app.py

'''
Author: Wxl
Date: 2024-03-11 13:58:58
LastEditors: wuxulong19950206 [email protected]
LastEditTime: 2024-03-11 22:26:59
FilePath: \apeech_rec\app.py
Description: 
Copyright (c) 2024 by ${git_name} email: ${git_email}, All Rights Reserved.
'''
# References:
# https://gradio.app/docs/#dropdown

import logging
import os
import wave
import tempfile
import time
import urllib.request
import uuid
from datetime import datetime

import gradio as gr

from examples import examples
from deploy_model import decode, get_pretrained_model,language_to_models

languages = list(language_to_models.keys())


def convert_to_wav(in_filename: str) -> str:
    """Convert the input audio file to a wave file"""
    out_filename = str(uuid.uuid4())
    out_filename = f"{in_filename}"

    logging.info(f"Converting '{in_filename}' to '{out_filename}'")
    _ = os.system(f"ffmpeg -hide_banner -i '{in_filename}' -ar 16000 '{out_filename}'")

    return out_filename


def build_html_output(s: str, style: str = "result_item_success"):
    return f"""
    <div class='result'>
        <div class='result_item {style}'>
          {s}
        </div>
    </div>
    """

def process_url(
    language: str,
    repo_id: str,
    decoding_method: str,
    num_active_paths: int,
    url: str,
):
    logging.info(f"Processing URL: {url}")
    with tempfile.NamedTemporaryFile() as f:
        try:
            urllib.request.urlretrieve(url, f.name)

            return process(
                in_filename=f.name,
                language=language,
                repo_id=repo_id,
                decoding_method=decoding_method,
                num_active_paths=num_active_paths,
            )
        except Exception as e:
            logging.info(str(e))
            return "", build_html_output(str(e), "result_item_error")
        
def process_uploaded_file(
    language: str,
    repo_id: str,
    decoding_method: str,
    num_active_paths: int,
    in_filename: str,
):
    if in_filename is None or in_filename == "":
        return "", build_html_output(
            "Please first upload a file and then click "
            'the button "submit for recognition"',
            "result_item_error",
        )

    logging.info(f"Processing uploaded file: {in_filename}")
    # try:
    return process(
        in_filename=in_filename,
        language=language,
        repo_id=repo_id,
        decoding_method=decoding_method,
        num_active_paths=num_active_paths,
    )
    # except Exception as e:
    #     logging.info(str(e))
    #     return "", build_html_output(str(e), "result_item_error")
def process_microphone(
    language: str,
    repo_id: str,
    decoding_method: str,
    num_active_paths: int,
    in_filename: str,
):
    if in_filename is None or in_filename == "":
        return "", build_html_output(
            "Please first click 'Record from microphone', speak, "
            "click 'Stop recording', and then "
            "click the button 'submit for recognition'",
            "result_item_error",
        )

    logging.info(f"Processing microphone: {in_filename}")
    try:
        return process(
            in_filename=in_filename,
            language=language,
            repo_id=repo_id,
            decoding_method=decoding_method,
            num_active_paths=num_active_paths,
        )
    except Exception as e:
        logging.info(str(e))
        return "", build_html_output(str(e), "result_item_error")
    
def process(
    language: str,
    repo_id: str,
    decoding_method: str,
    num_active_paths: int,
    in_filename: str,
):
    logging.info(f"language: {language}")
    logging.info(f"repo_id: {repo_id}")
    logging.info(f"decoding_method: {decoding_method}")
    logging.info(f"num_active_paths: {num_active_paths}")
    logging.info(f"in_filename: {in_filename}")
    filename = convert_to_wav(in_filename)

    now = datetime.now()
    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    logging.info(f"Started at {date_time}")

    start = time.time()

    recognizer = get_pretrained_model()

    text = decode(recognizer, filename)

    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    end = time.time()

    wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流
    num_frame = wav.getnframes() # 获取帧数
    num_channel=wav.getnchannels() # 获取声道数
    framerate=wav.getframerate() # 获取帧速率
    num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数
    str_data = wav.readframes(num_frame) # 读取全部的帧
    wav.close() # 关闭流
    duration = num_frame / framerate
    rtf = (end - start) / duration

    logging.info(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s")

    info = f"""
    Wave duration  : {duration: .3f} s <br/>
    Processing time: {end - start: .3f} s <br/>
    RTF: {end - start: .3f}/{duration: .3f} = {rtf:.3f} <br/>
    """
    if rtf > 1:
        info += (
            "<br/>We are loading the model for the first run. "
            "Please run again to measure the real RTF.<br/>"
        )

    logging.info(info)
    logging.info(f"\nrepo_id: {repo_id}\nhyp: {text}")

    return text, build_html_output(info)
title = "#汉语语音识别"
description = """
汉语普通话语音识别
"""

