readme: ---
  license: cc-by-nc-4.0
  library_name: fairseq
  task: audio-to-audio
  tags:
  - fairseq
  - audio
  - audio-to-audio
  - speech-to-speech-translation
  
  datasets:
  - MuST-C
  
  ---
  ## xm_transformer_unity_en-hk
  
  Speech-to-speech translation model with two-pass decoder (UnitY) from fairseq:
  - English-Hokkien
  - Trained with supervised data in TED domain, and weakly supervised data in TED and Audiobook domain. See [here]( https://research.facebook.com/publications/hokkien-direct-speech-to-speech-translation) 
  for training details.
  - Speech synthesis with [facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS](https://huggingface.co/facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS)
  - [Project Page](https://github.com/facebookresearch/fairseq/tree/ust/examples/hokkien)
  
  ## 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_unity_en-hk",
      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("/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_HK_layer12.km2500_frame_TAT-TTS", 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)
  ```
  