Create app.py

app.py

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+import os
+import base64
+import gradio as gr
+import openai
+from pydub import AudioSegment
+import io
+import tempfile
+import speech_recognition as sr
+
+# Initialize the OpenAI client
+client = openai.OpenAI(
+    base_url="https://llama3-2-3b.lepton.run/api/v1/",
+    api_key=os.environ.get('LEPTON_API_TOKEN')
+)
+
+def transcribe_audio(audio):
+    # Convert the audio to wav format
+    audio = AudioSegment.from_file(audio)
+    audio = audio.set_frame_rate(16000).set_channels(1)
+    
+    # Save as wav file
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
+        audio.export(temp_audio.name, format="wav")
+        temp_audio_path = temp_audio.name
+
+    # Perform speech recognition
+    recognizer = sr.Recognizer()
+    with sr.AudioFile(temp_audio_path) as source:
+        audio_data = recognizer.record(source)
+        text = recognizer.recognize_google(audio_data)
+
+    # Clean up the temporary file
+    os.unlink(temp_audio_path)
+
+    return text
+
+def process_audio(audio):
+    # Transcribe the input audio
+    transcription = transcribe_audio(audio)
+
+    # Process the transcription with the API
+    completion = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "user", "content": transcription},
+        ],
+        max_tokens=128,
+        stream=True,
+        extra_body={
+            "require_audio": "true",
+            "tts_preset_id": "jessica",
+        }
+    )
+
+    response_text = ""
+    audios = []
+
+    for chunk in completion:
+        if not chunk.choices:
+            continue
+        content = chunk.choices[0].delta.content
+        audio = getattr(chunk.choices[0], 'audio', [])
+        if content:
+            response_text += content
+        if audio:
+            audios.extend(audio)
+
+    # Combine audio chunks and save as MP3
+    audio_data = b''.join([base64.b64decode(audio) for audio in audios])
+    
+    # Save the audio to a temporary file
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_audio:
+        temp_audio.write(audio_data)
+        temp_audio_path = temp_audio.name
+
+    return response_text, temp_audio_path
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(type="filepath"),
+    outputs=[
+        gr.Textbox(label="Response Text"),
+        gr.Audio(label="Response Audio")
+    ],
+    title="Audio-to-Audio Demo",
+    description="Upload an audio file to get a response in both text and audio format."
+)
+
+# Launch the interface
+iface.launch()