On-device Models

Apart from SeamlessM4T-LARGE (2.3B) and SeamlessM4T-MEDIUM (1.2B) models, we are also developing a small model (281M) targeting for on-device inference. This folder contains an example to run an exported small model covering most tasks (ASR/S2TT/S2ST). The model could be executed on popular mobile devices with Pytorch Mobile (https://pytorch.org/mobile/home/).

Overview

Model	Disk Size	Supported Tasks	Supported Languages
UnitY-Small	862MB	S2ST, S2TT, ASR	eng, fra, hin, por, spa
UnitY-Small-S2T	637MB	S2TT, ASR	eng, fra,hin, por, spa

UnitY-Small-S2T is a pruned version of UnitY-Small without 2nd pass unit decoding.

Inference

To use exported model, users don't need seamless_communication or fairseq2 dependency.

import torchaudio
import torch
audio_input, _ = torchaudio.load(TEST_AUDIO_PATH) # Load waveform using torchaudio

s2t_model = torch.jit.load("unity_on_device_s2t.ptl") # Load exported S2T model
text = s2t_model(audio_input, tgt_lang=TGT_LANG) # Forward call with tgt_lang specified for ASR or S2TT
print(f"{lang}:{text}")

s2st_model = torch.jit.load("unity_on_device.ptl")
text, units, waveform = s2st_model(audio_input, tgt_lang=TGT_LANG) # S2ST model also returns waveform
print(f"{lang}:{text}")
torchaudio.save(f"{OUTPUT_FOLDER}/{lang}.wav", waveform.unsqueeze(0), sample_rate=16000) # Save output waveform to local file

Also running the exported model doesn't need python runtime. For example, you could load this model in C++ following this tutorial, or building your own on-device applications similar to this example

Metrics

S2TT BLEU on FLEURS

Eng-X | TGT_LANG | BLEU | |---------|----------------------| | fra|?| | hin|?| | por|?| | spa|?|

X-Eng | SRC_LANG | BLEU | |---------|----------------------| | fra|?| | hin|?| | por|?| | spa|?|

S2ST BLEU on FLEURS