app.py

import gradio as gr
import torch
import whisper
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

### ————————————————————————————————————————

title="Whisper to Biomedical NER"

### ————————————————————————————————————————

whisper_model = whisper.load_model("medium")

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

biomed_ner_pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu

def parse_ber(text):
    raw = biomed_ner_pipe(text)
    ner_content = {
        "text": text,
        "entities": [
            {
                "entity": x["entity_group"],
                "word": x["word"],
                "score": x["score"],
                "start": x["start"],
                "end": x["end"],
            }
            for x in raw
        ]
    }
    return ner_content
    
def translate_and_classify(audio):
    
    print("""
    —
    Sending audio to Whisper ...
    —
    """)
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)
    
    mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
    
    _, probs = whisper_model.detect_language(mel)
    
    transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False)
    translate_options = whisper.DecodingOptions(task="translate", fp16 = False)
    
    transcription = whisper.decode(whisper_model, mel, transcript_options)
    translation = whisper.decode(whisper_model, mel, translate_options)
    
    print("Language Spoken: " + transcription.language)
    print("Transcript: " + transcription.text)  
    print("Translated: " + translation.text)
    
    detected_ner = parse_ner(translation.text)
    print("Detected Named Entities: ", detected_ner)
    return transcription.text, detected_ner

css = """
        .gradio-container {
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: black;
            background: black;
        }
        input[type='range'] {
            accent-color: black;
        }
        .dark input[type='range'] {
            accent-color: #dfdfdf;
        }
        .container {
            max-width: 730px;
            margin: auto;
            padding-top: 1.5rem;
        }
        #gallery {
            min-height: 22rem;
            margin-bottom: 15px;
            margin-left: auto;
            margin-right: auto;
            border-bottom-right-radius: .5rem !important;
            border-bottom-left-radius: .5rem !important;
        }
        #gallery>div>.h-full {
            min-height: 20rem;
        }
        .details:hover {
            text-decoration: underline;
        }
        .gr-button {
            white-space: nowrap;
        }
        .gr-button:focus {
            border-color: rgb(147 197 253 / var(--tw-border-opacity));
            outline: none;
            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
            --tw-border-opacity: 1;
            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
            --tw-ring-opacity: .5;
        }
        #advanced-btn {
            font-size: .7rem !important;
            line-height: 19px;
            margin-top: 12px;
            margin-bottom: 12px;
            padding: 2px 8px;
            border-radius: 14px !important;
        }
        #advanced-options {
            display: none;
            margin-bottom: 20px;
        }
        .footer {
            margin-bottom: 45px;
            margin-top: 35px;
            text-align: center;
            border-bottom: 1px solid #e5e5e5;
        }
        .footer>p {
            font-size: .8rem;
            display: inline-block;
            padding: 0 10px;
            transform: translateY(10px);
            background: white;
        }
        .dark .footer {
            border-color: #303030;
        }
        .dark .footer>p {
            background: #0b0f19;
        }
"""
with gr.Blocks(css = css) as demo:
    gr.Markdown("""
                ## Biomedical Named Entity Recognition From Speech with Whisper
            """)
    gr.HTML('''
     <p style="margin-bottom: 10px; font-size: 94%">
                Whisper is a general-purpose speech recognition model released by OpenAI that can perform multilingual speech recognition as well as speech translation and language identification. Combined with an biomedical named entity recognition model,this allows for detecting key terms directly from speech in multiple languages and can potentially be used to assist in data-driven analysis in clinical settings related to physical and mental health
              </p>
              ''')
    
    with gr.Column():
            #gr.Markdown(""" ### Record audio """)
        with gr.Tab("Record Audio"):
            audio_input_r = gr.Audio(label = 'Record Audio Input',source="microphone",type="filepath")
            transcribe_audio_r = gr.Button('Transcribe')
        
        with gr.Tab("Upload Audio as File"):
            audio_input_u = gr.Audio(label = 'Upload Audio',source="upload",type="filepath")
            transcribe_audio_u = gr.Button('Transcribe')

        with gr.Row():
            transcript_output = gr.Textbox(label="Transcription in the language you spoke", lines = 3)
            biomed_ner_output = gr.HighlightedText(label = "Detected Named Entities")
    
    transcribe_audio_r.click(translate_and_classify, inputs = audio_input_r, outputs = [transcript_output,biomed_ner_output])
    transcribe_audio_u.click(translate_and_classify, inputs = audio_input_u, outputs = [transcript_output,biomed_ner_output])   
    gr.HTML('''
        <div class="footer">
                    <p>Whisper Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> -   
                    <a href="https://huggingface.co/d4data/biomedical-ner-all" style="text-decoration: underline;" target="_blank">Biomedical NER Model</a>
                    </p>
        </div>
        ''')
    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.whisper_biomd_ner)")
    
    
demo.launch()