init

.env

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+API_KEY=io-v2-eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJvd25lciI6ImU4OGE2MDE0LTVhMWQtNGY2OS05M2EyLTRkOGQ5NTMxYTE5NSIsImV4cCI6NDkxMDcwMTgxNH0.E-Ywo2jzcG3c8I7eRuufI5FuHFSOJN7AK2I65xqnsQ8qH_UuS8L6Fst6LSui6AVaZbAs_gpbNFUhe4GRhmgDbQ

app.py

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+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('''
+    <div align=center>
+        <h1>
+            Faster Whisper WebUI
+        </h1>
+    </div>
+    ''')
+
+    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()

outputs/output.txt

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+[00:00:00,000 -> 00:00:03,680]  Современные технологии стремительно принякают во всех свера нашей жизни.
+[00:00:04,240 -> 00:00:08,340]  Мы пользуемся смартфонами, интернетом, искусственным интеллектом и умными помощниками.
+[00:00:09,000 -> 00:00:10,320]  Мир меняется буквально на глазах.
+[00:00:10,740 -> 00:00:13,460]  Работа собирает автомобиля алгоритм и пишут статьи,
+[00:00:13,640 -> 00:00:17,180]  а голосую систему могут озвучивать текст с почти человеческой интернации.
+[00:00:17,600 -> 00:00:21,860]  Однако, несмотря на все достижение, важно помнить, технологии – это все в большин инструмент.
+[00:00:22,500 -> 00:00:24,660]  То, как мы их применяем зависит от нас.
+[00:00:25,120 -> 00:00:29,080]  Мы должны использовать прогресс-разумно, сохраняя человеческие ценности,
+[00:00:29,080 -> 00:00:31,880]  забудьтеся будущим и друг от друга.
+[00:00:32,400 -> 00:00:34,660]  Развитите технологий нет меняет важность доброты,
+[00:00:34,940 -> 00:00:35,980]  внимание и этики.
+[00:00:36,220 -> 00:00:38,340]  Пусть технологический прогресс делая жизнью
+[00:00:38,340 -> 00:00:40,960]  допни, но настоящую ценность предоёт и человек.
+[00:00:41,200 -> 00:00:43,660]  Все мыслими, чувствами и стремлянем к лучшему.

services/llm.py

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+import openai
+
+class Llm:
+    def __init__(self, apiKey:str):
+        self.client = openai.OpenAI(
+            api_key=apiKey,
+            base_url='https://api.intelligence.io.solutions/api/v1/'
+        )
+
+    def generate(self, model:str, systemPrompt:str, userPrompt:str, temp:float):
+        '''
+        prompt[user_promtp, system_ptompt]
+        Function generate text by prompt
+        '''
+
+        # Получаем ответ от нейросети
+        response = self.client.chat.completions.create(
+            model=model,
+            messages=[
+                {'role': 'system', 'content': systemPrompt},
+                {'role': 'user', 'content': userPrompt},
+            ],
+            temperature=temp,
+            stream=False
+        ) 
+
+        # Достаем текст
+        text = str(response.choices[0].message.content)
+
+        return text, text
+