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"]