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import gradio as gr
import speech_recognition as sr
from pydub import AudioSegment
import os
def audio_preproccessing():
os.system("deepFilter 'Audio.wav'")
os.rename("Audio_DeepFilterNet3.wav", "Audio.wav")
print("Audio Preprocessing Done!")
def transcribe_audio(audio_file_path, input_language,denoising,music):
# Auto-detect format from file extension
file_ext = os.path.splitext(audio_file_path)[1][1:] # e.g. "mp3", "wav"
# Add silence padding
audio = AudioSegment.from_file(audio_file_path, format=file_ext)
two_sec_silence = AudioSegment.silent(duration=2500)
audio = two_sec_silence + audio + two_sec_silence
audio.export("Audio.wav", format="wav")
file_path2 = "Audio.wav"
#if music=='Yes':
if denoising=='Yes':
audio_preproccessing()
recognizer = sr.Recognizer()
try:
with sr.AudioFile(file_path2) as source:
recognizer.adjust_for_ambient_noise(source)
audio_data = recognizer.record(source)
# Google API call
text = recognizer.recognize_google(audio_data, language=input_language)
return text
except sr.UnknownValueError:
return "Could not understand the audio"
except sr.RequestError as e:
return f"Could not request results; {e}"
# Gradio interface
iface = gr.Interface(
fn=transcribe_audio,
inputs=[
gr.Audio(sources=["upload","microphone"], type="filepath", label="Audio Input"),
#gr.Textbox(label="Language code (e.g., en-US, fa-IR)")
gr.Dropdown(choices=["fa-IR", "en-US", "ar-SA"], label="Choose the right language:"),
#gr.Checkbox(label="Apply Denoising?")
gr.Dropdown(choices=["No","Yes"], label="Need Denoising?"),
#gr.Dropdown(choices=["No","Yes"], label="Does the input audio have music?")
],
#outputs="text",
outputs=gr.Textbox(label="Transcription results", lines=10),
title="Speech-to-Text Service",
description="Upload or record audio and get transcription using our STT service."
)
iface.launch() |