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# download model
import modules.hf as hf
# load models
import models.voice as voice
import models.whisper as whisper
voice.load()
voice.loadVoc()
#libs
import modules.register as register
from models.censor import Wash
import requests
import os
def download_audio(url, output_file):
"""
Downloads an audio file from the given URL and saves it locally.
If the file already exists, it returns the path without downloading again.
:param url: URL of the audio file
:param output_file: Path where the audio will be saved
:return: Path to the audio file
"""
if os.path.exists(output_file):
print(f"File already exists: {output_file}")
return output_file
try:
response = requests.get(url, stream=True)
response.raise_for_status() # Raise an HTTPError for bad responses (4xx and 5xx)
with open(output_file, 'wb') as file:
for chunk in response.iter_content(chunk_size=8192):
file.write(chunk)
print(f"Audio downloaded successfully: {output_file}")
return output_file
except requests.exceptions.RequestException as e:
print(f"Error downloading audio: {e}")
return None
# generate audio function
censorModel = Wash()
def generate_audio(key, text, censor=False, offset=0, speed=0.9, crossfade=0.1):
"""Generate audio from text"""
data = register.get_audio(key)
if(data["isOnline"] == "True"):
audio = download_audio(data["audio_path"], f'{key}.wav')
txt = data["transcription"].decode('utf-8')
print(txt)
audio, spectogram = voice.infer(audio, txt, text, remove_silence=True)
else:
audio, spectogram = voice.infer(data["audio_path"], data["transcription"], text, remove_silence=True, speed=speed, crossfade=crossfade)
if(censor):
audio = censorModel.process_audio(audio, offset)
return audio
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