Llava_Endpoint / text_to_speech.py
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Create text_to_speech.py
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# text_to_speech.py
import os
from elevenlabs import VoiceSettings, ElevenLabs
ELEVENLABS_API_KEY = "sk_29eaa30255601541b3a80f470ac8c45987c007b8e2dfb791"
client = ElevenLabs(api_key=ELEVENLABS_API_KEY)
def text_to_speech_file(text: str) -> str:
# Calling the text-to-speech conversion API with detailed parameters
response = client.text_to_speech.convert(
voice_id="XrExE9yKIg1WjnnlVkGX", # Adam pre-made voice
output_format="mp3_22050_32",
text=text,
model_id="eleven_turbo_v2_5", # use the turbo model for low latency
voice_settings=VoiceSettings(
stability=0.0,
similarity_boost=1.0,
style=0.0,
use_speaker_boost=True,
),
)
# Generating a unique file name for the output MP3 file
save_file_path = "output.wav"
# Writing the audio to a file
with open(save_file_path, "wb") as f:
for chunk in response:
if chunk:
f.write(chunk)
print(f"{save_file_path}: A new audio file was saved successfully!")
return save_file_path