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| import gradio as gr | |
| import librosa | |
| import torch | |
| import numpy as np | |
| # Lazy init placeholders | |
| processor = None | |
| model = None | |
| decoder = None | |
| def init_models(): | |
| global processor, model, decoder | |
| if processor is not None: | |
| return | |
| from transformers import AutoProcessor, AutoModelForCTC | |
| from pyctcdecode import build_ctcdecoder | |
| processor = AutoProcessor.from_pretrained("ai4bharat/indicwav2vec-hindi") | |
| model = AutoModelForCTC.from_pretrained("ai4bharat/indicwav2vec-hindi") | |
| vocab = processor.tokenizer.get_vocab() | |
| sorted_vocab = sorted(vocab.items(), key=lambda kv: kv[1]) | |
| tokens = [tok for tok, _ in sorted_vocab] | |
| arpa_path = "./hindi_small_4gram_pruned_clean.arpa" | |
| binary_path = "./hindi_small_4gram_pruned.binary" | |
| unigrams = {} | |
| with open(arpa_path, encoding="utf-8") as f: | |
| in_unigrams = False | |
| for line in f: | |
| line = line.strip() | |
| if not in_unigrams: | |
| if line == "\\1-grams:": | |
| in_unigrams = True | |
| continue | |
| if line.startswith("\\"): | |
| break | |
| parts = line.split(maxsplit=2) | |
| if len(parts) >= 2: | |
| unigrams[parts[1]] = float(parts[0]) | |
| decoder = build_ctcdecoder(tokens, binary_path, unigrams=unigrams) | |
| def transcribe(audio_path): | |
| if not audio_path: | |
| return "No audio provided." | |
| init_models() | |
| # librosa.load will load and resample | |
| audio_np, sr = librosa.load(audio_path, sr=16000) | |
| audio_np = audio_np.astype(np.float32) | |
| inputs = processor(audio_np, sampling_rate=sr, return_tensors="pt") | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits.cpu().numpy()[0] | |
| return decoder.decode(logits) | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=gr.Audio(type="filepath", label="Upload a WAV file"), | |
| outputs="text", | |
| title="Indic ASR Demo (Hindi)", | |
| description="Upload a Hindi audio file (.wav) to see the transcription." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False) | |