File size: 2,325 Bytes
68653c3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
# def translate_and_speak(text):
# input_text = "en " + text
# encoded = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
# generated_tokens = model.generate(**encoded, max_length=128, num_beams=5, early_stopping=True)
# output = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
# for tag in ["__en__", "__sa__", "en", "sa"]:
# output = output.replace(tag, "")
# sanskrit_text = output.strip()
# # Convert to speech
# tts = gTTS(sanskrit_text, lang='hi')
# with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
# tts.save(fp.name)
# audio_path = fp.name
# return sanskrit_text, audio_path
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import torch
from gtts import gTTS
import gradio as gr
import tempfile
# Load model and tokenizer
model__name = "Helsinki-NLP/opus-mt-en-hi"
# Use GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model_name = "SweUmaVarsh/m2m100-en-sa-translation"
tokenizer = M2M100Tokenizer.from_pretrained(model_name)
model = M2M100ForConditionalGeneration.from_pretrained(model_name)
def translate_and_speak(text):
input_text = "en " + text
encoded = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
generated_tokens = model.generate(**encoded, max_length=128, num_beams=5, early_stopping=True)
output = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
for tag in ["__en__", "__sa__", "en", "sa"]:
output = output.replace(tag, "")
sanskrit_text = output.strip()
# Convert to speech
tts = gTTS(sanskrit_text, lang='hi')
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
tts.save(fp.name)
audio_path = fp.name
return sanskrit_text, audio_path
iface = gr.Interface(
fn=translate_and_speak,
inputs=gr.Textbox(label="Enter English Text"),
outputs=[gr.Textbox(label="Sanskrit Translation"), gr.Audio(label="Sanskrit Speech")],
title="Final Year Project: English to Sanskrit Translator (IT 'A' 2021–2025)",
description="Enter a sentence in English to get its Sanskrit translation and audio output."
)
iface.launch()
|