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# frontend.py
import gradio as gr
import httpx
examples = [
["Can you turn my English into German?", "./show_case/common_voice_en_19664034.mp3"], # En-De
["Can you identify the initial word that connects to 'currency_name' in this audio clip?", "./show_case/audio-1434542201-headset.wav"], # ER
["What do you think the speaker's message is intended to be in this audio?", "./show_case/audio-1434542201-headset.wav"], # IC
["What does the person say?", "./show_case/p225_002.wav"], # DFake
# ["Assess whether this speech's pronunciation is Real or Fake.", "./show_case/Real.wav"], # DFake
["Assess whether this speech's pronunciation is Real or Fake.", "./show_case/Fake.wav"], # DFake
["What emotional weight does the speaker's tone carry?\nPick one answer from A, B, C, and D.\nA: fear\nB: sadness\nC: joy\nD: neutral", "./show_case/SER(emotion)_example.wav"], #SER(emotion)
# ["Assess whether this speech's pronunciation is Real or Fake.", "./show_case/SVD_14154_file31512.mp3.wav_16k.wav_norm.wav_mono.wav_silence.wav"], # SVD
["Choose the most suitable answer from options A, B, C, and D to respond the question in next line, you may only choose A or B or C or D.\nThe number of speakers delivering this speech is what?\nA. 4\nB. 2\nC.1\nD. 3", "./show_case/SNV_example.wav"], #SNV
["Identify the language of the conversation you just heard.","./show_case/SLR_example.wav"], #SLR
["tell the gender of the speaker in this audio.","./show_case/SGR_018.wav"], #SGR
["What's the sound we're hearing in this audio from?","./show_case/Sound_Vocal_example.wav"], #Sound_vocal
["What is your best guess at the setting of this sound clip?","./show_case/Scene_example.wav"], #Sound_cochl
["Choose the most suitable answer from options A, B, C, and D to respond the question in next line, Please think step by step and you may only choose A or B or C or D.\nRecognize the segment where 'project' is spoken by the speaker.\nA. [5.28, 5.39]\nB. [0.92, 1.39]\nC. [4.75, 5.28]\nD. [3.86, 4.23]","./show_case/SG_audio_1.wav"], #SG
["What type of business does the first person's son have?","./show_case/SFT_Fisher_example.wav"] #SFT_Fisher
]
async def call_api(text: str, audio_path: str):
# 读取音频文件
with open(audio_path, "rb") as f:
audio_bytes = f.read()
# 发送到后端API
async with httpx.AsyncClient() as client:
files = {"audio_file": (audio_path, audio_bytes)}
data = {"text": text}
response = await client.post(
"http://36.151.70.8:30113/process/",
files=files,
data=data
)
return response.json()["result"]
iface = gr.Interface(
fn=call_api,
inputs=[
gr.Textbox(label="Enter text instruction", value="What does the person say?"),
gr.Audio(type="filepath", label="Upload Audio", value="./show_case/p225_002.wav")
],
outputs=gr.Textbox(label="Model output"),
examples=examples,
allow_flagging="never",
cache_examples=False
)
iface.launch()
if __name__ == '__main__':
# curl -X POST -F "text=What does the person say?" -F "audio_file=@./test_audio.wav" http://36.151.70.8:30113/process/
pass
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