import os
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper

model = whisper.load_model("small")

        
def inference(audio):
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)
    
    mel = whisper.log_mel_spectrogram(audio).to(model.device)
    
    _, probs = model.detect_language(mel)
    
    options = whisper.DecodingOptions(fp16 = False)
    result = whisper.decode(model, mel, options)
    
    print(result.text)
    return result.text


block = gr.Blocks(css=css)


with block:
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                audio = gr.Audio(
                    label="Input Audio",
                    show_label=False,
                    source="microphone",
                    type="filepath"
                )

                btn = gr.Button("Transcribe")
        text = gr.Textbox(show_label=False, elem_id="result-textarea")
        
        btn.click(inference, inputs=[audio], outputs=[text],   api_name="transcribe")
 
        gr.HTML('''
        <div class="footer">
                    <p>This space is used by VideoChain, the all-in-one API to generate video, audio and more.</p>
        </div>
        ''')

block.launch()