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import os
import requests
from tqdm import tqdm
from google.cloud import storage
from huggingface_hub import hf_hub_download
def _download_starting_files() -> None:
"""
Downloads the embeddings from a bucket
"""
# Initialise a client
credentials = os.getenv('GOOGLE_APPLICATION_CREDENTIALS')
storage_client = storage.Client.from_service_account_json(credentials)
bucket = storage_client.get_bucket('embeddings-bella')
# Get both embeddings
blob = bucket.blob("gpt_cond_latent.npy")
blob.download_to_filename('assets/gpt_cond_latent.npy')
blob = bucket.blob("speaker_embedding.npy")
blob.download_to_filename('assets/speaker_embedding.npy')
def _download_file(url, destination):
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get('content-length', 0))
block_size = 1024
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
with open(destination, 'wb') as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
def download_model():
# Define files and their corresponding URLs
files_to_download = {
'LICENSE.txt': 'https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/LICENSE.txt?download=true',
'README.md': 'https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/README.md?download=true',
'config.json': 'https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/config.json?download=true',
'model.pth': 'https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/model.pth?download=true',
'vocab.json': 'https://huggingface.co/coqui/XTTS-v2/resolve/v2.0.2/vocab.json?download=true',
}
if not os.path.exists("tts_model"):
os.makedirs("tts_model")
# Download files if they don't exist
print("[COQUI TTS] STARTUP: Checking Model is Downloaded.")
for filename, url in files_to_download.items():
destination = f'tts_model/{filename}'
print(f"[COQUI TTS] STARTUP: Downloading {filename}...")
_download_file(url, destination)
# Downloads the embeddings from GCP
# _download_starting_files()
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