import gradio as gr from faster_whisper import WhisperModel from pathlib import Path from dotenv import load_dotenv import os from services.llm import Llm load_dotenv() # Стандартный API ключ DEFAULT_API_KEY=os.getenv('API_KEY') # Задаем выходную директорию OUTPUT_PATH='outputs' # Стандартный системный промпт DEFAULT_SYSTEM_PROMPT='''You are a diligent university student who has recorded a lecture as an audio file and later transcribed it into raw text. Your task is to rewrite this unstructured transcript into a clear, logically organized, and detailed lecture summary (lecture notes). Guidelines: 1. Structure: - Organize the text into a hierarchy of sections and subsections. - Use headings, bullet points, or numbering where appropriate. - Present the material in a logical flow (from introduction → main points → details → examples → conclusion). 2. Clarity & Cohesion: - Remove filler words, repetitions, and irrelevant fragments. - Rewrite incomplete sentences into full, grammatically correct sentences. - Ensure smooth transitions between topics, making the summary feel continuous and well-connected. 3. Depth & Detail: - Capture all important concepts, definitions, examples, and explanations from the lecture. - Expand shorthand or fragmented thoughts into full, precise explanations. - Where appropriate, rephrase or clarify confusing passages for better understanding. 4. Accuracy: - Preserve the lecturer’s original meaning, intent, and terminology. - Avoid adding personal opinions or new information that was not in the lecture. 5. Style: - Write in a formal, academic tone suitable for study notes. - Aim for readability: concise sentences, but thorough coverage of concepts. - Use emphasis (e.g., bold or italic text) only when it improves comprehension. Final Output: A cohesive, detailed, and well-structured lecture summary, suitable for later studying and revision. Use only russian language! ''' faster_whisper_models_list = ['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2', 'large-v3', 'large', 'distil-large-v2', 'distil-large-v3', 'distil-large-v3.5', 'large-v3-turbo', 'turbo'] llm_models_list = ['openai/gpt-oss-120b', 'Qwen/Qwen3-235B-A22B-Thinking-2507', 'deepseek-ai/DeepSeek-R1-0528', 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8', 'openai/gpt-oss-20b', 'Intel/Qwen3-Coder-480B-A35B-Instruct-int4-mixed-ar', 'meta-llama/Llama-3.2-90B-Vision-Instruct', 'mistralai/Mistral-Nemo-Instruct-2407', 'Qwen/Qwen2.5-VL-32B-Instruct', 'meta-llama/Llama-3.3-70B-Instruct', 'mistralai/Devstral-Small-2505', 'mistralai/Magistral-Small-2506', 'mistralai/Mistral-Large-Instruct-2411', 'CohereForAI/aya-expanse-32b'] # Функция транскрибации def recognize(model, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText): model = WhisperModel(model, device='cuda', compute_type='float16') # Задаем модель segments, _ = model.transcribe( # Распознаем текст audioFile, beam_size=beamSize, vad_filter=vadFilter, vad_parameters={ "min_silence_duration_ms": minSilenceDurationMs, "speech_pad_ms": speechPadMs }, temperature= [temp0, temp1, temp2], word_timestamps=wordTimestamps, no_speech_threshold=noSpeechThreshold, condition_on_previous_text=conditionOnPreviousText ) text = '' for seg in segments: text += f"[{format_timestamp(seg.start)} -> {format_timestamp(seg.end)}] {seg.text}" + '\n' return(text) def format_timestamp(seconds: float) -> str: millis = int(seconds * 1000) hours = millis // (3600 * 1000) minutes = (millis % (3600 * 1000)) // (60 * 1000) seconds_int = (millis % (60 * 1000)) // 1000 millis = millis % 1000 return f"{hours:02d}:{minutes:02d}:{seconds_int:02d},{millis:03d}" # Функция сохранения файла def saveFile(filename, text): directory = Path(OUTPUT_PATH) filePath = directory / filename filePath.parent.mkdir(parents=True, exist_ok=True) filePath.write_text(text, encoding='utf-8') with gr.Blocks() as demo: gr.HTML('''

Faster Whisper WebUI

''') with gr.Row(): with gr.Column(): # Колонка в левой части экрана с основным взаимодействием with gr.Accordion(label='Recognization'): with gr.Column(): audioFile = gr.Audio(label='Load audio for transcribe', type="filepath") recognizeBtn = gr.Button('recognize', variant='primary') with gr.Accordion(label='Recognized text'): recognizedText = gr.TextArea(label='') # Колонка в правой части экрана с настройками with gr.Column(): with gr.Accordion(label='Settings'): # Первое поле на всю ширину в акордионе настроек filename = gr.Textbox(label='Output filename', value='output.txt') # Акордион настроек faster whisper with gr.Accordion(label='Faster whisper settings'): with gr.Row(): # Левая колонка в акордионе with gr.Column(): fastWhisperModel = gr.Dropdown(label='Model', choices=faster_whisper_models_list, interactive=True) beamSize = gr.Number(label='beam_size', value=8, interactive=True) noSpeechThreshold = gr.Number(label='no_speech_threshold', value=0.5, interactive=True) vadFilter = gr.Checkbox(label='vad_filter', value=True, interactive=True) wordTimestamps = gr.Checkbox(label='word_timestamps', value=True, interactive=True) conditionOnPreviousText = gr.Checkbox(label='condition_on_previous_text', value=False, interactive=True) # Правая колонка в акордионе with gr.Column(): with gr.Accordion(label='Vad parameters'): minSilenceDurationMs = gr.Number(label='min_silence_duration_ms', value=300, interactive=True) speechPadMs = gr.Number(label='speech_pad_ms', value=200, interactive=True) with gr.Accordion(label='Temperature'): temp0 = gr.Number(label='temp_0', value=0.0, interactive=True) temp1 = gr.Number(label='temp_1', value=0.2, interactive=True) temp2 = gr.Number(label='temp_2', value=0.4, interactive=True) # Нижний акордион настроек для api ключа llm with gr.Accordion(label='ai.io.net api settings'): apiKey = gr.Textbox(label='API key', value=DEFAULT_API_KEY, interactive=True) with gr.Accordion(label='System prompt'): systemPrompt = gr.Textbox(label='', value=DEFAULT_SYSTEM_PROMPT, interactive=True) with gr.Row(): llmModel = gr.Dropdown(label='models', choices=llm_models_list, value=llm_models_list[0], interactive=True) llmTemperature = gr.Number(label='Temperature', value=0.8, interactive=True ) with gr.Column(): with gr.Accordion(label='LLM'): with gr.Accordion(label='Refined text raw'): refinedText = gr.Textbox(label='') with gr.Accordion(label='Refined text md formated'): refinedTextMD = gr.Markdown(label='') recognizeBtn.click(recognize, outputs=[recognizedText], inputs=[fastWhisperModel, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText]) recognizedText.change(Llm(apiKey.value).generate, inputs=[llmModel, systemPrompt, recognizedText, llmTemperature], outputs=[refinedText, refinedTextMD]) demo.launch()