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| import streamlit as st | |
| import openai | |
| import os | |
| from pydub import AudioSegment | |
| from pydub.silence import split_on_silence | |
| from dotenv import load_dotenv | |
| from tempfile import NamedTemporaryFile | |
| import math | |
| from docx import Document | |
| import time | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| # Set your OpenAI API key | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| # Comprehensive dictionary of languages supported by Whisper (ISO 639-1 codes) | |
| # This list is based on the languages supported by the official Whisper model. | |
| languages = { | |
| "Afrikaans": "af", | |
| "Albanian": "sq", | |
| "Amharic": "am", | |
| "Arabic": "ar", | |
| "Armenian": "hy", | |
| "Assamese": "as", | |
| "Azerbaijani": "az", | |
| "Basque": "eu", | |
| "Belarusian": "be", | |
| "Bengali": "bn", | |
| "Bosnian": "bs", | |
| "Bulgarian": "bg", | |
| "Burmese": "my", | |
| "Catalan": "ca", | |
| "Cebuano": "ceb", | |
| "Chichewa": "ny", | |
| "Chinese": "zh", | |
| "Corsican": "co", | |
| "Croatian": "hr", | |
| "Czech": "cs", | |
| "Danish": "da", | |
| "Dutch": "nl", | |
| "English": "en", | |
| "Esperanto": "eo", | |
| "Estonian": "et", | |
| "Filipino": "tl", | |
| "Finnish": "fi", | |
| "French": "fr", | |
| "Frisian": "fy", | |
| "Galician": "gl", | |
| "Georgian": "ka", | |
| "German": "de", | |
| "Greek": "el", | |
| "Gujarati": "gu", | |
| "Haitian Creole": "ht", | |
| "Hausa": "ha", | |
| "Hawaiian": "haw", | |
| "Hebrew": "he", | |
| "Hindi": "hi", | |
| "Hmong": "hmn", | |
| "Hungarian": "hu", | |
| "Icelandic": "is", | |
| "Igbo": "ig", | |
| "Indonesian": "id", | |
| "Irish": "ga", | |
| "Italian": "it", | |
| "Japanese": "ja", | |
| "Javanese": "jw", | |
| "Kannada": "kn", | |
| "Kazakh": "kk", | |
| "Khmer": "km", | |
| "Kinyarwanda": "rw", | |
| "Korean": "ko", | |
| "Kurdish": "ku", | |
| "Kyrgyz": "ky", | |
| "Lao": "lo", | |
| "Latin": "la", | |
| "Latvian": "lv", | |
| "Lithuanian": "lt", | |
| "Luxembourgish": "lb", | |
| "Macedonian": "mk", | |
| "Malagasy": "mg", | |
| "Malay": "ms", | |
| "Malayalam": "ml", | |
| "Maltese": "mt", | |
| "Maori": "mi", | |
| "Marathi": "mr", | |
| "Mongolian": "mn", | |
| "Nepali": "ne", | |
| "Norwegian": "no", | |
| "Nyanja": "ny", | |
| "Odia": "or", | |
| "Pashto": "ps", | |
| "Persian": "fa", | |
| "Polish": "pl", | |
| "Portuguese": "pt", | |
| "Punjabi": "pa", | |
| "Romanian": "ro", | |
| "Russian": "ru", | |
| "Samoan": "sm", | |
| "Scots Gaelic": "gd", | |
| "Serbian": "sr", | |
| "Sesotho": "st", | |
| "Shona": "sn", | |
| "Sindhi": "sd", | |
| "Sinhala": "si", | |
| "Slovak": "sk", | |
| "Slovenian": "sl", | |
| "Somali": "so", | |
| "Spanish": "es", | |
| "Sundanese": "su", | |
| "Swahili": "sw", | |
| "Swedish": "sv", | |
| "Tajik": "tg", | |
| "Tamil": "ta", | |
| "Tatar": "tt", | |
| "Telugu": "te", | |
| "Thai": "th", | |
| "Turkish": "tr", | |
| "Turkmen": "tk", | |
| "Ukrainian": "uk", | |
| "Urdu": "ur", | |
| "Uyghur": "ug", | |
| "Uzbek": "uz", | |
| "Vietnamese": "vi", | |
| "Welsh": "cy", | |
| "Xhosa": "xh", | |
| "Yiddish": "yi", | |
| "Yoruba": "yo", | |
| "Zulu": "zu" | |
| } | |
| # Create a selectbox for language selection; default is English. | |
| selected_lang_name = st.selectbox("Select transcription language", sorted(languages.keys()), index=sorted(languages.keys()).index("English")) | |
| selected_language = languages[selected_lang_name] | |
| def split_audio_on_silence(audio_file_path, min_silence_len=500, silence_thresh=-40, keep_silence=250): | |
| """ | |
| Split an audio file into chunks using silence detection. | |
| """ | |
| audio = AudioSegment.from_file(audio_file_path) | |
| chunks = split_on_silence( | |
| audio, | |
| min_silence_len=min_silence_len, | |
| silence_thresh=silence_thresh, | |
| keep_silence=keep_silence | |
