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def download_dict():
return {
"vec768l12": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
"output": "./pretrain/checkpoint_best_legacy_500.pt"
},
"vec256l9": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
"output": "./pretrain/checkpoint_best_legacy_500.pt"
},
"hubertsoft": {
"url": "https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt",
"output": "./pretrain/hubert-soft-0d54a1f4.pt"
},
"whisper-ppg": {
"url": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"output": "./pretrain/medium.pt"
}
}
def get_speech_encoder(config_path="configs/config.json"):
import json
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
speech_encoder = config["model"]["speech_encoder"]
dict = download_dict()
return dict[speech_encoder]["url"], dict[speech_encoder]["output"]
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