Add support for downloading files from YouTube using yt-dlp
Browse files- app.py +29 -10
- download.py +38 -0
- requirements.txt +2 -1
app.py
CHANGED
@@ -1,14 +1,19 @@
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from io import StringIO
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import os
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import tempfile
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-
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import gradio as gr
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from utils import slugify, write_srt, write_vtt
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import whisper
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import ffmpeg
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#import os
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#os.system("pip install git+https://github.com/openai/whisper.git")
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@@ -42,9 +47,8 @@ class UI:
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def __init__(self, inputAudioMaxDuration):
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self.inputAudioMaxDuration = inputAudioMaxDuration
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def transcribeFile(self, modelName, languageName, uploadFile, microphoneData, task):
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source =
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sourceName = os.path.basename(source)
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selectedLanguage = languageName.lower() if len(languageName) > 0 else None
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selectedModel = modelName if modelName is not None else "base"
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@@ -78,7 +82,20 @@ class UI:
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download.append(createFile(vtt, downloadDirectory, filePrefix + "-subs.vtt"));
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download.append(createFile(text, downloadDirectory, filePrefix + "-transcript.txt"));
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return text, vtt
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def createFile(text: str, directory: str, fileName: str) -> str:
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# Write the text to a file
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@@ -99,6 +116,7 @@ def getSubs(segments: Iterator[dict], format: str) -> str:
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segmentStream.seek(0)
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return segmentStream.read()
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def createUi(inputAudioMaxDuration, share=False):
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ui = UI(inputAudioMaxDuration)
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@@ -113,13 +131,14 @@ def createUi(inputAudioMaxDuration, share=False):
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demo = gr.Interface(fn=ui.transcribeFile, description=ui_description, inputs=[
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gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"], value="medium", label="Model"),
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gr.Dropdown(choices=sorted(LANGUAGES), label="Language"),
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gr.Audio(source="upload", type="filepath", label="Upload Audio"),
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gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
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gr.Dropdown(choices=["transcribe", "translate"], label="Task"),
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], outputs=[
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gr.Text(label="Transcription"),
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gr.Text(label="Segments")
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gr.File(label="Download")
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])
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demo.launch(share=share)
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from typing import Iterator
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from io import StringIO
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import os
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import pathlib
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import tempfile
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# External programs
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import whisper
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import ffmpeg
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# UI
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import gradio as gr
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from download import downloadUrl
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from utils import slugify, write_srt, write_vtt
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#import os
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#os.system("pip install git+https://github.com/openai/whisper.git")
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def __init__(self, inputAudioMaxDuration):
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self.inputAudioMaxDuration = inputAudioMaxDuration
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def transcribeFile(self, modelName, languageName, urlData, uploadFile, microphoneData, task):
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source, sourceName = getSource(urlData, uploadFile, microphoneData)
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selectedLanguage = languageName.lower() if len(languageName) > 0 else None
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selectedModel = modelName if modelName is not None else "base"
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download.append(createFile(vtt, downloadDirectory, filePrefix + "-subs.vtt"));
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download.append(createFile(text, downloadDirectory, filePrefix + "-transcript.txt"));
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return download, text, vtt
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def getSource(urlData, uploadFile, microphoneData):
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if urlData:
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# Download from YouTube
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source = downloadUrl(urlData)
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else:
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# File input
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source = uploadFile if uploadFile is not None else microphoneData
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file_path = pathlib.Path(source)
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sourceName = file_path.stem[:18] + file_path.suffix
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return source, sourceName
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def createFile(text: str, directory: str, fileName: str) -> str:
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# Write the text to a file
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segmentStream.seek(0)
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return segmentStream.read()
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def createUi(inputAudioMaxDuration, share=False):
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ui = UI(inputAudioMaxDuration)
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demo = gr.Interface(fn=ui.transcribeFile, description=ui_description, inputs=[
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gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"], value="medium", label="Model"),
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gr.Dropdown(choices=sorted(LANGUAGES), label="Language"),
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gr.Text(label="URL (YouTube, etc.)"),
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gr.Audio(source="upload", type="filepath", label="Upload Audio"),
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gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
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gr.Dropdown(choices=["transcribe", "translate"], label="Task"),
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], outputs=[
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gr.File(label="Download"),
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gr.Text(label="Transcription"),
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gr.Text(label="Segments")
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])
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demo.launch(share=share)
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download.py
ADDED
@@ -0,0 +1,38 @@
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import os
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from tempfile import mkdtemp
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from yt_dlp import YoutubeDL
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from yt_dlp.postprocessor import PostProcessor
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class FilenameCollectorPP(PostProcessor):
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def __init__(self):
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super(FilenameCollectorPP, self).__init__(None)
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self.filenames = []
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def run(self, information):
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self.filenames.append(information["filepath"])
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return [], information
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def downloadUrl(url: str):
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destinationDirectory = mkdtemp()
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ydl_opts = {
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"format": "bestaudio/best",
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'playlist_items': '1',
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'paths': {
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'home': destinationDirectory
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}
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}
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filename_collector = FilenameCollectorPP()
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with YoutubeDL(ydl_opts) as ydl:
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ydl.add_post_processor(filename_collector)
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ydl.download([url])
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if len(filename_collector.filenames) <= 0:
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raise Exception("Cannot download " + url)
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result = filename_collector.filenames[0]
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print("Downloaded " + result)
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return result
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requirements.txt
CHANGED
@@ -1,4 +1,5 @@
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git+https://github.com/openai/whisper.git
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transformers
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ffmpeg-python==0.2.0
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-
gradio
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git+https://github.com/openai/whisper.git
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transformers
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ffmpeg-python==0.2.0
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gradio
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yt-dlp
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