Delete README.md

README.md

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----
-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
-- TAT
-- Hokkien dramas
-
----
-## xm_transformer_s2ut_hk-en
-
-Speech-to-speech translation model with single-pass decoder (S2UT) from fairseq:
-- Hokkien-English
-- Trained with supervised data in TED, drama, [TAT](https://sites.google.com/speech.ntut.edu.tw/fsw/home/tat-corpus) domain, and weakly supervised data in drama domain. See [here](https://research.facebook.com/publications/hokkien-direct-speech-to-speech-translation) 
-for training details.
-- Speech synthesis with [facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur](https://huggingface.co/facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur)
-- [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_s2ut_hk-en",
-    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_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)
-```