mirror of
https://github.com/HumanAIGC-Engineering/gradio-webrtc.git
synced 2026-02-05 18:09:23 +08:00
Additional outputs tweaks + fix track constraints (#28)
* code * add code * add code
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@@ -1,37 +1,21 @@
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import logging
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import tempfile
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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 gradio_webrtc import AdditionalOutputs, ReplyOnPause, WebRTC
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from openai import OpenAI
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from pydub import AudioSegment
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from dotenv import load_dotenv
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load_dotenv()
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# Configure the root logger to WARNING to suppress debug messages from other libraries
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logging.basicConfig(level=logging.WARNING)
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# Create a console handler
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.DEBUG)
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# Create a formatter
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formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s")
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console_handler.setFormatter(formatter)
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# Configure the logger for your specific library
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logger = logging.getLogger("gradio_webrtc")
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logger.setLevel(logging.DEBUG)
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logger.addHandler(console_handler)
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client = OpenAI()
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def transcribe(audio: tuple[int, np.ndarray], transcript: list[dict]):
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print("audio", audio)
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segment = AudioSegment(
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audio[1].tobytes(),
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frame_rate=audio[0],
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@@ -39,12 +23,14 @@ def transcribe(audio: tuple[int, np.ndarray], transcript: list[dict]):
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channels=1,
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)
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transcript.append({"role": "user", "content": gr.Audio((audio[0], audio[1].squeeze()))})
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with tempfile.NamedTemporaryFile(suffix=".mp3") as temp_audio:
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segment.export(temp_audio.name, format="mp3")
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next_chunk = client.audio.transcriptions.create(
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model="whisper-1", file=open(temp_audio.name, "rb")
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).text
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transcript.append({"role": "user", "content": next_chunk})
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transcript.append({"role": "assistant", "content": next_chunk})
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yield AdditionalOutputs(transcript)
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