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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)
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