| ) | |
| return chunks | |
| def transcribe(audio_file): | |
| """ | |
| Transcribe an audio file using the OpenAI Whisper model. | |
| This uses the OpenAI API with the forced language set to the selected language. | |
| Args: | |
| audio_file (str): Path to the audio file. | |
| Returns: | |
| str: Transcribed text. | |
| """ | |
| with open(audio_file, "rb") as audio: | |
| response = openai.audio.transcriptions.create( | |
| model="whisper-1", | |
| file=audio, | |
| response_format="text", | |
| language=selected_language # Use the selected language code | |
| ) | |
| return response | |
| def process_audio_chunks(audio_chunks): | |
| """ | |
| Process and transcribe each audio chunk. | |
| Args: | |
| audio_chunks (list): List of AudioSegment chunks. | |
| Returns: | |
| str: Combined transcription from all chunks. | |
| """ | |
| transcriptions = [] | |
| min_length_ms = 100 # Minimum length required by OpenAI API (0.1 seconds) | |
| for i, chunk in enumerate(audio_chunks): | |
| if len(chunk) < min_length_ms: | |
| st.warning(f"Chunk {i} is too short to be processed.") | |
| continue | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file: | |
| chunk.export(temp_audio_file.name, format="wav") | |
| temp_audio_file_path = temp_audio_file.name | |
| transcription = transcribe(temp_audio_file_path) | |
| if transcription: | |
| transcriptions.append(transcription) | |
| st.write(f"Transcription for chunk {i}: {transcription}") | |
| os.remove(temp_audio_file_path) | |
| return " ".join(transcriptions) | |
| def save_transcription_to_docx(transcription, audio_file_path): | |
| """ | |
| Save the transcription as a .docx file. | |
| """ | |
| base_name = os.path.splitext(os.path.basename(audio_file_path))[0] | |
| output_file_name = f"{base_name}_full_transcription.docx" | |
| doc = Document() | |
| doc.add_paragraph(transcription) | |
| doc.save(output_file_name) | |
| return output_file_name | |
| st.title("Audio Transcription with OpenAI's Whisper") | |
| # Allow uploading of audio or video files | |
| uploaded_file = st.file_uploader("Upload an audio or video file", type=["wav", "mp3", "ogg", "m4a", "mp4", "mov"]) | |
| if 'transcription' not in st.session_state: | |
| st.session_state.transcription = None | |
| if uploaded_file is not None and st.session_state.transcription is None: | |
| st.audio(uploaded_file) | |
| # Save uploaded file temporarily | |
| file_extension = uploaded_file.name.split(".")[-1] | |
| original_file_name = uploaded_file.name.rsplit('.', 1)[0] | |
| temp_audio_file = f"temp_audio_file.{file_extension}" | |
| with open(temp_audio_file, "wb") as f: | |
| f.write(uploaded_file.getbuffer()) | |
| processing_start = time.time() | |
| with st.spinner('Transcribing...'): | |
| audio_chunks = split_audio_on_silence( | |
| temp_audio_file, | |
| min_silence_len=500, | |
| silence_thresh=-40, | |
| keep_silence=250 | |
| ) | |
| transcription = process_audio_chunks(audio_chunks) | |
| if transcription: | |
| st.session_state.transcription = transcription | |
| st.success('Transcription complete!') | |
| output_docx_file = save_transcription_to_docx(transcription, uploaded_file.name) | |
| st.session_state.output_docx_file = output_docx_file | |
| processing_duration = time.time() - processing_start | |
| st.info(f"Total processing time: {processing_duration:.2f} seconds.") | |
| if os.path.exists(temp_audio_file): | |
| os.remove(temp_audio_file) | |
| if st.session_state.transcription: | |
| st.text_area("Transcription", st.session_state.transcription, key="transcription_area_final") | |
| with open(st.session_state.output_docx_file, "rb") as docx_file: | |
| st.download_button( | |
| label="Download Transcription (.docx)", | |
| data=docx_file, | |
| file_name=st.session_state.output_docx_file, | |
| mime='application/vnd.openxmlformats-officedocument.wordprocessingml.document' | |
| ) | |