# Seamless Communication — Multilingual Speech and Text Translation by Meta

> Seamless Communication is Meta's suite of foundation models for speech-to-speech, speech-to-text, text-to-speech, and text-to-text translation across nearly 100 languages with expressive and streaming capabilities.

## Install

Save in your project root:

# Seamless Communication — Multilingual Speech and Text Translation by Meta

## Quick Use
```bash
pip install fairseq2
python -c "
from seamless_communication.inference import Translator
translator = Translator('seamlessM4T_v2_large', vocoder_name_or_card='vocoder_v2', device='cuda:0')
text, wav, sr = translator.predict(
    input='Hello, how are you today?',
    task_str='s2st',
    tgt_lang='fra'
)
print(text)
"
```

## Introduction
Seamless Communication is Meta's open-source project delivering foundational models for multimodal, multilingual translation. The family includes SeamlessM4T (Massively Multilingual & Multimodal Machine Translation), SeamlessExpressive for preserving vocal style, and SeamlessStreaming for low-latency real-time translation.

## What Seamless Communication Does
- Translates speech-to-speech across nearly 100 languages
- Performs speech-to-text, text-to-speech, and text-to-text translation in a single model
- Preserves vocal expression, emotion, and speaking rate during translation with SeamlessExpressive
- Enables real-time simultaneous translation with SeamlessStreaming
- Handles code-switched and noisy audio inputs robustly

## Architecture Overview
SeamlessM4T v2 uses a two-pass architecture. The first pass employs a shared encoder-decoder for text generation from either speech or text input, supporting 96 input languages for speech and 196 for text. The second pass synthesizes target speech through a text-to-unit model and a HiFi-GAN vocoder. SeamlessExpressive adds a prosody encoder that captures pitch, rhythm, and emotion from the source audio and transfers these attributes to the synthesized output. SeamlessStreaming wraps the model in an Efficient Monotonic Multihead Attention framework for simultaneous translation.

## Self-Hosting & Configuration
- Install via `pip install fairseq2` and clone the seamless_communication repository
- Model checkpoints are downloaded automatically from Hugging Face on first use
- Requires a CUDA GPU with at least 16 GB VRAM for the large model variant
- Configure source and target languages via the `tgt_lang` and `src_lang` parameters
- Streaming mode is enabled via the `SeamlessStreamingS2ST` or `SeamlessStreamingS2T` classes

## Key Features
- Unified architecture handles four translation modalities (S2ST, S2TT, T2ST, T2TT)
- SeamlessExpressive preserves vocal style, emotion, and prosody in translated speech
- SeamlessStreaming supports real-time simultaneous interpretation
- Covers 96 languages for speech input and 196 languages for text input
- Released with open weights, code, and the SeamlessAlign training data mining toolkit

## Comparison with Similar Tools
- **Google Translate API** — commercial service with broad language coverage; Seamless is open source and self-hostable
- **Whisper + NLLB** — pipeline approach combining ASR and text translation; Seamless handles speech-to-speech in a single model
- **Azure Speech Translation** — cloud service with real-time capabilities; Seamless offers comparable streaming locally
- **Coqui TTS** — open-source text-to-speech toolkit; focused on synthesis only, not cross-lingual translation
- **MMS (Massively Multilingual Speech)** — Meta's ASR covering 1000+ languages; complementary to Seamless which focuses on translation

## FAQ
**Q: Can I run Seamless on CPU only?**
A: Inference is possible on CPU but very slow. A CUDA GPU is strongly recommended.

**Q: Does SeamlessExpressive work for all language pairs?**
A: Expressive translation is available for a subset of high-resource language pairs. Check the model card for supported directions.

**Q: How does streaming mode handle latency?**
A: SeamlessStreaming begins translating before the speaker finishes, targeting under 2 seconds of latency for supported language pairs.

**Q: What license are the Seamless models under?**
A: The code is MIT-licensed. Model weights are released under the Seamless license which permits research and commercial use.

## Sources
- https://github.com/facebookresearch/seamless_communication
- https://ai.meta.com/research/seamless-communication/

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Author: AI Open Source