|
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", |
|
|
|
"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() |
|
|
