facebook/hf-seamless-m4t-medium

text-to-speech model by facebook

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SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. This repository hosts 🤗 Hugging Face's implementation of SeamlessM4T. You can find the original weights, as well as a guide on how to run them in the original hub repositories (large and medium checkpoints). 🌟 SeamlessM4T v2, an improved version of this version with a novel architecture, has been released here. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks. SeamlessM4T v2 is also supported by 🤗 Transformers, more on it in the model card of this new version or directly in 🤗 Transformers docs. SeamlessM4T Medium covers: This is the "medium" variant of the unified model, which enables multiple tasks without relying on multiple separate models: You can perform all the above tasks from one…

facebook/hf-seamless-m4t-medium on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-11-01T12:35:44.119Z

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  1. SeamlessM4T Medium
  2. 🤗 Usage
  3. Speech
  4. Text
  5. Tips
  6. 1. Use dedicated models
  7. 2. Change the speaker identity
  8. 3. Change the generation strategy
  9. 4. Generate speech and text at the same time

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Model overview

facebook/hf-seamless-m4t-medium is a text-to-speech 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
text-to-speech
Library
transformers
Recent downloads (30 days)
13,863
Cumulative likes
35
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
SeamlessM4TModel
License
cc-by-nc-4.0
Access
Not gated by Hugging Face
Created
2023-08-28T16:30:57.000Z
Last modified
2023-12-08T11:47:26.000Z

Compatibility and lineage

Language coverage was not reported.

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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Source: Hugging Face Hub. Metadata observed 2026-10-28T06:49:49.533Z. Daily imports are snapshots, not real-time monitoring.

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