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| import os | |
| import numpy as np | |
| import gradio as gr | |
| import whisper | |
| model = whisper.load_model("base") | |
| ##Bloom | |
| API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| def fun(audio) : #, state=''): | |
| text1 = model.transcribe(audio)["text"] | |
| text2 = lang_model_response(text) | |
| return text1, text2 | |
| def lang_model_response(prompt): | |
| print(f"*****Inside meme_generate - Prompt is :{prompt}") | |
| if len(prompt) == 0: | |
| prompt = """Can you help me please?""" | |
| json_ = {"inputs": prompt, | |
| "parameters": | |
| { | |
| "top_p": top_p, #0.90 default | |
| "max_new_tokens": 64, | |
| "temperature": temp, #1.1 default | |
| "return_full_text": True, | |
| "do_sample": True, | |
| }, | |
| "options": | |
| {"use_cache": True, | |
| "wait_for_model": True, | |
| },} | |
| response = requests.post(API_URL, headers=headers, json=json_) | |
| print(f"Response is : {response}") | |
| output = response.json() | |
| print(f"output is : {output}") | |
| output_tmp = output[0]['generated_text'] | |
| print(f"output_tmp is: {output_tmp}") | |
| solution = output_tmp[0] #output_tmp.split("\nQ:")[0] | |
| print(f"Final response after splits is: {solution}") | |
| #meme_image, new_prompt = write_on_image(solution) | |
| return solution | |
| def fun1(audio, state=''): | |
| text = model.transcribe(audio)["text"] | |
| state += text + " " | |
| return state, state | |
| gr.Interface( | |
| title = 'Testing Whisper', | |
| fn=fun, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath"), #streaming = True, | |
| # "state" | |
| ], | |
| outputs=[ | |
| "textbox", "textbox" | |
| ], | |
| live=True).launch() | |