seamless_communication VS tortoise-tts

Meta AI introduced a suite of AI language translation models that preserve expression and improve streaming [Details | GitHub]: SeamlessExpressive enables the transfer of tones, emotional expression and vocal styles in speech translation. You can try a demo of SeamlessExpressive using your own voice as an input here. SeamlessStreaming, a new model that enables streaming speech-to-speech and speech-to-text translations with <2 seconds of latency and nearly the same accuracy as an offline model. In contrast to conventional systems which translate when the speaker has finished their sentence, SeamlessStreaming translates while the speaker is still talking. t intelligently decides when it has enough context to output the next translated segment. SeamlessM4T v2, a foundational multilingual & multitask model for both speech & text. It's the successor to SeamlessM4T, demonstrating performance improvements across ASR, speech-to-speech, speech-to-text & text-to-speech tasks. Seamless, a model that merges capabilities from SeamlessExpressive, SeamlessStreaming and SeamlessM4T v2 into one.

The license details are listed on the project GitHub

https://github.com/facebookresearch/seamless_communication#l...

facebookresearch/seamless_communication: Speech translation

I wish that Meta would release models like SeamlessM4T[0] under the same license as llama, or an even better one.

There seem to be opportunities for people to use technology like this to improve lives, if it were licensed correctly, but I don't see how any commercial offering would compete with anything that Meta does.

Whisper is licensed more permissively and does a great job with speech to text in some languages, and it can translate to English only, but it can't translate between a large number of languages, and it doesn't have any kind of text to speech or speech to speech capabilities.

[0]: https://github.com/facebookresearch/seamless_communication

What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides. More specifically, conventional speech-to-speech translation systems rely on cascaded systems composed of multiple subsystems performing translation progressively, putting scalable and high-performing unified speech translation systems out of reach. To address these gaps, we introduce SeamlessM4T—Massively Multilingual & Multimodal Machine Translation—a single model that supports speech- to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic speech recognition for up to 100 languages. To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations, dubbed SeamlessAlign. Filtered and combined with human- labeled and pseudo-labeled data (totaling 406,000 hours), we developed the first multilingual system capable of translating from and into English for both speech and text. On Fleurs, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous state-of-the-art in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech. On CVSS and compared to a 2-stage cascaded model for speech- to-speech translation, SeamlessM4T-Large’s performance is stronger by 58%. Preliminary human evaluations of speech-to-text translation outputs evinced similarly impressive results; for translations from English, XSTS scores for 24 evaluated languages are consistently above 4 (out of 5). For into English directions, we see significant improvement over Whisper- Large-v2’s baseline for 7 out of 24 languages. To further evaluate our system, we developed Blaser 2.0, which enables evaluation across speech and text with similar accuracy compared to its predecessor when it comes to quality estimation. Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks (average improvements of 38% and 49%, respectively) compared to the current state-of-the-art model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety. Compared to the state-of-the-art, we report up to 63% of reduction in added toxicity in our translation outputs. Finally, all contributions in this work—including models, inference code, finetuning recipes backed by our improved modeling toolkit Fairseq2, and metadata to recreate the unfiltered 470,000 hours of SeamlessAlign —are open-sourced and accessible at https://github.com/facebookresearch/seamless_communication

281M and 235M param models too.

https://github.com/facebookresearch/seamless_communication/b...

I don't really know how the metrics they list compare to whisper, I'm very curious if these are fast enough for realtime speech2text? I think whisper technically could but it was difficult to do or something like that?

code: https://github.com/facebookresearch/seamless_communication

paper: https://ai.meta.com/research/publications/seamless-m4t/

demo: https://seamless.metademolab.com/

The quality also depends on the type of model. I'm not really sure what ESpeak-ng actually uses? The classical TTS approaches often use some statistical model (e.g. HMM) + some vocoder. You can get to intelligible speech pretty easily but the quality is bad (w.r.t. how natural it sounds).

There are better open source TTS models. E.g. check https://github.com/neonbjb/tortoise-tts or https://github.com/NVIDIA/tacotron2. Or here for more: https://www.reddit.com/r/MachineLearning/comments/12kjof5/d_...

I use Tortoise TTS. It's slow, a little clunky, and sometimes the output gets downright weird. But it's the best quality-oriented TTS I've found that I can run locally.

https://github.com/neonbjb/tortoise-tts

neonbjb/tortoise-tts

EDIT: It appears to be the exact same issue as this

It was primarily being used to train TTS models (see https://github.com/neonbjb/tortoise-tts), which largely fit into a single GPUs memory. So, for data parallelism, x8 PCIe isn't that much of a concern.

Previously TortoiseTTS was associated with PlayHT in some way, although the exact connection is a bit vague [0].

From the descriptions here it sounds a lot like AudioLM / SPEAR TTS / some of Meta's recent multilingual TTS approaches, although those models are not open source, sounds like PlayHT's approach is in a similar spirit. The discussion of "mel tokens" is closer to what I would call the classic TTS pipeline in many ways... PlayHT has generally been kind of closed about what they used, would be interesting to know more.

I assume the key factor here is high quality, emotive audio with good data cleaning processes. Probably not even a lot of data, at least in the scale of "a lot" in speech, e.g. ASR (millions of hours) or TTS (hundreds to thousands). As opposed to some radically new architectural piece never before seen in the literature, there are lots of really nice tools for emotive and expressive TTS buried in recent years of publications.

Tacotron 2 is perfectly capable of this type of stuff as well, as shown by Dessa [1] a few years ago (this writeup is a nice intro to TTS concepts). With the limit largely being, at some point you haven't heard certain phonetic sounds before in a voice, and need to do something to get plausible outcomes for new voices.

[0] Discussion here https://github.com/neonbjb/tortoise-tts/issues/182#issuecomm...

[1] https://medium.com/dessa-news/realtalk-how-it-works-94c1afda...

Tortoise GitHub repo - Source code, documentation, and usage guide