facebook/seamless-m4t-v2-large

automatic-speech-recognition model by facebook

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SeamlessM4T is our foundational all-in-one Massively Multilingual and Multimodal Machine Translation model delivering high-quality translation for speech and text in nearly 100 languages. SeamlessM4T models support the tasks of: SeamlessM4T models support: 🌟 We are releasing SeamlessM4T v2, an updated version with our novel UnitY2 architecture. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks. The v2 version of SeamlessM4T is a multitask adaptation of our novel UnitY2 architecture. Unity2 with its hierarchical character-to-unit upsampling and non-autoregressive text-to-unit decoding considerably improves over SeamlessM4T v1 in quality and inference speed. SeamlessM4T v2 is also supported by 🤗 Transformers, more on it in the dedicated section below. We provide the extensive evaluation results of seamlessM4T-Large and SeamlessM4T-Medium reported in the paper (as averages) in the metrics files above. The evaluation data ids for FLEURS, CoVoST2 and CVSS-C can be found here To reproduce…

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Model overview

facebook/seamless-m4t-v2-large is a automatic-speech-recognition model repository published by facebook on Hugging Face. The source reports the transformers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: automatic-speech-recognition
Library: transformers
Recent downloads (30 days): 304,760
Cumulative likes: 1,014
Hugging Face trending score: 3
Reported safetensors parameters: 2,309,249,669
Architecture: SeamlessM4Tv2Model
License: cc-by-nc-4.0
Access: Not gated by Hugging Face
Created: 2023-11-29T14:37:04.000Z
Last modified: 2024-01-04T12:48:26.000Z

Compatibility and lineage

Reported languages: af, am, ar, as, az, be, bn, bs, bg, ca, cs, zh, cy, da, de, el, en, et, fi, fr, or, om, ga, gl, gu, ha, he, hi, hr, hu, hy, ig, id, is, it, jv, ja, kn, ka, kk

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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