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import whisper
from groq import Groq
from gtts import gTTS
import gradio as gr
import os
import tempfile
# Load Whisper model for transcription
whisper_model = whisper.load_model("base")
# Set up Groq client for LLM interaction
os.environ["GROQ_API_KEY"] = "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"
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
def process_speech_to_speech(audio):
# Step 1: Transcribe the audio using Whisper
transcript = whisper_model.transcribe(audio)["text"]
# Step 2: Send transcription to the LLM via Groq
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": transcript}],
model="llama3-8b-8192"
)
response_text = chat_completion.choices[0].message.content
# Step 3: Convert LLM response to speech using gTTS
tts = gTTS(text=response_text, lang="en")
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
tts.save(fp.name)
audio_output = fp.name # Path to the generated speech file
return response_text, audio_output
# Gradio interface to deploy the application
iface = gr.Interface(
fn=process_speech_to_speech,
inputs=gr.Audio(type="filepath", label="Record your audio"),
outputs=[gr.Textbox(label="LLM Response"), gr.Audio(type="filepath", label="Spoken Response")],
live=True
)
iface.launch()
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