unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur
Speech-to-speech translation model from fairseq S2UT (paper/code):
- Spanish-English
- Trained on mTEDx, CoVoST 2, Europarl-ST and VoxPopuli
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
# 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)
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