Create app.py

app.py

@@ -0,0 +1,117 @@
+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)