app.py

import streamlit as st
import langcodes
from allosaurus.app import read_recognizer
from pathlib import Path

  
def get_path_to_wav_format(uploaded_file):
#  st.write(dir(uploaded_file))
#  st.write(type(uploaded_file))
#  st.write(uploaded_file)
  uploaded_bytes = uploaded_file.getvalue()
  actual_file_path = Path(uploaded_file.name)
  actual_file_path.write_bytes(uploaded_bytes)
    

  if ".wav" in uploaded_file.name:
    return Path(uploaded_file.name)
 if ".mp3" in uploaded_file.name:
   new_desired_path = actual_file_path.with_suffix(".wav")
   waveform, sample_rate = torchaudio.load(actual_file_path)
   st.info(waveform, sample_rate)
   torchaudio.save(new_desired_path, waveform, sample_rate)
   return new_desired_path


def get_langcode_for_allosaurus(input_code):
  langcode = "ipa" # the default allosaurus recognizer
  description = "the default universal setting, not specific to any language"
  try:
    lang = langcodes.get(input_code)
    langcode = lang.to_alpha3()
    description = lang.display_name()
  except langcodes.LanguageTagError as e:
    pass
  return langcode, description 
  
  
  


if __name__ == "__main__":
  input_code = st.text_input("(optional) 2 or 3-letter ISO code for input language", max_chars=3)
  langcode, description = get_langcode_for_allosaurus(input_code)
  st.write(f"Instructing Allosaurus to recognize using language {langcode}. That is, {description}")
  
  model = read_recognizer()
  
  uploaded_files = st.file_uploader("Choose a file", type=[
                                                          ".wav", 
#                                                          ".mp3", # TODO: convert .mp3 to .wav and save
                                                          ],
                                                    accept_multiple_files=True,
                                                          )
  
  for uploaded_file in uploaded_files:
    
    if uploaded_file is not None:
  
      
      
      st.audio(uploaded_file, format='audio/wav')
      
      wav_file = get_path_to_wav_format(uploaded_file)    
      st.write(wav_file)
      result = model.recognize(wav_file, langcode)
      st.write(result)