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import torch
import gradio as gr
import json
from transformers import pipeline
# Load the translation pipeline
text_translator = pipeline(
"translation",
model="facebook/nllb-200-distilled-600M",
torch_dtype=torch.bfloat16
)
# Load the JSON data for language codes
with open('language.json', 'r') as file:
language_data = json.load(file)
# Get all available languages (excluding duplicates with different scripts)
available_languages = []
seen_languages = set()
for entry in language_data:
base_language = entry['Language'].split('(')[0].strip()
if base_language not in seen_languages:
available_languages.append(base_language)
seen_languages.add(base_language)
# Sort languages alphabetically
available_languages.sort()
# Function to retrieve FLORES-200 code for a given language
def get_FLORES_code_from_language(language):
# Try exact match first
for entry in language_data:
if entry['Language'].lower() == language.lower():
return entry['FLORES-200 code']
# Fallback to matching base language name
for entry in language_data:
if entry['Language'].lower().startswith(language.lower()):
return entry['FLORES-200 code']
return None
# Translation function
def translate_text(text, destination_language):
dest_code = get_FLORES_code_from_language(destination_language)
if dest_code is None:
return f"Error: Could not find FLORES code for language {destination_language}"
try:
# Translation call
translation = text_translator(text, src_lang="eng_Latn", tgt_lang=dest_code)
return translation[0]["translation_text"]
except Exception as e:
return f"Error during translation: {str(e)}"
# Initialize the speech-to-text pipeline (Whisper model)
speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small")
# Function to transcribe audio to text
def transcribe_audio(audio_file, destination_language):
try:
transcription_result = speech_to_text(audio_file)
print(f"Transcription result: {transcription_result}") # Debugging output
if "text" in transcription_result:
transcription = transcription_result["text"]
else:
return "Error: Unable to transcribe audio."
# Translate the transcribed text
return translate_text(transcription, destination_language)
except Exception as e:
return f"Error during transcription: {str(e)}"
# Gradio interface
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(
"""
# 🌐 AI Translation Assistant
Translate **text or audio** into your desired language with professional accuracy.
"""
)
with gr.Tabs():
with gr.Tab("Text Translation"):
text_input = gr.Textbox(label="Input Text", lines=6, placeholder="Enter your text here...")
language_dropdown = gr.Dropdown(
choices=available_languages, label="Select Destination Language"
)
translated_text_output = gr.Textbox(label="Translated Text", lines=4)
translate_button = gr.Button("Translate")
translate_button.click(
translate_text, inputs=[text_input, language_dropdown], outputs=[translated_text_output]
)
with gr.Tab("Audio Translation"):
audio_input = gr.Audio(label="Upload Audio File", type="filepath")
audio_language_dropdown = gr.Dropdown(
choices=available_languages, label="Select Destination Language"
)
audio_translated_text_output = gr.Textbox(label="Transcribed and Translated Text", lines=2)
audio_translate_button = gr.Button("Transcribe and Translate")
audio_translate_button.click(
transcribe_audio,
inputs=[audio_input, audio_language_dropdown],
outputs=[audio_translated_text_output],
)
if __name__ == "__main__":
demo.launch()
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