# css style is copied from
# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
css = """
.result {display:flex;flex-direction:column}
.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
.result_item_error {background-color:#ff7070;color:white;align-self:start}
"""
def update_model_dropdown(language: str):
    if language in language_to_models:
        choices = language_to_models[language]
        return gr.Dropdown(
            choices=choices,
            value=choices[0],
            interactive=True,
        )

    raise ValueError(f"Unsupported language: {language}")


demo = gr.Blocks(css=css)
with demo:
    gr.Markdown(title)
    language_choices = list(language_to_models.keys())

    language_radio = gr.Radio(
        label="Language",
        choices=language_choices,
        value=language_choices[0],
    )
    model_dropdown = gr.Dropdown(
        choices=language_to_models[language_choices[0]],
        label="Select a model",
        value=language_to_models[language_choices[0]][0],
    )

    language_radio.change(
        update_model_dropdown,
        inputs=language_radio,
        outputs=model_dropdown,
    )

    decoding_method_radio = gr.Radio(
        label="Decoding method",
        choices=["greedy_search", "modified_beam_search"],
        value="greedy_search",
    )
    num_active_paths_slider = gr.Slider(
        minimum=1,
        value=4,
        step=1,
        label="Number of active paths for modified_beam_search",
    )

    with gr.Tabs():
        with gr.TabItem("Upload from disk"):
            uploaded_file = gr.Audio(
                sources=["upload"],  # Choose between "microphone", "upload"
                type="filepath",
                label="Upload from disk",
            )
            upload_button = gr.Button("Submit for recognition")
            uploaded_output = gr.Textbox(label="Recognized speech from uploaded file")
            uploaded_html_info = gr.HTML(label="Info")

            gr.Examples(
                examples=examples,
                inputs=[
                    language_radio,
                    model_dropdown,
                    decoding_method_radio,
                    num_active_paths_slider,
                    uploaded_file,
                ],
                outputs=[uploaded_output, uploaded_html_info],
                fn=process_uploaded_file,
            )
        with gr.TabItem("Record from microphone"):
            microphone = gr.Audio(
                sources=["microphone"],  # Choose between "microphone", "upload"
                type="filepath",
                label="Record from microphone",
            )

            record_button = gr.Button("Submit for recognition")
            recorded_output = gr.Textbox(label="Recognized speech from recordings")
            recorded_html_info = gr.HTML(label="Info")

            gr.Examples(
                examples=examples,
                inputs=[
                    language_radio,
                    model_dropdown,
                    decoding_method_radio,
                    num_active_paths_slider,
                    microphone,
                ],
                outputs=[recorded_output, recorded_html_info],
                fn=process_microphone,
            )
        with gr.TabItem("From URL"):
            url_textbox = gr.Textbox(
                max_lines=1,
                placeholder="URL to an audio file",
                label="URL",
                interactive=True,
            )

            url_button = gr.Button("Submit for recognition")
            url_output = gr.Textbox(label="Recognized speech from URL")
            url_html_info = gr.HTML(label="Info")
        upload_button.click(
            process_uploaded_file,
            inputs=[
                language_radio,
                model_dropdown,
                decoding_method_radio,
                num_active_paths_slider,
                uploaded_file,
            ],
            outputs=[uploaded_output, uploaded_html_info],
        )            
        record_button.click(
            process_microphone,
            inputs=[
                language_radio,
                model_dropdown,
                decoding_method_radio,
                num_active_paths_slider,
                microphone,
            ],
            outputs=[recorded_output, recorded_html_info],
        )
        url_button.click(
            process_url,
            inputs=[
                language_radio,
                model_dropdown,
                decoding_method_radio,
                num_active_paths_slider,
                url_textbox,
            ],
            outputs=[url_output, url_html_info],
        )

    gr.Markdown(description)

if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)

    demo.launch()