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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer |
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import torch |
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from gtts import gTTS |
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import gradio as gr |
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import tempfile |
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model__name = "Helsinki-NLP/opus-mt-en-hi" |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = model.to(device) |
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model_name = "SweUmaVarsh/m2m100-en-sa-translation" |
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tokenizer = M2M100Tokenizer.from_pretrained(model_name) |
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model = M2M100ForConditionalGeneration.from_pretrained(model_name) |
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def translate_and_speak(text): |
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input_text = "en " + text |
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encoded = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device) |
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generated_tokens = model.generate(**encoded, max_length=128, num_beams=5, early_stopping=True) |
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output = tokenizer.decode(generated_tokens[0], skip_special_tokens=True) |
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for tag in ["__en__", "__sa__", "en", "sa"]: |
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output = output.replace(tag, "") |
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sanskrit_text = output.strip() |
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tts = gTTS(sanskrit_text, lang='hi') |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp: |
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tts.save(fp.name) |
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audio_path = fp.name |
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return sanskrit_text, audio_path |
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iface = gr.Interface( |
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fn=translate_and_speak, |
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inputs=gr.Textbox(label="Enter English Text"), |
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outputs=[gr.Textbox(label="Sanskrit Translation"), gr.Audio(label="Sanskrit Speech")], |
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title="Final Year Project: English to Sanskrit Translator (IT 'A' 2021–2025)", |
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description="Enter a sentence in English to get its Sanskrit translation and audio output." |
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) |
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iface.launch() |
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