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app.py

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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()

requirements.txt

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+whisper
+groq
+gtts
+gradio
+tempfile