readme: ---
  license: cc-by-nc-4.0
  library_name: fairseq
  task: text-to-speech
  tags:
  - fairseq
  - audio
  - text-to-speech
  language: en
  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
  ---
  ## unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur
  
  Speech-to-speech translation model from fairseq S2UT ([paper](https://arxiv.org/abs/2204.02967)/[code](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/enhanced_direct_s2st_discrete_units.md)):
  - Spanish-English
  - Trained on mTEDx, CoVoST 2, Europarl-ST and VoxPopuli
  
  ## Usage
  
  ```python
  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
  # generator = task.build_generator([model], cfg)
  
  
  # # requires 16000Hz mono channel audio
  # audio, _ = torchaudio.load("/Users/lpw/git/api-inference-community/docker_images/fairseq/tests/samples/sample2.flac")
  
  # 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)
  ```