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Browse files- README_gradio.md +15 -0
- app.py +113 -0
- requirements.txt +6 -0
README_gradio.md
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---
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title: LLM Voice Chat (Gradio)
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emoji: 💻
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colorFrom: purple
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colorTo: red
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sdk: gradio
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sdk_version: 5.16.0
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app_file: app.py
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pinned: false
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license: mit
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short_description: LLM Voice by ElevenLabs (Gradio)
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tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN, secret|GROQ_API_KEY, secret|ELEVENLABS_API_KEY]
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from fastrtc import (
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ReplyOnPause,
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AdditionalOutputs,
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Stream,
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aggregate_bytes_to_16bit,
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get_twilio_turn_credentials,
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WebRTCError,
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stt,
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audio_to_bytes,
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)
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import numpy as np
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import gradio as gr
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from gradio.utils import get_space
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from groq import Groq
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from elevenlabs import ElevenLabs
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from dotenv import load_dotenv
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import time
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import os
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from fastapi import FastAPI
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load_dotenv()
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groq_client = Groq()
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tts_client = ElevenLabs(api_key=os.getenv("ELEVENLABS_API_KEY"))
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# See "Talk to Claude" in Cookbook for an example of how to keep
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# track of the chat history.
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def response(
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audio: tuple[int, np.ndarray],
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chatbot: list[dict] | None = None,
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):
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try:
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chatbot = chatbot or []
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messages = [{"role": d["role"], "content": d["content"]} for d in chatbot]
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start = time.time()
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# text = stt(audio)
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text = groq_client.audio.transcriptions.create(
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file=("audio-file.mp3", audio_to_bytes(audio)),
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model="whisper-large-v3-turbo",
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response_format="verbose_json",
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).text
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print("transcription", time.time() - start)
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print("prompt", text)
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chatbot.append({"role": "user", "content": text})
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yield AdditionalOutputs(chatbot)
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messages.append({"role": "user", "content": text})
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response_text = (
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groq_client.chat.completions.create(
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model="llama-3.1-8b-instant",
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max_tokens=512,
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messages=messages, # type: ignore
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)
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.choices[0]
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.message.content
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)
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chatbot.append({"role": "assistant", "content": response_text})
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iterator = tts_client.text_to_speech.convert_as_stream(
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text=response_text, # type: ignore
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voice_id="JBFqnCBsd6RMkjVDRZzb",
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model_id="eleven_multilingual_v2",
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output_format="pcm_24000",
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)
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for chunk in aggregate_bytes_to_16bit(iterator):
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audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1)
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yield (24000, audio_array)
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yield AdditionalOutputs(chatbot)
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except Exception as e:
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import traceback
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traceback.print_exc()
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raise WebRTCError(traceback.format_exc())
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chatbot = gr.Chatbot(type="messages")
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stream = Stream(
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modality="audio",
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mode="send-receive",
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handler=ReplyOnPause(response, input_sample_rate=16000),
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additional_outputs_handler=lambda a, b: b,
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additional_inputs=[chatbot],
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additional_outputs=[chatbot],
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rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
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concurrency_limit=20 if get_space() else None,
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)
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for id, block in stream.ui.blocks.items():
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if isinstance(block, gr.HTML):
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stream.ui.blocks[id] = gr.HTML(
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"""
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<h1 style='text-align: center'>
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LLM Voice Chat (Powered by Groq, ElevenLabs, and WebRTC ⚡️)
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</h1>
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"""
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)
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# Mount the STREAM UI to the FastAPI app
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# Because I don't want to build the UI manually
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app = FastAPI()
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gr.mount_gradio_app(app, stream.ui, path="/")
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if __name__ == "__main__":
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import os
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if (mode := os.getenv("MODE")) == "UI":
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stream.ui.launch(server_port=7860)
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elif mode == "PHONE":
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stream.fastphone(host="0.0.0.0", port=7860)
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else:
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=7860)
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requirements.txt
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fastrtc[stopword]
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python-dotenv
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openai
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twilio
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groq
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elevenlabs
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