explores the use of more abundant real-world data for building speech synthesizers. We train TTS systems using real-world speech from YouTube and podcasts. We observe the mismatch between training and inference alignments in mel-spectrogram based autoregressive models, leading to unintelligible synthesis, and demonstrate that learned discrete codes within multiple code groups effectively resolves this issue. We introduce our MQTTS system whose architecture is designed for multiple code generation and monotonic alignment, along with the use of a clean silence prompt to improve synthesis quality. We conduct ablation analyses to identify the efficacy of our methods. We show that MQTTS outperforms existing TTS systems in several objective and subjective measures.
GitHub
GitHub - b04901014/MQTTS
Contribute to b04901014/MQTTS development by creating an account on GitHub.
It is interesting how quickly people implement ideas. Like the one of podcast transcript with Whisper. Here is a selection
https://podscript.ai/
https://podtext.ai/
https://podscription.app/
https://podsearch.page/
Discussion https://news.ycombinator.com/item?id=34727695
https://podscript.ai/
https://podtext.ai/
https://podscription.app/
https://podsearch.page/
Discussion https://news.ycombinator.com/item?id=34727695
https://github.com/openai/whisper/discussions/937
Whisper model in CTranslate2, which is a fast inference engine for Transformer models. The project supports many useful inference features such as CPU and GPU execution, asynchronous execution, multi-GPU execution, 8-bit quantization, etc.
You can find a usage example here.
Note that it does not currently implement the full transcription loop, only the
For example, here's the transcription time of 13 minutes of audio on a V100 for the same accuracy:
Implementation Time with "small" model Time with "medium" model
Baseline 1m37s 3m16s
CTranslate2 0m25s 0m42s
Whisper model in CTranslate2, which is a fast inference engine for Transformer models. The project supports many useful inference features such as CPU and GPU execution, asynchronous execution, multi-GPU execution, 8-bit quantization, etc.
You can find a usage example here.
Note that it does not currently implement the full transcription loop, only the
model.decode part. So you would still need to implement the transcription logic from transcribe.py on top of it (iterate on each 30-second window, accumulate the context in the prompt, etc.).For example, here's the transcription time of 13 minutes of audio on a V100 for the same accuracy:
Implementation Time with "small" model Time with "medium" model
Baseline 1m37s 3m16s
CTranslate2 0m25s 0m42s
GitHub
Accelerate the Whisper decoding with CTranslate2 · openai whisper · Discussion #937
Hello, We integrated the Whisper model in CTranslate2, which is a fast inference engine for Transformer models. The project implements many useful inference features such as optimized CPU and GPU e...