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import gradio as gr
import re
import subprocess
import math
import shutil
import soundfile as sf
import tempfile
import os
import requests
import time
def _return_yt_html_embed(yt_url):
video_id = yt_url.split("?v=")[-1]
HTML_str = (
f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
" </center>"
)
return HTML_str
def transcribe_base(audio, language):
start_time = time.time()
d, sr = sf.read(audio)
data = {'audio': d.tolist(),
'sampling_rate': sr,
'language': language}
response = requests.post(os.getenv("api_url"), json=data).json()
transcription = response["text"]
speaker_class_string = response["speaker_class_string"]
end_time = time.time()
print("-"*50)
print(len(data["audio"])/float(sr))
print(end_time-start_time)
print("-"*50)
return transcription, speaker_class_string
def transcribe(audio_microphone, audio_upload, language):
print("Transcription request")
print(audio_microphone, audio_upload, language)
audio = audio_microphone if audio_microphone is not None else audio_upload
return transcribe_base(audio, language)
demo = gr.Blocks()
with demo:
gr.Markdown("# Speech recognition using Whisper models")
gr.Markdown("Orai NLP Technologies")
with gr.Tab("Trancribe Audio"):
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(sources="microphone", type="filepath"),
gr.Audio(sources="upload", type="filepath"),
gr.Dropdown(choices=[("Basque", "eu"),
("Spanish", "es"),
("English", "en")],
#("French", "fr"),
#("Italian", "it"),
value="eu")
],
outputs=[
gr.Textbox(label="Transcription", autoscroll=False),
gr.Textbox(label="Speaker Identification", autoscroll=False)
],
allow_flagging="never",
)
demo.queue(max_size=1)
demo.launch(share=False, max_threads=3, auth=(os.getenv("username"), os.getenv("password")), auth_message="Please provide a username and a password.")
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