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https://github.com/HumanAIGC-Engineering/gradio-webrtc.git
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6517a93472
* fix links * fix upload * add code * Add code --------- Co-authored-by: Freddy Boulton <freddyboulton@hf-freddy.local>
98 lines
2.8 KiB
Python
98 lines
2.8 KiB
Python
import os
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import time
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import gradio as gr
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import numpy as np
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from dotenv import load_dotenv
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from elevenlabs import ElevenLabs
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from fastapi import FastAPI
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from fastrtc import (
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AdditionalOutputs,
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ReplyOnPause,
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Stream,
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get_stt_model,
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get_twilio_turn_credentials,
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)
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from gradio.utils import get_space
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from groq import Groq
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from numpy.typing import NDArray
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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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stt_model = get_stt_model()
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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, NDArray[np.int16 | np.float32]],
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chatbot: list[dict] | None = None,
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):
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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_model.stt(audio)
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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=200,
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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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for i, chunk in enumerate(
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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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):
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if i == 0:
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yield AdditionalOutputs(chatbot)
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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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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=5 if get_space() else None,
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time_limit=90 if get_space() else None,
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ui_args={"title": "LLM Voice Chat (Powered by Groq, ElevenLabs, and WebRTC ⚡️)"},
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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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app = 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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os.environ["GRADIO_SSR_MODE"] = "false"
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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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stream.ui.launch(server_port=7860)
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