app.py

import gradio as gr
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, AutoModelForSeq2SeqLM, AutoTokenizer
from IndicTransToolkit import IndicProcessor
import librosa
import numpy as np

# Constants
BATCH_SIZE = 4
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# ---- Initialize Wav2Vec2 Model for Malayalam Speech Recognition ----
def initialize_wav2vec2_model(model_name):
    processor = Wav2Vec2Processor.from_pretrained(model_name)
    model = Wav2Vec2ForCTC.from_pretrained(model_name).to(DEVICE)
    model.eval()
    return processor, model

wav2vec2_model_name = "addy88/wav2vec2-malayalam-stt"
wav2vec2_processor, wav2vec2_model = initialize_wav2vec2_model(wav2vec2_model_name)

# ---- IndicTrans2 Model Initialization ----
def initialize_translation_model_and_tokenizer(ckpt_dir):
    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,
    ).to(DEVICE)
    model.eval()
    return tokenizer, model

en_indic_ckpt_dir = "ai4bharat/indictrans2-indic-en-1B"
en_indic_tokenizer, en_indic_model = initialize_translation_model_and_tokenizer(en_indic_ckpt_dir)
ip = IndicProcessor(inference=True)

# ---- Batch Translation Function ----
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

# ---- Gradio Function ----
def transcribe_and_translate(audio):
    try:
        # Load audio using librosa and force sample rate to 16kHz
        audio_input, sample_rate = librosa.load(audio, sr=16000)
        
        # Normalize audio
        if np.max(np.abs(audio_input)) != 0:
            audio_input = audio_input / np.max(np.abs(audio_input))
        
    except Exception as e:
        return f"Error reading audio: {e}", ""

    # Process audio
    input_values = wav2vec2_processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values.to(DEVICE)

    # Perform inference
    with torch.no_grad():
        logits = wav2vec2_model(input_values).logits
        predicted_ids = torch.argmax(logits, dim=-1)

    # Decode transcription
    malayalam_text = wav2vec2_processor.decode(predicted_ids[0].cpu(), skip_special_tokens=True)

    # 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 Wav2Vec2 → Translate to English using IndicTrans2."
)

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