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import whisper | |
import gradio as gr | |
import openai | |
import os | |
openai.api_key = os.environ["OPENAI_API_KEY"] | |
model = whisper.load_model("small") | |
#option 1 | |
def transcribe(audio): | |
model = whisper.load_model("base") | |
result = model.transcribe(audio) | |
return result["text"] | |
#option 2 | |
# def transcribe(audio): | |
# #time.sleep(3) | |
# # load audio and pad/trim it to fit 30 seconds | |
# audio = whisper.load_audio(audio) | |
# audio = whisper.pad_or_trim(audio) | |
# # make log-Mel spectrogram and move to the same device as the model | |
# mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
# # detect the spoken language | |
# _, probs = model.detect_language(mel) | |
# print(f"Detected language: {max(probs, key=probs.get)}") | |
# # decode the audio | |
# options = whisper.DecodingOptions(fp16 = False) | |
# result = whisper.decode(model, mel, options) | |
# return result.text | |
def process_text(input_text): | |
# Apply your function here to process the input text | |
output_text = input_text.upper() | |
return output_text | |
def get_completion(prompt, model='gpt-3.5-turbo'): | |
messages = [ | |
{"role": "system", "content": """You are a .... You are provided with the transcription of a ... . \ | |
Extract the following information from the transcription, replace curly brackets {} with relevant extracted information ... \ | |
...the rest of your prompt... | |
""" | |
}, | |
{"role": "user", "content": prompt} | |
] | |
response = openai.ChatCompletion.create( | |
model = model, | |
messages = messages, | |
temperature = 0, | |
) | |
return response.choices[0].message['content'] | |
with gr.Blocks() as demo: | |
gr.Markdown(""" | |
# Title <br> | |
Description | |
""") | |
title = "title" | |
audio = gr.Audio(type="filepath") | |
b1 = gr.Button("Transcribe audio") | |
b2 = gr.Button("<Placeholder for the prompted action>") | |
# b3 = gr.Button("Email report to your doctor") | |
text1 = gr.Textbox(lines=5) | |
text2 = gr.Textbox(lines=5) | |
prompt = text1 | |
b1.click(transcribe, inputs=audio, outputs=text1) | |
b2.click(get_completion, inputs=text1, outputs=text2) | |
# b1.click(transcribe, inputs=audio, outputs=text1) | |
# b2.click(get_completion, inputs=prompt, outputs=text2) | |
demo.launch() | |
#demo.launch(share=True, auth=("username", "password")) | |
# In this example, the process_text function just converts the input text to uppercase, but you can replace it with your desired function. The Gradio Blocks interface will have two buttons: "Transcribe audio" and "Process text". The first button transcribes the audio and fills the first textbox, and the second button processes the text from the first textbox and fills the second textbox. | |
# gr.Interface( | |
# title = 'OpenAI Whisper ASR Gradio Web UI', | |
# fn=transcribe, | |
# inputs=[ | |
# gr.inputs.Audio(source="microphone", type="filepath") | |
# ], | |
# outputs=[ | |
# "textbox" | |
# ], | |
# live=True).launch() | |