Create README.md

README.md

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+---
+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)
+```