| 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"] | |