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import os
# from dotenv import load_dotenv
import tempfile
import scipy.io.wavfile as wavfile
from openai import OpenAI
# from elevenlabs import ElevenLabs, VoiceSettings, play, stream
# Load API keys from .env file
# load_dotenv(override=True)
openai_api_key = os.getenv('OPENAI_API_KEY')
# elevenlabs_api_key = os.getenv('ELEVENLABS_API_KEY')
# Initialize clients
openai_client = OpenAI()
# elevenlabs_client = ElevenLabs(api_key=elevenlabs_api_key)
# Function to transcribe audio using OpenAI Whisper API
def transcribe(audio):
if audio is None:
return "No audio provided.", None
# Audio is received as a tuple (sample_rate, audio_data)
sample_rate, audio_data = audio
# Save the audio data to a temporary file
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
wavfile.write(temp_file.name, sample_rate, audio_data)
temp_file_path = temp_file.name
# Transcribe the audio file using OpenAI Whisper API
with open(temp_file_path, "rb") as audio_file:
transcription_response = openai_client.audio.transcriptions.create(
model="whisper-1",
file=audio_file,
)
transcription_text = transcription_response.text
return transcription_text
def tts(response_text):
# Now, use ElevenLabs to convert the transcription text to speech
# tts_response = elevenlabs_client.text_to_speech.convert(
# voice_id="CwhRBWXzGAHq8TQ4Fs17",
# optimize_streaming_latency="0",
# output_format="mp3_22050_32",
# text=response_text,
# voice_settings=VoiceSettings(
# stability=0.1,
# similarity_boost=0.3,
# style=0.2,
# ),
# )
# audio_file_path = "output_audio.mp3"
# with open(audio_file_path, "wb") as audio_file:
# for chunk in tts_response:
# audio_file.write(chunk)
# return audio_file_path
tts_client = OpenAI()
response = tts_client.audio.speech.create(
model="tts-1",
voice="onyx",
input=response_text,
)
# file_path = "output.mp3"
# if os.path.exists(file_path):
# # Delete the file
# os.remove(file_path)
response.stream_to_file("output.mp3")
return "output.mp3"
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