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import requests
import pytz
import streamlit as st
import os
from datetime import datetime
from audio_recorder_streamlit import audio_recorder
# Filepath for saving the text
file_path = 'text_output.txt'
API_URL = 'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
headers = {
"Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
"Content-Type": "audio/wav"
}
def query(filename):
with open(filename, "rb") as f:
data = f.read
#try:
response = requests.post(API_URL, headers=headers, data=data)
#except:
# st.write('Whisper Voice Speech to Text Model is asleep. Starting up now on T4 - please give 3 minutes then retry as KEDA scales up from zero to activate running container(s).')
return response.json()
def generate_filename(prompt, file_type):
central = pytz.timezone('US/Central')
safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
return f"{safe_date_time}_{safe_prompt}.{file_type}"
def save_and_play_audio(audio_recorder):
audio_bytes = audio_recorder()
if audio_bytes:
filename = generate_filename("Recording", "wav")
with open(filename, 'wb') as f:
f.write(audio_bytes)
st.audio(audio_bytes, format="audio/wav")
return filename
def transcribe_audio(filename):
output = query(filename)
return output
def save_transcription(transcription):
with open(file_path, 'a') as f:
f.write(f"{transcription}\n")
def load_previous_transcriptions():
if os.path.exists(file_path):
with open(file_path, 'r') as f:
return f.read()
return ""
def main():
st.title("Speech to Text 🎤📝")
st.write("Record your speech and get the text. 🗨️")
previous_transcriptions = load_previous_transcriptions()
text_area = st.text_area("Transcriptions:", previous_transcriptions, height=400)
filename = save_and_play_audio(audio_recorder)
if filename is not None:
try:
transcription = transcribe_audio(filename)
# Update the text area with new transcription
updated_transcriptions = f"{previous_transcriptions}\n{transcription}"
st.text_area("Transcriptions:", updated_transcriptions, height=400)
# Save the new transcription to file
save_transcription(transcription)
except:
st.write('Whisperer loading..')
if __name__ == "__main__":
main()
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