| import os | |
| import torch | |
| import torchaudio | |
| import tempfile | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import streamlit as st | |
| os.environ["TRANSFORMERS_CACHE"] = "/app/cache" | |
| os.makedirs("/app/cache", exist_ok=True) | |
| whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") | |
| whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") | |
| text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased") | |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
| def transcribe(audio_bytes): | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: | |
| tmp.write(audio_bytes) | |
| tmp_path = tmp.name | |
| waveform, sample_rate = torchaudio.load(tmp_path) | |
| input_features = whisper_processor(waveform.squeeze().numpy(), sampling_rate=sample_rate, return_tensors="pt").input_features | |
| predicted_ids = whisper_model.generate(input_features) | |
| transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] | |
| os.remove(tmp_path) | |
| return transcription | |
| def extract_text_features(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) | |
| outputs = text_model(**inputs) | |
| return outputs.logits.argmax(dim=1).item() | |
| def predict | |