Xm Transformer S2ut Hk En

🚀 xm_transformer_s2ut_hk - en

这是一个来自fairseq的单遍解码器（S2UT）语音到语音翻译模型，具备以下特点：

• 支持闽南语 - 英语的语音翻译。
• 使用TED、戏剧、TAT领域的监督数据，以及戏剧领域的弱监督数据进行训练。训练细节可参考此处。
• 采用facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur进行语音合成。
• 项目页面

🚀 快速开始

本项目是一个语音到语音的翻译模型，下面为你展示如何使用该模型进行语音翻译和合成。

💻 使用示例

基础用法

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)

📄 许可证

本项目采用CC - BY - NC 4.0许可证。

📋 信息表格