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| import os | |
| try: import gdown | |
| except ImportError: | |
| raise ImportError( | |
| "Sorry, gdown is required in order to download the new BigVGAN vocoder.\n" | |
| "Please install it with `pip install gdown` and try again." | |
| ) | |
| from urllib import request | |
| import progressbar | |
| D_STEM = "https://drive.google.com/uc?id=" | |
| DEFAULT_MODELS_DIR = os.path.join( | |
| os.path.expanduser("~"), ".cache", "tortoise", "models" | |
| ) | |
| MODELS_DIR = os.environ.get("TORTOISE_MODELS_DIR", DEFAULT_MODELS_DIR) | |
| MODELS = { | |
| "autoregressive.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth", | |
| "classifier.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/classifier.pth", | |
| "clvp2.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/clvp2.pth", | |
| "cvvp.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/cvvp.pth", | |
| "diffusion_decoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/diffusion_decoder.pth", | |
| "vocoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/vocoder.pth", | |
| "rlg_auto.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth", | |
| "rlg_diffuser.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth", | |
| # these links are from the nvidia gdrive | |
| "bigvgan_base_24khz_100band_g.pth": "https://drive.google.com/uc?id=1_cKskUDuvxQJUEBwdgjAxKuDTUW6kPdY", | |
| "bigvgan_24khz_100band_g.pth": "https://drive.google.com/uc?id=1wmP_mAs7d00KHVfVEl8B5Gb72Kzpcavp", | |
| } | |
| pbar = None | |
| def download_models(specific_models=None): | |
| """ | |
| Call to download all the models that Tortoise uses. | |
| """ | |
| os.makedirs(MODELS_DIR, exist_ok=True) | |
| def show_progress(block_num, block_size, total_size): | |
| global pbar | |
| if pbar is None: | |
| pbar = progressbar.ProgressBar(maxval=total_size) | |
| pbar.start() | |
| downloaded = block_num * block_size | |
| if downloaded < total_size: | |
| pbar.update(downloaded) | |
| else: | |
| pbar.finish() | |
| pbar = None | |
| for model_name, url in MODELS.items(): | |
| if specific_models is not None and model_name not in specific_models: | |
| continue | |
| model_path = os.path.join(MODELS_DIR, model_name) | |
| if os.path.exists(model_path): | |
| continue | |
| print(f"Downloading {model_name} from {url}...") | |
| if D_STEM in url: | |
| gdown.download(url, model_path, quiet=False) | |
| else: | |
| request.urlretrieve(url, model_path, show_progress) | |
| print("Done.") | |
| def get_model_path(model_name, models_dir=MODELS_DIR): | |
| """ | |
| Get path to given model, download it if it doesn't exist. | |
| """ | |
| if model_name not in MODELS: | |
| raise ValueError(f"Model {model_name} not found in available models.") | |
| model_path = os.path.join(models_dir, model_name) | |
| if not os.path.exists(model_path) and models_dir == MODELS_DIR: | |
| download_models([model_name]) | |
| return model_path | |
| if __name__ == "__main__": | |
| download_models() # to download all models | |