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| # Transform an audio to text script with language detection. | |
| # Author: Pratiksha Patel | |
| # Description: This script record the audio, transform it to text, detect the language of the file and save it to a txt file. | |
| # import required modules | |
| import torch | |
| import streamlit as st | |
| from audio_recorder_streamlit import audio_recorder | |
| from langdetect import detect | |
| import numpy as np | |
| from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq | |
| def transcribe_audio(audio_bytes): | |
| processor = AutoProcessor.from_pretrained("openai/whisper-large") | |
| model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large") | |
| audio_array = np.frombuffer(audio_bytes, dtype=np.int16) | |
| audio_tensor = torch.tensor(audio_array, dtype=torch.float64) / 32768.0 | |
| inputs = processor(feature_extractor=audio_tensor, sampling_rate=16000, return_tensors="pt") | |
| logits = model(**inputs).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.decode(predicted_ids[0]) | |
| return transcription | |
| # Streamlit app | |
| st.title("Audio to Text Transcription..") | |
| audio_bytes = audio_recorder(pause_threshold=3.0, sample_rate=16_000) | |
| if audio_bytes: | |
| st.audio(audio_bytes, format="audio/wav") | |
| transcription = transcribe_audio(audio_bytes) | |
| if transcription: | |
| st.write("Transcription:") | |
| st.write(transcription) | |
| else: | |
| st.write("Error: Failed to transcribe audio.") | |
| else: | |
| st.write("No audio recorded.") | |