add multiple audiofiles input and glue it
This commit is contained in:
7
app.py
7
app.py
@@ -11,8 +11,9 @@ from handlers.gradioHandler import GradioHandlers
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from handlers.fileHandlers import FileHandlers
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from services.fasterWhisper import FasterWhisper
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from handlers.convertMdToPdf import ConvertMdToPdf
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from handlers.glueAudio import GlueAudio
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gh = GradioHandlers(gr, Llm, ConvertMdToPdf, FileHandlers, FasterWhisper)
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gh = GradioHandlers(gr, Llm, ConvertMdToPdf, FileHandlers, FasterWhisper, GlueAudio)
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def main():
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with gr.Blocks() as demo:
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@@ -32,7 +33,7 @@ def main():
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with gr.Row():
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with gr.Accordion(label='Recognization and integration'):
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with gr.Column():
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audioFile = gr.Audio(label='Load audio for transcribe', type="filepath")
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audioFiles = gr.Files(label='Load audio for transcribe', type="filepath", file_types=['audio'])
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images = gr.Files(label='Upload images', file_types=['image'])
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recognizeBtn = gr.Button('recognize and integrate', variant='primary')
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@@ -99,7 +100,7 @@ def main():
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saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filename)
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saveFileCheckbox.change(gh.updateTextbox, inputs=saveFileCheckbox, outputs=filenamePdf)
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recognizeBtn.click(gh.FasterWhisper.recognize, outputs=[recognizedText], inputs=[fastWhisperModel, device, compute_type, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText])
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recognizeBtn.click(gh.handleRecognizeBtn, outputs=[recognizedText], inputs=[audioFiles, fastWhisperModel, device, compute_type, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText, gr.State(GLUED_AUDIO_FILENAME), gr.State(OUTPUT_PATH)])
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# Если пайплайн включен то тогда делаем автоматически
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# автоматический пайплайн
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@@ -13,6 +13,8 @@ DEFAULT_API_KEY=os.getenv('API_KEY')
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# Задаем выходную директорию
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OUTPUT_PATH='outputs'
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GLUED_AUDIO_FILENAME='glued.mp3'
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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.
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Your task is to rewrite this unstructured transcript into a clear, logically organized, and detailed lecture summary (lecture notes).
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@@ -1,11 +1,14 @@
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from pathlib import Path
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from markdown_pdf import MarkdownPdf # Для аннотации типов
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# Для аннотации типов
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from markdown_pdf import MarkdownPdf
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from pydub import AudioSegment
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class FileHandlers:
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# Функция сохранения файла
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def saveFile(self, filename, content, output_path):
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def saveFile(self, filename, content, output_path, format='mp3'):
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'''
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Сохраняет текст или pdf из markdown_pdf в файл с указанным названием и директорией.
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Сохраняет текст, pdf из markdown_pdf или склеенный аудиофайл в файл с указанным названием и директорией.
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Args:
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:param filename: название файла;
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@@ -16,8 +19,13 @@ class FileHandlers:
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directory = Path(output_path)
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filePath = directory / filename # Добавление пути директории
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filePath.parent.mkdir(parents=True, exist_ok=True) # Создание директории если не существует
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# Сохранение для разных типов
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if type(content) == MarkdownPdf:
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content.save(filePath)
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return content.save(filePath)
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elif type(content) == str:
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filePath.write_text(content, encoding='utf-8')
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return filePath.write_text(content, encoding='utf-8')
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elif type(content) == AudioSegment:
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return content.export(filePath, format=format)
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11
handlers/glueAudio.py
Normal file
11
handlers/glueAudio.py
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@@ -0,0 +1,11 @@
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from pydub import AudioSegment
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class GlueAudio():
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def glue(self, audioFiles):
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glued = AudioSegment.empty()
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for audioFile in audioFiles:
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audio = AudioSegment.from_file(audioFile)
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glued += audio
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return glued
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@@ -1,12 +1,19 @@
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class GradioHandlers:
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def __init__(self, gr, Llm, ConvertMdToPdf, FileHandlers, FasterWhisper):
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def __init__(self, gr, Llm, ConvertMdToPdf, FileHandlers, FasterWhisper, GlueAudio):
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# Объект для работы с файлами
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self.fh = FileHandlers()
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self.ga = GlueAudio()
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self.ConvertMdToPdf = ConvertMdToPdf()
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self.FasterWhisper = FasterWhisper()
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self.Llm = Llm
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self.gr = gr
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def handleRecognizeBtn(self, audioFiles, model, device, compute_type, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText, filename, outPath):
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audioFile = self.ga.glue(audioFiles)
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file = self.fh.saveFile(filename, audioFile, outPath)
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return self.FasterWhisper.recognize(model, device, compute_type, file, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText)
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# Функция улучшения текста
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def generateByCondition(self, api_key, llm_model, system_prompt, recognized_text, llm_temperature, is_pipeline_enabled, trigger, isSaveFile, filename, filenamePdf, output_path):
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llm = self.Llm(api_key)
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@@ -23,7 +23,7 @@ class FasterWhisper:
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for seg in segments:
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text += f"[{self.format_timestamp(seg.start)} -> {self.format_timestamp(seg.end)}] {seg.text}" + '\n'
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return(text)
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return text
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def format_timestamp(self, seconds: float) -> str:
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millis = int(seconds * 1000)
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