README.md

metadata

library_name: fairseq
task: audio-to-audio
tags:
  - fairseq
  - audio
  - audio-to-audio
  - speech-to-speech-translation
datasets:
  - mtedx
  - covost2
  - europarl_st
  - voxpopuli
widget:
  - example_title: Common Voice sample 1
    src: >-
      https://huggingface.co/facebook/xm_transformer_600m-es_en-multi_domain/resolve/main/common_voice_es_19966634.flac

xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022

Speech-to-speech translation model from fairseq S2UT (paper/code):

• Spanish-English
• Trained on mTEDx, CoVoST 2, Europarl-ST and VoxPopuli
• Speech synthesis with facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur

Usage

import json
import os
from pathlib import Path

import IPython.display as ipd
from fairseq import hub_utils
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.speech_to_text.hub_interface import S2THubInterface
from fairseq.models.text_to_speech import CodeHiFiGANVocoder
from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface

from huggingface_hub import snapshot_download
import torchaudio

cache_dir = os.getenv("HUGGINGFACE_HUB_CACHE")

models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
    "facebook/xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022",
    arg_overrides={"config_yaml": "config.yaml", "task": "speech_to_text"},
    cache_dir=cache_dir,
)
#model = models[0].cpu()
#cfg["task"].cpu = True
model = models[0].gpu()
cfg["task"].gpu = True
generator = task.build_generator([model], cfg)


# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")

sample = S2THubInterface.get_model_input(task, audio)
unit = S2THubInterface.get_prediction(task, model, generator, sample)

# speech synthesis           
library_name = "fairseq"
cache_dir = (
    cache_dir or (Path.home() / ".cache" / library_name).as_posix()
)
cache_dir = snapshot_download(
    f"facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur", cache_dir=cache_dir, library_name=library_name
)

x = hub_utils.from_pretrained(
    cache_dir,
    "model.pt",
    ".",
    archive_map=CodeHiFiGANVocoder.hub_models(),
    config_yaml="config.json",
    fp16=False,
    is_vocoder=True,
)

with open(f"{x['args']['data']}/config.json") as f:
    vocoder_cfg = json.load(f)
assert (
    len(x["args"]["model_path"]) == 1
), "Too many vocoder models in the input"

vocoder = CodeHiFiGANVocoder(x["args"]["model_path"][0], vocoder_cfg)
tts_model = VocoderHubInterface(vocoder_cfg, vocoder)

tts_sample = tts_model.get_model_input(unit)
wav, sr = tts_model.get_prediction(tts_sample)

ipd.Audio(wav, rate=sr)

Citation

@misc{https://doi.org/10.48550/arxiv.2204.02967,
  doi = {10.48550/ARXIV.2204.02967},  
  url = {https://arxiv.org/abs/2204.02967},  
  author = {Popuri, Sravya and Chen, Peng-Jen and Wang, Changhan and Pino, Juan and Adi, Yossi and Gu, Jiatao and Hsu, Wei-Ning and Lee, Ann},  
  keywords = {Computation and Language (cs.CL), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},  
  title = {Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation},  
  publisher = {arXiv},  
  year = {2022},  
  copyright = {arXiv.org perpetual, non-exclusive license}
}