IndicTrans2/huggingface_interface/IndicTransToolkit/main.py

import gradio as gr
import torch
from transformers import AutoModelForSeq2SeqLM, BitsAndBytesConfig, AutoTokenizer
from IndicTransToolkit import IndicProcessor
import speech_recognition as sr

# Constants
BATCH_SIZE = 4
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
quantization = None

# ---- IndicTrans2 Model Initialization ----
def initialize_model_and_tokenizer(ckpt_dir, quantization):
    if quantization == "4-bit":
        qconfig = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_compute_dtype=torch.bfloat16,
        )
    elif quantization == "8-bit":
        qconfig = BitsAndBytesConfig(
            load_in_8bit=True,
            bnb_8bit_use_double_quant=True,
            bnb_8bit_compute_dtype=torch.bfloat16,
        )
    else:
        qconfig = None

    tokenizer = AutoTokenizer.from_pretrained(ckpt_dir, trust_remote_code=True)
    model = AutoModelForSeq2SeqLM.from_pretrained(
        ckpt_dir,
        trust_remote_code=True,
        low_cpu_mem_usage=True,
        quantization_config=qconfig,
    )

    if qconfig is None:
        model = model.to(DEVICE)
        if DEVICE == "cuda":
            model.half()

    model.eval()
    return tokenizer, model

def batch_translate(input_sentences, src_lang, tgt_lang, model, tokenizer, ip):
    translations = []
    for i in range(0, len(input_sentences), BATCH_SIZE):
        batch = input_sentences[i : i + BATCH_SIZE]
        batch = ip.preprocess_batch(batch, src_lang=src_lang, tgt_lang=tgt_lang)
        inputs = tokenizer(
            batch,
            truncation=True,
            padding="longest",
            return_tensors="pt",
            return_attention_mask=True,
        ).to(DEVICE)

        with torch.no_grad():
            generated_tokens = model.generate(
                **inputs,
                use_cache=True,
                min_length=0,
                max_length=256,
                num_beams=5,
                num_return_sequences=1,
            )

        with tokenizer.as_target_tokenizer():
            generated_tokens = tokenizer.batch_decode(
                generated_tokens.detach().cpu().tolist(),
                skip_special_tokens=True,
                clean_up_tokenization_spaces=True,
            )

        translations += ip.postprocess_batch(generated_tokens, lang=tgt_lang)
        del inputs
        torch.cuda.empty_cache()

    return translations

# Initialize IndicTrans2
en_indic_ckpt_dir = "ai4bharat/indictrans2-indic-en-1B"
en_indic_tokenizer, en_indic_model = initialize_model_and_tokenizer(en_indic_ckpt_dir, quantization)
ip = IndicProcessor(inference=True)

# ---- Gradio Function ----
def transcribe_and_translate(audio):
    recognizer = sr.Recognizer()
    with sr.AudioFile(audio) as source:
        audio_data = recognizer.record(source)
        try:
            # Malayalam transcription using Google API
            malayalam_text = recognizer.recognize_google(audio_data, language="ml-IN")
        except sr.UnknownValueError:
            return "Could not understand audio", ""
        except sr.RequestError as e:
            return f"Google API Error: {e}", ""
    
    # Translation
    en_sents = [malayalam_text]
    src_lang, tgt_lang = "mal_Mlym", "eng_Latn"
    translations = batch_translate(en_sents, src_lang, tgt_lang, en_indic_model, en_indic_tokenizer, ip)
    
    return malayalam_text, translations[0]

# ---- Gradio Interface ----
iface = gr.Interface(
    fn=transcribe_and_translate,
    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
    outputs=[
        gr.Textbox(label="Malayalam Transcription"),
        gr.Textbox(label="English Translation")
    ],
    title="Malayalam Speech Recognition & Translation",
    description="Speak in Malayalam → Transcribe using Google Speech Recognition → Translate to English using IndicTrans2."
)

iface.launch(debug=True, share=True)