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| import torch | |
| import pickle | |
| import whisper | |
| import streamlit as st | |
| import torchaudio as ta | |
| from io import BytesIO | |
| from transformers import AutoProcessor, SeamlessM4TModel, WhisperProcessor, WhisperForConditionalGeneration | |
| if torch.cuda.is_available(): | |
| device = "cuda:0" | |
| torch_dtype = torch.float16 | |
| else: | |
| device = "cpu" | |
| torch_dtype = torch.float32 | |
| SAMPLING_RATE=16000 | |
| task = "transcribe" | |
| print(f"{device} Active!") | |
| # load Whisper model and processor | |
| processor = WhisperProcessor.from_pretrained("openai/whisper-small") | |
| model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") | |
| # Title of the app | |
| st.title("Audio Player with Live Transcription") | |
| # Sidebar for file uploader and submit button | |
| st.sidebar.header("Upload Audio Files") | |
| uploaded_files = st.sidebar.file_uploader("Choose audio files", type=["mp3", "wav"], accept_multiple_files=True) | |
| submit_button = st.sidebar.button("Submit") | |
| # def transcribe_audio(audio_data): | |
| # recognizer = sr.Recognizer() | |
| # with sr.AudioFile(audio_data) as source: | |
| # audio = recognizer.record(source) | |
| # try: | |
| # # Transcribe the audio using Google Web Speech API | |
| # transcription = recognizer.recognize_google(audio) | |
| # return transcription | |
| # except sr.UnknownValueError: | |
| # return "Unable to transcribe the audio." | |
| # except sr.RequestError as e: | |
| # return f"Could not request results; {e}" | |
| def detect_language(audio_file): | |
| whisper_model = whisper.load_model("base") | |
| mel = whisper.log_mel_spectrogram(trimmed_audio).to(whisper_model.device) | |
| # detect the spoken language | |
| _, probs = whisper_model.detect_language(mel) | |
| print(f"Detected language: {max(probs[0], key=probs[0].get)}") | |
| return max(probs[0], key=probs[0].get) | |
| if submit_button and uploaded_files is not None: | |
| st.write("Files uploaded successfully!") | |
| for uploaded_file in uploaded_files: | |
| # Display file name and audio player | |
| st.write(f"**File name**: {uploaded_file.name}") | |
| st.audio(uploaded_file, format=uploaded_file.type) | |
| # Transcription section | |
| st.write("**Transcription**:") | |
| # Read the uploaded file data | |
| waveform, sampling_rate = ta.load(uploaded_file.getvalue()) | |
| resampled_inp = ta.functional.resample(waveform, orig_freq=sampling_rate, new_freq=SAMPLING_RATE) | |
| input_features = processor(resampled_inp[0], sampling_rate=16000, return_tensors='pt').input_features | |
| if task == "translate": | |
| # Detect Language | |
| lang = detect_language(input_features) | |
| with open('languages.pkl', 'rb') as f: | |
| lang_dict = pickle.load(f) | |
| detected_language = lang_dict[lang] | |
| # Set decoder & Predict translation | |
| forced_decoder_ids = processor.get_decoder_prompt_ids(language=detected_language, task="translate") | |
| predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) | |
| else: | |
| predicted_ids = model.generate(input_features) | |
| # decode token ids to text | |
| transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
| st.write(transcription) | |
| # print(waveform, sampling_rate) | |
| # Run transcription function and display | |
| # import pdb;pdb.set_trace() | |
| # st.write(audio_data.getvalue()) | |