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| import streamlit as st | |
| import os | |
| import soundfile as sf | |
| import torch | |
| from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
| model_id = "distil-whisper/distil-large-v2" | |
| model = AutoModelForSpeechSeq2Seq.from_pretrained( | |
| model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True | |
| ) | |
| model.to(device) | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model=model, | |
| tokenizer=processor.tokenizer, | |
| feature_extractor=processor.feature_extractor, | |
| max_new_tokens=128, | |
| chunk_length_s=15, | |
| batch_size=16, | |
| torch_dtype=torch_dtype, | |
| device=device, | |
| ) | |
| def transcribe_audio(audio_file): | |
| # Save the audio file to a temporary file | |
| with open("temp_audio_file", "wb") as f: | |
| f.write(audio_file.getbuffer()) | |
| # Transcribe the audio file using the Whisper model | |
| result = pipe("temp_audio_file") | |
| return result["text"] | |
| # Streamlit app | |
| def main(): | |
| st.title('BETTER TRANSCRIBER') | |
| # Audio file uploader | |
| uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a", "ogg", "flac"]) | |
| if uploaded_file is not None: | |
| # Show a button to start the transcription process | |
| if st.button('Transcribe'): | |
| # Show a message while transcribing | |
| with st.spinner('Transcribing...'): | |
| text = transcribe_audio(uploaded_file) | |
| # Show the transcription | |
| st.subheader('Transcription:') | |
| st.write(text) | |
| else: | |
| st.write('Upload an audio file to get started.') | |
| if __name__ == "__main__": | |
| main() |