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import json |
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from pathlib import Path |
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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 fastapi import FastAPI |
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from fastapi.responses import HTMLResponse, StreamingResponse |
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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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audio_to_bytes, |
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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 AsyncClient |
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cur_dir = Path(__file__).parent |
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load_dotenv() |
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groq_client = AsyncClient() |
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async def transcribe(audio: tuple[int, np.ndarray]): |
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transcript = await 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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) |
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yield AdditionalOutputs(transcript.text) |
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stream = Stream( |
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ReplyOnPause(transcribe), |
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modality="audio", |
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mode="send", |
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additional_outputs=[ |
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gr.Textbox(label="Transcript"), |
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], |
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additional_outputs_handler=lambda a, b: a + " " + b, |
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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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) |
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app = FastAPI() |
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stream.mount(app) |
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@app.get("/transcript") |
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def _(webrtc_id: str): |
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async def output_stream(): |
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async for output in stream.output_stream(webrtc_id): |
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transcript = output.args[0] |
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yield f"event: output\ndata: {transcript}\n\n" |
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return StreamingResponse(output_stream(), media_type="text/event-stream") |
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@app.get("/") |
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def index(): |
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rtc_config = get_twilio_turn_credentials() if get_space() else None |
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html_content = (cur_dir / "index.html").read_text() |
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html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config)) |
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return HTMLResponse(content=html_content) |
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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, server_name="0.0.0.0") |
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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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