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| import base64 | |
| import gradio as gr | |
| import librosa | |
| import logging | |
| import os | |
| import soundfile as sf | |
| import subprocess | |
| import tempfile | |
| import urllib.request | |
| from datetime import datetime | |
| from time import time | |
| from examples import examples | |
| from model import UETASRModel | |
| def get_duration(filename: str) -> float: | |
| return librosa.get_duration(path=filename) | |
| def convert_to_wav(in_filename: str) -> str: | |
| out_filename = os.path.splitext(in_filename)[0] + ".wav" | |
| logging.info(f"Converting {in_filename} to {out_filename}") | |
| y, sr = librosa.load(in_filename, sr=16000) | |
| sf.write(out_filename, y, sr) | |
| 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( | |
| url: str, | |
| decoding_method: str, | |
| beam_size: int, | |
| max_symbols_per_step: int, | |
| ): | |
| logging.info(f"Processing URL: {url}") | |
| with tempfile.NamedTemporaryFile() as f: | |
| try: | |
| urllib.request.urlretrieve(url, f.name) | |
| return process(in_filename=f.name, | |
| decoding_method=decoding_method, | |
| beam_size=beam_size, | |
| max_symbols_per_step=max_symbols_per_step) | |
| except Exception as e: | |
| logging.info(str(e)) | |
| return "", build_html_output(str(e), "result_item_error") | |
| def process_uploaded_file( | |
| in_filename: str, | |
| decoding_method: str, | |
| beam_size: int, | |
| max_symbols_per_step: int, | |
| ): | |
| 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, | |
| decoding_method=decoding_method, | |
| beam_size=beam_size, | |
| max_symbols_per_step=max_symbols_per_step) | |
| except Exception as e: | |
| logging.info(str(e)) | |
| return "", build_html_output(str(e), "result_item_error") | |
| def process_microphone( | |
| in_filename: str, | |
| decoding_method: str, | |
| beam_size: int, | |
| max_symbols_per_step: int, | |
| ): | |
| 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 microphone: {in_filename}") | |
| try: | |
| return process(in_filename=in_filename, | |
| decoding_method=decoding_method, | |
| beam_size=beam_size, | |
| max_symbols_per_step=max_symbols_per_step) | |
| except Exception as e: | |
| logging.info(str(e)) | |
| return "", build_html_output(str(e), "result_item_error") | |
| def process( | |
| in_filename: str, | |
| decoding_method: str, | |
| beam_size: int, | |
| max_symbols_per_step: int, | |
| ): | |
| logging.info(f"in_filename: {in_filename}") | |
| filename = convert_to_wav(in_filename) | |
| now = datetime.now() | |
| date_time = now.strftime("%d/%m/%Y, %H:%M:%S.%f") | |
| logging.info(f"Started at {date_time}") | |
| repo_id = "thanhtvt/uetasr-conformer_30.3m" | |
| start = time() | |
| recognizer = UETASRModel(repo_id, | |
| decoding_method, | |
| beam_size, | |
| max_symbols_per_step) | |
| text = recognizer.predict(filename) | |
| date_time = now.strftime("%d/%m/%Y, %H:%M:%S.%f") | |
| end = time() | |
| duration = get_duration(filename) | |
| 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 required resources for the first run. " | |
| "Please run again to measure the real RTF.<br/>" | |
| ) | |
| logging.info(info) | |
| return text, build_html_output(info) | |
| title = "Educa ASR" | |
| description = """ | |
| A space demo for Automatic Speech Recognition. | |
| """ | |
| # 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} | |
| """ | |
| demo = gr.Blocks(css=css) | |
| with demo: | |
| gr.Markdown(title) | |
| decode_method_radio = gr.Radio( | |
| label="Decoding method", | |
| choices=["greedy_search", "beam_search"], | |
| value="greedy_search", | |
| interactive=True, | |
| ) | |
| beam_size_slider = gr.Slider( | |
| label="Beam size", | |
| minimum=1, | |
| maximum=20, | |
| step=1, | |
| value=1, | |
| interactive=False, | |
| ) | |
| def interact_beam_slider(decoding_method): | |
| if decoding_method == "greedy_search": | |
| return gr.update(value=1, interactive=False) | |
| else: | |
| return gr.update(interactive=True) | |
| decode_method_radio.change(interact_beam_slider, | |
| decode_method_radio, | |
| beam_size_slider) | |
| max_symbols_per_step_slider = gr.Slider( | |
| label="Maximum symbols per step", | |
| minimum=1, | |
| maximum=20, | |
| step=1, | |
| value=5, | |
| interactive=True, | |
| visible=True, | |
| ) | |
| with gr.Tabs(): | |
| with gr.TabItem("Upload from disk"): | |
| uploaded_file = gr.Audio( | |
| source="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=uploaded_file, | |
| outputs=[uploaded_output, uploaded_html_info], | |
| fn=process_uploaded_file, | |
| ) | |
| with gr.TabItem("Record from microphone"): | |
| microphone = gr.Audio( | |
| source="microphone", | |
| 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=microphone, | |
| outputs=[uploaded_output, uploaded_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=[ | |
| uploaded_file, | |
| decode_method_radio, | |
| beam_size_slider, | |
| max_symbols_per_step_slider, | |
| ], | |
| outputs=[uploaded_output, uploaded_html_info], | |
| ) | |
| record_button.click( | |
| process_microphone, | |
| inputs=[ | |
| microphone, | |
| decode_method_radio, | |
| beam_size_slider, | |
| max_symbols_per_step_slider, | |
| ], | |
| outputs=[recorded_output, recorded_html_info], | |
| ) | |
| url_button.click( | |
| process_url, | |
| inputs=[ | |
| url_textbox, | |
| decode_method_radio, | |
| beam_size_slider, | |
| max_symbols_per_step_slider, | |
| ], | |
| 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() | |