#!/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 time from datetime import datetime import gradio as gr import torch import torchaudio 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_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, ) 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], ) 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()