#!/usr/bin/env python3 # # Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) # # See LICENSE for clarification regarding multiple authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # References: # https://gradio.app/docs/#dropdown import base64 import logging import os import tempfile import time from datetime import datetime import gradio as gr import torch import torchaudio import urllib.request from examples import examples from model import decode, get_pretrained_model, language_to_models, sample_rate 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 = in_filename + ".wav" logging.info(f"Converting '{in_filename}' to '{out_filename}'") _ = os.system(f"ffmpeg -hide_banner -i '{in_filename}' -ar 16000 '{out_filename}'") _ = os.system( f"ffmpeg -hide_banner -loglevel error -i '{in_filename}' -ar 16000 '{out_filename}.flac'" ) with open(out_filename + ".flac", "rb") as f: s = "\n" + out_filename + "\n" s += base64.b64encode(f.read()).decode() logging.info(s) return out_filename def build_html_output(s: str, style: str = "result_item_success"): return f"""
{s}
""" 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") @torch.no_grad() 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( repo_id, decoding_method=decoding_method, num_active_paths=num_active_paths, ) text = decode(recognizer, filename) date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f") end = time.time() metadata = torchaudio.info(filename) duration = metadata.num_frames / sample_rate rtf = (end - start) / duration logging.info(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s") info = f""" Wave duration : {duration: .3f} s
Processing time: {end - start: .3f} s
RTF: {end - start: .3f}/{duration: .3f} = {rtf:.3f}
""" if rtf > 1: info += ( "
We are loading the model for the first run. " "Please run again to measure the real RTF.
" ) logging.info(info) logging.info(f"\nrepo_id: {repo_id}\nhyp: {text}") return text, build_html_output(info) title = "# Automatic Speech Recognition with Next-gen Kaldi" description = """ This space shows how to do automatic speech recognition with Next-gen Kaldi. Please visit for streaming speech recognition with **Next-gen Kaldi**. It is running on CPU within a docker container provided by Hugging Face. See more information by visiting the following links: - - - - If you want to deploy it locally, please see """ # 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.update(choices=choices, value=choices[0]) 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( source="upload", # Choose between "microphone", "upload" type="filepath", optional=False, 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( source="microphone", # Choose between "microphone", "upload" type="filepath", optional=False, 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) torch.set_num_threads(1) torch.set_num_interop_threads(1) torch._C._jit_set_profiling_executor(False) torch._C._jit_set_profiling_mode(False) torch._C._set_graph_executor_optimize(False) if __name__ == "__main__": formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" logging.basicConfig(format=formatter, level=logging.INFO) demo.launch()