import json
import gradio as gr
import numpy as np
import wenetruntime as wenet

wenet.set_log_level(2)
decoder = wenet.Decoder(lang='chs')


def recognition(audio):
    print(audio)
    if audio is None:
        return "Input Error! Please enter one audio!"
    sr, y = audio
    assert sr in [48000, 16000]
    if sr == 48000:  # Optional resample to 16000
        y = (y / max(np.max(y), 1) * 32767)[::3].astype("int16")
    ans = decoder.decode(y.tobytes(), True)
    if ans == None:
        return "ERROR! No text output! Please try again!"
    # ans (json)
    # {
    #    'nbest' : [{"sentence" : ""}], 'type' : 'final_result
    # }
    ans = json.loads(ans)
    print(ans)
    txt = ans['nbest'][0]['sentence']
    return txt


# input
inputs = [
    gr.inputs.Audio(source="microphone",
                    type="numpy",
                    label='Speaker#1')
]

output = gr.outputs.Textbox(label="Output Text")

# examples = ['examples/BAC009S0764W0121.wav']
text = "Speech Recognition in WeNet | 基于 WeNet 的语音识别"

# description
description = ("WeSpeaker Demo ! Try it with your own voice !")

article = (
    "<p style='text-align: center'>"
    "<a href='https://github.com/wenet-e2e/wespeaker' target='_blank'>Github: Learn more about WeSpeaker</a>"
    "</p>")

interface = gr.Interface(
    fn=recognition,
    inputs=inputs,
    outputs=output,
    title=text,
    description=description,
    article=article,
    theme='huggingface',
)

interface.launch(enable_queue=True)