47 lines
2.1 KiB
Python
47 lines
2.1 KiB
Python
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import os
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from urllib import request
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from tqdm import tqdm
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DEFAULT_MODELS_DIR = os.path.join(os.path.expanduser("~"), ".cache", "tortoise", "models")
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MODELS_DIR = os.environ.get("TORTOISE_MODELS_DIR", DEFAULT_MODELS_DIR)
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MODELS_DIR = "/data/speech_synth/models/"
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MODELS = {
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"autoregressive.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth",
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"classifier.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/classifier.pth",
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"clvp2.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/clvp2.pth",
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"diffusion_decoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/diffusion_decoder.pth",
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"vocoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/vocoder.pth",
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"rlg_auto.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth",
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"rlg_diffuser.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth",
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}
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def download_models(specific_models=None):
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"""
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Call to download all the models that Tortoise uses.
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"""
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os.makedirs(MODELS_DIR, exist_ok=True)
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for model_name, url in MODELS.items():
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if specific_models is not None and model_name not in specific_models:
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continue
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model_path = os.path.join(MODELS_DIR, model_name)
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if os.path.exists(model_path):
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continue
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print(f"Downloading {model_name} from {url}...")
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with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
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request.urlretrieve(url, model_path, lambda nb, bs, fs, t=t: t.update(nb * bs - t.n))
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print("Done.")
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def get_model_path(model_name, models_dir=MODELS_DIR):
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"""
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Get path to given model, download it if it doesn't exist.
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"""
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if model_name not in MODELS:
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raise ValueError(f"Model {model_name} not found in available models.")
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model_path = os.path.join(models_dir, model_name)
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if not os.path.exists(model_path) and models_dir == MODELS_DIR:
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download_models([model_name])
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return model_path
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